title: Better Approximation Bounds for the Joint Replenishment Problem

abstract: The Joint Replenishment Problem (JRP) deals with optimizing shipments of goods from a supplier to retailers through a shared warehouse. Each shipment involves transporting goods from the supplier to the warehouse, at a fixed cost C, followed by a redistribution of these goods from the warehouse to the retailers that ordered them, where transporting goods to a retailer ρ has a fixed cost cρ. In addition, we incur waiting costs for each order, possibly an arbitrary non-decreasing function of time, different for each order. The objective is to minimize the overall cost of satisfying all orders, namely the sum of all shipping and waiting costs. ::: ::: JRP has been well studied in Operations Research and, more recently, in the area of approximation algorithms. For arbitrary waiting cost functions, the best known approximation ratio is 1.8. This ratio can be reduced to a 1.574 for the JRP-D model, where there is no cost for waiting but orders have deadlines. As for hardness results, it is known that the problem is APX-hard and that the natural linear program for JRP has integrality gap at least 1.245. Both results hold even for JRP-D. In the online scenario, the best lower and upper bounds on the competitive ratio are 2.64 and 3, respectively. The lower bound of 2.64 applies even to the restricted version of JRP, denoted JRP-L, where the waiting cost function is linear. ::: ::: We provide several new approximation results for JRP. In the offline case, we give an algorithm with ratio a 1.791, breaking the barrier of 1.8. We also show that the integrality gap of the linear program for JRP-L is at least 12/11 a 1.09. In the online case, we show a lower bound of a 2.754 on the competitive ratio for JRP-L (and thus JRP as well), improving the previous bound of 2.64. We also study the online version of JRP-D, for which we prove that the optimal competitive ratio is 2.


title: Fiber Optic Sensors in Structural Health Monitoring

abstract: Structural Health Monitoring (SHM) can be understood as the integration of sensing and intelligence to enable the structure loading and damage-provoking conditions to be recorded, analyzed, localized, and predicted in such a way that nondestructive testing becomes an integral part of them. In addition, SHM systems can include actuation devices to take proper reaction or correction actions. SHM sensing requirements are very well suited for the application of optical fiber sensors (OFS), in particular, to provide integrated, quasi-distributed or fully distributed technologies. In this tutorial, after a brief introduction of the basic SHM concepts, the main fiber optic techniques available for this application are reviewed, emphasizing the four most successful ones. Then, several examples of the use of OFS in real structures are also addressed, including those from the renewable energy, transportation, civil engineering and the oil and gas industry sectors. Finally, the most relevant current technical challenges and the key sector markets are identified. This paper provides a tutorial introduction, a comprehensive background on this subject and also a forecast of the future of OFS for SHM. In addition, some of the challenges to be faced in the near future are addressed.


title: An Algorithm of Parking Planning for Smart Parking System

abstract: There are so many vehicles in the world and the number of vehicles is increasing rapidly. To alleviate the parking problems caused by that, the smart parking system has been developed. The parking planning is one of the most important parts of it. An effective parking planning strategy makes the better use of parking resources possible. In this paper, we present a feasible method to do parking planning. We transform the parking planning problem into a kind of linear assignment problem. We take vehicles as jobs and parking spaces as agents. We take distances between vehicles and parking spaces as costs for agents doing jobs. Then we design an algorithm for this particular assignment problem and solve the parking planning problem. The method proposed can give timely and efficient guide information to vehicles for a real time smart parking system. Finally, we show the effectiveness of the method with experiments over some data, which can simulate the situation of doing parking planning in the real world.


title: The TokuFS Streaming File System

abstract: The TokuFS file system outperforms write-optimized file systems by an order of magnitude on microdata write workloads, and outperforms read-optimized file systems by an order of magnitude on read workloads. Microdata write workloads include creating and destroying many small files, performing small unaligned writes within large files, and updating metadata. TokuFS is implemented using Fractal Tree indexes, which are primarily used in databases. TokuFS employs block-level compression to reduce its disk usage.


title: DeepGraph: Graph Structure Predicts Network Growth

abstract: The topological (or graph) structures of real-world networks are known to be predictive of multiple dynamic properties of the networks. Conventionally, a graph structure is represented using an adjacency matrix or a set of hand-crafted structural features. These representations either fail to highlight local and global properties of the graph or suffer from a severe loss of structural information. There lacks an effective graph representation, which hinges the realization of the predictive power of network structures. ::: In this study, we propose to learn the represention of a graph, or the topological structure of a network, through a deep learning model. This end-to-end prediction model, named DeepGraph, takes the input of the raw adjacency matrix of a real-world network and outputs a prediction of the growth of the network. The adjacency matrix is first represented using a graph descriptor based on the heat kernel signature, which is then passed through a multi-column, multi-resolution convolutional neural network. Extensive experiments on five large collections of real-world networks demonstrate that the proposed prediction model significantly improves the effectiveness of existing methods, including linear or nonlinear regressors that use hand-crafted features, graph kernels, and competing deep learning methods.


title: A decade of research in statistics: a topic model approach

abstract: Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution.


title: New filtering approaches for phishing email

abstract: Phishing emails usually contain a message from a credible looking source requesting a user to click a link to a website where she/he is asked to enter a password or other confidential information. Most phishing emails aim at withdrawing money from financial institutions or getting access to private information. Phishing has increased enormously over the last years and is a serious threat to global security and economy. There are a number of possible countermeasures to phishing. These range from communication-oriented approaches like authentication protocols over blacklisting to content-based filtering approaches. ::: ::: We argue that the first two approaches are currently not broadly implemented or exhibit deficits. Therefore content-based phishing filters are necessary and widely used to increase communication security. A number of features are extracted capturing the content and structural properties of the email. Subsequently a statistical classifier is trained using these features on a training set of emails labeled as ham (legitimate), spam or phishing. This classifier may then be applied to an email stream to estimate the classes of new incoming emails. ::: ::: In this paper we describe a number of novel features that are particularly well-suited to identify phishing emails. These include statistical models for the low-dimensional descriptions of email topics, sequential analysis of email text and external links, the detection of embedded logos as well as indicators for hidden salting. Hidden salting is the intentional addition or distortion of content not perceivable by the reader. For empirical evaluation we have obtained a large realistic corpus of emails prelabeled as spam, phishing, and ham (legitimate). In experiments our methods outperform other published approaches for classifying phishing emails. We discuss the implications of these results for the practical application of this approach in the workflow of an email provider. Finally we describe a strategy how the filters may be updated and adapted to new types of phishing.


title: A Single-Transistor Silicon Synapse

abstract: We have developed a new floating-gate silicon MOS transistor for analog learning applications. The memory storage is nonvolatile; hot-electron injection and electron tunneling permit bidirectional memory updates. Because these updates depend on both the stored memory value and the transistor terminal voltages, the synapse can implement a learning function. We have derived a memory-update rule from the physics of the tunneling and injection processes, and have investigated synapse learning in a prototype array. Unlike conventional EEPROM devices, the synapse allows simultaneous memory reading and writing. Synapse transistor arrays can therefore compute both the array output, and local memory updates, in parallel. The synapse is small, and typically is operated at subthreshold current levels; it will permit the development of dense, low-power silicon learning systems.


title: A 33-dBm 1.9-GHz silicon-on-insulator CMOS stacked-FET power amplifier

abstract: A single-stage stacked-FET power amplifier (PA) is demonstrated using a 0.28-µm silicon-on-insulator (SOI) CMOS technology. To overcome the low breakdown voltage limit of MOSFETs, a stacked-FET structure is employed, where four transistors are connected in series so that their output voltage swings are added in phase. The stacked-FET PA has been designed to withstand up to 9 V of supply voltage before reaching its breakdown limit. The measurement results show that, with a 6.5-V supply, the PA achieves a small-signal gain of 13.2 dB, a saturated output power of 33 dBm, and a maximum power-added-efficiency (PAE) of 47% at 1.9 GHz. This is the first reported stacked-FET PA in submicron SOI CMOS technology that delivers multi-Watt output power in the GHz range. It also maintains high power efficiency over a wide range of supply voltages.


title: An Effective Clustering Approach to Stock Market Prediction

abstract: In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each subcluster belong to the same class. Then, for each sub-cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits.


title: Pattern Discovery Techniques for Music Audio

abstract: Human listeners are able to recognize structure in music through the perception of repetition and other relationships within a piece of music. This work aims to automate the task of music analysis. Music is “explained” in terms of embedded relationships, especially repetition of segments or phrases. The steps in this process are the transcription of audio into a representation with a similarity or distance metric, the search for similar segments, forming clusters of similar segments, and explaining music in terms of these clusters. Several transcription methods are considered: monophonic pitch estimation, chroma (spectral) representation, and polyphonic transcription followed by harmonic analysis. Also, several algorithms that search for similar segments are described. These techniques can be used to perform an analysis of musical structure, as illustrated by examples.


