title: Affect recognition using EEG signal

abstract: Emotion states greatly influence many areas in our daily lives, such as: learning, decision making and interaction with others. Therefore, the ability to detect and recognize one's emotional states is essential in intelligence Human Machine Interaction (HMI). The aim of this study was to develop a new system that can sense and communicate emotion changes expressed by the Central Nervous System (CNS) through the use of EEG signals. More specifically, this study was carried out to develop an EEG-based subject-dependent affect recognition system to quantitatively measure and categorize three affect states: Positively excited, neutral and negatively excited. In this paper, we discussed implementation issues associated with each key stage of a fully automated affect recognition system: emotion elicitation protocol, feature extraction and classification. EEG recordings from 5 subjects with IAPS images as stimuli from the eNTERFACE06 database were used for simulation purposes. Discriminating features were extracted in both time and frequency domains (statistical, narrow-band, HOC, and wavelet entropy) to better understand the oscillatory nature of the brain waves. Through the use of k Nearest Neighbor classifier (kNN), we obtained mean correct classification rates of 90.77% on the three emotion classes when K equals 5. This demonstrated the feasibility of brain waves as a mean to categorize a user's emotion state. Secondly, we also assessed the suitability of commercially available EEG headsets such as Emotive Epoc for emotion recognition applications. This study was carried out by comparing the sensor location, signal integrity with those of Biosemi Active II. A new set of recognition performance was presented with reduced number of channels.


title: Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey

abstract: In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.


title: BEGA - a biaxial excitation generator for automobiles: comprehensive characterization and test results

abstract: This work presents the design and test results for a biaxial excitation generator/motor for automobiles (BEGA), which has a three phase stator and a salient pole excited hetero-polar rotor with multiple flux-barriers filled with low cost permanent magnets. For this new generator, the low voltage regulation is obtained by the flux-barrier PM combination with field (excitation) low power control and a full power diode rectifier in the stator. Good power/volume and superior efficiency (up to 80%) are obtained at costs comparable to those of existing Lundell generator. The generator configuration, principle, equations, finite element field analysis, design optimization, performance characteristics and test results, together with the generating system simulations make the core of the paper.


title: Compact Ultrawideband Rectangular Aperture Antenna and Band-Notched Designs

abstract: A simple and compact ultrawideband (UWB) aperture antenna with extended band-notched designs is presented. The antenna consists of a rectangular aperture on a printed circuit board ground plane and a T-shaped exciting stub. The proposed planar coplanar waveguide fed antenna is easy to be integrated with radio-frequency/microwave circuitry for low manufacturing cost. The antenna is successfully designed, implemented, and measured. A compact aperture area of 13 times23mm2 is obtained with promising performances, including broadband matched impedance, stable radiation patterns, and constant group delay. The correlation between the mode-based field distributions and radiation patterns is discussed. Extended from the proposed antenna, three advanced band-notched (5-6 GHz) designs are also presented as a desirable feature for UWB applications


title: Investigation of wide-band microstrip slot antenna

abstract: This paper presents the simulation and experimental investigations of a printed microstrip slot antenna. It is a quarter wavelength monopole slot cut in the finite ground plane edge, and fed electromagnetically by a microstrip transmission line. It provides a wide impedance bandwidth adjustable by variation of its parameters, such as the relative permittivity and thickness of the substrate, width, and location of the slot in the ground plane, and feed and ground plane dimensions. The ground plane is small, 50 mm/spl times/80 mm, and is about the size of a typical PC wireless card. At the center frequency of 3.00 GHz, its width of 50 mm is about /spl lambda//2 and influences the slot impedance and bandwidth significantly. An impedance bandwidth (S/sub 11/=-10 dB) of up to about 60% is achieved by individually optimizing its parameters. The simulation results are confirmed experimentally. A dual complementary slot antenna configuration is also investigated for the polarization diversity.


title: Effect of the deadtime on the ZVS in phase-shifted full-bridge DC/DC converter for electric vehicle application

abstract: Phase-shifted full-bridge (PSFB) DC/DC converter with synchronous rectifiers, which has the advantages of high conversion efficiency, high power density, and low electromagnetic interference, is a widely used topology for power conversion in the electric vehicle. But it is not easy to achieve zero voltage switching (ZVS) operation under light load condition because the energy in leakage inductance is insufficient. This paper proposes that ZVS operation is also closely related to the deadtime of lagging-leg switches. Analysis and optimized design is presented. Feasibility and effectivity are verified with simulation and experimental results of a prototype converter.


