title: Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus Photographs

abstract: The purpose of computer-aided detection or diagnosis (CAD) technology has so far been to serve as a second reader. If, however, all relevant lesions in an image can be detected by CAD algorithms, use of CAD for automatic reading or prescreening may become feasible. This work addresses the question how to fuse information from multiple CAD algorithms, operating on multiple images that comprise an exam, to determine a likelihood that the exam is normal and would not require further inspection by human operators. We focus on retinal image screening for diabetic retinopathy, a common complication of diabetes. Current CAD systems are not designed to automatically evaluate complete exams consisting of multiple images for which several detection algorithm output sets are available. Information fusion will potentially play a crucial role in enabling the application of CAD technology to the automatic screening problem. Several different fusion methods are proposed and their effect on the performance of a complete comprehensive automatic diabetic retinopathy screening system is evaluated. Experiments show that the choice of fusion method can have a large impact on system performance. The complete system was evaluated on a set of 15 000 exams (60 000 images). The best performing fusion method obtained an area under the receiver operator characteristic curve of 0.881. This indicates that automated prescreening could be applied in diabetic retinopathy screening programs.


title: Design of a 0.6-V 0.2-mW CMOS MEMS accelerometer

abstract: This paper presents a low-voltage low-power monolithic complementary metal-oxide-semiconductor (CMOS) micro-electromechanical-system (MEMS) accelerometer design. This design utilizes low-voltage design techniques without using low-threshold devices or internal supply voltage boosting. The accelerometer, designed in the 0.18-μm CMOS MEMS process, contains the micro-mechanical structure, readout circuits, and a 16-bit delta-sigma analog-to-digital converter (ΔΣ ADC). It occupies an area of only 0.8 × 1 mm2 and draws 0.33 mA of current from a 0.6-V supply. The simulated sensitivity is 3000 LSB/g and the nonlinearity is 0.78% within the ±6 g sensing range.


title: III–V/Si Vernier-Ring Comb Lasers (VRCLs)

abstract: We present a novel reconfigurable comb laser design that enables channel sparing, and demonstrate its functionality in a III–V/Si external cavity configuration. The laser is comprised of a single shared tunable Si ring mirror Vernier-paired with an array of tunable channel ring filters each coupled to their respective reflective semiconductor optical amplifier. This comb laser is not only tunable, capable of shifting all comb lines together, but it is flexible, able to change the spacing of channels in the comb within a fixed grid defined by the ring mirror. This flexibility results in features not previously demonstrated in a comb, such as mixed channel spacing and channel sparing capability. This is a practical candidate for flexible, scalable wavelength division multiplexed links targeting the next generation of data centers for the cloud.


title: Residual Dense Network for Image Restoration

abstract: Recently, deep convolutional neural network (CNN) has achieved great success for image restoration (IR) and provided hierarchical features at the same time. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images, thereby resulting in relatively-low performance. In this work, we propose a novel and efficient residual dense network (RDN) to address this problem in IR, by making a better tradeoff between efficiency and effectiveness in exploiting the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via densely connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory mechanism. To adaptively learn more effective features from preceding and current local features and stabilize the training of wider network, we proposed local feature fusion in RDB. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. We demonstrate the effectiveness of RDN with several representative IR applications, single image super-resolution, Gaussian image denoising, image compression artifact reduction, and image deblurring. Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually.


title: Current State of Text Sentiment Analysis from Opinion to Emotion Mining

abstract: Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.


Introduction to automata theory, languages, and computation, 2nd edition


title: A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM

abstract: This paper proposes a sensorless speed control strategy for a permanent-magnet synchronous motor (PMSM) based on a new sliding-mode observer (SMO), which substitutes a sigmoid function for the signum function with a variable boundary layer. In order to apply a sensorless PMSM control which is robust against parameter fluctuations and disturbances, a high-speed SMO is proposed, which estimates the rotor position and the angular velocity from the back EMF. In the conventional SMO, a low-pass filter and an additional position compensation of the rotor are used to reduce the chattering problem that is commonly found in the SMO using the signum function. In order to overcome the time delay caused by the low-pass filter, in this research, a sigmoid function is used for the switching function instead of the signum function. Also, the variation in the stator resistance is estimated to improve the steady-state performance of the SMO. The stability of the proposed SMO was verified using the Lyapunov second method to determine the observer gain. The validity of the proposed high-speed PMSM sensorless velocity control has been demonstrated with simulations and real experiments.


title: A Review of Power Harvesting from Vibration Using Piezoelectric Materials

abstract: The process of acquiring the energy surround- ing a system and converting it into usable electrical energy is termed power harvesting. In the last few years, there has been a surge of research in the area of power harvesting. This increase in research has been brought on by the mod- ern advances in wireless technology and low-power electron- ics such as microelectromechanical systems. The advances have allowed numerous doors to open for power harvesting systems in practical real-world applications. The use of pie- zoelectric materials to capitalize on the ambient vibrations surrounding a system is one method that has seen a dramat- ic rise in use for power harvesting. Piezoelectric materials have a crystalline structure that provides them with the ability to transform mechanical strain energy into electrical charge and, vice versa, to convert an applied electrical potential into mechanical strain. This property provides these materials with the ability to absorb mechanical energy from their surround- ings, usually ambient vibration, and transform it into electrical energy that can be used to power other devices. While piezo- electric materials are the major method of harvesting energy, other methods do exist; for example, one of the conventional methods is the use of electromagnetic devices. In this paper we discuss the research that has been performed in the area of power harvesting and the future goals that must be achieved for power harvesting systems to find their way into everyday use. and replacement of the battery can become a tedious task. In the case of wireless sensors, these devices can be placed in very remote locations such as structural sensors on a bridge or global positioning system (GPS) tracking devices on ani- mals in the wild. When the battery is extinguished of all its power, the sensor must be retrieved and the battery re- placed. Because of the remote placement of these devices, obtaining the sensor simply to replace the battery can be- come a very expensive task or even impossible. For in- stance, in civil infrastructure applications it is often desirable to embed the sensor, making battery replacement unfeasible. If ambient energy in the surrounding medium could be ob- tained, then it could be used to replace or charge the battery. One method is to use piezoelectric materials to obtain ener- gy lost due to vibrations of the host structure. This captured energy could then be used to prolong the life of the power supply or in the ideal case provide endless energy for the electronic devices lifespan. For these reasons, the amount of research devoted to power harvesting has been rapidly in- creasing. In this paper we review and detail some of the top- ics in power harvesting that have been receiving the most research, including energy harvesting from mechanical vi- bration, biological systems, and the effects of power har- vesting on the vibration of a structure.


title: Trawling the Web for emerging cyber-communities

abstract: The Web harbors a large number of communities — groups of content-creators sharing a common interest — each of which manifests itself as a set of interlinked Web pages. Newgroups and commercial Web directories together contain of the order of 20,000 such communities; our particular interest here is on emerging communities — those that have little or no representation in such fora. The subject of this paper is the systematic enumeration of over 100,000 such emerging communities from a Web crawl: we call our process trawling. We motivate a graph-theoretic approach to locating such communities, and describe the algorithms, and the algorithmic engineering necessary to find structures that subscribe to this notion, the challenges in handling such a huge data set, and the results of our experiment. © 1999 Published by Elsevier Science B.V. All rights reserved.


title: What makes good research in software engineering

abstract: Physics, biology, and medicine have well-refined public explanations of their research processes. Even in simplified form, these provide guidance about what counts as “good research” both inside and outside the field. Software engineering has not yet explicitly identified and explained either our research processes or the ways we recognize excellent work. Science and engineering research fields can be characterized in terms of the kinds of questions they find worth investigating, the research methods they adopt, and the criteria by which they evaluate their results. I will present such a characterization for software engineering, showing the diversity of research strategies and the way they shift as ideas mature. Understanding these strategies should help software engineers design research plans and report the results clearly; it should also help explain the character of software engineering research to computer science at large and to other scientists.


title: A reverse-coupled bipolar coil structure for an integrated LCC-compensated inductive power transfer system

abstract: This paper proposes a reverse-coupled bipolar coil structure to realize an integrated LCC-compensated inductive power transfer (IPT) system. Two unipolar coils are connected reversely, resulting in a bipolar structure, which is used as the compensation inductor in an LCC-compensated IPT system. This bipolar coil is integrated into the main coil to save space, and its dimensions are designed to eliminate its magnetic coupling with the same-side main coil and simplify the circuit analysis. The working principle of the integrated IPT system is presented, which shows that the proposed bipolar coil can help improve the system performance with position and angular misalignment in the horizontal plane. A prototype is designed and implemented to validate the proposed bipolar coil structure. The primary coil dimension is 450mm×450mm, and the secondary coil size is 300mm ×300mm. Experiments show that it can achieve 2.15kW power transfer with 89.9% dc-dc efficiency across an airgap of 200mm. When there is 150mm misalignment, the system power and efficiency are not significantly affected, and it can still maintain 80.5% of the well-aligned power with 89.0% efficiency.


