title: Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded SCADA via Spectral Analysis

abstract: Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is highly periodic. However, it is sometimes multiplexed, due to multi-threaded scheduling. In previous work we introduced a Statechart model which includes multiple Deterministic Finite Automata (DFA), one per cyclic pattern. We demonstrated that Statechart-based anomaly detection is highly effective on multiplexed cyclic traffic when the individual cyclic patterns are known. The challenge is to construct the Statechart, by unsupervised learning, from a captured trace of the multiplexed traffic, especially when the same symbols (ICS messages) can appear in multiple cycles, or multiple times in a cycle. Previously we suggested a combinatorial approach for the Statechart construction, based on Euler cycles in the Discrete Time Markov Chain (DTMC) graph of the trace. This combinatorial approach worked well in simple scenarios, but produced a false-alarm rate that was excessive on more complex multiplexed traffic. In this paper we suggest a new Statechart construction method, based on spectral analysis. We use the Fourier transform to identify the dominant periods in the trace. Our algorithm then associates a set of symbols with each dominant period, identifies the order of the symbols within each period, and creates the cyclic DFAs and the Statechart. We evaluated our solution on long traces from two production ICS: one using the Siemens S7-0x72 protocol and the other using Modbus. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulate multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The resulting Statecharts model the traces with an overall median false-alarm rate as low as 0.16% on the synthetic datasets, and with zero false-alarms on production S7-0x72 traffic. Moreover, the spectral analysis Statecharts consistently out-performed the previous combinatorial Statecharts, exhibiting significantly lower false alarm rates and more compact model sizes.


title: Software protection on the go: a large-scale empirical study on mobile app obfuscation

abstract: The prosperity of smartphone markets has raised new concerns about software security on mobile platforms, leading to a growing demand for effective software obfuscation techniques. Due to various differences between the mobile and desktop ecosystems, obfuscation faces both technical and non-technical challenges when applied to mobile software. Although there have been quite a few software security solution providers launching their mobile app obfuscation services, it is yet unclear how real-world mobile developers perform obfuscation as part of their software engineering practices. Our research takes a first step to systematically studying the deployment of software obfuscation techniques in mobile software development. With the help of an automated but coarse-grained method, we computed the likelihood of an app being obfuscated for over a million app samples crawled from Apple App Store. We then inspected the top 6600 instances and managed to identify 601 obfuscated versions of 539 iOS apps. By analyzing this sample set with extensive manual effort, we made various observations that reveal the status quo of mobile obfuscation in the real world, providing insights into understanding and improving the situation of software protection on mobile platforms.


title: AttentiveLearner2: A Multimodal Approach for Improving MOOC Learning on Mobile Devices

abstract: We propose AttentiveLearner2, a multimodal mobile learning system for MOOCs running on unmodified smartphones. AttentiveLearner2 uses both the front and back cameras of a smartphone as two complementary and fine-grained feedback channels in real time: the back camera monitors learners’ photoplethysmography (PPG) signals and the front camera tracks their facial expressions during MOOC learning. AttentiveLearner2 implicitly infers learners’ affective and cognitive states during learning by analyzing learners’ PPG signals and facial expressions. In a 26-participant user study, we found that it is feasible to detect 6 types of emotion during learning via collected PPG signals and facial expressions and these modalities are complement with each other.


title: Community-aware network sparsification

abstract: Network sparsification aims to reduce the number of edges of a network while maintaining its structural properties; such properties include shortest paths, cuts, spectral measures, or network modularity. Sparsification has multiple applications, such as, speeding up graph-mining algorithms, graph visualization, as well as identifying the important network edges. In this paper we consider a novel formulation of the network-sparsification problem. In addition to the network, we also consider as input a set of communities. The goal is to sparsify the network so as to preserve the network structure with respect to the given communities. We introduce two variants of the community-aware sparsification problem, leading to sparsifiers that satisfy different connectedness community properties. From the technical point of view, we prove hardness results and devise effective approximation algorithms. Our experimental results on a large collection of datasets demonstrate the effectiveness of our algorithms.


title: Performance of UHF W-BAN antennas in a real environment scenario

abstract: This work presents performance of robust wearable antennas intended to operate in Wireless Body Area Networks (W-BAN) in UHF, TETRAPOL communication band, 380-400 MHz. We propose a Planar Inverted F Antenna (PIFA) as reliable antenna type for UHF W-BAN applications. In order to satisfy the robustness requirements of the UHF band, both from communication and mechanical aspect, a new technology for building these antennas was proposed. The antennas are built out of flexible conductive sheets encapsulated inside a silicone based elastomer, Polydimethylsiloxane (PDMS). The proposed antennas are resistive to washing, bending and perforating. From the communication point of view, opting for a PIFA antenna type we solve the problem of coupling to the wearer and thus improve the overall communication performance of the antenna. Several different tests and comparisons were performed in order to check the stability of the proposed antennas when they are placed on the wearer or left in a common everyday environ- ment, on the ground, table etc. S11 deviations are observed and compared with the commercially available wearable antennas. As a final check, the antennas were tested in the frame of an existing UHF TETRAPOL communication system. All the measurements were performed in a real university campus scenario, showing reliable and good performance of the proposed PIFA antennas. © 2014 IEEE.


title: Microstrip-to-stripline planar transitions on LTCC

abstract: This paper presents the design of microstrip to stripline transitions built on the same layer of substrate of a single LTCC wafer. To achieve this geometry a cavity is opened on the substrate layer just above the stripline removing the upper ground plane, morphing the stripline into a microstrip line. The main interest of these transitions is the ability to excite stripline circuits directly from the same layer, as well as use them as 50Ω matched interconnection between buried lines and MMIC's. Two transitions of this kind are realized and measured. Comparison of measured circuits and simulations provide insight on what are the effects of the fabrication tolerance of critical structures, such as substrate cavities and ground planes.


title: Analyzing real-time PCR data by the comparative CT method

abstract: Two different methods of presenting quantitative gene expression exist: absolute and relative quantification. Absolute quantification calculates the copy number of the gene usually by relating the PCR signal to a standard curve. Relative gene expression presents the data of the gene of interest relative to some calibrator or internal control gene. A widely used method to present relative gene expression is the comparative CT method also referred to as the method. This protocol provides an overview of the comparative CT method for quantitative gene expression studies. Also presented here are various examples to present quantitative gene expression data using this method.


title: Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches

abstract: 1. The extracellular patch clamp method, which first allowed the detection of single channel currents in biological membranes, has been further refined to enable higher current resolution, direct membrane patch potential control, and physical isolation of membrane patches. 2. A description of a convenient method for the fabrication of patch recording pipettes is given together with procedures followed to achieve giga-seals i.e. pipette-membrane seals with resistances of 10(9) - 10(11) omega. 3. The basic patch clamp recording circuit, and designs for improved frequency response are described along with the present limitations in recording the currents from single channels. 4. Procedures for preparation and recording from three representative cell types are given. Some properties of single acetylcholine-activated channels in muscle membrane are described to illustrate the improved current and time resolution achieved with giga-seals. 5. A description is given of the various ways that patches of membrane can be physically isolated from cells. This isolation enables the recording of single channel currents with well-defined solutions on both sides of the membrane. Two types of isolated cell-free patch configurations can be formed: an inside-out patch with its cytoplasmic membrane face exposed to the bath solution, and an outside-out patch with its extracellular membrane face exposed to the bath solution. 6. The application of the method for the recording of ionic currents and internal dialysis of small cells is considered. Single channel resolution can be achieved when recording from whole cells, if the cell diameter is small (less than 20 micrometer). 7. The wide range of cell types amenable to giga-seal formation is discussed.


title: Ethanol Extract of Dianthus chinensis L. Induces Apoptosis in Human Hepatocellular Carcinoma HepG2 Cells In Vitro

abstract: Dianthus chinensis L. is used to treat various diseases including cancer; however, the molecular mechanism by which the ethanol extract of Dianthus chinensis L. (EDCL) induces apoptosis is unknown. In this study, the apoptotic effects of EDCL were investigated in human HepG2 hepatocellular carcinoma cells. Treatment with EDCL significantly inhibited cell growth in a concentration- and time-dependent manner by inducing apoptosis. This induction was associated with chromatin condensation, activation of caspases, and cleavage of poly (ADP-ribose) polymerase protein. However, apoptosis induced by EDCL was attenuated by caspase inhibitor, indicating an important role for caspases in EDCL responses. Furthermore, EDCL did not alter the expression of bax in HepG2 cells but did selectively downregulate the expression of bcl-2 and bcl-xl, resulting in an increase in the ratio of bax:bcl-2 and bax:bcl-xl. These results support a mechanism whereby EDCL induces apoptosis through the mitochondrial pathway and caspase activation in HepG2 cells.