title: Real time kinect based robotic arm manipulation with five degree of freedom

abstract: We propose a real time, gesture based robotic arm manipulation using kinect sensor. This method uses kinect depth data, skeletal data and joint orientation data for end effector movement including roll, pitch and pinch. For the initial data, we extracted skeletal of the person, hand position and hand state. In closed hand state condition, the position of the hand is extracted and the three dimension data is used for inverse kinematics of the joints of the robotic arm. In open hand state, tilting and rolling of hand is used for pitch and roll respectively. In lasso state, pinch of end effector is perceived. The robotic arm used for the experimentation is Scorbot ER III. The system shows promising results for non-invasive robotic arm manipulation.


title: Far-Field Superoscillatory Metamaterial Superlens

abstract: We demonstrate a metamaterial superlens: a planar array of discrete subwavelength metamolecules with individual scattering characteristics tailored to vary spatially to create subdiﬀraction superoscillatory focus of, in principle, arbitrary shape and size. Metamaterial free-space lenses with previously unattainable eﬀective numerical apertures – as high as 1.52 – and foci as small as 0.33λ in size are demonstrated. Super-resolution imaging with such lenses is experimentally veriﬁed breaking the conventional diﬀraction limit of resolution and exhibiting resolution close to the size of the focus. Our approach will enable far-ﬁeld label-free super-resolution nonalgorithmic microscopies at harmless levels of intensity, including imaging inside cells, nanostructures, and silicon chips, without impregnating them with ﬂuorescent materials.


title: When comparisons arise.

abstract: People acquire information about their abilities by comparison, and research suggests that people restrict such comparisons to those whom they consider sources of diagnostic information. We suggest that diagnosticity is often considered only after comparisons are made and that people do not fail to make nondiagnostic comparisons so much as they mentally undo them. In 2 studies, participants made nondiagnostic comparisons even when they knew they should not, and quickly unmade them when they were able. These results suggest that social comparisons may be relatively spontaneous, effortless, and unintentional reactions to the performances of others and that they may occur even when people consider such reactions logically inappropriate.


title: Performance of the bootstrap Rasch model test under violations of non-intersecting item response functions

abstract: The Rasch model is known in particular for its properties of parameter separability and specific objectivity. The extent to which this property is attained depends on the magnitude of the discrepancy between the data and the model. The use of reliable model fit tests which can detect model violations is therefore essential before a psychological test is used and inferences based on the requirements of the Rasch model are drawn. This paper provides a critical analysis of the performance of the parametric bootstrap model test (von Davier, 1997) in the presence of non-parallel item response functions as violations of a basic requirement of the dichotomous Rasch model. Based on results from simulated data it is shown that in general the bootstrap test leads too often to failures to reject non-fitting data.


title: Importance of Sex Differences in Impulse Control and Addictions

abstract: Historically, sex- or gender-related differences in addictions have been understudied. When neglected, both sexes may not receive the full benefit of medical research. Although hormone fluctuations in women are rarely investigated with respect to treatments, levels of estrogen and progesterone may have large impacts on the efficacies of behavioral or pharmaceutical interventions (1–7). The National Institutes of Health (NIH) have been advocating for investigating gender-related differences and hormonal influences (8), including with respect to impulse control and its contributions to addictions. Despite the importance of studying sex differences, the standard integration of sex-difference considerations, including in preclinical research using cell lines and animals, has yet to occur.


title: When Is a Sprint a Sprint? A Review of the Analysis of Team-Sport Athlete Activity Profile

abstract: The external load of a team-sport athlete can be measured by tracking technologies, including global positioning systems (GPS), local positioning systems (LPS) and vision-based systems. These technologies allow for the calculation of displacement, velocity and acceleration during a match or training session. The accurate quantification of these variables is critical so that meaningful changes in team-sport athlete external load can be detected. High-velocity running, including sprinting, may be important for specific team-sport match activities, including evading an opponent or creating a shot on goal. Maximal accelerations are energetically demanding and frequently occur from a low velocity during team-sport matches. Despite extensive research, conjecture exists regarding the thresholds by which to classify the high velocity and acceleration activity of a team-sport athlete. There is currently no consensus on the definition of a sprint or acceleration effort, even within a single sport. The aim of this narrative review was to examine the varying velocity and acceleration thresholds reported in athlete activity profiling. The purposes of this review were therefore to (1) identify the various thresholds used to classify high-velocity or –intensity running plus accelerations; (2) examine the impact of individualized thresholds on reported team-sport activity profile; (3) evaluate the use of thresholds for court-based team-sports and; (4) discuss potential areas for future research. The presentation of velocity thresholds as a single value, with equivocal qualitative descriptors, is confusing when data lies between two thresholds. In Australian football, sprint efforts have been defined as activity > 4.00 m·s-1 or > 4.17 m·s-1. Acceleration thresholds differ across the literature, with > 1.11 m·s-2, 2.78 m·s-2, 3.00 m·s-2 and 4.00 m·s-2 utilized across a number of sports. It is difficult to compare literature on field-based sports due to inconsistencies in velocity and acceleration thresholds, even within a single sport. Velocity and acceleration thresholds have been determined from physical capacity tests. Limited research exists on the classification of velocity and acceleration data by female team-sport athletes. Alternatively, data mining techniques may be used to report team-sport athlete external load, without the requirement of arbitrary or physiologically defined thresholds.


title: Not a Simple Yes or No: Uncertainty in Indirect Answers

abstract: There is a long history of using logic to model the interpretation of indirect speech acts. Classical logical inference, however, is unable to deal with the combinations of disparate, conflicting, uncertain evidence that shape such speech acts in discourse. We propose to address this by combining logical inference with probabilistic methods. We focus on responses to polar questions with the following property: they are neither yes nor no, but they convey information that can be used to infer such an answer with some degree of confidence, though often not with enough confidence to count as resolving. We present a novel corpus study and associated typology that aims to situate these responses in the broader class of indirect question--answer pairs (IQAPs). We then model the different types of IQAPs using Markov logic networks, which combine first-order logic with probabilities, emphasizing the ways in which this approach allows us to model inferential uncertainty about both the context of utterance and intended meanings.


title: Current Feedback Compensation Circuit for 2T1C LED Displays: Method

abstract: A novel current feedback programming principle and circuit architecture are presented, compatible with LED displays utilizing the 2T1C pixel structure. The new pixel programming approach is compatible with all TFT backplane technologies and can compensate for non-uniformities in both threshold voltage and carrier mobility of the OLED pixel drive TFT, due to a feedback loop that modulates the gate of the driving transistor according to the OLED current. The circuit can be internal or external to the integrated display data driver. Based on simulations and data gathered through a fabricated prototype driver, a pixel drive current of 20 nA can be programmed within an addressing time ranging from 10 $\mu{\rm s}$ to 50 $\mu{\rm s}$ .


title: Iron and Magnet Losses and Torque Calculation of Interior Permanent Magnet Synchronous Machines Using Magnetic Equivalent Circuit

abstract: We present a faster and simpler approach for the calculation of iron and magnet losses and torque of an interior permanent-magnet synchronous machine (IPMSM) than finite-element methods (FEM). It uses a magnetic equivalent circuit (MEC) based on large elements and takes into account magnetic saturation and magnet eddy currents. The machine is represented by nonlinear and constant reluctance elements and flux sources. Solution of the nonlinear magnetic circuit is obtained by an iterative method. The results allow the calculation of losses and torque of the machine. Due to the approximations used in the formulation of the MEC, this method is less accurate but faster than nonlinear transient magnetic FEM, and is more useful for the comparison of different machine designs during design optimization.


title: Drill Sergeant: Supporting Physical Construction Projects through an Ecosystem of Augmented Tools

abstract: Mapping techniques from software tutorials onto physical craft processes can assist novices in building multi-material assemblies. By providing in-situ step instructions and progress tracking, generating dynamic feedback on technique, and adapting tutorial content to a user's specific context and preferences, an ecosystem of smart tools can guide users through complete project tutorials. We demonstrate how such techniques can be enabled by augmenting common workshop tools (drill/driver, saw, router) with measurement, state sensing and interactive feedback; and by sequencing instructions across multiple tools. We validate the benefits of a smart tool ecosystem through reflections on a series of author-created design examples and informal feedback from four fab lab users.