title: A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data

abstract: In this paper we introduce an approximation of the Weisfeiler-Lehman graph kernel algorithm aimed at improving the computation time of the kernel when applied to Resource Description Framework (RDF) data. Typically, applying graph kernels to RDF is done by extracting subgraphs from a large RDF graph and computing the kernel on this set of subgraphs. In contrast, our algorithm computes the Weisfeiler-Lehman kernel directly on the large RDF graph, but still retains the subgraph information. We show that this algorithm is faster than the regular Weisfeiler-Lehman kernel for RDF data and has at least the same performance. Furthermore, we show that our method has similar or better performance, and is faster, than other recently introduced graph kernels for RDF.


title: Fast matting using large kernel matting Laplacian matrices

abstract: Image matting is of great importance in both computer vision and graphics applications. Most existing state-of-the-art techniques rely on large sparse matrices such as the matting Laplacian [12]. However, solving these linear systems is often time-consuming, which is unfavored for the user interaction. In this paper, we propose a fast method for high quality matting. We first derive an efficient algorithm to solve a large kernel matting Laplacian. A large kernel propagates information more quickly and may improve the matte quality. To further reduce running time, we also use adaptive kernel sizes by a KD-tree trimap segmentation technique. A variety of experiments show that our algorithm provides high quality results and is 5 to 20 times faster than previous methods.


title: Adjacency-Faithfulness and Conservative Causal Inference

abstract: Most causal inference algorithms in the literature (e.g., Pearl (2000), Spirtes et al. (2000), Heckerman et al. (1999)) exploit an assumption usually referred to as the causal Faithfulness or Stability condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call "Adjacency-Faithfulness" and "Orientation-Faithfulness". We point out that assuming Adjacency-Faithfulness is true, it is in principle possible to test the validity of Orientation-Faithfulness. Based on this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has to be modified to be (asymptotically) correct under the weaker, Adjacency-Faithfulness assumption. Roughly the modified algorithm, called Conservative PC (CPC), checks whether Orientation-Faithfulness holds in the orientation phase, and if not, avoids drawing certain causal conclusions the PC algorithm would draw. However, if the stronger, standard causal Faithfulness condition actually obtains, the CPC algorithm is shown to output the same pattern as the PC algorithm does in the large sample limit. We also present a simulation study showing that the CPC algorithm runs almost as fast as the PC algorithm, and outputs significantly fewer false causal arrowheads than the PC algorithm does on realistic sample sizes. We end our paper by discussing how score-based algorithms such as GES perform when the Adjacency-Faithfulness but not the standard causal Faithfulness condition holds, and how to extend our work to the FCI algorithm, which allows for the possibility of latent variables.


title: Solution of Economic Load Dispatch problem in Power System using Lambda Iteration and Back Propagation Neural Network Methods

abstract: Economic load dispatch is the process of allocating the required load demand between the available generators in power system while satisfying all units and system equality and inequality constraints. Economic Load Dispatch solutions are found by solving the conventional methods such as lambda iteration, Gradient search method, Linear Programming and Dynamic Programming while at the same minimizing fuel costs, but convergence is too slow, so in order to get fast convergence and accurate results we are using artificial neural network. Artificial neural network is well-known in the area of power systems. It is a very powerful solution algorithm because of its rapid convergence near the solution. This property is especially useful for power system applications because an initial guess near the solution is easily attained. In this paper a three generator system is considered and by using lambda iteration method Economic Load Dispatch is determined and 150 patterns for different loads will be derived from same method to train neural network. As it is too slow method, we proposed a soft computing based approach i.e. Back Propagation Neural Network (BPNN) for determining the optimal flow. This method provides fast and accurate results when compared with the conventional method.