title: A systematic review of physical activity and sedentary behaviour research in the oil-producing countries of the Arabian Peninsula

abstract: The dramatic rise in Noncommunicable Diseases (NCD) in the oil-producing countries of the Arabian Peninsula is driven in part by insufficient physical activity, one of the five main contributors to health risk in the region. The aim of this paper is to review the available evidence on physical activity and sedentary behaviour for this region. Based on the findings, we prioritize an agenda for research that could inform policy initiatives with regional relevance. We reviewed regional evidence on physical activity and sedentary behaviour to identify the needs for prevention and policy-related research. A literature search of peer-reviewed publications in the English language was conducted in May 2016 using PubMed, Web of Science and Google Scholar. 100 studies were identified and classified using the Behavioural Epidemiology Framework. Review findings demonstrate that research relevant to NCD prevention is underdeveloped in the region. A majority of the studies were epidemiological in approach with few being large-scale population-based studies using standardised measures. Correlates demonstrated expected associations with health outcomes, low levels of physical activity (particularly among young people), high levels of sedentary behaviour (particularly among men and young people) and expected associations of known correlates (e.g. gender, age, education, time, self-motivation, social support, and access). Very few studies offered recommendations for translating research findings into practice. Further research on the determinants of physical activity and sedentary behaviour in the Arabian Peninsula using standard assessment tools is urgently needed. Priority research includes examining these behaviours across the four domains (household, work, transport and leisure). Intervention research focusing on the sectors of education, health and sports sectors is recommended. Furthermore, adapting and testing international examples to the local context would help identify culturally relevant policy and programmatic interventions for the region.


title: ADAM: a testbed for exploring the use of data mining in intrusion detection

abstract: Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM (Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection.


title: Simple Does It: Weakly Supervised Instance and Semantic Segmentation

abstract: Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.


title: The influence of shallow trench isolation angle on hot carrier effect of STI-based LDMOS transistors

abstract: Hot carrier reliability imposes challenges in the design of STI-based laterally diffused metal-oxide-semiconductor (LDMOS) devices as the device feature is miniaturized. Efforts to quantify the degradation are crucial in countering the device reliability risk. This paper investigates the effect of shallow trench isolation (STI) angle on hot carrier effect (HCI) of STI-based LDMOS devices. The effect on critical device parameters specifically the saturation drain current (Id sat ), on-resistance (Ron) as well as the rate of impact ionization of the device had been studied and discussed in detail. From the result obtained, it is found that the drain current for device with 100° STI angle is reduced by 58.78% compared to device with 45° STI angle. Larger STI angle shows higher HCI degradation and the physical mechanism behind the results is analyzed from the Sentaurus 2D techplot.


title: Oral Applications of Cyanoacrylate Adhesives: A Literature Review

abstract: Cyanoacrylate adhesives have been used in medicine and dentistry with some controversial opinions. The aim of this review was to summarize the relevant literature regarding the use of cyanoacrylate adhesives for oral wounds during dental and surgical procedures, with focus on the applications, indications, advantages, and disadvantages. In conclusion, in vivo and clinical studies have demonstrated in the last few years convincing results regarding the safety, efficacy, ease of application, and feasibility of all types of cyanoacrylate adhesives used in intra- and extraoral procedures.


title: Interval Type-2 Fuzzy Logic Toolbox

abstract: This paper presents the development and design of a graphical user interface and a command line programming toolbox for construction, edition and observation of Interval Type-2 Fuzzy Inference Systems. The Interval Type-2 Fuzzy Logic System Toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the Toolbox. The Toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods and the evaluation of Interval Type-2 Fuzzy Inference Systems.


title: GestureGAN for Hand Gesture-to-Gesture Translation in the Wild

abstract: Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we propose a novel hand Gesture Generative Adversarial Network (GestureGAN). GestureGAN consists of a single generator $G$ and a discriminator $D$, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of"channel pollution"while back-propagating the gradients. In addition, we present the Fr\'echet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier. Our model and code are available at https://github.com/Ha0Tang/GestureGAN.


title: Skill Rating for Generative Models

abstract: We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between generators and discriminators provide an effective way to evaluate generative models. We introduce two methods for summarizing tournament outcomes: tournament win rate and skill rating. Evaluations are useful in different contexts, including monitoring the progress of a single model as it learns during the training process, and comparing the capabilities of two different fully trained models. We show that a tournament consisting of a single model playing against past and future versions of itself produces a useful measure of training progress. A tournament containing multiple separate models (using different seeds, hyperparameters, and architectures) provides a useful relative comparison between different trained GANs. Tournament-based rating methods are conceptually distinct from numerous previous categories of approaches to evaluation of generative models, and have complementary advantages and disadvantages.


title: Dynamics, Hover Configurations, and Rotor Failure Restabilization of a Morphing Quadrotor

abstract: We present the design of a morphing quadrotor which is capable of arm rotation and extension. Using Lagrangian mechanics, we derive the multibody dynamics. We also derive the set of equations which describe all possible hover configurations of the vehicle. Using these equations, we develop a technique to find a hover configuration in the event a rotor has limited or zero thrust. We then develop a control technique to stabilize the vehicle at these configurations after a rotor has failed. Finally, we present a simulation which demonstrates the viability of recovering from complete and sudden rotor failure.


title: MEMORY-BASED LANGUAGE PROCESSING: Psycholinguistic Research in the 1990s

abstract: ▪ Abstract There are two main domains of research in psycholinguistics: sentence processing, concerned with how the syntactic structures of sentences are computed, and text processing, concerned with how the meanings of larger units of text are understood. In recent sentence processing research, a new and controversial theme is that syntactic computations may rely heavily on statistical information about the relative frequencies with which different syntactic structures occur in the language. In text processing, recent research has focused on what information the words and ideas of a text evoke from long- term memory quickly, passively, and at low processing cost. Research in both domains has begun to use the information that can be obtained from large corpora of naturally occurring texts.


title: Plagiarism-A Survey

abstract: Plagiarism in the sense of "theft of intellectual property" has been around for as long as humans have produced work of art and research. However, easy access to the Web, large databases, and telecommunication in general, has turned plagiarism into a serious problem for publishers, researchers and educational institutions. In this paper, we concentrate on textual plagiarism (as opposed to plagiarism in music, paintings, pictures, maps, technical drawings, etc.). We first discuss the complex general setting, then report on some results of plagiarism detection software and finally draw attention to the fact that any serious investigation in plagiarism turns up rather unexpected side-effects. We believe that this paper is of value to all researchers, educators and students and should be considered as seminal work that hopefully will encourage many still deeper investigations.


Sentiment Analysis ( Almost ) from Scratch


title: Constance Mellon's "Library Anxiety": An Appreciation and a Critique.

abstract: Gillian S. Gremmels is Library Director at Davidson College; e-mail: jigremmels@davidson.edu. © 2015 Gillian S. Gremmels, Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/3.0/) CC BY-NC. Introduction Even after almost thirty years, I vividly remember reading Constance A. Mellon’s article, “Library Anxiety: A Grounded Theory and Its Development,” when it appeared in the March 1986 issue of College & Research Libraries.1 I was a reference librarian at DePauw University in Indiana at the time, helping students individually and in “bibliographic instruction” classes. The resonance I felt when I read about library anxiety was powerful: yes, I thought; this is what I’m seeing in my students, who seem overwhelmed by the library, in need of librarians’ help, yet reluctant to approach us. To have the phenomenon confirmed by research and given a name authenticated it and enabled my colleagues and me to address it directly with students and faculty. I was also intrigued by Mellon’s method. I had never heard of grounded theory before, but a method that based its conclusions on students’ own rich descriptions of their realities greatly appealed to me. A few years later, when I was studying naturalistic/interpretivist research methodology at Indiana University, I read Barney G. Glaser and Anselm L. Strauss’s book, The Discovery of Grounded Theory, and did my own grounded theory study on choices made by academic reference librarians while working with students. 2 My experience evidently was not unique. Mellon’s article was chosen as one of the seven most important in the 75-year history of College & Research Libraries because it made “library anxiety,” a phenomenon observed by practitioners, official and uncovered its origins. The article also legitimized the use of qualitative research methods by giving grounded theory wide recognition in a premier journal. In this essay, I summarize Mellon’s method and findings, trace the article’s influence, offer a critique, and suggest some directions for future research.