title: Space Structure and Clustering of Categorical Data

abstract: Learning from categorical data plays a fundamental role in such areas as pattern recognition, machine learning, data mining, and knowledge discovery. To effectively discover the group structure inherent in a set of categorical objects, many categorical clustering algorithms have been developed in the literature, among which $k$ -modes-type algorithms are very representative because of their good performance. Nevertheless, there is still much room for improving their clustering performance in comparison with the clustering algorithms for the numeric data. This may arise from the fact that the categorical data lack a clear space structure as that of the numeric data. To address this issue, we propose, in this paper, a novel data-representation scheme for the categorical data, which maps a set of categorical objects into a Euclidean space. Based on the data-representation scheme, a general framework for space structure based categorical clustering algorithms (SBC) is designed. This framework together with the applications of two kinds of dissimilarities leads two versions of the SBC-type algorithms. To verify the performance of the SBC-type algorithms, we employ as references four representative algorithms of the $k$ -modes-type algorithms. Experiments show that the proposed SBC-type algorithms significantly outperform the $k$ -modes-type algorithms.


title: Rethinking Visualization: A High-Level Taxonomy

abstract: We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relates and overlap, and that may inspire research ideas in hybrid visualization areas


title: Dual wide-band four-unit MIMO antenna system for 4G/LTE and WLAN mobile phone applications

abstract: A compact planar multiband four-unit multiple-input multiple-output (MIMO) antenna system with high isolation is developed. At VSWR ≤ 2.75, the proposed MIMO antenna operates in the frequency range of LTE Band-1, 2, 3, 7, 40 and WLAN 2.4 GHz band. A T-strip and dumbbell shaped slots are studied to mitigate mutual coupling effects. The measured worst case isolation is better that 15.3 dB and envelope correlation coefficient is less than 0.01. The received signals satisfy the equal power gain condition and radiation patterns confirm the pattern diversity to combat multipath fading effects. At 29 dB SNR, the achieved MIMO channel capacity is about 22.2 b/s/Hz. These results infer that the proposed MIMO antenna is an attractive candidate for 4G-LTE mobile phone applications.


title: Advanced vehicle monitoring and tracking system based on Raspberry Pi

abstract: An advanced vehicle monitoring and tracking system is designed for monitoring the school vehicle from any location A to location B at real time and provide safety environment to the traveler. The proposed system would make good use of new technology that based on Embedded Linux board namely Raspberry Pi and its advanced feature of storing database at real time. The proposed system works on Global Positioning System (GPS) and Global System for Mobile Communication (GSM) which is used for vehicle tracking and monitoring mechanism. For this purpose SIM908 Module is used which includes all the three things namely GPS GPRS GSM. The GPS gives current location of the vehicle; GPRS sends the tracking information to the server and the GSM is used for sending alert message to vehicle's owner mobile. The proposed system would place inside the vehicle whose position is to be determined on the web page and monitored at real time. In the proposed system, there is comparison between the current vehicle path and already specified path inside the file system of Raspberry pi. Hence if the driver drives the vehicle on the wrong path then the alert message will be sent from the proposed system to the vehicle's owner mobile and if the vehicle's speed goes beyond the specified value of the speed, then also the warning message will be sent from system to the owner mobile. The proposed system also took care of the traveler's safety by using LPG Gas leakage sensor MQ6 and temperature sensor DS18B20.


Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction


title: Rule-Based Exploratory Testing of Graphical User Interfaces

abstract: This paper introduces rule-based exploratory testing, an approach to GUI testing that combines aspects of manual exploratory testing with rule-based test automation. This approach uses short, automated rules to increase the bug-detection capability of recorded exploratory test sessions. A preliminary evaluation found that this approach can be used to detect both general and application-specific bugs, but that rules for general bugs are easier to transfer between applications. Also, despite the advantages of keyword-based testing, it interferes with the transfer of rules between applications.


FreeSentry: Protecting Against Use-After-Free Vulnerabilities Due to Dangling Pointers


title: All-inorganic cesium lead iodide perovskite solar cells with stabilized efficiency beyond 15%

abstract: As the black cesium lead iodide (CsPbI3) tends to transit into a yellow δ-phase at ambient, it is imperative to develop a stabilized black phase for photovoltaic applications. Herein, we report a distorted black CsPbI3 film by exploiting the synergistic effect of hydroiodic acid (HI) and phenylethylammonium iodide (PEAI) additives. It is found that the HI induces formation of hydrogen lead iodide (HPbI3+x), an intermediate to the distorted black phase with appropriate band gap of 1.69 eV; while PEAI provides nucleation for optimized crystallization. More importantly, it stabilizes the distorted black phase by hindering phase transition via its steric effects. Upon optimization, we have attained solar cell efficiency as high as 15.07%. Specifically, the bare cell without any encapsulation shows negligible efficiency loss after 300 h of light soaking. The device keeps 92% of its initial cell efficiency after being stored for 2 months under ambient conditions. Black phase cesium lead iodide perovskite is regarded as a promising candidate for solar cells, but it easily transits to undesired yellow phase. Herein, Wang et al. stabilized the black phase using molecular additives to achieve device efficiency beyond 15% with high light soaking stability.


title: Integrated battery charger for electric scooter

abstract: The paper deals with a battery charger integrated into the traction hardware of an electric scooter, for recharging the scooter batteries by means of a single-phase AC source. A mechanical switch and a rectifier bridge are the only additional components required to transform the electric scooter powetrain into a PFC battery charger, suitable for current-controlled or voltage-controlled recharge. The AC current is controlled at unitary power factor with no harmonic distortion. Switching harmonics are also drastically reduced by means of phase-interleaving. The battery charge is regulated according to the requests of the Battery Monitor System (BMS) that is embedded into the battery packs. The effectiveness of the integrated battery charger is demonstrated here on an electric scooter with high voltage Li-Ion battery (260V) and DC/DC/AC power conversion scheme. The integrated PFC charger concept is also valid for electric vehicles with AC traction drives based on a direct DC/AC conversion scheme, as demonstrated throughout the paper.


title: GaN MMIC based T/R-Module Front-End for X-Band Applications

abstract: Amplifiers for a next generation of T/R-modules in future active array antennas are realized as monolithically integrated circuits (MMIC) on the bases of novel AlGaN/GaN HEMT structures. Both, low noise and power amplifiers are designed for X-band frequencies. The MMICs are designed, simulated and fabricated using a novel via-hole microstrip technology. Output power levels of 6.8 W (38 dBm) for the driver amplifier (DA) and 20 W (43 dBm) for the high power amplifier (HPA) are measured. The measured noise figure of the low noise amplifier (LNA) is in the range of 1.5 dB. A T/R-module front-end with mounted GaN MMICs is designed based on a multilayer LTCC technology.


Ontology Learning from Text: An Overview


title: Big Data Clustering: A Review

abstract: Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today’s novel solutions. The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based on today’s available technologies and frameworks.


title: A Survey of In-Band Full-Duplex Transmission: From the Perspective of PHY and MAC Layers

abstract: In-band full-duplex (IBFD) transmission represents an attractive option for increasing the throughput of wireless communication systems. A key challenge for IBFD transmission is reducing self-interference. Fortunately, the power associated with residual self-interference can be effectively canceled for feasible IBFD transmission with combinations of various advanced passive, analog, and digital self-interference cancellation schemes. In this survey paper, we first review the basic concepts of IBFD transmission with shared and separated antennas and advanced self-interference cancellation schemes. Furthermore, we also discuss the effects of IBFD transmission on system performance in various networks such as bidirectional, relay, and cellular topology networks. This survey covers a wide array of technologies that have been proposed in the literature as feasible for IBFD transmission and evaluates the performance of the IBFD systems compared to conventional half-duplex transmission in connection with theoretical aspects such as the achievable sum rate, network capacity, system reliability, and so on. We also discuss the research challenges and opportunities associated with the design and analysis of IBFD systems in a variety of network topologies. This work also explores the development of MAC protocols for an IBFD system in both infrastructure-based and ad hoc networks. Finally, we conclude our survey by reviewing the advantages of IBFD transmission when applied for different purposes, such as spectrum sensing, network secrecy, and wireless power transfer.


title: Real-Time Face Identification via CNN and Boosted Hashing Forest

abstract: The family of real-time face representations is obtained via Convolutional Network with Hashing Forest (CNHF). We learn the CNN, then transform CNN to the multiple convolution architecture and finally learn the output hashing transform via new Boosted Hashing Forest (BHF) technique. This BHF generalizes the Boosted SSC approach for hashing learning with joint optimization of face verification and identification. CNHF is trained on CASIA-WebFace dataset and evaluated on LFW dataset. We code the output of single CNN with 97% on LFW. For Hamming embedding we get CBHF-200 bit (25 byte) code with 96.3% and 2000-bit code with 98.14% on LFW. CNHF with 2000×7-bit hashing trees achieves 93% rank-1 on LFW relative to basic CNN 89.9% rank-1. CNHF generates templates at the rate of 40+ fps with CPU Core i7 and 120+ fps with GPU GeForce GTX 650.