Mention and Entity Description Co-Attention For Entity Disambiguation


Effect of magnetron target temperature on deposition rate and structure of Zr–ZrO2thin films


title: LPV Identification of Wind Turbine Rotor Vibrational Dynamics Using Periodic Disturbance Basis Functions

abstract: This brief presents an identification experiment performed on the coupled dynamics of the edgewise bending vibrations of the rotor blades and the in-plane motion of the drivetrain of three-bladed wind turbines. These dynamics vary with rotor speed, and are subject to periodic wind flow disturbances. This brief demonstrates that this time-varying behavior can be captured in a linear parameter-varying (LPV) model with the rotor speed as the scheduling signal, and with additional sinusoidal inputs that are used as basis functions for the periodic wind flow disturbances. By including these inputs, the predictor-based LPV subspace identification approach (LPV PBSIDopt) was tailored for wind turbine applications. Using this tailor-made approach, the LPV model is identified from data measured with the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory's National Wind Technology Center.


title: Text mining for causal relations

abstract: Given a semantic relation, the automatic extraction of linguistic patterns that express that relation is a rather difficult problem. This paper presents a semi-automatic method of discovering generally applicable lexico-syntactic patterns that refer to the causal relation. The patterns are found automatically, but their validation is done semi-automatically.


title: Clustera: an integrated computation and data management system

abstract: This paper introduces Clustera, an integrated computation and data management system. In contrast to traditional cluster-management systems that target specific types of workloads, Clustera is designed for extensibility, enabling the system to be easily extended to handle a wide variety of job types ranging from computationally-intensive, long-running jobs with minimal I/O requirements to complex SQL queries over massive relational tables. Another unique feature of Clustera is the way in which the system architecture exploits modern software building blocks including application servers and relational database systems in order to realize important performance, scalability, portability and usability benefits. Finally, experimental evaluation suggests that Clustera has good scale-up properties for SQL processing, that Clustera delivers performance comparable to Hadoop for MapReduce processing and that Clustera can support higher job throughput rates than previously published results for the Condor and CondorJ2 batch computing systems.


title: A Review of Deep Learning Methods Applied on Load Forecasting

abstract: The utility industry has invested widely in smart grid (SG) over the past decade. They considered it the future electrical grid while the information and electricity are delivered in two-way flow. SG has many Artificial Intelligence (AI) applications such as Artificial Neural Network (ANN), Machine Learning (ML) and Deep Learning (DL). Recently, DL has been a hot topic for AI applications in many fields such as time series load forecasting. This paper introduces the common algorithms of DL in the literature applied to load forecasting problems in the SG and power systems. The intention of this survey is to explore the different applications of DL that are used in the power systems and smart grid load forecasting. In addition, it compares the accuracy results RMSE and MAE for the reviewed applications and shows the use of convolutional neural network CNN with k-means algorithm had a great percentage of reduction in terms of RMSE.


title: A Java processor architecture for embedded real-time systems

abstract: Architectural advancements in modern processor designs increase average performance with features such as pipelines, caches, branch prediction, and out-of-order execution. However, these features complicate worst-case execution time analysis and lead to very conservative estimates. JOP (Java Optimized Processor) tackles this problem from the architectural perspective - by introducing a processor architecture in which simpler and more accurate WCET analysis is more important than average case performance. This paper presents a Java processor designed for time-predictable execution of real-time tasks. JOP is the implementation of the Java virtual machine in hardware. JOP is intended for applications in embedded real-time systems and the primary implementation technology is in a field programmable gate array. This paper demonstrates that a hardware implementation of the Java virtual machine results in a small design for resource-constrained devices.


title: Using Multiple Barometers to Detect the Floor Location of Smart Phones with Built-in Barometric Sensors for Indoor Positioning

abstract: Following the popularity of smart phones and the development of mobile Internet, the demands for accurate indoor positioning have grown rapidly in recent years. Previous indoor positioning methods focused on plane locations on a floor and did not provide accurate floor positioning. In this paper, we propose a method that uses multiple barometers as references for the floor positioning of smart phones with built-in barometric sensors. Some related studies used barometric formula to investigate the altitude of mobile devices and compared the altitude with the height of the floors in a building to obtain the floor number. These studies assume that the accurate height of each floor is known, which is not always the case. They also did not consider the difference in the barometric-pressure pattern at different floors, which may lead to errors in the altitude computation. Our method does not require knowledge of the accurate heights of buildings and stories. It is robust and less sensitive to factors such as temperature and humidity and considers the difference in the barometric-pressure change trends at different floors. We performed a series of experiments to validate the effectiveness of this method. The results are encouraging.


title: A Novel Switched-Coupled-Inductor DC–DC Step-Up Converter and Its Derivatives

abstract: This paper presents a novel dc–dc converter configuration, which successfully integrates two technologies, including a switched capacitor and a switched coupled inductor, into one converter. By adopting a coupled inductor to charge a switched capacitor, the voltage gain can be effectively increased, and the turns ratio of the coupled inductor can be also reduced. Not only lower conduction losses but also higher power conversion efficiency is benefited from a lower part count and lower turns ratios. The proposed converter is simply composed of six components, which can be further derived to varied converters for different purposes, such as a bidirectional converter. The operating principle and steady-state analysis are discussed in this paper. A 250-W laboratory hardware prototype is completed and verified. The voltage gain is up to 11. The highest efficiency is 97.2%, and the full-load efficiency is kept at 93.6%.


title: Remote-Controllable Power Outlet System for Home Power Management

abstract: In this paper we describe the Wireless Power-Controlled Outlet Module (WPCOM) with a scalable mechanism for home power management which we have developed. The WPCOM integrates the multiple AC power sockets and a simple low-power microcontroller into a power outlet to switch the power of the sockets ON/OFF and to measure the power consumption of plugged electric home appliances. Our WPCOM consists of six scalable modules, that is, the Essential Control Module, the Bluetooth Module, the GSM Module, the Ethernet Module, the SD Card Module and the Power Measuring Module, which together provide an indoor wireless, and an outdoor remote control and monitor of electric home appliances. We have designed a PDA control software and remote control software which support the Graphic User Interface, thus allowing the user to easily monitor the electric home appliances through the PDA and the Internet individually. In addition, we use a Short Message Service to achieve control and monitoring through a GSM cellular mobile phone for remote use anytime and anywhere.


title: Resonant frequency biofeedback training to increase cardiac variability: rationale and manual for training.

abstract: Heart rate and blood pressure, as well as other physiological systems, among healthy people, show a complex pattern of variability, characterized by multifrequency oscillations. There is evidence that these oscillations reflect the activity of homeostatic reflexes. Biofeedback training to increase the amplitude of respiratory sinus arrhythmia (RSA) maximally increases the amplitude of heart rate oscillations only at approximately 0.1 Hz. To perform this task people slow their breathing to this rate to a point where resonance occurs between respiratory-induced oscillations (RSA) and oscillations that naturally occur at this rate, probably triggered in part by baroreflex activity. We hypothesize that this type of biofeedback exercises the baroreflexes, and renders them more efficient. A manual is presented for carrying out this method. Supporting data are provided in Lehrer, Smetankin, and Potapova (2000) in this issue.


title: Injecting Relational Structural Representation in Neural Networks for Question Similarity

abstract: Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning an SVM model using Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs trained with our approach can learn more accurate models, especially after fine tuning on GS.


title: Fake Faces Identification via Convolutional Neural Network

abstract: Generative Adversarial Network (GAN) is a prominent generative model that are widely used in various applications. Recent studies have indicated that it is possible to obtain fake face images with a high visual quality based on this novel model. If those fake faces are abused in image tampering, it would cause some potential moral, ethical and legal problems. In this paper, therefore, we first propose a Convolutional Neural Network (CNN) based method to identify fake face images generated by the current best method [20], and provide experimental evidences to show that the proposed method can achieve satisfactory results with an average accuracy over 99.4%. In addition, we provide comparative results evaluated on some variants of the proposed CNN architecture, including the high pass filter, the number of the layer groups and the activation function, to further verify the rationality of our method.