title: The Tokuhashi Score: Significant Predictive Value for the Life Expectancy of Patients With Breast Cancer With Spinal Metastases

abstract: STUDY DESIGN ::: Retrospective study of 55 consecutive patients with spinal metastases secondary to breast cancer who underwent surgery. ::: ::: ::: OBJECTIVE ::: To evaluate the predictive value of the Tokuhashi score for life expectancy in patients with breast cancer with spinal metastases. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: The score, composed of 6 parameters each rated from 0 to 2, has been proposed by Tokuhashi and colleagues for the prognostic assessment of patients with spinal metastases. ::: ::: ::: METHODS ::: A total of 55 patients surgically treated for vertebral metastases secondary to breast cancer were studied. The score was calculated for each patient and, according to Tokuhashi, the patients were divided into 3 groups with different life expectancy according to their total number of scoring points. In a second step, the grouping for prognosis was modified to get a better correlation of the predicted and definitive survival. ::: ::: ::: RESULTS ::: Applying the Tokuhashi score for the estimation of life expectancy of patients with breast cancer with vertebral metastases provided very reliable results. However, the original analysis by Tokuhashi showed a limited correlation between predicted and real survival for each prognostic group. Therefore, our patients were divided into modified prognostic groups regarding their total number of scoring points, leading to a higher significance of the predicted prognosis in each group (P < 0.0001), and a better correlation of the predicted and real survival. ::: ::: ::: CONCLUSION ::: The modified Tokuhashi score assists in decision making based on reliable estimators of life expectancy in patients with spinal metastases secondary to breast cancer.


Chip-Tool Interface Temperature Prediction Model for Turning Process


title: SD-NFV as an Energy Efficient Approach for M2M Networks Using Cloud-Based 6LoWPAN Testbed

abstract: Machine-to-Machine (M2M) communication is the leading technology for realising the Internet-of-Things (IoT). The M2M sensor nodes are characterized by low-power and low-data rates devices which have increased exponentially over the years. IPv6 over low power wireless personal area network (6LoWPAN) is the first protocol that provides IPv6 connectivity to the wireless M2M sensor nodes. Having a tremendous number of M2M sensor nodes execute independent control decision leads to difficulty in network control and management. In addition, these evergrowing devices generate massive traffic and cause energy scarcity, which affects the M2M sensor node lifetime. Recently, software defined-networking (SDN) and network functioning virtualization (NFV) are being used in M2M sensor networks to add programmability and flexibility features in order to adopt the exponential increment in wireless M2M traffic and enable network configuration even after deployment. This paper presents a proof-of-concept implementation which aims to analyze how SDN, NFV, and cloud computing can interact together in the 6LoWPAN gateway to provide simplicity and flexibility in network management. The proposed approach is called customized software defined-NFV (SD-NFV), and has been tested and verified by implementing a real-time 6LoWPAN testbed. The experimental results indicated that the SD-NFV approach reduced the network discovery time by 60% and extended the node’s lifetime by 65% in comparison to the traditional 6LoWPAN network. The implemented testbed has one sink which is the M2M 6LoWPAN gateway where the network coordinator and the SDN controller are executed. There are many possible ways to implement 6LoWPAN testbed but limited are based on open standards development boards (e.g., Arduino, Raspberry Pi, and Beagle Bones). In the current testbed, the Arduino board is chosen and the SDN controller is customized and written using C++ language to fit the 6LoWPAN network requirements. Finally, SDN and NFV have been envisioned as the most promising techniques to improve network programmability, simplicity, and management in cloud-based 6LoWPAN gateway.


title: Feature-based brain MRI retrieval for Alzheimer disease diagnosis

abstract: In this paper we consider the application of the feature-based approach to medical image retrieval, particularly brain MRI scans for early Alzheimer's disease diagnosis. The key idea is to provide the doctor with the images which have similar visual properties and have full case record, giving the ability to make more informed decision in the prodromal phase of the disease. With regard to the state-of-the art SIFT features in a Bag-of-Visual-Words approach we propose to use the Laguerre Circular Harmonic Functions coefficients as feature vectors. An additional pre-classification step based on estimation of Alzheimer's disease early image abnormalities is proposed to improve overall precision.


title: Measuring the relationship between social media use and addictive behavior and depression and suicide ideation among university students

abstract: Abstract Addictive behavior to social network sites is considered an alarming phenomenon where other psychopathological problems can be manifested. The purpose of the study is to analyze the relationship between the use and the addictive behavior of social media and the use of mobile devices, depression, and suicidal ideation. The questionnaires were applied to a sample of 374 university students where 58.6% were women and 41.4% men, with an average age of 20.01 years (SD = 1.84). Unlike the use of social media, addictive behavior was significantly related to depression and suicidal ideation. 36.1% of the sample reported having at least one idea in relation to suicide in the last two weeks. We propose an explanatory model that was adjusted appropriately and explained the addictive behavior with the frequency of mobile phone use, daily hours, depression, and suicidal ideation, the last one in a negative direction. It is concluded that, unlike excessive use, addictive behavior is associated with negative psychological characteristics. However, addictive behavior can also be considered a protective factor against suicidal ideation when relating to depression.