title: Sentiment analysis for dialectical Arabic

abstract: This article investigates sentiment analysis in Arabic tweets with the presence of dialectical words. Sentiment analysis deals with extracting opinionated phrases from reviews, comments or tweets. i.e. to decide whether a given review or comment is positive, negative or neutral. Sentiment analysis has many applications and is very vital for many organizations. In this article, we utilize machine learning techniques to determine the polarity of tweets written in Arabic with the presence of dialects. Dialectical Arabic is abundantly present in social media and micro blogging channels. Dialectical Arabic presents challenges for topical classifications and for sentiment analysis. One example of such challenges is that stemming algorithms do not perform well with dialectical words. Another example is that dialectical Arabic uses an extended set of stopwords. In this research we introduce a framework that is capable of performing sentiment analysis on tweets written using either Modern Standard Arabic or Jordanian dialectical Arabic. The core of this framework is a dialect lexicon which maps dialectical words into their corresponding Modern Standard Arabic words. The experimentation reveals that the dialect lexicon improves the accuracies of the classifiers.


title: Building an Automatic Thesaurus to Enhance Building an Automatic Thesaurus to Enhance Building an Automatic Thesaurus to Enhance Building an Automatic Thesaurus to Enhance Information Retrieval

abstract: One of the major problems of modern Information Retrieval (IR) systems is the vocabulary Problem that c oncerns with the discrepancies between terms used for describing documents and the terms used by the researcher to describe their information need. We have implemented an automatic thesurs, the system was built using Vector Space Model (VSM). In this model, we used Cosine measure similarity. In this paper we use selected 242 Arabic


title: Classification of networks based on inherent structural characteristics

abstract: This paper seeks to identify and classify power system networks based on their inherent definitive structural property. The participation between the load buses is observable from the eigenvectors obtained from a Schur complement of the Y-admittance matrix. This is used to classify power system networks as either topologically strong or weak. Results show that in a topologically weak network, load voltages are below the nominal voltage limit. Conversely, in a topologically strong network they are above the nominal limit.


COMPARING FAST AND FRUGAL TREES AND BAYESIAN NETWORKS FOR RISK ASSESSMENT


title: Changing the agriculture and environment conversation

abstract: The land sharing/sparing debate has stagnated. Finding a way forward requires that we ask new questions and, crucially, focus on human well-being and ecosystem services.


title: Design lessons from the fastest q&a site in the west

abstract: This paper analyzes a Question & Answer site for programmers, Stack Overflow, that dramatically improves on the utility and performance of Q&A systems for technical domains. Over 92% of Stack Overflow questions about expert topics are answered - in a median time of 11 minutes. Using a mixed methods approach that combines statistical data analysis with user interviews, we seek to understand this success. We argue that it is not primarily due to an a priori superior technical design, but also to the high visibility and daily involvement of the design team within the community they serve. This model of continued community leadership presents challenges to both CSCW systems research as well as to attempts to apply the Stack Overflow model to other specialized knowledge domains.


title: Thermal-Comfort Design of Personalized Casts

abstract: This paper introduces a novel method for designing personalized orthopedic casts which are aware of thermal-comfort while satisfying mechanical requirements. Our pipeline starts from thermal images taken by an infrared camera, by which the distribution of thermal-comfort sensitivity is generated on the surface of a 3D scanned model. We formulate a hollowed Voronoi tessellation pattern to represent the covered region for a web-like cast design. The pattern is further optimized according to the thermal-comfort sensitivity calculated from thermal images. Working together with a thickness variation method, we generate a solid model for a personalized cast maximizing both thermal comfort and mechanical stiffness. To demonstrate the effectiveness of our approach, 3D printed models of personalized casts are tested on body parts of different individuals.


title: Design of a Low-Power CMOS LVDS I/O Interface Circuit

abstract: The paper presents the design and implementation of LVDS (low-voltage differential signaling) receiver circuit, fully compatible with LVDS standard. The proposed circuit is composed of the telescopic amplifier and the comparator with internal hysteresis. The receiver supports 3.5 Gbps data rate with 7.4 mA current at 1.8 V supply according to post-layout circuit simulations. The circuit has the power consumption of 13.1 MW. Comparing with the conventional circuit, the circuit is achieved to reduce the power consumption by 19.1% and the data rate by 14.3%. The validity and effectiveness of the proposed circuit are verified through the circuit simulation with Samsung 0.18 μm CMOS (complementary metal-oxide-semiconductor) standard technology under the 1.8 V supply voltage.


title: A Frequency Domain Step-Size Control Method for LMS Algorithms

abstract: Frequency-domain (FD) adaptive filters are able to reduce the numerical complexity by using the overlap-and-save implementation method compared to time-domain (TD) ones. Similar to the TD algorithms, the selection of step size trades off between the convergence rate and steady state performance. In this letter, we propose a bin-wise block-varying step-size control method for the FD least-mean-square (LMS) algorithm. It achieves both fast convergence rate and low steady state error. In addition, compared to the TD step-size control method, the proposed method performs better when the filter input is a non-white process. Effectiveness of the proposed algorithm is demonstrated through computer simulations.


title: Fast, Accurate Detection of 100,000 Object Classes on a Single Machine

abstract: In the paper [1] published in CVPR, we presented a method that can directly use deformable part models (DPMs) trained as in [3]. After training, HOG based part filters are hashed, and, during inference, counts of hashing collisions summed over all hash bands serve as a proxy for part-filter / sliding-window dot products, i.e., filter responses. These counts are an approximation and so we take the original HOG-based filters for the top hash counts and calculate the exact dot products for scoring. It is possible to train DPM models not on HOG data but on a hashed WTA [4] version of this data. The resulting part filters are sparse, real-valued vectors the size of WTA vectors computed from sliding windows. Given the WTA hash of a window, we exactly recover dot products of the top responses using an extension of locality-sensitive hashing. In this supplement, we sketch a method for training such WTA-based models.


Bridge Management System with Integrated Life Cycle Cost Optimization


title: Query Based Arabic Text Summarization 1

abstract: With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, in this case domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper we introduce a query based, Arabic text, single document summarization using an existing Arabic language thesaurus and an extracted knowledge base. We use an Arabic corpus to extract domain knowledge represented by topic related concepts/


title: Assessing Computational Thinking in CS Unplugged Activities

abstract: Computer Science (CS) Unplugged activities have been deployed in many informal settings to present computing concepts in an engaging manner. To justify use in the classroom, however, it is critical for activities to have a strong educational component. For the past three years, we have been developing and refining a CS Unplugged curriculum for use in middle school classrooms. In this paper, we describe an assessment that maps questions from a comprehensive project to computational thinking (CT) skills and Bloom's Taxonomy. We present results from two different deployments and discuss limitations and implications of our approach.


title: A framework for visual saliency detection with applications to image thumbnailing

abstract: We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.


title: Experimental validation of a leg-wheel hybrid mobile robot Quattroped

abstract: This video submission presents the experimental validation and testing of a leg-wheel hybrid mobile robot Quattroped. By combining the smooth and efficient motion of wheels on the flat ground with the great mobility of legs on rough terrains, the design of the robot aims for agile and versatile yet efficient locomotion in both natural and artificial environments. Compared to most hybrid platforms, which have separate mechanisms of wheels and legs, this robot is implemented with a transformation mechanism that directly changes the morphology of wheels (i.e., a full circle) into half-circle legs, each with 2 active degrees-of-freedom (i.e., combining two half circles as a leg ). The experimental testing includes flat terrain driving and turning in the wheeled mode, leg-wheel mode switching, and step crossing, bar crossing, natural rough terrain walking, and stair climbing in the legged mode.


title: Weakly Supervised Dense Event Captioning in Videos

abstract: Dense event captioning aims to detect and describe all events of interest contained in a video. Despite the advanced development in this area, existing methods tackle this task by making use of dense temporal annotations, which is dramatically source-consuming. This paper formulates a new problem: weakly supervised dense event captioning, which does not require temporal segment annotations for model training. Our solution is based on the one-to-one correspondence assumption, each caption describes one temporal segment, and each temporal segment has one caption, which holds in current benchmark datasets and most real-world cases. We decompose the problem into a pair of dual problems: event captioning and sentence localization and present a cycle system to train our model. Extensive experimental results are provided to demonstrate the ability of our model on both dense event captioning and sentence localization in videos.