Synthesizing Union Tables from the Web


title: Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

abstract: The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.


title: Facial smile detection using convolutional neural networks

abstract: Facial expression analysis plays a key role in analyzing emotions and human behaviors. Smile detection is a special task in facial expression analysis with various potential applications such as photo selection, user experience analysis, smiling payment and patient monitoring. Conventional approaches often extract low-level face descriptors and detect smile based on a strong binary classifier. In this paper, we propose an effective architecture of Convolutional Neural Networks (CNNs) to detect smile in real-time speed with high accuracy. The experimental results show that our proposed network outperforms recent state-of-the-art methods.


title: Guided Procedural Modeling

abstract: Procedural methods present one of the most powerful techniques for authoring a vast variety of computer graphics models. However, their massive applicability is hindered by the lack of control and a low predictability of the results. In the classical procedural modeling pipeline, the user usually defines a set of rules, executes the procedural system, and by examining the results attempts to infer what should be changed in the system definition in order to achieve the desired output. We present guided procedural modeling, a new approach that allows a high level of top-down control by breaking the system into smaller building blocks that communicate. In our work we generalize the concept of the environment. The user creates a set of guides. Each guide defines a region in which a specific procedural model operates. These guides are connected by a set of links that serve for message passing between the procedural models attached to each guide. The entire model consists of a set of guides with procedural models, a graph representing their connection, and the method in which the guides interact. The modeling process is performed by modifying each of the described elements. The user can control the high-level description by editing the guides or manipulate the low-level description by changing the procedural rules. Changing the connectivity allows the user to create new complex forms in an easy and intuitive way. We show several examples of procedural structures, including an ornamental pattern, a street layout, a bridge, and a model of trees. We also demonstrate interactive examples for quick and intuitive editing using physics-based mass-spring system.


title: Automatic license plate recognition

abstract: Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.


title: The RADStack: Open Source Lambda Architecture for Interactive Analytics

abstract: The Real-time Analytics Data Stack, colloquially referred to as the RADStack, is an open-source data analytics stack designed to provide fast, flexible queries over up-to-the-second data. It is designed to overcome the limitations of either a purely batch processing system (it takes too long to surface new events) or a purely real-time system (it’s difficult to ensure that no data is left behind and there is often no way to correct data after initial processing). It will seamlessly return best-effort results on very recent data combined with guaranteed-correct results on older data. In this paper, we introduce the architecture of the RADStack and discuss our methods of providing interactive analytics and a flexible data processing environment to handle a variety of real-world workloads.


title: A broadband end launched coaxial-to-waveguide transition for waveguide phased arrays

abstract: A design approach has been presented that is capable of producing well-matched transitions into very dispersive waveguides used in a wideband phased array environment. In addition, a very accurate method of analysis has also been developed.


title: Building A Shallow Arabic Morphological Analyser In One Day

abstract: The paper presents a rapid method of developing a shallow Arabic morphological analyzer. The analyzer will only be concerned with generating the possible roots of any given Arabic word. The analyzer is based on automatically derived rules and statistics. For evaluation, the analyzer is compared to a commercially available Arabic Morphological Analyzer.


IMPLEMNTATION OF SIMULINK BASED MODEL USING SOBEL EDGE DETECTOR FOR DENTAL PROBLEMS Deepika Nagpal


title: Enhanced local binary covariance matrices (ELBCM) for texture analysis and object tracking

abstract: This paper presents a novel way to embed local binary texture information in the form of local binary patterns (LBP) into the covariance descriptor. Contrary to previous publications, our method is not based on the LBP decimal values where arithmetic operations have no texture meaning. Our method uses the angles described by the uniform LBP patterns and includes them into the set of features used to build the covariance descriptor. Our representation is not only more compact but more robust because it is less affected by noise and small neighborhood rotations. Experimental evaluations corroborate the performance of our descriptor for texture analysis and tracking applications. Our descriptor rivals with state-of-the-art methods and beats other covariance-based descriptors.


title: Effect of adding single-joint exercises to a multi-joint exercise resistance-training program on strength and hypertrophy in untrained subjects.

abstract: The aim of this study was to examine the effect of adding single-joint (SJ) exercises to a multi-joint (MJ) exercise resistance-training program on upper body muscle size and strength. Twenty-nine untrained young men participated in a 10-week training session. They were randomly divided in 2 groups: the MJ group performed only MJ exercises (lat pulldown and bench press); the MJ+SJ group performed the same MJ exercises plus SJ exercises (lat pulldown, bench press, elbow flexion, and elbow extension). Before and after the training period, the muscle thickness (MT) of the elbow flexors was measured with ultrasound, and peak torque (PT) was measured with an isokinetic dynamometer. There was a significant (p < 0.05) increase in MT (6.5% for MJ and 7.04% for MJ+SJ) and PT (10.40% for MJ and 12.85% for MJ+SJ) in both groups, but there were no between-group differences. Therefore, this study showed that the inclusion of SJ exercises in a MJ exercise training program resulted in no additional benefits in terms of muscle size or strength gains in untrained young men.


title: Internet of Drones

abstract: The Internet of Drones (IoD) is a layered network control architecture designed mainly for coordinating the access of unmanned aerial vehicles to controlled airspace, and providing navigation services between locations referred to as nodes. The IoD provides generic services for various drone applications, such as package delivery, traffic surveillance, search and rescue, and more. In this paper, we present a conceptual model of how such an architecture can be organized and we specify the features that an IoD system based on our architecture should implement. For doing so, we extract key concepts from three existing large scale networks, namely the air traffic control network, the cellular network, and the Internet, and explore their connections to our novel architecture for drone traffic management. A simulation platform for IoD is being implemented, which can be accessed from www.IoDnet.org in the future.


title: A Precision Pseudo Resistor Bias Scheme for the Design of Very Large Time Constant Filters

abstract: In this brief, a novel pseudo resistor bias scheme capable of achieving improved performances against process parameter variations is presented. The use of a matched structure allows for achieving a simulated statistical variation $\sigma/\mu$ one order of magnitude better than conventional bias schemes proposed in the literature. The entire circuit was designed to be able to emulate a tunable resistor whose resistance could be digitally set in the range of 400 $\text{M}\Omega$ –90 $\text{G}\Omega$ . The design of a very low frequency bandpass filter (BPF) for biopotential recording was also covered as a suitable application. The tunability offered by the bias scheme was employed, in this case, to realize a high-pass cutoff frequency variable in the range of 10 Hz–2 kHz. An integrated circuit including eight acquisition channels, each of which is composed of the designed BPF and a conventional 10-bit analog-to-digital converter, was finally realized in a CMOS 0.35- $\mu\text{m}$ process. The chip was successfully tested showing a measured $\sigma/\mu$ variation of the high-pass cutoff frequency equal to 0.13 when different channels on the same die are considered.