title: DOCK: Detecting Objects by transferring Common-sense Knowledge

abstract: We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories. In our setting, the training data for the source categories have bounding box annotations, while those for the target categories only have image-level annotations. Current state-of-the-art approaches focus on image-level visual or semantic similarity to adapt a detector trained on the source categories to the new target categories. In contrast, our key idea is to (i) use similarity not at the image-level, but rather at the region-level, and (ii) leverage richer common-sense (based on attribute, spatial, etc.) to guide the algorithm towards learning the correct detections. We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that common-sense knowledge can substantially improve detection performance over existing transfer-learning baselines.


title: MaskConnect: Connectivity Learning by Gradient Descent

abstract: Although deep networks have recently emerged as the model of choice for many computer vision problems, in order to yield good results they often require time-consuming architecture search. To combat the complexity of design choices, prior work has adopted the principle of modularized design which consists in defining the network in terms of a composition of topologically identical or similar building blocks (a.k.a. modules). This reduces architecture search to the problem of determining the number of modules to compose and how to connect such modules. Again, for reasons of design complexity and training cost, previous approaches have relied on simple rules of connectivity, e.g., connecting each module to only the immediately preceding module or perhaps to all of the previous ones. Such simple connectivity rules are unlikely to yield the optimal architecture for the given problem.


title: Exact variable-length anomaly detection algorithm for univariate and multivariate time series

abstract: The problem of anomaly detection in time series has received a lot of attention in the past two decades. However, existing techniques cannot locate where the anomalies are within anomalous time series, or they require users to provide the length of potential anomalies. To address these limitations, we propose a self-learning online anomaly detection algorithm that automatically identifies anomalous time series, as well as the exact locations where the anomalies occur in the detected time series. In addition, for multivariate time series, it is difficult to detect anomalies due to the following challenges. First, anomalies may occur in only a subset of dimensions (variables). Second, the locations and lengths of anomalous subsequences may be different in different dimensions. Third, some anomalies may look normal in each individual dimension but different with combinations of dimensions. To mitigate these problems, we introduce a multivariate anomaly detection algorithm which detects anomalies and identifies the dimensions and locations of the anomalous subsequences. We evaluate our approaches on several real-world datasets, including two CPU manufacturing data from Intel. We demonstrate that our approach can successfully detect the correct anomalies without requiring any prior knowledge about the data.


title: Trusted Neural Networks for Safety-Constrained Autonomous Control

abstract: We propose Trusted Neural Network (TNN) models, which are deep neural network models that satisfy safety constraints critical to the application domain. We investigate different mechanisms for incorporating rule-based knowledge in the form of first-order logic constraints into a TNN model, where rules that encode safety are accompanied by weights indicating their relative importance. This framework allows the TNN model to learn from knowledge available in form of data as well as logical rules. We propose multiple approaches for solving this problem: (a) a multi-headed model structure that allows trade-off between satisfying logical constraints and fitting training data in a unified training framework, and (b) creating a constrained optimization problem and solving it in dual formulation by posing a new constrained loss function and using a proximal gradient descent algorithm. We demonstrate the efficacy of our TNN framework through experiments using the open-source TORCS~\cite{BernhardCAA15} 3D simulator for self-driving cars. Experiments using our first approach of a multi-headed TNN model, on a dataset generated by a customized version of TORCS, show that (1) adding safety constraints to a neural network model results in increased performance and safety, and (2) the improvement increases with increasing importance of the safety constraints. Experiments were also performed using the second approach of proximal algorithm for constrained optimization --- they demonstrate how the proposed method ensures that (1) the overall TNN model satisfies the constraints even when the training data violates some of the constraints, and (2) the proximal gradient descent algorithm on the constrained objective converges faster than the unconstrained version.


title: High-Performance Work Systems and Occupational Safety.