title: The Three Pillars of Machine Programming

abstract: In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.


title: Adaptive remodeling of skeletal muscle energy metabolism in high-altitude hypoxia: Lessons from AltitudeOmics

abstract: Metabolic responses to hypoxia play important roles in cell survival strategies and disease pathogenesis in humans. However, the homeostatic adjustments that balance changes in energy supply and demand to maintain organismal function under chronic low oxygen conditions remain incompletely understood, making it difficult to distinguish adaptive from maladaptive responses in hypoxia-related pathologies. We integrated metabolomic and proteomic profiling with mitochondrial respirometry and blood gas analyses to comprehensively define the physiological responses of skeletal muscle energy metabolism to 16 days of high-altitude hypoxia (5260 m) in healthy volunteers from the AltitudeOmics project. In contrast to the view that hypoxia down-regulates aerobic metabolism, results show that mitochondria play a central role in muscle hypoxia adaptation by supporting higher resting phosphorylation potential and enhancing the efficiency of long-chain acylcarnitine oxidation. This directs increases in muscle glucose toward pentose phosphate and one-carbon metabolism pathways that support cytosolic redox balance and help mitigate the effects of increased protein and purine nucleotide catabolism in hypoxia. Muscle accumulation of free amino acids favor these adjustments by coordinating cytosolic and mitochondrial pathways to rid the cell of excess nitrogen, but might ultimately limit muscle oxidative capacity in vivo Collectively, these studies illustrate how an integration of aerobic and anaerobic metabolism is required for physiological hypoxia adaptation in skeletal muscle, and highlight protein catabolism and allosteric regulation as unexpected orchestrators of metabolic remodeling in this context. These findings have important implications for the management of hypoxia-related diseases and other conditions associated with chronic catabolic stress.


title: A jumping robot using soft pneumatic actuator

abstract: This paper presents the development of a new type of robot capable of vertical and directional jumping. The robot uses soft silicone elastomer based pneumatic actuators as legs that accelerate the platform upwards by rapid pressurization. The robot is able to control and adjust the direction of the jumping by altering the timing patterns in which the individual legs are activated.


title: Image warping for a painterly effect

abstract: We propose a two-stage approach to painterly rendering of photographs, where the image plane is first warped to produce a distorted or caricatured effect and then the resulting image is rendered with a painterly effect. We use SLIC superpixels to obtain an oversegmentation, and assign spring parameters uniformly to all pixels within a region; then, the mass-spring simulation distorts the plane in a random but content-sensitive way. With aggressive warping, the subsequent painterly rendering can be done lightly and need not remove much detail. The resulting renderings convey a sense of being painted and leave a sense of being handmade and not overly beholden to the photographic scene.


title: Encouraging pro-environmental behaviour: An integrative review and research agenda

abstract: Environmental quality strongly depends on human behaviour patterns. We review the contribution and the potential of environmental psychology for understanding and promoting pro-environmental behaviour. A general framework is proposed, comprising: (1) identification of the behaviour to be changed, (2) examination of the main factors underlying this behaviour, (3) design and application of interventions to change behaviour to reduce environmental impact, and (4) evaluation of the effects of interventions. We discuss how environmental psychologists empirically studied these four topics, identify apparent shortcomings so far, and indicate major issues for future research.


title: AwareCare: Development and validation of an observational measure of awareness in people with severe dementia

abstract: Signs of sensory and perceptual awareness can be observed in people with very severe dementia, and may be influenced by the extent to which the environment offers appropriate stimulation. We developed an observational tool, AwareCare, which care staff can use to identify signs of awareness in residents with very severe dementia, based on the concept of the Wessex Head Injury Matrix (WHIM). Using WHIM items as a guide, and following focus groups with care staff and family members, an expert panel identified 28 environmental stimuli and 35 response categories for the initial version of AwareCare. After baseline assessments of cognition, well-being and quality of life were taken, 40 residents were observed individually for 30 minutes on 5 occasions. Based on the observational data, 10 stimulus categories and 14 response categories were identified for further analysis and formed the final version of AwareCare. All participants showed awareness to varying degrees. Social stimuli elicited the most responses. Gr...


title: A systematic review and taxonomy of explanations in decision support and recommender systems

abstract: With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.


title: Designing of self tuning PID controller for AR drone quadrotor

abstract: In this paper, a gradient decent based methodology is employed to tune the Proportional-Integral-Derivative (PID) controller parameters for AR Drone quadrotor. The three PID controller parameters, proportional gain (K p ), integral gain (K i ) and derivative gain (K d ) are tuned online while flying. The proposed technique has been demonstrated through two test cases. One is the way-point navigation and other is the leader-follower formation control. The experimental result as well as simulations result have shown for both the cases.


title: Fully-isolated silicon RF LDMOS for high-efficiency mobile power conversion and RF amplification

abstract: In this paper a fully isolated bulk Si RF LDMOS device platform is reported which has been optimized for highly efficient mobile power conversion and RF power amplification. The self-aligned RF LDMOS NFET achieves a specific on-resistance R ds, on of 0.94 ohm-mm2, a breakdown voltage >9V, an R sp ∗Q gg product of 8.3 mohm nC, and a cutoff frequency F t > 43 GHz. Complementary PFET RF LDMOS exhibit an R ds, on of 3.6 ohm-mm2 with a cutoff frequency F t > 16GHz. Integrated DC-DC SMPS fabricated with these RF LDMOS have a 2.3x area reduction over conventional 5V CMOS and a gate driver efficiency increase of at least 75%. RF LDMOS NFET power amplifier cores (RF PA) under CW load pull and 3.3V supply voltage exhibit gains (Gt) of 18dB and 12dB at 2.4GHz and 5.8GHz, respectively, with a 1dB compression power density of 22dBm/mm of gate width and a maximum PAE > 70%. RF LDMOS power cells meet the 802.11n spectral mask requirement for a 20MHz 64 QAM modulated signal with P out = 19dBm. These DC and RF results are the best reported for integrated Si LDMOS device structures, enabling the next generation of highly efficient miniaturized mobile power converters and new RF integration paradigms for mobile front-end modules.


title: Helical Cerenkov effect, a novel radiation source

abstract: The observability of the helical Cerenkov effect as a novel radiation source is discussed. Depending on the value of the index of refraction of the medium, the strength of the uniform magnetic field, and the electron beam energy, helical Cerenkov radiation can occur in the same spectral regions as the ordinary Cerenkov effect, that is, from microwave to visible wavelengths. From the kinematics point of view, I argue that for a microwave wavelength of 10/sup -1/cm this effect should be observable in a medium with an index of refraction of 1.4, with a beam energy of 3 MeV, and a uniform magnetic field of 4 T. On the specific level, however, for the sake of simplicity, I discuss the observability of this effect for visible light with the central wavelength of 5/spl times/10/sup -5/ cm which can be achieved with 2 MeV in beam energy, silica aerogel as a medium (with an index of refraction of 1.075), and uniform magnetic fields from 5 to 10 T. For a 10-T magnetic field, I calculate that in the visible region of 250 to 750 nm an electron will produce a photon per 10 cm of traveled length. As to the stimulated helical Cerenkov emission, I estimate that respectable gains are possible even if the beam passes close to the dielectric rather than through it. In addition to being potentially a new radiation source, the helical Cerenkov effect could possibly be used as a detector of radiation by energetic electrons that are trapped in a medium by strong magnetic fields. >


title: Low energy operation in WSNs: A survey of preamble sampling MAC protocols

abstract: The limited energy resources of sensor nodes are among the most important constraints in Wireless Sensor Networks (WSNs). Consequently, the Medium Access Control (MAC) layer design is crucial, due to its influence on the transceiver, which is the most energy-consuming component of a sensor node. Among the different MAC protocols designed for WSNs, preamble sampling techniques provide extremely low energy consumption at low loads and have a notably simple operation and a lack of synchronisation requirements, which are characteristics that are especially appealing to WSNs. In this work, a survey of the different types of MAC protocols designed for WSNs is presented with a special focus on preamble sampling MAC protocols. The aim of this work is to give a detailed overview and classification of the most relevant preamble sampling MAC protocols, being motivated by the extremely large number of MAC protocols designed for WSNs in recent years. Moreover, a simple set of guidelines for matching the most suitable MAC protocol category to a given application is provided in this work.