Tutorial: Recent Advances in Deep Learning.


title: Description logics of context

abstract: Copyright: 2013 Oxford University Press. This is an ABSTRACT ONLY. The definitive version is published in Journal of Logic and Computation, pp 1-47


title: Broadband Circularly Polarized Patch Antenna With Parasitic Strips

abstract: A patch antenna with parasitic strips is presented for wideband circular polarization. In order to broaden the axial-ratio (AR) bandwidth of the original corner-truncated patch, four parasitic strips are sequentially rotated and gap-coupled around the patch. A capacitive-coupled feed with a small disc on the top of the feeding probe is utilized to obtain a wide impedance-matching bandwidth. Moreover, another disc-loaded shorting pin is placed at the center of the antenna to adjust the squint beams and improve the gains along $ + z$ -axis. Measured results show that the proposed antenna with a low profile of $0.13{\lambda _0}$ obtains a global bandwidth of 24% (2.32–2.95 GHz) for $\vert{S_{11}}\vert , ${\rm AR} , and average gain of 8 dBic.


title: The beat spectrum: a new approach to rhythm analysis

abstract: We introduce the beat spectrum, a new method of automatically characterizing the rhythm and tempo of music and audio. The beat spectrum is a measure of acoustic self-similarity as a function of time lag. Highly structured or repetitive music will have strong beat spectrum peaks at the repetition times. This reveals both tempo and the relative strength of particular beats, and therefore can distinguish between different kinds of rhythms at the same tempo. We also introduce the beat spectrogram which graphically illustrates rhythm variation over time. Unlike previous approaches to tempo analysis, the beat spectrum does not depend on particular attributes such as energy or frequency, and thus will work for any music or audio in any genre. We present tempo estimation results which are accurate to within 1% for a variety of musical genres. This approach has a variety of applications, including music retrieval by similarity and automatically generating music videos. Anyone who has ever tapped a foot in time to music has performed rhythm analysis. Though simple for humans, this task is considerably more difficult to automate. We introduce a new measure of tempo analysis called the beat spectrum. This is a measure of acoustic self-similarity versus lag time, computed from a representation of spectrally similarity. Peaks in the beat spectrum correspond to major rhythmic components of the source audio. The repetition time of each component can be determined by the lag time of the corresponding peak, while the relative amplitudes of different peaks reflects the strengths of their corresponding rhythmic components. We also present the beat spectrogram which graphically illustrates rhythmic variation over time. The beat spectrogram is an image formed from the beat spectrum over successive windows. Strong rhythmic components are visible as bright bars in the beat spectrogram, making changes in tempo or time signature visible. In addition, a measure of audio novelty can be computed that measures how novel the source audio is at any time [2]. Instances when this measure is large correspond to significant audio changes. Periodic peaks correspond to rhythmic periodicity in the music. In the final section, we present various applications of the beat spectrum, including music retrieval by rhythmic similarity, an “automatic DJ” that can smoothly sequence music with similar tempos and automatic music video generation.


title: The Good, The Bad and the Ugly: A Meta-analytic Review of Positive and Negative Effects of Violent Video Games

abstract: Objective Video game violence has become a highly politicized issue for scientists and the general public. There is continuing concern that playing violent video games may increase the risk of aggression in players. Less often discussed is the possibility that playing violent video games may promote certain positive developments, particularly related to visuospatial cognition. The objective of the current article was to conduct a meta-analytic review of studies that examine the impact of violent video games on both aggressive behavior and visuospatial cognition in order to understand the full impact of such games. Methods A detailed literature search was used to identify peer-reviewed articles addressing violent video game effects. Effect sizes r (a common measure of effect size based on the correlational coefficient) were calculated for all included studies. Effect sizes were adjusted for observed publication bias. Results Results indicated that publication bias was a problem for studies of both aggressive behavior and visuospatial cognition. Once corrected for publication bias, studies of video game violence provided no support for the hypothesis that violent video game playing is associated with higher aggression. However playing violent video games remained related to higher visuospatial cognition (r x = 0.36). Conclusions Results from the current analysis did not support the conclusion that violent video game playing leads to aggressive behavior. However, violent video game playing was associated with higher visuospatial cognition. It may be advisable to reframe the violent video game debate in reference to potential costs and benefits of this medium.


title: Patterns of cognitive appraisal in emotion.

abstract: There has long been interest in describing emotional experience in terms of underlying dimensions, but traditionally only two dimensions, pleasantness and arousal, have been reliably found. The reasons for these findings are reviewed, and integrating this review with two recent theories of emotions (Roseman, 1984; Scherer, 1982), we propose eight cognitive appraisal dimensions to differentiate emotional experience. In an investigation of this model, subjects recalled past experiences associated with each of 15 emotions, and rated them along the proposed dimensions. Six orthogonal dimensions, pleasantness, anticipated effort, certainty, attentional activity, self-other responsibility/control, and situational control, were recovered, and the emotions varied systematically along each of these dimensions, indicating a strong relation between the appraisal of one's circumstances and one's emotional state. The patterns of appraisal for the different emotions, and the role of each of the dimensions in differentiati ng emotional experience are discussed. Most people think of emotions in categorical terms: "I was scared," or "I was sad," or "I was frustrated." In complicated situations they may say, "I felt sad and frustrated." The idea that there is a small set of fundamentally different emotions, has a long and illustrious history in science as well, dating back at least to Aristotle and reemerging in the theory of the four humors, in the works of eighteenthcentury philosophers, and in Darwin (1872/ 1965). In recent years the categorical approach to the study of emotions has become prominent in psychology, stimulated by the monumental work of Sylvan Tomkins (1962, 1963, 1982; Ekman & Friesen, 1971; hard, 1971, 1972, 1977; Izard & Buechler, 1980). This view of emotional experience admirably captures our intuition that happiness, anger, and fear are basic feeling-states, easily recognizable, and fundamentally different from each other.


title: Genome-Wide Linkage Analysis of the Acute Coronary Syndrome Suggests a Locus on Chromosome 2

abstract: A positive family history is a recognized cardiovascular risk factor, and genome-wide scans may reveal susceptibility loci for coronary artery disease. The acute coronary syndrome, consisting of myocardial infarction and unstable angina, is the most important manifestation of coronary disease and is characterized by atherosclerotic plaque disruption and coronary thrombosis. From ≈6000 hospital admissions to cardiology units, we identified affected sibling pairs (n=61) who had documented acute coronary syndrome before the age of 70 years. A 10-cM resolution genetic map and MAPMAKER/SIBS were used for genome-wide linkage analysis. One locus on chromosome 2q36-q37.3 showed linkage with a lod score of 2.63 ( P P


title: Real-time imitation of human whole-body motions by humanoids

abstract: In this paper, we present a system that enables humanoid robots to imitate complex whole-body motions of humans in real time. In our approach, we use a compact human model and consider the positions of the endeffectors as well as the center of mass as the most important aspects to imitate. Our system actively balances the center of mass over the support polygon to avoid falls of the robot, which would occur when using direct imitation. For every point in time, our approach generates a statically stable pose. Hereby, we do not constrain the configurations to be in double support. Instead, we allow for changes of the support mode according to the motions to imitate. To achieve safe imitation, we use retargeting of the robot’s feet if necessary and find statically stable configurations by inverse kinematics. We present experiments using human data captured with an Xsens MVN motion capture system. The results show that a Nao humanoid is able to reliably imitate complex whole-body motions in real time, which also include extended periods of time in single support mode, in which the robot has to balance on one foot.


Dialogue Generation With GAN


title: When Waiting is not an Option : Learning Options with a Deliberation Cost

abstract: Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance. While the problem of"how"to learn options is increasingly well understood, the question of"what"good options should be has remained elusive. We formulate our answer to what"good"options should be in the bounded rationality framework (Simon, 1957) through the notion of deliberation cost. We then derive practical gradient-based learning algorithms to implement this objective. Our results in the Arcade Learning Environment (ALE) show increased performance and interpretability.


Advances in Process Mining: Artificial negative events and othertechniques.


title: Corporate Learning in Times of Digital Transformation: A Conceptual Framework and Service Portfolio for the Learning Function in Banking Organisations

abstract: Digital transformation is one of the major challenges in all industries. It embraces the realignment of technology and new business models to more effectively engage digital customers at every touchpoint in the costumer experience lifecycle. Therefore, successful digital transformation begins with an understanding of digital consumer behavior, preferences and choices. It then leads to major consumer-centric changes within the organisation that address these needs. Such a consumer-centric and self-directed client structure has significant consequences for banks. Banks will need to continue existing services, while developing strategies to manage the shift in mix. ::: ::: For that reason, banking organisations are challenged to consider exploration new business fields and not only focus on exploitation anymore. Finally they need to ensure a successful balance between both. This means they have to establish a general development orientation and afford implementing innovations at the same time they focus on efficiency by utilizing and optimizing the existing. Pressure for doing this is definitely there and causes in current dynamic changes in the finance market, new competitors in the branch and little differentiation among offered banking services. Hence, they need to enhance their continuous learning ability, which is an essential precondition for coping with innovations. ::: ::: This paper follows the research question “how can the learning function foster the enhancement of the banking organisation’s learning and innovation ability in times of digital transformation?” This is closely linked to the kind of services a learning function needs to enhance or integrate for supporting and designing a learning organization. With the idea underlying enterprise 2.0, social media platforms offer valuable opportunities for doing this. From an educational management perspective, we focus on the design levels of individuals, teams, and organisation in banks. ::: ::: Derived from our theoretical framework we suggest a conceptual model for managing organisational learning for continuous innovation based on four action areas. We examine the appropriateness of this framework in the practice field through eleven case studies in banking organisations. This approach allows a further development of these areas and to develop an understanding of what are current and future starting points for action in banking organisations. Finally, we identify new roles and services of the learning function for supporting the organisations.