title: Data security in cloud

abstract: Cloud computing is moving increasingly to a destination with no return: the consolidation is an essential tool for the future existence of the internet world. Cloud computing is used as private, hybrid cloud, big data analytics and backup. Today the trend followed is to store the databases and software's in cloud environment. The services are taken whenever required. Without installing software's in systems users can go to cloud where already software is available and make use of it similarly for storing data also. Security is the main aspect now for the confidential data what we store in cloud. The methodology followed is, to collect resource/data, store data in the cloud, data processing by giving access to fetch the resource to user IP, checking hacker's information and admin responding by restricting respective IP. The restricted IP user can never access the resources anymore. Big data security in cloud, results in tracking unauthorized users (hackers) who try to access the resources. Admin have the special privilege to block the hackers machine by disabling IP address of respective machine. In this current work the data is stored in the server in encrypted fashion and only the admin is given the writes to decrypt the data. If an unauthorized user is trying to access any file or data from the cloud the admin can block the users IP address from accessing the data so that the security for data will be given.


title: Orchestral fully convolutional networks for small lesion segmentation in brain MRI

abstract: White matter (WM) lesion identification and segmentation has proved of clinical importance for diagnosis, treatment and neurological outcomes. Convolutional neural networks (CNN) have demonstrated their success for large lesion load segmentation, but are not sensitive to small deep WM and sub-cortical lesion segmentation. We propose to use multi-scale and supervised fully convolutional networks (FCN) to segment small WM lesions in 22 anemic patients. The multiple scales enable us to identify the small lesions while reducing many false alarms, and the multi-supervised scheme allows a better management of the unbalanced data. Compared to a single FCN (Dice score ~0.31), the performance on the testing dataset of our proposed networks achieved a Dice score of 0.78.


title: of LaborRelative Income, Happiness and Utility: An Explanation for the Easterlin Paradox and Other Puzzles

abstract: The well-known Easterlin paradox points out that average happiness has remained constant over time despite sharp rises in GNP per head. At the same time, a micro literature has typically found positive correlations between individual income and individual measures of subjective well-being. This paper suggests that these two findings are consistent with the presence of relative income terms in the utility function. Income may be evaluated relative to others (social comparison) or to oneself in the past (habituation). We review the evidence on relative income from the subjective well-being literature. We also discuss the relation (or not) between happiness and utility, and discuss some nonhappiness research (behavioral, experimental, neurological) related to income comparisons. We last consider how relative income in the utility function can affect economic models of behavior in the domains of consumption, investment, economic growth, savings, taxation, labor supply, wages, and migration.


title: Reasoning with Belief Functions: An Analysis of Compatibility

abstract: Abstract An earlier position paper has examined the applicability of belief-functions methodology in three reasoning tasks: (1) representation of incomplete knowledge, (2) belief-updating, and (3) evidence pooling. My conclusions were that the use of belief functions encounters basic difficulties along all three tasks, and that extensive experimental and theoretical studies should be undertaken before belief functions could be applied safely. This article responds to the discussion, in this issue, of my conclusions and the degree to which they affect the applicability of belief functions in automated reasoning tasks.


title: Further steps towards efficient runtime verification: handling probabilistic cost models

abstract: We consider high-level models that specify system behaviors probabilistically and support the specification of cost attributes. Specifically, we focus on Discrete Time Markov Reward Models (D-MRMs), i.e. state machines where probabilities can be associated with transitions and rewards (costs) can be associated with states and transitions. Through probabilities we model assumptions on the behavior of environment in which an application is embedded. Rewards can instead model the cost assumptions involved in the system's operations. A system is designed to satisfy the requirements, under the given assumptions. Design-time assumptions, however, can turn out to be invalid at runtime, and therefore it is necessary to verify whether changes may lead to requirements violations. If they do, it is necessary to adapt the behavior in a self-healing manner to continue to satisfy the requirements. We have previously presented an approach to support efficient runtime probabilistic model checking of DTMCs for properties expressed in PCTL. In this paper we extend the approach to D-MRMs and reward properties. The benefits of the approach are justified both theoretically and empirically on significant test cases.


title: A word-order based graph representation for relevance identification

abstract: In this paper we propose a new word-order based graph representation for text. In our graph representation vertices represent words or phrases and edges represent relations between contiguous words or phrases. The graph representation also includes dependency information. Our text representation is suitable for applications involving the identification of relevance or paraphrases across texts, where word-order information would be useful. We show that this word-order based graph representation performs better than a dependency tree representation while identifying the relevance of one piece of text to another.


title: Ant Colony Optimization: A Swarm Intelligence based Technique

abstract: The growing complexity of real-world problems has motivated computer scientists to search for efficient problemsolving methods. Divide and conquer techniques are one way to solve large and difficult problems. Division of large work into smaller parts and combining the solution of small problems to get the solution of large one has been a practice in computer research since long time. Swarm also exhibits the behavior of division of work and cooperation to achieve difficult tasks. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples which show that nature has been an unending source of inspiration. Artificial Swarm/Ant foraging utilizes various forms of indirect communication, involving the implicit transfer of information from agent to agent through modification of the environment. Using this approach, one can design efficient searching methods that can find solution to complex optimization problems. Over times, several algorithms have been designed and used that are inspired by the foraging behavior of real ants colonies to find solutions to difficult problems. In this paper the idea of Ant Colonies is presented with brief introduction to its applications in different areas of problem solving in computer science.


title: Downlink Coordinated Multi-Point with Overhead Modeling in Heterogeneous Cellular Networks

abstract: Coordinated multi-point (CoMP) communication is attractive for heterogeneous cellular networks (HCNs) for interference reduction. However, previous approaches to CoMP face two major hurdles in HCNs. First, they usually ignore the inter-cell overhead messaging delay, although it results in an irreducible performance bound. Second, they consider the grid or Wyner model for base station locations, which is not appropriate for HCN BS locations which are numerous and haphazard. Even for conventional macrocell networks without overlaid small cells, SINR results are not tractable in the grid model nor accurate in the Wyner model. To overcome these hurdles, we develop a novel analytical framework which includes the impact of overhead delay for CoMP evaluation in HCNs. This framework can be used for a class of CoMP schemes without user data sharing. As an example, we apply it to downlink CoMP zero-forcing beamforming (ZFBF), and see significant divergence from previous work. For example, we show that CoMP ZFBF does not increase throughput when the overhead channel delay is larger than 60% of the channel coherence time. We also find that, in most cases, coordinating with only one other cell is nearly optimum for downlink CoMP ZFBF.


title: Sora Identity: Secure, Digital Identity on the Blockchain

abstract: Digital identity is the cornerstone of a digital economy. However, proving identity remotely is difficult to do. To complicate things further, identity is usually not a global, absolute construct, but the information shared with different parties differs, based on the relationship to the user. Therefore, a viable solution for digital identity should enable users to have full control over their personal information and share only the information that they wish to share with each service. Blockchain technology can help to realize a self-sovereign identity that puts the user in control of her information, by enabling a decentralized way to handle public key infrastructure. In the current contribution, we present the Sora identity system, which is a mobile app that utilizes blockchain technology to create a secure protocol for storing encrypted personal information, as well as sharing verifiable claims about personal information.


title: Simple Smartphone-Based Guiding System for Visually Impaired People

abstract: Visually impaired people are often unaware of dangers in front of them, even in familiar environments. Furthermore, in unfamiliar environments, such people require guidance to reduce the risk of colliding with obstacles. This study proposes a simple smartphone-based guiding system for solving the navigation problems for visually impaired people and achieving obstacle avoidance to enable visually impaired people to travel smoothly from a beginning point to a destination with greater awareness of their surroundings. In this study, a computer image recognition system and smartphone application were integrated to form a simple assisted guiding system. Two operating modes, online mode and offline mode, can be chosen depending on network availability. When the system begins to operate, the smartphone captures the scene in front of the user and sends the captured images to the backend server to be processed. The backend server uses the faster region convolutional neural network algorithm or the you only look once algorithm to recognize multiple obstacles in every image, and it subsequently sends the results back to the smartphone. The results of obstacle recognition in this study reached 60%, which is sufficient for assisting visually impaired people in realizing the types and locations of obstacles around them.


title: Efficient edge detection from tactile data

abstract: A real-time tactile image processing algorithm for edge contact is presented in this paper. Based on basic elasticity results, closed-form solutions for calculating contact force and local contact geometries (i.e., location and orientation of the line of contact) from the first three moments of a tactile image are derived. Computational complexity of the proposed algorithm and those of the previous approaches are compared, and passive tactile sensing experiments are performed. It is shown that the proposed algorithm has the advantage of persevering force information and is more consistent and computationally efficient.