title: A model-based Bayesian framework for ECG beat segmentation

abstract: The study of electrocardiogram (ECG) waveform amplitudes, timings and patterns has been the subject of intense research, for it provides a deep insight into the diagnostic features of the heart's functionality. In some recent works, a Bayesian filtering paradigm has been proposed for denoising and compression of ECG signals. In this paper, it is shown that this framework may be effectively used for ECG beat segmentation and extraction of fiducial points. Analytic expressions for the determination of points and intervals are derived and evaluated on various real ECG signals. Simulation results show that the method can contribute to and enhance the clinical ECG beat segmentation performance.


title: High volume of endurance training impairs adaptations to 12 weeks of strength training in well-trained endurance athletes

abstract: The purpose of the present study was to compare the effect of 12 weeks of strength training combined with a large volume of endurance training with the effect of strength training alone on the strength training adaptations. Well-trained cyclists with no strength training experience performed heavy strength training twice a week in addition to a high volume of endurance training during a 12-week preparatory period (S + E; n = 11). A group of non-strength trained individuals performed the same strength training as S + E, but without added endurance training (S; n = 7). Thigh muscle cross-sectional area, 1 repetition maximum (1RM) in leg exercises, squat jump performance, and peak rate of force development (RFD) were measured. Following the intervention period, both S + E and S increased 1RM strength, thigh muscle cross-sectional area, and squat jump performance (p < 0.05), and the relative improvements in S were greater than in S + E (p < 0.05). S increased peak RFD while S + E did not, and this improvement was greater than in S + E (p < 0.05). To the best of our knowledge, this is the first controlled study to demonstrate that the strength training response on muscle hypertrophy, 1RM strength, squat jump performance, and peak RFD is attenuated in well-trained endurance athletes during a period of concurrent endurance training.


title: Milk Composition Responses to Unilateral Arterial Infusionof Complete and Histidine-Lacking Amino Acid Mixtures to the Mammary Glands of Cows

abstract: Abstract To evaluate a close mammary infusion technique for the study of milk protein responses to blood amino acid profile, five early-lactation, multiparous Holstein cows were surgically fitted with catheters in both external iliac arteries. Animals were infused into one arterial catheter with five different solutions on 5 consecutive days in a Latin square design. Infusions began at 0800h and continued until 1800h. The five infusates were a 3% saline control, 15g/h of complete amino acid mix, 15g/h of imbalanced amino acid mix (minus His), 30g/h of complete amino acid mix, and 30g/h of imbalanced amino acid mix (minus His). Cows were fed a total mixed ration twice daily containing 16% crude protein and 1.7Mcal/kg of net energy for lactation. Infusion of the complete amino acid mix elevated amino acid concentrations in arterial plasma two- to threefold but caused only a small dose-dependent increase in milk protein content and yield. Fat percentage in milk was decreased from 4.08 to 3.35% by the complete amino acid infusions so that the protein: fat ratio climbed from 0.76 on the control to 0.99 with 30g/h of amino acid. Removal of His from the infusate caused plasma His concentrations to drop but had no effect on any other circulating amino acids. Milk composition was restored to control levels by removal of the single amino acid. A short-term circulating amino acid imbalance depresses milk protein percentage and increases milk fat content in dairy cows.


Cancer-Associated Immune Resistance and Evasion of Immune Surveillance in Colorectal Cancer


Neuroscience and Education : Issues and Opportunities


title: A Bio-Inspired Robot With Undulatory Fins and Its Control Methods

abstract: This paper proposes a bio-inspired robot with undulatory fins and summarizes its control methods. First, three basic motions, forward/backward swimming, diving/rising motion, and turning, are implemented and evaluated by experiments. Next, a hybrid control that combines active disturbance rejection control with a fuzzy strategy is presented to achieve closed-loop depth and course control according to the evaluation of the three basic motions. Finally, waypoint tracking with a line-of-sight guidance system based on a finite-state machine for this bio-inspired robot is presented. The results of swimming experiments are provided to illustrate the validity of the proposed methods.


title: Chronic Obstructive Pulmonary Disease and Lung Cancer: Underlying Pathophysiology and New Therapeutic Modalities

abstract: Chronic obstructive pulmonary disease (COPD) and lung cancer are major lung diseases affecting millions worldwide. Both diseases have links to cigarette smoking and exert a considerable societal burden. People suffering from COPD are at higher risk of developing lung cancer than those without, and are more susceptible to poor outcomes after diagnosis and treatment. Lung cancer and COPD are closely associated, possibly sharing common traits such as an underlying genetic predisposition, epithelial and endothelial cell plasticity, dysfunctional inflammatory mechanisms including the deposition of excessive extracellular matrix, angiogenesis, susceptibility to DNA damage and cellular mutagenesis. In fact, COPD could be the driving factor for lung cancer, providing a conducive environment that propagates its evolution. In the early stages of smoking, body defences provide a combative immune/oxidative response and DNA repair mechanisms are likely to subdue these changes to a certain extent; however, in patients with COPD with lung cancer the consequences could be devastating, potentially contributing to slower postoperative recovery after lung resection and increased resistance to radiotherapy and chemotherapy. Vital to the development of new-targeted therapies is an in-depth understanding of various molecular mechanisms that are associated with both pathologies. In this comprehensive review, we provide a detailed overview of possible underlying factors that link COPD and lung cancer, and current therapeutic advances from both human and preclinical animal models that can effectively mitigate this unholy relationship.


title: Information security policy compliance: an empirical study of rationality-based beliefs and information security awareness

abstract: Many organizations recognize that their employees, who are often considered the weakest link in information security, can also be great assets in the effort to reduce risk related to information security. Since employees who comply with the information security rules and regulations of the organization are the key to strengthening information security, understanding compliance behavior is crucial for organizations that want to leverage their human capital. ::: ::: This research identifies the antecedents of employee compliance with the information security policy (ISP) of an organization. Specifically, we investigate the rationality-based factors that drive an employee to comply with requirements of the ISP with regard to protecting the organization's information and technology resources. Drawing on the theory of planned behavior, we posit that, along with normative belief and self-efficacy, an employee's attitude toward compliance determines intention to comply with the ISP. As a key contribution, we posit that an employee's attitude is influenced by benefit of compliance, cost of compliance, and cost of noncompliance, which are beliefs about the overall assessment of consequences of compliance or noncompliance. We then postulate that these beliefs are shaped by the employee's outcome beliefs concerning the events that follow compliance or noncompliance: benefit of compliance is shaped by intrinsic benefit, safety of resources, and rewards, while cost of compliance is shaped by work impediment; and cost of noncompliance is shaped by intrinsic cost, vulnerability of resources, and sanctions. We also investigate the impact of information security awareness (ISA) on outcome beliefs and an employee's attitude toward compliance with the ISP. ::: ::: Our results show that an employee's intention to comply with the ISP is significantly influenced by attitude, normative beliefs, and self-efficacy to comply. Outcome beliefs significantly affect beliefs about overall assessment of consequences, and they, in turn, significantly affect an employee's attitude. Furthermore, ISA positively affects both attitude and outcome beliefs. As the importance of employees' following their organizations' information security rules and regulations increases, our study sheds light on the role of ISA and compliance-related beliefs in an organization's efforts to encourage compliance.


title: The mask detection technology for occluded face analysis in the surveillance system.

abstract: The surveillance systems have been widely used in automatic teller machines (ATMs), banks, convenient stores, etc. For example, when a customer uses the ATM, the surveillance systems will record his/her face information. The information will help us understand and trace who withdrew money. However, when criminals use the ATM to withdraw illegal money, they usually block their faces with something (in Taiwan, criminals usually use safety helmets or masks to block their faces). That will degrade the purpose of the surveillance system. In previous work, we already proposed a technology for safety helmet detection. In this paper, we propose a mask detection technology based upon automatic face recognition methods. We use the Gabor filters to generate facial features and utilize geometric analysis algorithms for mask detection. The technology can give an early warning to save-guards when any "customer" or "intruder" blocks his/her face information with a mask. Besides, the technology can assist face detection in the automatic face recognition system. Experimental results show the performance and reliability of the proposed technology.


title: Fast and lock-free concurrent priority queues for multi-thread systems

abstract: We present an efficient and practical lock-free implementation of a concurrent priority queue that is suitable for both fully concurrent (large multi-processor) systems as well as pre-emptive (multi-process) systems. Many algorithms for concurrent priority queues are based on mutual exclusion. However, mutual exclusion causes blocking which has several drawbacks and degrades the overall performance of the system. Non-blocking algorithms avoid blocking, and several implementations have been proposed. Previously known non-blocking algorithms of priority queues did not perform well in practice because of their complexity, and they are often based on non-available atomic synchronization primitives. Our algorithm is based on the randomized sequential list structure called Skiplist, and a real-time extension of our algorithm is also described. In our performance evaluation we compare our algorithm with a well-representable set of earlier known implementations of priority queues. The experimental results clearly show that our lock-free implementation outperforms the other lock-based implementations in practical scenarios for 3 threads and more, both on fully concurrent as well as on pre-emptive systems.


title: Modeling word perception using the Elman network

abstract: This paper presents an automatic acquisition process to acquire the semantic meaning for the words. This process obtains the representation vectors for stemmed words by iteratively improving the vectors, using a trained Elman network. Experiments performed on a corpus composed of Shakespeare's writings show its linguistic analysis and categorization abilities.