abstract: Two studies were conducted investigating the relationship between high-performance work systems (HPWS) and occupational safety. In Study 1, data were obtained from company human resource and safety directors across 138 organizations. LISREL VIII results showed that an HPWS was positively related to occupational safety at the organizational level. Study 2 used data from 189 front-line employees in 2 organizations. Trust in management and perceived safety climate were found to mediate the relationship between an HPWS and safety performance measured in terms of personal-safety orientation (i.e., safety knowledge, safety motivation, safety compliance, and safety initiative) and safety incidents (i.e., injuries requiring first aid and near misses). These 2 studies provide confirmation of the important role organizational factors play in ensuring worker safety.


title: On the relationship between parametric and geometric active contours

abstract: Geometric active contours have many advantages over parametric active contours, such as computational simplicity and the ability to change the curve topology during deformation. While many of the capabilities of the older parametric active contours have been reproduced in geometric active contours, the relationship between the two has not always been clear. We develop a precise relationship between the two which includes spatially-varying coefficients, both tension and rigidity, and non-conservative external forces. The result is a very general geometric active contour formulation for which the intuitive design principles of parametric active contours can be applied. We demonstrate several novel applications in a series of simulations.


title: Cloud security issues and challenges

abstract: The cloud computing provides on demand services over the Internet with the help of a large amount of virtual storage. The main features of cloud computing is that the user does not have any setup of expensive computing infrastructure and the cost of its services is less. In the recent years, cloud computing integrates with the industry and many other areas, which has been encouraging the researcher to research on new related technologies. Due to the availability of its services & scalability for computing processes individual users and organizations transfer their application, data and services to the cloud storage server. Regardless of its advantages, the transformation of local computing to remote computing has brought many security issues and challenges for both consumer and provider. Many cloud services are provided by the trusted third party which arises new security threats. The cloud provider provides its services through the Internet and uses many web technologies that arise new security issues. This paper discussed about the basic features of the cloud computing, security issues, threats and their solutions. Additionally, the paper describes several key topics related to the cloud, namely cloud architecture framework, service and deployment model, cloud technologies, cloud security concepts, threats, and attacks. The paper also discusses a lot of open research issues related to the cloud security.


title: Dorsal augmentation with diced cartilage enclosed with temporal fascia in secondary endonasal rhinoplasty.

abstract: BACKGROUND ::: Placement of diced cartilage enclosed within an autologous fascia sleeve (DC-F) for nasal dorsum reconstruction is common in open rhinoplasty, but there are no data regarding its use in closed rhinoplasty (the endonasal approach). ::: ::: ::: OBJECTIVES ::: The authors describe a technique for augmenting the nasal dorsum in secondary rhinoplasty with DC-F grafts via an endonasal approach. ::: ::: ::: METHODS ::: In this study, the authors retrospectively review the cases of 18 patients who underwent closed rhinoplasty with the authors' technique between 2008 and 2011. Cartilage harvested from the septum, rib, or concha was diced into 0.5- to 1-mm cubes. A rectangle of deep temporal fascia (approximately 5 × 5 cm) was harvested by means of a single V-shaped incision overlying the temporal fossa, then wrapped around another 1-mL syringe and secured. The fascial cylinder was filled with the desired amount of diced cartilage and then sutured closed at both ends. This graft was placed into the dorsum of the nose via the endonasal approach. ::: ::: ::: RESULTS ::: Average age of the patients was 32 years. The patients were followed for a minimum of 15 months. Donor site for cartilage harvest was conchal in 8 patients, septal and conchal in 6 cases, and costal in 4. Complications were encountered in 3 patients, only 1 of whom required surgical revision for contour irregularity. No resorption of cartilage was encountered in any patient after 15 months. Smooth continuity of the nasal dorsum was achieved in all our patients. ::: ::: ::: CONCLUSIONS ::: Despite the lack of a large volume of patients for overwhelming and conclusive results, our study provides further confirmation that this technique is indeed an attractive option for nasal dorsum reconstruction.


title: Predicting risk of suicide using resting state heart rate

abstract: This study investigates the potential of using heart rate-related measurements to aid clinicians in predicting suicide risk. For this purpose, heart rate was recorded during 10 minutes resting state from 15 patients with suicide ideation and 15 healthy subjects using an affordable and wearable sensor. Our results showed statistically significant differences (p<0.05) in two time-domain features measuring overall heart rate variability and short term heart rate variations. KNN and SVM classifiers were implemented on the features obtained. Our results showed that using heart rate-related features the risk of suicide could be predicted by an average accuracy of 80%.