title: Design of an ex-proof untethered in-pipe inspection robot

abstract: In this work, the design of a natural gas pipeline inspection robot that generates energy from gas flow, performs onboard image processing and handles propulsion through biometal fiber (BMF) driven arms is presented. For an in-pipe inspection of an explosive gas filled pipe to be performed properly in real operational conditions, the robot should be free of external cable to prevent gas leakage from the pipe, ex-proof to prevent any spark that can lead to a fire or explosion, and adaptive to highly varying pipe diameters and tight curvatures on L and T joints. Novel solutions that address the mentioned requirements are proposed for systems of propulsion, energy source, data acquisition and communication. Design of robot that combines all these solutions is explained in detail.


title: SIMD Vectorized Hashing for Grouped Aggregation

abstract: Grouped aggregation is a commonly used analytical function. The common implementation of the function using hashing techniques suffers lower throughput rate due to the collision of the insert keys in the hashing techniques. During collision, the underlying technique searches for an alternative location to insert keys. Searching an alternative location increases the processing time for an individual key thereby degrading the overall throughput. In this work, we use Single Instruction Multiple Data (SIMD) vectorization to search multiple slots at an instant followed by direct aggregation of results. We provide our experimental results of our vectorized grouped aggregation with various open-addressing hashing techniques using several dataset distributions and our inferences on them. Among our findings, we observe different impacts of vectorization on these techniques. Namely, linear probing and two-choice hashing improve their performance with vectorization, whereas cuckoo and hopscotch hashing show a negative impact. Overall, we provide in this work a basic structure of a dedicated SIMD accelerated grouped aggregation framework that can be adapted with different hashing techniques.


title: TABOR: A Graphical Model-based Approach for Anomaly Detection in Industrial Control Systems

abstract: Industrial Control Systems (ICS) such as water and power are critical to any society. Process anomaly detection mechanisms have been proposed to protect such systems to minimize the risk of damage or loss of resources. In this paper, a graphical model-based approach is proposed for profiling normal operational behavior of an operational ICS referred to as SWaT (Secure Water Treatment). Timed automata are learned as a model of regular behaviors shown in sensors signal like fluctuations of water level in tanks. Bayesian networks are learned to discover dependencies between sensors and actuators. The models are used as a one-class classifier for process anomaly detection, recognizing irregular behavioral patterns and dependencies. The detection results can be interpreted and the abnormal sensors or actuators localized due to the interpretability of the graphical models. This approach is applied to a dataset collected from SWaT. Experimental results demonstrate the model's superior performance on both precision and run-time over methods including support vector machine and deep neural networks. The underlying idea is generic and applicable to other industrial control systems such as power and transportation.


title: Compact and Sharp-Rejection Bandstop Filter Using Uniplanar Double Spiral Resonant Cells

abstract: A novel compact bandstop filter composed of three cascaded uniplanar double spiral resonant cells (UDSRCs) for high attenuation rates is presented. Through the equivalent circuit prediction and parametric analysis, it is found that the UDSRC exhibits two controllable transmission zeros with great design flexibility through tuning the geometry parameters in a small range. Then, the influence of the stage separation between each UDSRC is investigated in order to get the appropriate stage separation. After optimization, a demonstration bandstop filter has been fabricated and measured. The results show that the attenuation rates on the lower and upper sides are 95 dB/GHz and 155 dB/GHz, respectively. Without any shunt stubs introduced, the length and width of the three cells are 28% and 4% of the guided wavelength at the midstopband frequency.


A Linear Time Algorithm for Deciding Interval Graph Isomorphism


title: Visual servoing for humanoid grasping and manipulation tasks

abstract: Using visual feedback to control the movement of the end-effector is a common approach for robust execution of robot movements in real-world scenarios. Over the years several visual servoing algorithms have been developed and implemented for various types of robot hardware. In this paper, we present a hybrid approach which combines visual estimations with kinematically determined orientations to control the movement of a humanoid arm. The approach has been evaluated with the humanoid robot ARMAR III using the stereo system of the active head for perception as well as the torso and arms equipped with five finger hands for actuation. We show how a robust visual perception is used to control complex robots without any hand-eye calibration. Furthermore, the robustness of the system is improved by estimating the hand position in case of failed visual hand tracking due to lightning artifacts or occlusions. The proposed control scheme is based on the fusion of the sensor channels for visual perception, force measurement and motor encoder data. The combination of these different data sources results in a reactive, visually guided control that allows the robot ARMAR-III to execute grasping tasks in a real-world scenario.


title: Development of Indonesian Large Vocabulary Continuous Speech Recognition System within A-STAR Project

abstract: The paper outlines the development of a large vocabulary continuous speech recognition (LVCSR) system for the Indonesian language within the Asian speech translation (A-STAR) project. An overview of the A-STAR project and Indonesian language characteristics will be briefly described. We then focus on a discussion of the development of Indonesian LVCSR, including data resources issues, acoustic modeling, language modeling, the lexicon, and accuracy of recognition. There are three types of Indonesian data resources: daily news, telephone application, and BTEC tasks, which are used in this project. They are available in both text and speech forms. The Indonesian speech recognition engine was trained using the clean speech of both daily news and telephone application tasks. The optimum performance achieved on the BTEC task was 92.47% word accuracy. 1 A-STAR Project Overview The A-STAR project is an Asian consortium that is expected to advance the state-of-the-art in multilingual man-machine interfaces in the Asian region. This basic infrastructure will accelerate the development of large-scale spoken language corpora in Asia and also facilitate the development of related fundamental information communication technologies (ICT), such as multi-lingual speech translation, Figure 1: Outline of future speech-technology services connecting each area in the Asian region through network. multi-lingual speech transcription, and multi-lingual information retrieval. These fundamental technologies can be applied to the human-machine interfaces of various telecommunication devices and services connecting Asian countries through the network using standardized communication protocols as outlined in Fig. 1. They are expected to create digital opportunities, improve our digital capabilities, and eliminate the digital divide resulting from the differences in ICT levels in each area. The improvements to borderless communication in the Asian region are expected to result in many benefits in everyday life including tourism, business, education, and social security. The project was coordinated together by the Advanced Telecommunication Research (ATR) and the National Institute of Information and Communications Technology (NICT) Japan in cooperation with several research institutes in Asia, such as the National Laboratory of Pattern Recognition (NLPR) in China, the Electronics and Telecommunication Research Institute (ETRI) in Korea, the Agency for the Assessment and Application Technology (BPPT) in Indonesia, the National Electronics and Computer Technology Center (NECTEC) in Thailand, the Center for Development of Advanced Computing (CDAC) in India, the National Taiwan University (NTU) in Taiwan. Partners are still being sought for other languages in Asia. More details about the A-STAR project can be found in (Nakamura et al., 2007). 2 Indonesian Language Characteristic The Indonesian language, or so-called Bahasa Indonesia, is a unified language formed from hundreds of languages spoken throughout the Indonesian archipelago. Compared to other languages, which have a high density of native speakers, Indonesian is spoken as a mother tongue by only 7% of the population, and more than 195 million people speak it as a second language with varying degrees of proficiency. There are approximately 300 ethnic groups living throughout 17,508 islands, speaking 365 native languages or no less than 669 dialects (Tan, 2004). At home, people speak their own language, such as Javanese, Sundanese or Balinese, even though almost everybody has a good understanding of Indonesian as they learn it in school. Although the Indonesian language is infused with highly distinctive accents from different ethnic languages, there are many similarities in patterns across the archipelago. Modern Indonesian is derived from the literary of the Malay dialect. Thus, it is closely related to the Malay spoken in Malaysia, Singapore, Brunei, and some other areas. Unlike the Chinese language, it is not a tonal language. Compared with European languages, Indonesian has a strikingly small use of gendered words. Plurals are often expressed by means of word repetition. It is also a member of the agglutinative language family, meaning that it has a complex range of prefixes and suffixes, which are attached to base words. Consequently, a word can become very long. More details on Indonesian characteristics can be found in (Sakti et al., 2004). 3 Indonesian Phoneme Set The Indonesian phoneme set is defined based on Indonesian grammar described in (Alwi et al., 2003). A full phoneme set contains 33 phoneme symbols in total, which consists of 10 vowels (including diphthongs), 22 consonants, and one silent symbol. The vowel articulation pattern of the Indonesian language, which indicates the first two resonances of the vocal tract, F1 (height) and F2 (backness), is shown in Fig. 2.