The FAIR Guiding Principles for scientific data management and stewardship


title: Real-time Obstacle Avoidance for Fast Mobile Robots

abstract: A real-time obstacle avoidance approach for mobile robots has been developed and implemented. It permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advance toward the target. The novelty of this approach, entitled the virtual force field method, lies in the integration of two known concepts: certainty grids for obstacle representation and potential fields for navigation. This combination is especially suitable for the accommodation of inaccurate sensor data as well as for sensor fusion and makes possible continuous motion of the robot with stopping in front of obstacles. This navigation algorithm also takes into account the dynamic behavior of a fast mobile robot and solves the local minimum trap problem. Experimental results from a mobile robot running at a maximum speed of 0.78 m/s demonstrate the power of the algorithm. >


title: Automated Detection of Firearms and Knives in a CCTV Image

abstract: Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.


title: Group reactions to loyalty and disloyalty

abstract: The causes and consequences of loyalty and disloyalty in groups have received little attention from social psychologists. This chapter analyzes how groups respond to loyalty (defined as staying in a group, even though one could obtain a better outcome by leaving, because staying benefits the group) and disloyalty (defined as leaving a group, because one can obtain a better outcome by doing so, even though leaving harms the group). Attention is given to factors that influence the valence and intensity of group responses to loyalty and disloyalty on the part of both ingroup and outgroup members.


title: 5G Cellular User Equipment: From Theory to Practical Hardware Design

abstract: Research and development on the next generation wireless systems, namely 5G, has experienced explosive growth in recent years. In the physical layer, the massive multiple-input-multiple-output (MIMO) technique and the use of high GHz frequency bands are two promising trends for adoption. Millimeter-wave (mmWave) bands, such as 28, 38, 64, and 71 GHz, which were previously considered not suitable for commercial cellular networks, will play an important role in 5G. Currently, most 5G research deals with the algorithms and implementations of modulation and coding schemes, new spatial signal processing technologies, new spectrum opportunities, channel modeling, 5G proof of concept systems, and other system-level enabling technologies. In this paper, we first investigate the contemporary wireless user equipment (UE) hardware design, and unveil the critical 5G UE hardware design constraints on circuits and systems. On top of the said investigation and design tradeoff analysis, a new, highly reconfigurable system architecture for 5G cellular user equipment, namely distributed phased arrays based MIMO (DPA-MIMO) is proposed. Finally, the link budget calculation and data throughput numerical results are presented for the evaluation of the proposed architecture.


title: Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

abstract: We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numerically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.


title: Data Protection from Insider Threats

abstract: As data represent a key asset for today's organizations, the problem of how to protect this data from theft and misuse is at the forefront of these organizations' minds. Even though today several data security techniques are available to protect data and computing infrastructures, many such techniques -- such as firewalls and network security tools -- are unable to protect data from attacks posed by those working on an organization's "inside." These "insiders" usually have authorized access to relevant information systems, making it extremely challenging to block the misuse of information while still allowing them to do their jobs. This book discusses several techniques that can provide effective protection against attacks posed by people working on the inside of an organization. ::: ::: Chapter One introduces the notion of insider threat and reports some data about data breaches due to insider threats. Chapter Two covers authentication and access control techniques, and Chapter Three shows how these general security techniques can be extended and used in the context of protection from insider threats. Chapter Four addresses anomaly detection techniques that are used to determine anomalies in data accesses by insiders. These anomalies are often indicative of potential insider data attacks and therefore play an important role in protection from these attacks. ::: ::: Security information and event management (SIEM) tools and fine-grained auditing are discussed in Chapter Five. These tools aim at collecting, analyzing, and correlating -- in real-time -- any information and event that may be relevant for the security of an organization. As such, they can be a key element in finding a solution to such undesirable insider threats. Chapter Six goes on to provide a survey of techniques for separation-of-duty (SoD). SoD is an important principle that, when implemented in systems and tools, can strengthen data protection from malicious insiders. However, to date, very few approaches have been proposed for implementing SoD in systems. In Chapter Seven, a short survey of a commercial product is presented, which provides different techniques for protection from malicious users with system privileges -- such as a DBA in database management systems. Finally, in Chapter Eight, the book concludes with a few remarks and additional research directions. ::: ::: Table of Contents: Introduction / Authentication / Access Control / Anomaly Detection / Security Information and Event Management and Auditing / Separation of Duty / Case Study: Oracle Database Vault / Conclusion


title: A survey of visual sensor network platforms

abstract: Recent developments in low-cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks. Various visual sensor platforms have been developed with the aim of integrating visual data to wireless sensor applications. The objective of this article is to survey current visual sensor platforms according to in-network processing and compression/coding techniques together with their targeted applications. Characteristics of these platforms such as level of integration, data processing hardware, energy dissipation, radios and operating systems are also explored and discussed.


title: Conversational semantics with social commitments

abstract: Message semantics are traditionally defined in terms of mental states, which is a trend that is criticized for assuming the sincerity and cooperativeness of agents. To circumvent these limitations, several proposals have been put forth to define the semantics of messages using social commitments. We follow this trend and present a conversational model where the meaning of messages is based on their use as coordinating devices advancing conversations that advance the state of social commitments and the state of the activities in which agents participate.


title: A 2-W W-Band GaN Traveling-Wave Amplifier With 25-GHz Bandwidth

abstract: A high-power gallium–nitride (GaN) monolithic microwave integrated circuit (MMIC) operating over the 75–100-GHz band is reported. Using an on-chip traveling-wave power-combining network, it achieves a continuous wave output power level of 34 dBm (2.5 W) $\pm$ 1 dB over the 75–100-GHz bandwidth and a peak power of 3 W at 84 GHz. Operating in a pulsed mode (10% duty), the MMIC chip produces a peak power of 3.6 W at 83 GHz. This work establishes new levels of performance for GaN MMICs at these frequencies. In comparison to the previous work, these results represent improvements in output power, bandwidth, and gain/power flatness with frequency across the full 75–100-GHz band. This paper also presents design details on the combining network and the MMIC not previously reported.


title: A Novel Security-Enhanced Agile Software Development Process Applied in an Industrial Setting

abstract: A security-enhanced agile software development process, SEAP, is introduced in the development of a mobile money transfer system at Ericsson Corp. A specific characteristic of SEAP is that it includes a security group consisting of four different competences, i.e., Security manager, security architect, security master and penetration tester. Another significant feature of SEAP is an integrated risk analysis process. In analyzing risks in the development of the mobile money transfer system, a general finding was that SEAP either solves risks that were previously postponed or solves a larger proportion of the risks in a timely manner. The previous software development process, i.e., The baseline process of the comparison outlined in this paper, required 2.7 employee hours spent for every risk identified in the analysis process compared to, on the average, 1.5 hours for the SEAP. The baseline development process left 50% of the risks unattended in the software version being developed, while SEAP reduced that figure to 22%. Furthermore, SEAP increased the proportion of risks that were corrected from 12.5% to 67.1%, i.e., More than a five times increment. This is important, since an early correction may avoid severe attacks in the future. The security competence in SEAP accounts for 5% of the personnel cost in the mobile money transfer system project. As a comparison, the corresponding figure, i.e., For security, was 1% in the previous development process.


title: Improving sales diversity by recommending users to items

abstract: Sales diversity is considered a key feature of Recommender Systems from a business perspective. Sales diversity is also linked with the long-tail novelty of recommendations, a quality dimension from the user perspective. We explore the inversion of the recommendation task as a means to enhance sales diversity - and indirectly novelty - by selecting which users an item should be recommended to instead of the other way around. We address the inverted task by two approaches: a) inverting the rating matrix, and b) defining a probabilistic reformulation which isolates the popularity component of arbitrary recommendation algorithms. We find that the first approach gives rise to interesting reformulations of nearest-neighbor algorithms, which essentially introduce a new neighbor selection policy. The second approach, as well as the first, ultimately result in substantial sales diversity enhancements, and improved trade-offs with recommendation precision and novelty. Two experiments on movie and music recommendation datasets show the effectiveness of the resulting approach, even when compared to direct optimization approaches of the target metrics proposed in prior work.