title: Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels

abstract: The practical deployment of massive multiple-input multiple-output (MIMO) in the future fifth generation (5G) wireless communication systems is challenging due to its high-hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things. Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician K-factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the tradeoff between the achievable rate and the energy efficiency with respect to key system parameters, including the quantization bits, number of BS antennas, Rician K-factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate tradeoff compared with the ideal infinite-resolution and low-resolution ADC architectures.


title: Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision

abstract: Understanding the 3D world is a fundamental problem in computer vision. However, learning a good representation of 3D objects is still an open problem due to the high dimensionality of the data and many factors of variation involved. In this work, we investigate the task of single-view 3D object reconstruction from a learning agent's perspective. We formulate the learning process as an interaction between 3D and 2D representations and propose an encoder-decoder network with a novel projection loss defined by the projective transformation. More importantly, the projection loss enables the unsupervised learning using 2D observation without explicit 3D supervision. We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes. Results show superior performance and better generalization ability for 3D object reconstruction when the projection loss is involved.


title: Mangroves: obligate or facultative halophytes? A review

abstract: National Natural Science Foundation of China [40776046]; National Basic Research Program of China [2009CB426306]; China State Administration of Oceanography [200905009]


title: Raising Student Social Presence in Online Classes.

abstract: Distance and online education is increasing in importance in the offerings of many universities. As this occurs, instructors are developing ways to use new media to support traditional teaching practices. This is an important effort, as online education is still viewed with a degree of skepticism by some. By successfully transferring accepted teaching practice into online education, instructors are helping to demonstrate that online education can be of high quality. Some instructors are also developing different teaching practices to take advantage of the new media. This too .is important, because the technologies of online education may support learning strategies that are not feasible in traditional teaching modes. A subject of importance in both efforts is that of student social presence. Social presence, or the perceived saliency of members of the online group, may be important to the satisfaction of students in online classes. This paper explores issues relating to social presence in online classes and suggests ways to increase student social presence in online classes. The relative richness of seven media types (face-to-face, video conferencing, synchronous audio, text-based chat, e-mail, asynchronous audio, and threaded discussion) is ranked according to four criteria (feedback, multiple cues, message . tailoring, and emotions). (Contains 17 references.) (Author/MES) Reproductions supplied by EDRS are the best that can be made from the original document. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY


title: Advisory warnings based on cooperative perception

abstract: The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The development process was accompanied by a series of driving simulator studies to determine both the potential to reduce traffic conflicts and important design characteristics of early advisory warning signals. The most important details of the prototype system's components inter-vehicle information-fusion and situation analysis are described and the achieved warning timings are compared to the results of the driving simulator studies.


title: Robust Estimation of Road Frictional Coefficient

abstract: Knowledge of tire force potential, i.e., tire-road frictional coefficient, is important for vehicle active safety systems because tire-road friction is an effective measure of the safety margin of vehicle dynamics. For vehicle handling dynamics, the frictional coefficient is highly coupled with tire slip angle, therefore, they need to be estimated simultaneously when the latter is not measured. This paper presents an estimation algorithm based on a robust adaptive observer methodology. Stability and robustness of this observer are analyzed numerically. The performance is analyzed using computer simulations and experiments under various road and steering conditions.


title: The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population

abstract: Background. A self-report screening scale of adult attention-deficit/hyperactivity disorder (ADHD), the World Health Organization (WHO) Adult ADHD Self-Report Scale (ASRS) was developed in conjunction with revision of the WHO Composite International Diagnostic Interview (CIDI). The current report presents data on concordance of the ASRS and of a short-form ASRS screener with blind clinical diagnoses in a community sample. Method. The ASRS includes 18 questions about frequency of recent DSM-IV Criterion A symptoms of adult ADHD. The ASRS screener consists of six out of these 18 questions that were selected based on stepwise logistic regression to optimize concordance with the clinical classification. ASRS responses were compared to blind clinical ratings of DSM-IV adult ADHD in a sample of 154 respondents who previously participated in the US National Comorbidity Survey Replication (NCS-R), oversampling those who reported childhood ADHD and adult persistence. Results. Each ASRS symptom measure was significantly related to the comparable clinical symptom rating, but varied substantially in concordance (Cohen's κ in the range 0·16–0·81). Optimal scoring to predict clinical syndrome classifications was to sum unweighted dichotomous responses across all 18 ASRS questions. However, because of the wide variation in symptom-level concordance, the unweighted six-question ASRS screener outperformed the unweighted 18-question ASRS in sensitivity (68·7% v . 56·3%), specificity (99·5% v . 98·3%), total classification accuracy (97·9% v . 96·2%), and κ (0·76 v . 0·58). Conclusions. Clinical calibration in larger samples might show that a weighted version of the 18-question ASRS outperforms the six-question ASRS screener. Until that time, however, the unweighted screener should be preferred to the full ASRS, both in community surveys and in clinical outreach and case-finding initiatives.


title: Power system oscillation damping by means of VSC-HVDC systems

abstract: This chapter shows the potential contribution that voltage source converter-high-voltage direct current (VSC-HVDC) systems can have to enhance power system stability, in particular, through damping of power system oscillations. It presents two application examples to demonstrate the damping contribution capability of HVDC links, although the analysis can be extendable to HVDC grids of any size by considering the proper coordination. The first example considers the capability of offshore wind power generation connected onshore through an HVDC link to damp low-frequency oscillations (LFOs). A number of power system stabilizers that may be included in the power converter are compared. The focus of the second example is, through a simplified system representation, to use analytical and simulation tools to investigate the occurrence of subsynchronous resonance (SSR) in an onshore transmission system. Damping of SSR using submarine VSC-HVDC links is demonstrated through numerical simulations, with experimental results validating the proposed control scheme.


title: Visualization of High-Dimensional Point Clouds Using Their Density Distribution's Topology

abstract: We present a novel method to visualize multidimensional point clouds. While conventional visualization techniques, like scatterplot matrices or parallel coordinates, have issues with either overplotting of entities or handling many dimensions, we abstract the data using topological methods before presenting it. We assume the input points to be samples of a random variable with a high-dimensional probability distribution which we approximate using kernel density estimates on a suitably reconstructed mesh. From the resulting scalar field we extract the join tree and present it as a topological landscape, a visualization metaphor that utilizes the human capability of understanding natural terrains. In this landscape, dense clusters of points show up as hills. The nesting of hills indicates the nesting of clusters. We augment the landscape with the data points to allow selection and inspection of single points and point sets. We also present optimizations to make our algorithm applicable to large data sets and to allow interactive adaption of our visualization to the kernel window width used in the density estimation.


title: Efficient memory safety for TinyOS

abstract: Reliable sensor network software is difficult to create: applications are concurrent and distributed, hardware-based memory protection is unavailable, and severe resource constraints necessitate the use of unsafe, low-level languages. Our work improves this situation by providing efficient memory and type safety for TinyOS 2 applications running on the Mica2, MicaZ, and TelosB platforms. Safe execution ensures that array and pointer errors are caught before they can corrupt RAM. Our contributions include showing that aggressive optimizations can make safe execution practical in terms of resource usage; developing a technique for efficiently enforcing safety under interrupt-driven concurrency; extending the nesC language and compiler to support safety annotations; finding previously unknown bugs in TinyOS; and, finally, showing that safety can be exploited to increase the availability of sensor networks applications even when memory errors are left unfixed.


title: Prediction of financial information manipulation by using support vector machine and probabilistic neural network

abstract: Different methods have been used to predict financial information manipulation that can be defined as the distortion of the information in the financial statements. The purpose of this paper is to predict financial information manipulation by using support vector machine (SVM) and probabilistic neural network (PNN). A number of financial ratios are used as explanatory variables. Test performance of classification accuracy, sensitivity and specificity statistics for PNN and SVM are compared with the results of discriminant analysis, logistics regression (logit), and probit classifiers, which have been used in other studies. We have found that the performance of SVM and PNN are higher than that of the other classifiers analyzed before. Thus, both classifiers can be used as automated decision support system for the detection of financial information manipulation.