title: Microstrip Transmission Line Sensor for Rice Quality Detection: An Overview

abstract: This paper describes the comparative analysis of different types of microstrip transmission sensor for rice quality detection. Basically the cylindrical slot antenna and microstrip line based structure have been discussed. This paper shows focus on the advantage and tremendous characteristics of microstrip couple line sensor over the other microwave sensors and the substantial amount of efficiency has been achieved by the use of Coupled line filter approach. The analysis on the basis of various parameters like, characteristics impedance, microstrip width and dielectric constant of the substrate, modes of reflection coefficient, insertion loss, radiation, moisture content in rice grain, and the frequency applied. By applying the different principles and methods and finally with Vector Network Analyzer the reflection coefficient measurement reading give us the measurement of broken rice. Keywords-Coupled Line Filter, Microstrip, Moisture Content (m.c), Slot Antenna, Vector Network Analyzer (VNA).


title: Coupling heterogeneous production systems by a multi-agent based cyber-physical production system

abstract: Todays increasing volatility of market demands and customer requirements are forcing industrial enterprises to realize and ensure an increased flexibility of production systems. Since current automation concepts and architectures for production systems do not provide the required flexibility sufficiently, new approaches have to be developed. This paper proposes an approach that implements the quickly evolving concept of Cyber-Physical Systems for the special case of production systems by means of software agents. A joined demonstrator of such Cyber-Physical Production System is described and used for the evaluation of the proposed multi-agent approach.


title: Kinect and RGBD Images: Challenges and Applications

abstract: Kinect is a device introduced in November 2010 as an accessory of Xbox 360. The acquired data has different and complementary natures, combining geometry with visual attributes. For this reason, Kinect is a flexible tool that can be used in applications from several areas such as: Computer Graphics, Image Processing, Computer Vision and Human-Machine Interaction. In this way, the Kinect is a widely used device in industry (games, robotics, theater performers, natural interfaces, etc.) and in research. We will initially present some concepts about the device: the architecture and the sensor. We then will discuss about the data acquisition process: capturing, representation and filtering. Capturing process consists of obtaining a colored image (RGB) and performing a depth measurement (D), with structured light technique. This data is represented by a structure called RGBD Image. We will also talk about the main tools available for developing applications on various platforms. Furthermore, we will discuss some recent projects based on RGBD Images. In particular, those related to Object Recognition, 3D Reconstruction, Augmented Reality, Image Processing, Robotic, and Interaction. In this survey, we will show some research developed by the academic community and some projects developed for the industry. We intend to show the basic principles to begin developing applications using Kinect, and present some projects developed at the VISGRAF Lab. And finally, we intend to discuss the new possibilities, challenges and trends raised by Kinect.


title: A brief analysis of the key technologies and applications of educational data mining on online learning platform

abstract: With the rapid development of the Internet and communication technology, online education has drawn more and more attention, online learning platforms, on the other hand, store massive learner behavioral data and educational data. How to effectively analyze and utilize the data to improve the quality of online education has become a key issue urgently needed to be solved in the field of big data in education(BDE), educational data mining(EDM) is exactly an effective and practical method and means of applying BDE. Therefore, EDM is an important academic research hotspot in the field of EDM. Firstly, the paper introduces the basic concepts of BDE, EDM and online learning platform, and then elaborates on the process of how educational data mining transforms raw data into knowledge. Finally, the key technologies of data mining are classified according to their uses, and gives its application in online education scene. The paper can provide some guidance for the research and application of educational data mining based on online education.


title: Science and data science

abstract: Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.


title: Peer group association, the acceptance of norms and violent behaviour: A longitudinal analysis of reciprocal effects

abstract: Differential association and social learning theory assume delinquent peers to be instigators and reinforcers of delinquent behaviour and norms favourable of delinquency. Control theory, on the other hand, assumes that delinquents will group together with peers that share a common normative and behavioural background. Interactional theory as an integrative paradigm argues that both – influence and selection – processes might be active simultaneously and are embedded in a reciprocal causal relationship. This paper tests the reciprocity between the association with delinquent peer groups, the acceptance of pro-violent norms and violent delinquency during adolescence with data from a German longitudinal panel study in a longitudinal structural equation model. Results indicate that peers, norms and violence are interactionally related and that influence and selection processes are active simultaneously. Moreover, further structural dimensions are able to explain delinquent peer group association, the acceptan...


title: Research on power factor improvement technology of quasi-Z-source

abstract: This paper proposes a novel control strategy for electrolytic capacitor-less quasi-Z-source inverter, which fed adjustable-speed drive (ASD) system with almost unit input power factor. The system has no shoot through issues and gains high reliability. By using E-caps-less topology, the life time can be greatly increased. In order to guarantee the high power factor and the ability of resisting grid voltage sags, the voltage and current double closed loop structure is used to control the QZSI. Choose PMSM driving system as the control object, a new control strategy is adopted to improve the power factor further. The effectiveness of the proposed system has been verified by simulation.


title: Design and Implementation of High Performance MIL-STD-1553B Bus Controller

abstract: In recent times, due to usage of avionics subsystems and the amount of data processed by them there is an increased demand for the usage of digital techniques in aircrafts. MIL-STD-1553 has evolved as an international standard for military applications. To meet the real world specifications, it is required to optimize the area and power and to improve the performance of the data bus. Now-a-days, speed of the system is very much important. But with the speedy improvements in modern avionics system, traditional MIL-STD-1553 bus cannot reach the need of high speed applications. This project aims at designing high performance MIL-STD-1553B bus controller which is compatible to Data Device Corporation (DDC). In most of the aircraft applications DDC devices have been used. The proposed system is designed using Verilog HDL and simulation is done in ModelSim. The area and time calculations have done in Design Compiler Tool of Synopsys. The functionality of the developed architecture is verified in Xilinx ISE 13.4.


title: Caring for Agents and Agents that Care: Building Empathic Relations with Synthetic Agents

abstract: When building agents and synthetic characters, and in order to achieve believability, we must consider the emotional relations established between users and characters, that is, we must consider the issue of "empathy". Defined in broad terms as "An observer reacting emotionally because he perceives that another is experiencing or about to experience an emotion", empathy is an important element to consider in the creation of relations between humans and agents. In this paper we will focus on the role of empathy in the construction of synthetic characters, providing some requirements for such construction and illustrating the presented concepts with a specific system called FearNot!. FearNot! was developed to address the difficult and often devastating problem of bullying in schools. By using role playing and empathic synthetic characters in a 3D environment, FearNot! allows children from 8 to 12 to experience a virtual scenario where they can witness (in a third-person perspective) bullying situations. To build empathy into FearNot! we have considered the following components: agentýs architecture; the charactersý embodiment and emotional expression; proximity with the user and emotionally charged situations.We will describe how these were implemented in FearNot! and report on the preliminary results we have with it.


Specimen Identification and Key Construction with Time-Sharing Computers


title: Nearest Neighbor Pattern Classification

abstract: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.


title: Profit optimizing customer churn prediction with Bayesian network classifiers

abstract: Customer churn prediction is becoming an increasingly important business analytics problem for telecom operators. In order to increase the efficiency of customer retention campaigns, churn prediction models need to be accurate as well as compact and interpretable. Although a myriad of techniques for churn prediction has been examined, there has been little attention for the use of Bayesian Network classifiers. This paper investigates the predictive power of a number of Bayesian Network algorithms, ranging from the Naive Bayes classifier to General Bayesian Network classifiers. Furthermore, a feature selection method based on the concept of the Markov Blanket, which is genuinely related to Bayesian Networks, is tested. The performance of the classifiers is evaluated with both the Area under the Receiver Operating Characteristic Curve and the recently introduced Maximum Profit criterion. The Maximum Profit criterion performs an intelligent optimization by targeting this fraction of the customer base which would maximize the profit generated by a retention campaign. The results of the experiments are rigorously tested and indicate that most of the analyzed techniques have a comparable performance. Some methods, however, are more preferred since they lead to compact networks, which enhances the interpretability and comprehensibility of the churn prediction models.


title: DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

abstract: Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, we show that DeepSense is feasible to implement on smartphones and embedded devices thanks to its moderate energy consumption and low latency.