title: EdgeFlow: A Technique for Boundary Detection

abstract: A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. Segmentation results on a large and diverse collections of natural images are provided, demonstrating the usefulness of this method to content based image retrieval.


title: Ultra-low power digital subthreshold logic circuits

abstract: Numerous efforts in balancing the trade-off between power, area and performance have been done in the medium performance, medium power region of the design spectrum. However, not much study has been done at the two extreme ends of the design spectrum, namely the ultra-low power with acceptable performance at one end (the focus of this paper), and high performance with power within limit at the other. One solution to achieve the ultra-low power requirement is to operate the digital logic gates in the subthreshold region. We analyze both CMOS and Pseudo-NMOS logic families operating in the subthreshold region. We compare the results with CMOS in the normal strong inversion region and with other known low-power logic, namely, energy recovery logic. Our results show an energy per switching reduction of two orders of magnitude for an 8/spl times/8 carry save array multiplier when it is operated in the subthreshold region.


title: Residual-life distributions from component degradation signals: A Bayesian approach

abstract: Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.


title: Effects of low-intensity bench press training with restricted arm muscle blood flow on chest muscle hypertrophy: a pilot study

abstract: Summary Single-joint resistance training with blood flow restriction (BFR) results in significant increases in arm or leg muscle size and single-joint strength. However, the effect of multijoint BFR training on both blood flow restricted limb and nonrestricted trunk muscles remain poorly understood. To examine the impact of BFR bench press training on hypertrophic response to non-restricted (chest) and restricted (upper-arm) muscles and multi-joint strength, 10 young men were randomly divided into either BFR training (BFR-T) or non-BFR training (CON-T) groups. They performed 30% of one repetition maximal (1-RM) bench press exercise (four sets, total 75 reps) twice daily, 6 days week )1 for 2 weeks. During the exercise session, subjects in the BFR-T group placed elastic cuffs proximally on both arms, with incremental increases in external compression starting at 100 mmHg and ending at 160 mmHg. Before and after the training, triceps brachii and pectoralis major muscle thickness (MTH), bench press 1-RM and serum anabolic hormones were measured. Two weeks of training led to a significant increase (P<0.05) in 1-RM bench press strength in BFR-T (6%) but not in CON-T ()2%). Triceps and pectoralis major MTH increased 8% and 16% (P<0.01), respectively, in BFR-T, but not in CON-T ()1% and 2%, respectively). There were no changes in baseline concentrations of anabolic hormones in either group. These results suggest that BFR bench press training leads to significant increases in muscle size for upper arm and chest muscles and 1-RM strength.


title: Learning to detect patterns of crime

abstract: Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ones may have been committed by the same individual(s). If automated, data-driven tools for crime pattern detection are made available to assist analysts, these tools could help police to better understand patterns of crime, leading to more precise attribution of past crimes, and the apprehension of suspects. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a "seed" of a few crimes. Series Finder incorporates both the common characteristics of all patterns and the unique aspects of each specific pattern, and has had promising results on a decade's worth of crime pattern data collected by the Crime Analysis Unit of the Cambridge Police Department.


title: Effectiveness of a Handwriting Intervention With At-Risk Kindergarteners

abstract: We examined the effectiveness of an occupational therapist-led handwriting intervention for special education and at-risk kindergarteners.We incorporated a two-group, pretest-posttest design. Both groups consisted of kindergarteners receiving individualized education program (IEP) or Response to Intervention (RtI) support. An occupational therapist provided biweekly group handwriting instruction using the Size Matters Handwriting Program to students in the intervention group (n = 23). The control group (n = 12) received the standard handwriting instruction.Students in the intervention group demonstrated significantly greater gains in handwriting legibility than students in the control group. Students in the intervention group also demonstrated significantly greater gains in the prereading skills of uppercase letter recognition, lowercase letter recognition, and letter sound recognition.This study provides preliminary support for an occupational therapist-led handwriting intervention to improve writing legibility and letter recognition in kindergarteners receiving RtI and IEP supports.


title: Cost-sensitive learning for large-scale hierarchical classification

abstract: We study hierarchical classication of products in electronic commerce, classifying a text description of a product into one of the leaf classes of a tree-structure taxonomy. In particular, we investigate two essential problems, performance evaluation and learning, in a synergistic way. Unless we know what is the appropriate performance evaluation metric for a task, we are not going to learn a classier of maximum utility for the task. Given the characteristics of the task of hierarchical product classication, we shed insight into how and why common evaluation metrics such as error rate can be misleading, which is applicable for treating other real world applications. The analysis leads to a new performance evaluation metric that tailors this task to reect a vendor’s business goal of maximizing revenue. The proposed metric has an intuitive meaning as the average revenue loss, which depends on both the value of individual products and the hierarchical distance between the true class and the predicted class. Correspondingly, our learning algorithm uses multiclass SVM with margin re-scaling to optimize the proposed metric, instead of error rate or other common metrics. Margin re-scaling is sensitive to the scaling of loss functions. We propose a loss normalization approach to appropriately calibrating the scaling of loss functions, which is applicable to general classication and structured prediction tasks whenever using structured SVM with margin re-scaling. Experiments on a large dataset show that our approach outperforms standard multi-class SVM in terms of the proposed metric, eectively reducing the average revenue loss.


title: OmniMapper: A modular multimodal mapping framework

abstract: Simultaneous Localization and Mapping (SLAM) is not a problem with a one-size-fits-all solution. The literature includes a variety of SLAM approaches targeted at different environments, platforms, sensors, CPU budgets, and applications. We propose OmniMapper, a modular multimodal framework and toolbox for solving SLAM problems. The system can be used to generate pose graphs, do feature-based SLAM, and also includes tools for semantic mapping. Multiple measurement types from different sensors can be combined for multimodal mapping. It is open with standard interfaces to allow easy integration of new sensors and feature types. We present a detailed description of the mapping approach, as well as a software framework that implements this, and present detailed descriptions of its applications to several domains including mapping with a service robot in an indoor environment, large-scale mapping on a PackBot, and mapping with a handheld RGBD camera.


title: Performance analysis of precoding techniques for Massive MU-MIMO systems

abstract: Massive Multi-user Multiple Input Multiple Output (MU-MIMO) systems can yield a considerable increase in the capacity and BER for the requirement of higher data rate by using large number of antennas at transmitter and receiver side. In case of multi-user MIMO, a multi-antenna transmitter communicates simultaneously with multiple receivers. But the MU-MIMO system suffers from huge inter-user interference. By using precoding techniques in massive MU-MIMO the inter-user interference of the system are analyzed and it can be substantially reduced. In precoding technique the transmit diversity is exploited by weighting information stream so that it reduces the corrupted effect in communication channel, for this purpose the CSI are needed to be known by the transmitter. It is analyzed that at 6dB of SNR MMSE precoding outperforms error rate of 10−3.65 compared to Block diagonalization and 10−3.62 to Tomlinson-Harashima precoding method for Massive MU-MIMO system.


title: Designing and Implementing Malicious Hardware

abstract: Hidden malicious circuits provide an attacker with a stealthy attack vector. As they occupy a layer below the entire software stack, malicious circuits can bypass traditional defensive techniques. Yet current work on trojan circuits considers only simple attacks against the hard-ware itself, and straightforward defenses. More complex designs that attack the software are unexplored, as are the countermeasures an attacker may take to bypass proposed defenses. ::: ::: We present the design and implementation of Illinois Malicious Processors (IMPs). There is a substantial design space in malicious circuitry; we show that an attacker, rather than designing one specific attack, can instead design hardware to support attacks. Such flexible hardware allows powerful, general purpose attacks, while remaining surprisingly low in the amount of additional hardware. We show two such hardware designs, and implement them in a real system. Further, we show three powerful attacks using this hardware, including a login backdoor that gives an attacker complete and high-level access to the machine. This login attack requires only 1341 additional gates: gates that can be used for other attacks as well. Malicious processors are more practical, more flexible, and harder to detect than an initial analysis would suggest.


title: Real Time Road Signs Recognition

abstract: This paper presents a road signs detection and classification system based on a three-step algorithm composed of color segmentation, shape recognition, and a neural network. The final goal of this algorithm is to detect and classify almost all road signs present along Italian roads. Color segmentation was suggested by the aim to achieve real time execution, since color-based segmentation is faster than the one based on shape. In order to save computational time, only the RGB color space, directly supplied by the chosen camera, or color spaces that can be obtained with linear transformations, are considered. Two different methods are used for shape detection, one is based on pattern matching with simple models and the other one is based on edge detection and geometrical cues. The complete set of signs taken in account has been divided in several categories according to their shape and color. Finally for each road signs set a neural network is built and trained.


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