Fpga Based High Speed Parallel Cyclic Redundancy Check


title: Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks

abstract: Eye movements in the case of freely viewing natural scenes are believed to be guided by local contrast, global contrast, and top-down visual factors. Although a lot of previous works have explored these three saliency cues for several years, there still exists much room for improvement on how to model them and integrate them effectively. This paper proposes a novel computation model to predict eye fixations, which adopts a multiresolution convolutional neural network (Mr-CNN) to infer these three types of saliency cues from raw image data simultaneously. The proposed Mr-CNN is trained directly from fixation and nonfixation pixels with multiresolution input image regions with different contexts. It utilizes image pixels as inputs and eye fixation points as labels. Then, both the local and global contrasts are learned by fusing information in multiple contexts. Meanwhile, various top-down factors are learned in higher layers. Finally, optimal combination of top-down factors and bottom-up contrasts can be learned to predict eye fixations. The proposed approach significantly outperforms the state-of-the-art methods on several publically available benchmark databases, demonstrating the superiority of Mr-CNN. We also apply our method to the RGB-D image saliency detection problem. Through learning saliency cues induced by depth and RGB information on pixel level jointly and their interactions, our model achieves better performance on predicting eye fixations in RGB-D images.


title: Improving vulnerability discovery models

abstract: Security researchers are applying software reliability models to vulnerability data, in an attempt to model the vulnerability discovery process. I show that most current work on these vulnerability discovery models (VDMs) is theoretically unsound. I propose a standard set of definitions relevant to measuring characteristics of vulnerabilities and their discovery process. I then describe the theoretical requirements of VDMs and highlight the shortcomings of existing work, particularly the assumption that vulnerability discovery is an independent process.


title: OpenEval: Web Information Query Evaluation

abstract: In this paper, we investigate information validation tasks that are initiated as queries from either automated agents or humans. We introduce OpenEval, a new online information validation technique, which uses information on the web to automatically evaluate the truth of queries that are stated as multiargument predicate instances (e.g., DrugHasSideEffect(Aspirin, GI Bleeding))). OpenEval gets a small number of instances of a predicate as seed positive examples and automatically learns how to evaluate the truth of a new predicate instance by querying the web and processing the retrieved unstructured web pages. We show that OpenEval is able to respond to the queries within a limited amount of time while also achieving high F1 score. In addition, we show that the accuracy of responses provided by OpenEval is increased as more time is given for evaluation. We have extensively tested our model and shown empirical results that illustrate the effectiveness of our approach compared to related techniques.


Floppy Lateral Position


title: Route prediction using trip observations and map matching

abstract: This paper uses location data traces (from GPS, Mobile Signals etc.) of past trips of vehicles to develop algorithm for predicting the end-to-end route of a vehicle. Focus is on overall route prediction rather than predicting road segments in short term. Researches in past for route prediction makes use of raw location data traces data decomposed into trips for such route predictions. This paper introduces an additional step to convert trips composed of location data traces points to trips of road network edges. This requires the algorithm to make use of road networks. We show that efficiency in storage and time complexity can be achieved without sacrificing the accuracy by doing so. Moreover, its well-known that location traces data has inherent inaccuracies due to hardware limitations of devices. Most of the researches don't handle it. This paper presents the results of route prediction algorithms under inaccuracies in data.


title: A microwave photonics-based inverse synthetic aperture radar system

abstract: We demonstrate a microwave imaging radar experimental platform, using photonic generation of linear frequency modulated waveform centered at 10GHz with 4GHz bandwidth. Distance and velocity resolution of about 5cm and 2m/s are obtained experimentally.


title: Self-biasing effects induced by RF step-stress in Ka-band LNAs based on InAlN/GaN HEMT technology

abstract: Nitride technologies are proposing a large variety of active devices to address high-power modules, but also robust low-noise receivers at high frequencies. High Electron Mobility Transistors (HEMT) are essentially developed on AlGaN/GaN heterostructure, but InAlN/GaN alternative seems very promising due to lattice matched layers (using 17% of In content) at the interface between layers where the channel (2DEG) occurs, and to a better mobility in the 2DEG. This paper presents a study on Ka-band Low Noise Amplifiers featuring a Noise Figure of 3.3dB between 29–30.5 GHz for the single stage version under study. RF step-stresses and CW stresses have been applied on four different LNAs, evidencing fluctuation of charges in the active device. Self-biasing of the HEMT is emphasized and assessed through the evaluation of the 2nd order harmonic at 59 GHz. Initial noise and dynamic performances can be fully recovered after a long period with no RF signal, or by applying a positive voltage on the gate to remove charges under the gated zone of the transistor. These results on the stability of the noise figure of LNAs prove that jamming signals can be harmless on GaN based receivers, even if some more improvements have to be achieved on these not yet mature technologies.


title: Plasmonics for extreme light concentration and manipulation

abstract: Plasmonic structures are ideally suited to manipulate light on a scale that is much smaller than the wavelength of the plasmon resonance. This review discusses the applications arising from such extreme light concentration, which range from photonic devices and photovoltaics to localized thermal effects.


title: Accurate GSM Indoor Localization

abstract: Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median accuracy of 5 meters in large multi-floor buildings. The key idea that makes accurate GSM-based indoor localization possible is the use of wide signal-strength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but too weak to be used for efficient communication. Experiments conducted on three multi-floor buildings show that our system achieves accuracy comparable to an 802.11-based implementation, and can accurately differentiate between floors in both wooden and steel-reinforced concrete structures.


Hidden Markov Model and Genetic Algorithm Based Credit Card Fraud Detection


title: p88110: A Graphical Simulator for Computer Architecture and Organization Courses

abstract: Studying fundamental computer architecture and organization topics requires a significant amount of practical work if students are to acquire a good grasp of the theoretical concepts presented in classroom lectures or textbooks. The use of simulators is commonly adopted in order to reach this objective. However, as most of the available educational simulators focus on specific topics, different laboratory assignments usually require the use of different simulators. This paper presents a graphical and interactive reduced instruction set computer (RISC) processor and memory simulator that allows active learning of some theoretical concepts covered in computer architecture and organization courses. The simulator can be configured to present different processor views, from a simple serial one, without caches or pipelines, to a more realistic one with caches and superscalar execution. This approach allows a set of increasingly complex code-based laboratory assignments to be developed using a single simulator, covering topics ranging from assembly language programming to the analysis of the different kind of cache misses, pipeline hazards or branch prediction hits and misses produced during a program execution. The simulator has been included in a an automatic assessment system that helps the students to complete the assignments and helps teachers to evaluate the correctness of the students' solutions in different environments, such as high-enrollment courses or distance education. Since 1996, both the simulator and the automatic assessment system have been successfully used by more than 5000 students in computer architecture and organization courses at the Technical University of Madrid (UPM), Spain.


title: Cascaded DC–DC Converter Photovoltaic Systems: Power Optimization Issues

abstract: This paper investigates the issues of ensuring global power optimization for cascaded dc-dc converter architectures of photovoltaic (PV) generators irrespective of the irradiance conditions. The global optimum of such connections of PV modules is generally equivalent with performing the maximum power point tracking (MPPT) on all the modules. The most important disturbance occurs when the irradiance levels of modules happen to be sensibly different from a module to another - in this case, voltage-limitation requirements may be broken. The proposed supervisory algorithm then attempts to establish the best suboptimal power regime. Validation has been achieved by MATLAB/Simulink numerical simulation in the case of a single-phase grid-connected PV system, where individual MPPTs have been implemented by an extremum-seeking control, a robust and less-knowledge-demanding perturb-and-observe method.