title: Efficient and reliable low-power backscatter networks

abstract: There is a long-standing vision of embedding backscatter nodes like RFIDs into everyday objects to build ultra-low power ubiquitous networks. A major problem that has challenged this vision is that backscatter communication is neither reliable nor efficient. Backscatter nodes cannot sense each other, and hence tend to suffer from colliding transmissions. Further, they are ineffective at adapting the bit rate to channel conditions, and thus miss opportunities to increase throughput, or transmit above capacity causing errors. This paper introduces a new approach to backscatter communication. The key idea is to treat all nodes as if they were a single virtual sender. One can then view collisions as a code across the bits transmitted by the nodes. By ensuring only a few nodes collide at any time, we make collisions act as a sparse code and decode them using a new customized compressive sensing algorithm. Further, we can make these collisions act as a rateless code to automatically adapt the bit rate to channel quality --i.e., nodes can keep colliding until the base station has collected enough collisions to decode. Results from a network of backscatter nodes communicating with a USRP backscatter base station demonstrate that the new design produces a 3.5× throughput gain, and due to its rateless code, reduces message loss rate in challenging scenarios from 50% to zero.


title: Expression flow for 3D-aware face component transfer

abstract: We address the problem of correcting an undesirable expression on a face photo by transferring local facial components, such as a smiling mouth, from another face photo of the same person which has the desired expression. Direct copying and blending using existing compositing tools results in semantically unnatural composites, since expression is a global effect and the local component in one expression is often incompatible with the shape and other components of the face in another expression. To solve this problem we present Expression Flow, a 2D flow field which can warp the target face globally in a natural way, so that the warped face is compatible with the new facial component to be copied over. To do this, starting with the two input face photos, we jointly construct a pair of 3D face shapes with the same identity but different expressions. The expression flow is computed by projecting the difference between the two 3D shapes back to 2D. It describes how to warp the target face photo to match the expression of the reference photo. User studies suggest that our system is able to generate face composites with much higher fidelity than existing methods.


title: PPG-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks

abstract: Non invasive and continuous measurement of blood pressure can enable an effective 24/7 monitoring of hypertensive patients to timely prevent cardiovascular problems and precisely regulate anti-hypertension cures. Unfortunately, to date, blood pressure can be only measured through either cuff-based or invasive instruments that cannot be continuously used due to their characteristics. This paper proposes an overview of techniques and approaches based on the photoplethysmographic (PPG) signal for non invasive and continuous measurement of blood pressure. In particular, the PPG signal can be easily acquired from an optical sensor applied on the epidermis and used, alone or integrated with the ECG signal, to estimate the blood pressure. On the basis of such methods new instruments and sensor-based systems can be developed and integrated with computer-based health care systems that aim at supporting continuous and remote monitoring of assisted livings.


Exploring usefulness of video-based instruction as a pedagogical tool in learning of Mathematics


abstract: Despite decades of research, developing software that is fit for purpose, developed on time, and within budget remains a challenge. Many researchers have advocated the use of artificial intelligence techniques such as knowledge-based systems, neural networks, and data mining as a way of addressing these difficulties. Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects provides an overview of useful techniques in artificial intelligence for future software development along with critical assessment for further advancement. A compendium of latest industry findings, this Premier Reference Source offers researchers, academicians, and practitioners developmental ideas within the field.


title: Locus: locating bugs from software changes

abstract: Various information retrieval (IR) based techniques have been proposed recently to locate bugs automatically at the file level. However, their usefulness is often compromised by the coarse granularity of files and the lack of contextual information. To address this, we propose to locate bugs using software changes, which offer finer granularity than files and provide important contextual clues for bug-fixing. We observe that bug inducing changes can facilitate the bug fixing process. For example, it helps triage the bug fixing task to the developers who committed the bug inducing changes or enables developers to fix bugs by reverting these changes. Our study further identifies that change logs and the naturally small granularity of changes can help boost the performance of IR-based bug localization. Motivated by these observations, we propose an IR-based approach Locus to locate bugs from software changes, and evaluate it on six large open source projects. The results show that Locus outperforms existing techniques at the source file level localization significantly. MAP and MRR in particular have been improved, on average, by 20.1% and 20.5%, respectively. Locus is also capable of locating the inducing changes within top 5 for 41.0% of the bugs. The results show that Locus can significantly reduce the number of lines needing to be scanned to locate the bug compared with existing techniques.


title: RHex: A Simple and Highly Mobile Hexapod Robot

abstract: In this paper, the authors describe the design and control of RHex, a power autonomous, untethered, compliant-legged hexapod robot. RHex has only six actuators—one motor located at each hip— achieving mechanical simplicity that promotes reliable and robust operation in real-world tasks. Empirically stable and highly maneuverable locomotion arises from a very simple clock-driven, open-loop tripod gait. The legs rotate full circle, thereby preventing the common problem of toe stubbing in the protraction (swing) phase. An extensive suite of experimental results documents the robot’s significant “intrinsic mobility”—the traversal of rugged, broken, and obstacle-ridden ground without any terrain sensing or actively controlled adaptation. RHex achieves fast and robust forward locomotion traveling at speeds up to one body length per second and traversing height variations well exceeding its body clearance.


title: K-Means-Based Consensus Clustering: A Unified View

abstract: The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, however the existing research efforts are still preliminary and fragmented. To that end, in this paper, we provide a systematic study of K-means-based consensus clustering (KCC). Specifically, we first reveal a necessary and sufficient condition for utility functions which work for KCC. This helps to establish a unified framework for KCC on both complete and incomplete data sets. Also, we investigate some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with many missing values.


title: FingerFlux: near-surface haptic feedback on tabletops

abstract: We introduce FingerFlux, an output technique to generate near-surface haptic feedback on interactive tabletops. Our system combines electromagnetic actuation with permanent magnets attached to the user's hand. FingerFlux lets users feel the interface before touching, and can create both attracting and repelling forces. This enables applications such as reducing drifting, adding physical constraints to virtual controls, and guiding the user without visual output. We show that users can feel vibration patterns up to 35 mm above our table, and that FingerFlux can significantly reduce drifting when operating on-screen buttons without looking.


title: Employing spatial analysis in indoor positioning and tracking using wi-fi access points

abstract: A practical WiFi-based positioning system has to be adaptable to the variations of indoor environmental dynamic factors. In this work, we propose a novel Wi-Fi indoor positioning and tracking framework which employs the spatial analysis and image processing techniques. The Wi-Fi surfaces can be dynamically constructed and updated and thus help to address the challenges of signal spatial heterogeneity and environmental variations. A mobile app for indoor positioning application has been developed as a proof of concept. Based on the experiments we conducted at the Esri campus, this method can achieve about 2-meter positioning accuracy. The proposed methodology and theoretical frame-work can guide engineers to implement cost-effective indoor positioning infrastructure, and thus offer insights on future smart campus applications. The introduced spatial analysis and geoprocessing workflow may also bring the attention of GIScientists to make more efforts to conquer the indoor positioning and tracking challenges.


title: Recognizing Partial Biometric Patterns

abstract: Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching. In this area, deep learning based methods are widely applied to match these partial captured objects caused by occlusions, variations of postures or just partial out of view in person re-identification and partial face recognition. However, most current methods are not able to identify an individual in case that some parts of the object are not obtainable, while the rest are specialized to certain constrained scenarios. To this end, we propose a robust general framework for arbitrary biometric matching scenarios without the limitations of alignment as well as the size of inputs. We introduce a feature post-processing step to handle the feature maps from FCN and a dictionary learning based Spatial Feature Reconstruction (SFR) to match different sized feature maps in this work. Moreover, the batch hard triplet loss function is applied to optimize the model. The applicability and effectiveness of the proposed method are demonstrated by the results from experiments on three person re-identification datasets (Market1501, CUHK03, DukeMTMC-reID), two partial person datasets (Partial REID and Partial iLIDS) and two partial face datasets (CASIA-NIR-Distance and Partial LFW), on which state-of-the-art performance is ensured in comparison with several state-of-the-art approaches. The code is released online and can be found on the website: https://github.com/lingxiao-he/Partial-Person-ReID.


title: Identity and Access Management with Blockchain in Electronic Healthcare Records

abstract: Blockchain has proved itself to be tamper resistant and secure. It is increasingly getting attention from companies changing from centralized to decentralized systems. This paper proposes a system for identity and access management using blockchain technology to support authentication and authorization of entities in a digital system. A prototype demonstrates the application of blockchain in identity and access management using the Hyperledger Fabric framework. It provides a proof of concept based on a use case concerning Electronic Health Records from the healthcare domain where an immutable and auditable history is desired for data concerning patients. Basic authentication and authorization operations are able to execute in 2-3 seconds with an initial size of blockchain of about 3.8 MB covering physicians in Denmark.


title: Wounding patterns and human performance in knife attacks: optimising the protection provided by knife-resistant body armour.