title: Unconstrained face detection: State of the art baseline and challenges

abstract: A large scale study of the accuracy and efficiency of face detection algorithms on unconstrained face imagery is presented. Nine different face detection algorithms are studied, which are acquired through either government rights, open source, or commercial licensing. The primary data set utilized for analysis is the IAPRA Janus Benchmark A (IJB-A), a recently released unconstrained face detection and recognition dataset which, at the time of this study, contained 67,183 manually localized faces in 5,712 images and 20,408 video frames. The goal of the study is to determine the state of the art in face detection with respect to unconstrained imagery which is motivated by the saturation of recognition accuracies on seminal unconstrained face recognition datasets which are filtered to only contain faces detectable by a commodity face detection algorithm. The most notable finding from this study is that top performing detectors still fail to detect the vast majority of faces with extreme pose, partial occlusion, and/or poor illumination. In total, over 20% of faces fail to be detected by all nine detectors studied. The speed of the detectors was generally correlated with accuracy: faster detectors were less accurate than their slower counterparts. Finally, key considerations and guidance is provided for performing face detection evaluations. All software using these methods to conduct the evaluations and plot the accuracies are made available in the open source.


title: CT 1.6.4 Fast unfolding of community hierarchies in large networks

abstract: Introduction The typical size of large networks such as social network services, mobile phone networks or the web now counts in millions when not billions of nodes and these scales demand new methods to retrieve comprehensive information from their structure. A promising approach consists in decomposing the networks into communities of strongly connected nodes, with the nodes belonging to different communities only sparsely connected. Finding exact optimal partitions in networks is known to be computationally intractable, mainly due to the explosion of the number of possible partitions as the number of nodes increases. It is therefore of high interest to propose algorithms to find reasonably “good” solutions of the problem in a reasonably “fast” way. One of the fastest algorithms consists in optimizing the modularity of the partition in a greedy way (Clauset et al, 2004), a method that, even improved, does not allow to analyze more than a few millions nodes (Wakita et al, 2007).


title: Novel Architectures for High-Speed and Low-Power 3-2, 4-2 and 5-2 Compressors

abstract: The 3-2, 4-2 and 5-2 compressors are the basic components in many applications, in particular partial product summation in multipliers. In this paper novel architectures and designs of high speed, low power 3-2, 4-2 and 5-2 compressors capable of operating at ultra-low voltages are presented. The power consumption, delay and area of these new compressor architectures are compared with existing and recently proposed compressor architectures and are shown to perform better. The proposed architecture lays emphasis on the use of multiplexers in arithmetic circuits that result in high speed and efficient design. Also in all existing implementations of XOR gate and multiplexers, both output and its complement are available but current designs of compressors do not use these outputs efficiently. In the proposed architecture these outputs are efficiently utilized to improve the performance of compressors. The combination of low power, low transistor count and lesser delay makes the new compressors a viable option for efficient design


title: A prepaid meter using mobile communication

abstract: Energy meters in India have dominantly been electromechanical in nature but are gradually being replaced by more ::: sophisticated and accurate digital and electronic meters. A high percentage of electricity revenue is lost to power theft, incorrect meter reading and billing, and reluctance of consumers towards paying electricity bills on time. Considerable amount of revenue losses can be reduced by using Prepaid Energy Meters. A prepaid energy meter enables power utilities to collect energy bills from the consumers prior to the usage of power by delivering only as much as what has been paid for. This paper suggests a prepaid energy meter behaving like a prepaid mobile phone. The meter contains a prepaid card analogous to mobile SIM card. The prepaid card communicates with the power utility using mobile communication infrastructure. Once the prepaid card is out of balance, the consumer load is disconnected from the utility supply by the contactor. The power utility can recharge the prepaid card remotely through mobile communication based on customer requests. A prior billing is bound to do away with the problems of unpaid bills and human error in meter readings, thereby ensuring justified revenue for the utility. The proposed prepaid meter is implemented in a software model and Matlab has been used for simulation.


title: Classification of human activities on UWB radar using a support vector machine

abstract: The classification of human activity using radar sensors has potential application in many different fields, including law enforcement, military and security operations. One unique way to classify human motion is the use of Ultra Wide Band (UWB) Radar. Work has been done to detect the presence of humans with UWB radar using the CLEAN algorithm [1]. For a more detailed analysis of human activities, micro-Doppler signatures [3] have been used to characterize human gait and other human activities. However, the potential for classifying different human activities using UWB radar has not been extensively studied yet. If the UWB radar can provide target motion information as well as range information, the effectiveness of the radar would be increased.