title: Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin

abstract: In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several attacks threaten the ability to participate in pools. The block withholding (BWH) attack makes the pool reward system unfair by letting malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH attacks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another attack called selfish mining, an attacker can unfairly earn extra rewards by deliberately generating forks. In this paper, we propose a novel attack called a fork after withholding (FAW) attack. FAW is not just another attack. The reward for an FAW attacker is always equal to or greater than that for a BWH attacker, and it is usable up to four times more often per pool than in BWH attack. When considering multiple pools - the current state of the Bitcoin network - the extra reward for an FAW attack is about 56% more than that for a BWH attack. Furthermore, when two pools execute FAW attacks on each other, the miner's dilemma may not hold: under certain circumstances, the larger pool can consistently win. More importantly, an FAW attack, while using intentional forks, does not suffer from practicality issues, unlike selfish mining. We also discuss partial countermeasures against the FAW attack, but finding a cheap and efficient countermeasure remains an open problem. As a result, we expect to see FAW attacks among mining pools.


title: Robust Blur Kernel Estimation for License Plate Images From Fast Moving Vehicles

abstract: As the unique identification of a vehicle, license plate is a key clue to uncover over-speed vehicles or the ones involved in hit-and-run accidents. However, the snapshot of over-speed vehicle captured by surveillance camera is frequently blurred due to fast motion, which is even unrecognizable by human. Those observed plate images are usually in low resolution and suffer severe loss of edge information, which cast great challenge to existing blind deblurring methods. For license plate image blurring caused by fast motion, the blur kernel can be viewed as linear uniform convolution and parametrically modeled with angle and length. In this paper, we propose a novel scheme based on sparse representation to identify the blur kernel. By analyzing the sparse representation coefficients of the recovered image, we determine the angle of the kernel based on the observation that the recovered image has the most sparse representation when the kernel angle corresponds to the genuine motion angle. Then, we estimate the length of the motion kernel with Radon transform in Fourier domain. Our scheme can well handle large motion blur even when the license plate is unrecognizable by human. We evaluate our approach on real-world images and compare with several popular state-of-the-art blind image deblurring algorithms. Experimental results demonstrate the superiority of our proposed approach in terms of effectiveness and robustness.


title: A survey on fall detection: Principles and approaches

abstract: Fall detection is a major challenge in the public health care domain, especially for the elderly, and reliable surveillance is a necessity to mitigate the effects of falls. The technology and products related to fall detection have always been in high demand within the security and the health-care industries. An effective fall detection system is required to provide urgent support and to significantly reduce the medical care costs associated with falls. In this paper, we give a comprehensive survey of different systems for fall detection and their underlying algorithms. Fall detection approaches are divided into three main categories: wearable device based, ambience device based and vision based. These approaches are summarised and compared with each other and a conclusion is derived with some discussions on possible future work.


title: Effect of Air-Gap Between a Ferrite Plate and Metal Strips on Magnetic Shielding

abstract: This paper explores the effect of an air-gap between a ferrite plate and metal strips on magnetic shielding in the presence of a periodic line current source based on an analytical solution. The Fourier transform is used to represent the magnetic vector potentials in the spectral domain, and the mode-matching technique is enforced to obtain a modal solution in rapidly converging series form, leading to numerically efficient results. Magnetic shielding effectiveness is investigated in terms of the air-gap thickness between the ferrite plate and metal strips, the metal strip width, and the position of a periodic line current source.


title: Sentiment analysis on product reviews

abstract: Sentiment analysis is used for Natural language Processing, text analysis, text preprocessing, Stemming etc. are the major research field in current time. Sentiment analysis using different techniques and tools for analyze the unstructured data in a manner that objective results can be generated from them. Basically, these techniques allow a computer to understand what is being said by humans. Sentiment analysis uses different techniques to determine the sentiment of a text or sentence. The Internet is a large repository of natural language. People share their thoughts and experiences which are subjective in nature. Many a time, getting suitable information about a product can became tedious for customers. Companies may not be fully aware of customer requirements. Product reviews can be analyzed to understand the sentiment of the people towards a particular topic. However, these are voluminous; therefore a summary of positive and negative reviews needs to be generated. In this paper, the main focus is on the review of algorithms and techniques used for extract feature wise summary of the product and analyzed them to form an authentic review. Future work will include more product reviews websites and will focus on higher level natural language processing tasks. Using best and new techniques or tool for more accurate result in which the system except only those keywords which are in dataset rest of the words are eliminated by the system.


title: Towards Open Set Deep Networks

abstract: Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is easy to generate images that humans would never classify as a particular object class, yet networks classify such images high confidence as that given class - deep network are easily fooled with images humans do not consider meaningful. The closed set nature of deep networks forces them to choose from one of the known classes leading to such artifacts. Recognition in the real world is open set, i.e. the recognition system should reject unknown/unseen classes at test time. We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class. A key element of estimating the unknown probability is adapting Meta-Recognition concepts to the activation patterns in the penultimate layer of the network. OpenMax allows rejection of "fooling" and unrelated open set images presented to the system; OpenMax greatly reduces the number of obvious errors made by a deep network. We prove that the OpenMax concept provides bounded open space risk, thereby formally providing an open set recognition solution. We evaluate the resulting open set deep networks using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation data, and thousands of fooling and open set images. The proposed OpenMax model significantly outperforms open set recognition accuracy of basic deep networks as well as deep networks with thresholding of SoftMax probabilities.


title: Short-term plasticity and long-term potentiation mimicked in single inorganic synapses

abstract: The electronic properties of inorganic devices such as memristors can be used to simulate neurological behaviour. In particular, ionic and electronic effects in a silver sulphide device are now shown to mimic short- and long-term synaptic functions.


title: Modeling primary school pre-service teachers' Technological Pedagogical Content Knowledge (TPACK) for meaningful learning with information and communication technology (ICT)

abstract: Within the field of educational technology, Technological Pedagogical Content Knowledge (TPACK) has been theorized as a seven-factor construct to describe teacher's integration of information and communication technology (ICT) in their teaching. However, this framework has yet to be successfully validated through survey instruments. This paper examines the construct validity of a TPACK survey that was contextualized for the pedagogical approaches employed in a 12-week ICT course designed with reference to the TPACK framework for Singaporean primary school pre-service teachers. Using this framework, the researchers were able to uncover five of the seven TPACK constructs which were a better model fit as compared with several extant studies of TPACK surveys. Using these results, pre and post-course structural equation models were constructed to explain the relationships amongst the different constructs of teachers' TPACK perceptions. It was found that pedagogical knowledge had a direct impact on TPACK at the beginning of the course. As teachers made connections between their technological knowledge and pedagogical knowledge to form technological pedagogical knowledge during the course, the direct relation between pedagogical knowledge and TPACK became insignificant where as the relations between pedagogical knowledge and technological pedagogical knowledge, and technological pedagogical knowledge and TPACK were strengthened. The comparison between the pre and post-course models also revealed that the pre-service teachers' perceived relations between content knowledge and TPACK changes from insignificant to significant. The implications of these findings and suggestions to improve the construct validation of the TPACK framework are discussed in this paper.


title: Adaptive Algorithm Using Hot-Electron Injection for Programming Analog Computational Memory Elements Within 0.2% of Accuracy Over 3.5 Decades

abstract: This paper describes a new predictive algorithm that can be used for programming large arrays of analog computational memory elements within 0.2% of accuracy for 3.5 decades of currents. The average number of pulses required are 7-8 (20 mus each). This algorithm uses hot-electron injection for accurate programming and Fowler-Nordheim tunneling for global erase. This algorithm has been tested for programming 1024times16 and 96times16 floating-gate arrays in 0.25 mum and 0.5 mum n-well CMOS processes, respectively


title: Evaluation of Stereo Matching Costs on Images with Radiometric Differences

abstract: Stereo correspondence methods rely on matching costs for computing the similarity of image locations. We evaluate the insensitivity of different costs for passive binocular stereo methods with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants like the absolute difference, the sampling-insensitive absolute difference, and normalized cross correlation, as well as their zero-mean versions. We also consider filters like LoG, mean, and bilateral background subtraction (BilSub) and nonparametric measures like Rank, SoftRank, Census, and Ordinal. Finally, hierarchical mutual information (HMI) is considered as pixelwise cost. Using stereo data sets with ground-truth disparities taken under controlled changes of exposure and lighting, we evaluate the costs with a local, a semiglobal, and a global stereo method. We measure the performance of all costs in the presence of simulated and real radiometric differences, including exposure differences, vignetting, varying lighting, and noise. Overall, the ranking of methods across all data sets and experiments appears to be consistent. Among the best costs are BilSub, which performs consistently very well for low radiometric differences; HMI, which is slightly better as pixelwise matching cost in some cases and for strong image noise; and Census, which showed the best and most robust overall performance.


title: A controllable, fast and stable basis for vortex based smoke simulation

abstract: We introduce a novel method for describing and controlling a 3D smoke simulation. Using harmonic analysis and principal component analysis, we define an underlying description of the fluid flow that is compact and meaningful to non-expert users. The motion of the smoke can be modified with high level tools, such as animated current curves, attractors and tornadoes. Our simulation is controllable, interactive and stable for arbitrarily long periods of time. The simulation's computational cost increases linearly in the number of motion samples and smoke particles. Our adaptive smoke particle representation conveniently incorporates the surface-like characteristics of real smoke.