abstract: BACKGROUND ::: Stab attacks generate high loads, and to defeat them, armour needs to be of a certain thickness and stiffness. Slash attacks produce much lower loads and armour designed to defeat them can be far lighter and more flexible. ::: ::: ::: METHODS AND SUBJECTS ::: Phase 1: Human performance in slash attacks: 87 randomly selected students at the Royal Military College of Science were asked to make one slash attack with an instrumented blade on a vertically mounted target. No instructions on how to slash the target were given. The direction, contact forces and velocity of each attack were recorded. Phase 2: Clinical experience with edged weapon attacks: The location and severity of all penetrating injuries in patients attending the Glasgow Royal Infirmary between 1993 and 1996 were charted on anatomical figures. ::: ::: ::: RESULTS ::: Phase 1: Two types of human slash behaviour were evident: a 'chop and drag' blow and a 'sweep motion' type of attack. 'Chop and drag' attacks had higher peak forces and velocities than sweep attacks. Shoulder to waist blows (diagonal) accounted for 82% of attacks, 71% of attackers used a long diagonal slash with an average cut length of 34 cm and 11% used short diagonal attacks with an average cut length of 25 cm. Only 18% of attackers slashed across the body (short horizontal); the average measured cut length of this type was 28 cm. The maximum peak force for the total sample population was 212 N; the maximum velocity was 14.88 m s(-1). The 95 percentile force for the total sample population was 181 N and the velocity was 9.89 m s(-1). Phase 2: 431 of the 500 patients had been wounded with edged weapons. The average number of wounds sustained by victims in knife assaults was 2.4. The distribution of wounds by frequency and severity are presented. ::: ::: ::: CONCLUSIONS ::: Anti-slash protection is required for the arms, neck, shoulders, and thighs. The clinical experience of knife-attack victims provides information on the relative vulnerabilities of different regions of the body. It is anticipated that designing a tunic-type of Police uniform that is inherently stab and slash resistant will eventually replace the current obvious and often bulky extra protective vest. Attempts at making a combined garment will need to be guided by ergonomic considerations and field testing. A similar anatomical regional risk model might also be appropriate in the design of anti-ballistic armour and combined anti-ballistic and knife-resistant armour.


title: Three-Port Series-Resonant DC–DC Converter to Interface Renewable Energy Sources With Bidirectional Load and Energy Storage Ports

abstract: In this paper, a three-port converter with three active full bridges, two series-resonant tanks, and a three-winding transformer is proposed. It uses a single power conversion stage with high-frequency link to control power flow between batteries, load, and a renewable source such as fuel cell. The converter has capabilities of bidirectional power flow in the battery and the load port. Use of series-resonance aids in high switching frequency operation with realizable component values when compared to existing three-port converter with only inductors. The converter has high efficiency due to soft-switching operation in all three bridges. Steady-state analysis of the converter is presented to determine the power flow equations, tank currents, and soft-switching region. Dynamic analysis is performed to design a closed-loop controller that will regulate the load-side port voltage and source-side port current. Design procedure for the three-port converter is explained and experimental results of a laboratory prototype are presented.


abstract: A Web services-based collaborative scheme for credit card fraud detection is proposed. With the proposed scheme, participant banks can share the knowledge about fraud patterns in a heterogeneous and distributed environment and further enhance their fraud detection capability and reduce financial loss.


title: Three-dimensional displacements of a piezoelectric tube scanner

abstract: This article gives the quantitative three-dimensional displacements of a piezoelectric tube scanner subject to arbitrary voltages. The results including the influences of the tip’s position and length are reported. The displacements are determined by the piezoelectric strain/charge constant d31 and the geometrical parameters of the scanner. Experiment results show the feasibility of the proposed method to calibrate the effective piezoelectric constant of a scanner. The coupling between vertical and transverse scanning displacements is discussed. Comparison of the newly developed formulas with the previous formulas and finite element calculation is carried out. The theoretical basis of the recently proposed “circular arc bending model” is found and the exact form of the model is also derived. Numerical results show that the exact form agrees with the experimental results much better than the previous form. The formulas presented here can be used for the design, calibration, and further application of piezo...


title: Deep Variational Inference Without Pixel-Wise Reconstruction

abstract: Variational autoencoders (VAEs), that are built upon deep neural networks have emerged as popular generative models in computer vision. Most of the work towards improving variational autoencoders has focused mainly on making the approximations to the posterior flexible and accurate, leading to tremendous progress. However, there have been limited efforts to replace pixel-wise reconstruction, which have known shortcomings. In this work, we use real-valued non-volume preserving transformations (real NVP) to exactly compute the conditional likelihood of the data given the latent distribution. We show that a simple VAE with this form of reconstruction is competitive with complicated VAE structures, on image modeling tasks. As part of our model, we develop powerful conditional coupling layers that enable real NVP to learn with fewer intermediate layers.


title: A reinforcement learning approach to autonomous decision-making in smart electricity markets

abstract: The vision of a Smart Electric Grid relies critically on substantial advances in intelligent decentralized control mechanisms. We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies. Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting from these signals. We show how feature selection and regularization can be leveraged to automatically optimize brokers for particular market conditions, and demonstrate the performance of our design in extensive experiments using real-world energy market data.


title: Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey

abstract: The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics.


title: Design of Proxy Signature in the Digital Signature Algorithm (DSA)

abstract: The use of proxy signature has attracted considerable attention and has been deployed in many applications. Unfortunately, most of the proxy signature schemes, which have been proposed up to now, are not based on standard signature such as Digital Signature Algorithm (DSA). Consequently, those proposals have inevitably been considered infeasible because of obvious security weaknesses so they suffer from new attacks. In fact, with the application of standard signature like DSA, which has been fully discussed, a proxy signature scheme could be well accepted for the deployment in many applications. Therefore, we propose a new proxy signature scheme which combines the DSA properties with proxy signatures. In other words, our scheme keeps not only the properties of the DSA but also fulfills the requirements of proxy signatures. We hope our new proxy signature based on DSA combining Public-key infrastructure (PKI) mechanism could be used in practice as well as the conventional DSA.


title: Is Bigger Better? Comparing User-Generated Passwords on 3x3 vs. 4x4 Grid Sizes for Android's Pattern Unlock

abstract: Android's graphical authentication mechanism requires users to unlock their devices by "drawing" a pattern that connects a sequence of contact points arranged in a 3x3 grid. Prior studies demonstrated that human-generated 3x3 patterns are weak (CCS'13); large portions can be trivially guessed with sufficient training. An obvious solution would be to increase the grid size to increase the complexity of chosen patterns. In this paper we ask the question: Does increasing the grid size increase the security of human-generated patterns? We conducted two large studies to answer this question, and our analysis shows that for both 3x3 and 4x4 patterns, there is a high incidence of repeated patterns and symmetric pairs (patterns that derive from others based on a sequence of flips and rotations), and many 4x4 patterns are expanded versions of 3x3 patterns. Leveraging this information, we developed an advanced guessing algorithm and used it to quantified the strength of the patterns using the partial guessing entropy. We find that guessing the first 20% (G0.2) of patterns for both 3x3 and 4x4 can be done as efficiently as guessing a random 2-digit PIN. While guessing larger portions of 4x4 patterns (G0.5) requires 2-bits more entropy than guessing the same ratio of 3x3 patterns, it remains on the order of cracking random 3-digit PINs. Of the patterns tested, our guessing algorithm successful cracks 15% of 3x3 patterns within 20 guesses (a typical phone lockout) and 19% of 4x4 patterns within 20 guesses; however, after 50,000 guesses, we correctly guess 95.9% of 3x3 patterns but only 66.7% of 4x4 patterns. While there may be some benefit to expanding the grid size to 4x4, we argue the majority of patterns chosen by users will remain trivially guessable and insecure against broad guessing attacks.


title: Planar Shared Antenna Structure for NFC and UHF-RFID Reader Applications

abstract: A novel shared antenna structure is presented in this communication for near-field communication and ultrahigh-frequency (UHF)-radio frequency identification (RFID) reader applications. This design is developed based on a circularly polarized gap-loaded loop antenna and then evolved to contain a two-turn coil for inductive coupling. The design procedure and the operating principles in the two scenarios are interpreted. Experimental results confirm the good performances of the proposed design with low profile and simple structure. The measured 15 dB impedance matching and 3 dB axial ratio bandwidths can fully cover the entire UHF-RFID band of 840–960 MHz. In the high-frequency band, the optimal matching circuit of the proposed structure is determined and an average transmission coefficient of 0.61 is achieved within the operating range of 50 mm.


title: Connectionist Temporal Fusion for Sign Language Translation

abstract: Continuous sign language translation (CSLT) is a weakly supervised problem aiming at translating vision-based videos into natural languages under complicated sign linguistics, where the ordered words in a sentence label have no exact boundary of each sign action in the video. This paper proposes a hybrid deep architecture which consists of a temporal convolution module (TCOV), a bidirectional gated recurrent unit module (BGRU), and a fusion layer module (FL) to address the CSLT problem. TCOV captures short-term temporal transition on adjacent clip features (local pattern), while BGRU keeps the long-term context transition across temporal dimension (global pattern). FL concatenates the feature embedding of TCOV and BGRU to learn their complementary relationship (mutual pattern). Thus we propose a joint connectionist temporal fusion (CTF) mechanism to utilize the merit of each module. The proposed joint CTC loss optimization and deep classification score-based decoding fusion strategy are designed to boost performance. With only once training, our model under the CTC constraints achieves comparable performance to other existing methods with multiple EM iterations. Experiments are tested and verified on a benchmark, i.e. the RWTH-PHOENIX-Weather dataset, which demonstrate the effectiveness of our proposed method.


title: An Architecture Proposal for Network Traffic Monitoring with IoT Traffic Classification Support

abstract: Devices of the Internet-of-Things such as sensors, actuators and smart objects are being incorporated into homes and companies to allow more interaction and communication. As new devices are introduced in IoT ecosystems, there is a need to improve control, security and infrastructure management, since network operators will not be able to know which devices are being used or whether network operations are being performed correctly for these devices. IoT ecosystems are heterogeneous, dynamic with the constant inclusion of new services and require interoperability. Environments with these characteristics present a challenge for any classification methodology. This paper shows a proposal for the construction of a tool for network monitoring that supports IoT devices traffic classification and identification. The proposed tool will provide three main functionalities: a network monitor, a passive classifier, and anactive classifier.


Current status of airborne active phased array (AESA) radar systems and future trends


title: Trust in recommender systems

abstract: Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.


title: Heterogeneous Information Network Embedding for Meta Path based Proximity

abstract: A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity between vertices in the original graph. This way, typical search and mining methods can be applied in the embedded space with the help of off-the-shelf multidimensional indexing approaches. Existing network embedding techniques focus on homogeneous networks, where all vertices are considered to belong to a single class.


title: Peer-to-Peer Markets

abstract: Peer-to-peer markets such as eBay, Uber, and Airbnb allow small suppliers to compete with traditional providers of goods or services. We view the primary function of these markets as making it easy for buyers to find sellers and engage in convenient, trustworthy transactions. We discuss elements of market design that make this possible, including search and matching algorithms, pricing, and reputation systems. We then develop a simple model of how these markets enable entry by small or flexible suppliers, and the resulting impact on existing firms. Finally, we consider the regulation of peer-to-peer markets, and the economic arguments for different approaches to licensing and certification, data, and employment regulation.


CORILSE: a Spanish Sign Language Repository for Linguistic Analysis.


What Is Disease and When Does It Begin


title: Scale-free brain functional networks

abstract: Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a) the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b) the characteristic path length is small and comparable with those of equivalent random networks, and (c) the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.


title: Comparison of the ESHRE–ESGE and ASRM classifications of Müllerian duct anomalies in everyday practice

abstract: STUDY QUESTION ::: Does the European Society of Human Reproduction and Embryology-European Society for Gynaecological Endoscopy (ESHRE-ESGE) classification of female genital tract malformations significantly increase the frequency of septate uterus diagnosis relative to the American Society for Reproductive Medicine (ASRM) classification? ::: ::: ::: SUMMARY ANSWER ::: Use of the ESHRE-ESGE classification, compared with the ASRM classification, significantly increased the frequency of septate uterus recognition. ::: ::: ::: WHAT IS KNOWN ALREADY ::: The ESHRE-ESGE criteria were supposed to eliminate the subjective diagnoses of septate uterus by the ASRM criteria and replace the complementary absolute morphometric criteria. However, the clinical value of the ESHRE-ESGE classification in daily practice is difficult to appreciate. The application of the ESHRE-ESGE criteria has resulted in a significantly increased recognition of residual septum after hysteroscopic metroplasty, with a possible risk of overdiagnosis of septate uterus and problems for its management. ::: ::: ::: STUDY DESIGN, SIZE, AND DURATION ::: A prospective observational study was performed with 261 women consecutively enrolled between June and September 2013. ::: ::: ::: PARTICIPANTS/MATERIALS, SETTING, AND METHODS ::: Non-pregnant women of reproductive age presented for evaluation to a private medical center. A gynecological examination and 3D ultrasonography were performed to assess the anatomy of the uterus, cervix and vagina. Congenital anomalies were diagnosed using the ASRM classification with additional morphometric criteria as well as with the ESHRE-ESGE classification. We compared the frequency and concordance of diagnoses of septate uterus and all congenital malformations of the uterus according to both classifications. The morphological characteristics of septate uterus recognized by both criteria were compared. ::: ::: ::: MAIN RESULTS AND ROLE OF CHANCE ::: Of the 261 patients enrolled in this study, septate uterus was diagnosed in 44 (16.9%) and 16 (6.1%) patients using the ESGE-ESHRE and ASRM criteria, respectively [relative risk (RR)ESHRE-ESGE:ASRM 2.74; 95% confidence interval (CI), 1.6-4.72; P < 0.01]. At least one congenital anomaly were diagnosed in 58 (22.2%) and 43 (16.5%) patients using the ESHRE-ESGE and ASRM classifications (RRESHRE-ESGE:ASRM, 1.35; 95% CI, 0.95-1.92, P = 0.1), respectively. The two criteria had moderate strength of agreement in the diagnosis of septate uterus (κ = 0.45, P < 0.01). There was good agreement in differentiation between anomaly and norm between the two assessment criteria (κ = 0.79, P < 0.01). The percentages of all congenital malformations and results of the differentiation between the anomaly and norm were obtained after excluding the confounding original ESHRE-ESGE criterion of dysmorphic uterus (internal indentation <50% uterine wall thickness). The morphology of septa identified by the ESHRE-ESGE [length of internal fundal indentation (mm): median 10.7; lower-upper quartile, 8.1-20] significantly differed (P < 0.01) from that identified by the ASRM criteria [length of internal fundal indentation (mm): median, 21.1; lower-upper quartile, 18.8-33.1]. Internal fundal indentation in 16 out of 44 (36.4%) cases was <1 cm in the septate uterus by ESHRE-ESGE and met the criteria for normal uterus by ASRM. ::: ::: ::: LIMITATIONS AND REASONS FOR CAUTION ::: The study participants were women who visited a diagnostic and treatment center specialized in uterine congenital malformations for a medical assessment, not from the general public. ::: ::: ::: WIDER IMPLICATIONS OF THE FINDINGS ::: Septate uterus diagnosis by ESHRE-ESGE was quantitatively dominated by morphological states corresponding to arcuate uterus or cases that were not diagnosed as congenital malformations by ASRM. Relative overdiagnosis of septate uterus by ESHRE-ESGE in these cases may lead to unnecessary overtreatment without the expected benefits. The ESHRE-ESGE classification criteria should be redefined due to confusions in the methodology. Until the criteria are revised, septate uterus should not be diagnosed using this classification system and it should not be used as an eligibility criterion for hysteroscopic metroplasty. ::: ::: ::: STUDY FUNDING/COMPETING INTERESTS ::: This work was supported by Jagiellonian University (grant no. K/ZDS/003821). The authors have no competing interests to declare.


title: On the Persistency of Excitation for Blind Channel Estimation in Cyclic Prefix Systems

abstract: Recently, a new subspace-based blind channel estimation algorithm in cyclic prefix (CP) system was reported. A persistency of excitation (PE) property of the input signal is required for the algorithm to work. In this paper, the probability of fulfilling the PE property under different situations is studied. Four factors in the algorithm affect the PE property of the input signal: 1) signal constellation used; 2) precoder coefficients; 3) number of consecutive blocks; 4) a number called the repetition index. Theoretical derivations as well as numerical simulations are given to demonstrate the main points of this paper. Important conclusions are 1) that the probability of fulfilling the PE property increases and converges to unity when the number of received blocks increases but is always upper-bounded by a value less than unity when the repetition index increases; 2) that the probability of fulfilling the PE property is smaller when the algorithm is applied in orthogonal frequency division multiplexing (OFDM) systems than in single-carrier-cyclic-prefix (SC-CP) systems.