Deep MIML Network.


title: Dense planar SLAM

abstract: Using higher-level entities during mapping has the potential to improve camera localisation performance and give substantial perception capabilities to real-time 3D SLAM systems. We present an efficient new real-time approach which densely maps an environment using bounded planes and surfels extracted from depth images (like those produced by RGB-D sensors or dense multi-view stereo reconstruction). Our method offers the every-pixel descriptive power of the latest dense SLAM approaches, but takes advantage directly of the planarity of many parts of real-world scenes via a data-driven process to directly regularize planar regions and represent their accurate extent efficiently using an occupancy approach with on-line compression. Large areas can be mapped efficiently and with useful semantic planar structure which enables intuitive and useful AR applications such as using any wall or other planar surface in a scene to display a user's content.


title: Development of the Conformity to Masculine Norms Inventory

abstract: This article describes the construction of the Conformity to Masculine Norms Inventory (CMNI), and 5 studies that examined its psychometric properties. Factor analysis indicated 11 distinct factors: Winning, Emotional Control, Risk-Taking, Violence, Dominance, Playboy, Self-Reliance, Primacy of Work, Power Over Women, Disdain for Homosexuals, and Pursuit of Status. Results from Studies 2-5 indicated that the CMNI had strong internal consistency estimates and good differential validity comparing men with women and groups of men on health-related questions; all of the CMNI subscales were significantly and positively related to other masculinity-related measures, with several subscales being related significantly and positively to psychological distress, social dominance, aggression, and the desire to be more muscular, and significantly and negatively to attitudes toward psychological help seeking and social desirability; and CMNI scores had high test-retest estimates for a 2-3 week period.


title: Bringing Robotics to Formal Education: The Thymio Open-Source Hardware Robot

abstract: Mobile robots are valuable tools for education because of both the enthusiasm they raise and the multidisciplinary nature of robotics technology. Mobile robots give access to a wide range of fields, such as complex mechanics, sensors, wireless transmission, mathematics, and computer science. However, despite their potential as educational tools, robots are still not as widespread in schools as they could be. In this article, we identify five key reasons: lack of diversity, high cost, noninclusive design, lack of educational material, and lack of stability over time. Then, we describe our answers to these problems, as we implemented them in the Thymio project: a mature mass-produced open-hardware robot, at a low price, with a multiage and gender-neutral feature set, and with a design promoting creativity, facilitating learning, and providing a wide range of interaction possibilities from built-in behaviors to text programming, passing through different visual programming environments. We highlight some neglected key issues that differentiate open-source hardware from open-source software, for instance the legal uncertainty of designing open hardware using professional computer-aided design (CAD) tools and the difficulty to distribute the development. Our solution to these being to increase the awareness of CAD editors to open-source hardware and to provide a two-layer development model for hardware.


title: Event-related brain potentials and human language

abstract: The human capacity to produce and comprehend language is one of the most distinctive characteristics of our species. However, understanding the cognitive and neural underpinnings of human language has proved difficult, in part because these processes are rapid, complex and (for the most part) inaccessible to conscious reflection. Methodologies are needed that provide continuous measurement during language processing and that do not rely on a conscious response. One such method involves the recording of event-related brain potentials (ERPs) elicited during language comprehension or production. ERPs are continuous, multidimensional records of the electrical activity that occurs in the brain during the process of interest. We review recent work demonstrating that ERPs are quite sensitive to (at least some of) the psychological and neural events underlying human language. Indeed, researchers have used ERPs to investigate the separability of syntactic and semantic processes, the on-line analysis of sentence constituent structure and the lexical processing capacities of language-disordered populations.


title: Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement

abstract: Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers. Here we present two novel techniques to address inherent challenges in the application of ML to the domain of mammography. We describe the use of genetic search of image enhancement methods, leading us to the use of a novel form of false color enhancement through contrast limited adaptive histogram equalization (CLAHE), as a method to optimize mammographic feature representation. We also utilize dual deep convolutional neural networks at different scales, for classification of full mammogram images and derivative patches combined with a random forest gating network as a novel architectural solution capable of discerning malignancy with a specificity of 0.91 and a specificity of 0.80. To our knowledge, this represents the first automatic stand-alone mammography malignancy detection algorithm with sensitivity and specificity performance similar to that of expert radiologists.


PAN 2017: Author Profiling - Gender and Language Variety Prediction


Sentiment Analysis of Movie Reviews using Machine Learning Techniques


Aspects of the diachronic (in)stability of complex morphology


Human Modeling for Autonomous Vehicles : Reachability Analysis , Online Learning , and Driver Monitoring for Behavior Prediction


title: A tree-edit-distance algorithm for comparing simple, closed shapes

abstract: A b s t r a c t We discuss a graph-algorithmic approach to comparing shapes. We focus in this paper on comparing simple closed curves in the plane. Our approach is to (1) represent such a shape by its skeleton, which is a tree embedded in the plane, and (2) compare two shapes by comparing their skeletons via tree edit-distance. In this paper, we define our version of tree edit-distance (it differs from that previously described in the literature), and give a polynomial-time algorithm to compute the distance between two trees.


An Event-Schematic, Cooperative, Cognitive Architecture Plays Super Mario


title: Dynamically Self-Reconfigurable Machine Learning Structure for FPGA Implementation

abstract: In this paper, we describe organization of a machine learning system based on dynamically reconfigurable architecture and self-organization. This system learns typical neural network tasks using self-organizing learning array algorithm described elsewhere. To develop this system, we adopt hardware-software codesign approach based on combining an array of VIRTEX XCV1000 FPGAs with custom software – Matlab/C++. The prototype structure is divided into hardware architecture, software programs and their interface. Hardware architecture dynamically implements the neurons training and voting. Software programs implement control of database and system level management, and are interfaced with hardware via PCI bus using developed C++ dynamic libraries and interface logic.


title: Ten years of a model of aesthetic appreciation and aesthetic judgments : The aesthetic episode - Developments and challenges in empirical aesthetics

abstract: About a decade ago, psychology of the arts started to gain momentum owing to a number of drives: technological progress improved the conditions under which art could be studied in the laboratory, neuroscience discovered the arts as an area of interest, and new theories offered a more comprehensive look at aesthetic experiences. Ten years ago, Leder, Belke, Oeberst, and Augustin (2004) proposed a descriptive information-processing model of the components that integrate an aesthetic episode. This theory offered explanations for modern art's large number of individualized styles, innovativeness, and for the diverse aesthetic experiences it can stimulate. In addition, it described how information is processed over the time course of an aesthetic episode, within and over perceptual, cognitive and emotional components. Here, we review the current state of the model, and its relation to the major topics in empirical aesthetics today, including the nature of aesthetic emotions, the role of context, and the neural and evolutionary foundations of art and aesthetics.


title: A Natural Language Tutorial Dialogue System for Physics

abstract: Abstract : We describe the WHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multi-sentential explanations. We explore approaches for achieving a deeper understanding of these explanations and dialogue management approaches and strategies for providing appropriate feedback on them.


title: Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

abstract: Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element ${x}$ is a member of a given set ${S}$ or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.


title: A Survey on Energy Internet Communications for Sustainability

abstract: Energy Internet (EI) is proposed as the evolution of smart grid, aiming to integrate various forms of energy into a highly flexible and efficient grid that provides energy packing and routing functions, similar to the Internet. As an essential part in EI system, a scalable and interoperable communication infrastructure is critical in system construction and operation. In this article, we survey the recent research efforts on EI communications. The motivation and key concepts of EI are first introduced, followed by the key technologies and standardizations enabling the EI communications as well as security issues. Open challenges in system complexity, efficiency, reliability are explored and recent achievements in these research topics are summarized as well.