title: A Comprehensive Feature Study for Appliance Recognition on High Frequency Energy Data

abstract: Awareness about the energy consumption of appliances can help to save energy in households. Non-intrusive Load Monitoring (NILM) is a feasible approach to provide consumption feedback at appliance level. In this paper, we evaluate a broad set of features for electrical appliance recognition, extracted from high frequency start-up events. These evaluations were applied on several existing high frequency energy datasets. To examine clean signatures, we ran all experiments on two datasets that are based on isolated appliance events; more realistic results were retrieved from two real household datasets. Our feature set consists of 36 signatures from related work including novel approaches, and from other research fields. The results of this work include a stand-alone feature ranking, promising feature combinations for appliance recognition in general and appliance-wise performances.


title: Single-Chip W-band SiGe HBT Transceivers and Receivers for Doppler Radar and Millimeter-Wave Imaging

abstract: This paper presents the first single-chip direct-conversion 77-85 GHz transceiver fabricated in SiGe HBT technology, intended for Doppler radar and millimeter-wave imaging, particularly within the automotive radar band of 77-81 GHz. A 1.3 mm times 0.9 mm 86-96 GHz receiver is also presented. The transceiver, fabricated in a 130 nm SiGe HBT technology with fT/fMAX of 230/300 GHz, consumes 780 mW, and occupies 1.3 mm times 0.9 mm of die area. Furthermore, it achieves 40 dB conversion gain in the receiver at 82 GHz, a 3 dB bandwidth extending from 77 to 85 GHz at 25degC, and covering the entire 77-81 GHz band up to 100degC, record 3.85 dB DSB noise figure measured at 82 GHz LO and 1 GHz IF, and an IP1dB of -35 dBm. The transmitter provides + 11.5 dBm of saturated output power at 77 GHz, and a divide64 static frequency divider is included on-die. Successful detection of a Doppler shift of 30 Hz at a range of 6 m is shown. The 86-96 GHz receiver achieves 31 dB conversion gain, a 3 dB bandwidth of 10 GHz, and 5.2 dB DSB noise figure at 96 GHz LO and 1 GHz IF, and -99 dBc/Hz phase noise at 1 MHz offset. System-level layout and integration techniques that address the challenges of low-voltage transceiver implementation are also discussed.


title: Physiological Effects of Concurrent Training in Elderly Men

abstract: The aim of the present study was to investigate the effects of concurrent strength and endurance training on neuromuscular and hormonal parameters in elderly men. 23 healthy men (65±4 years) were randomly assigned to 1 of 3 groups: concurrent (CG, n=8), strength (SG, n=8) or endurance group (EG, n=7). The programs consisted, of strength training, endurance training on a cycle ergometer or a combination of both in the same session 3 times per week over a duration of 12 weeks. Subjects were evaluated on parameters related to muscle strength, muscle activation and serum hormones. There were significant increases in lower-body strength in all groups (P<0.05), with higher increases in SG (67%) than CG (41%) and both were higher than EG (25%) (p<0.01). Only SG and CG increased upper-body strength (p<0.01), with no significant difference between the 2 groups. Furthermore, there were significant decreases in free testosterone in EG after training. Significant increases in isometric strength and maximal muscle activation (p<0.05) as well as decreases in the submaximal muscle activation to the same load, were only seen in SG (p<0.05). The present results suggest that the interference effect observed due to concurrent strength and endurance training could be related to impairment of neural adaptations.


title: GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems

abstract: We present an iterative method for solving linear systems, which has the property of minimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from t...


title: The Raven ’ s Progressive Matrices : Change and Stability over Culture and Time

abstract: Data relating to the stability and variation in the norms for the Raven's Progressive Matrices Test (a well-validated measure of basic cognitive functioning) for different cultural, ethnic, and socioeconomic groups on a worldwide and within-country basis are first summarized. Subsequent sections deal with variation over time. A possible explanation for the variation in norms over time and between ethnic groups within countries is offered.


title: Bid optimizing and inventory scoring in targeted online advertising

abstract: Billions of online display advertising spots are purchased on a daily basis through real time bidding exchanges (RTBs). Advertising companies bid for these spots on behalf of a company or brand in order to purchase these spots to display banner advertisements. These bidding decisions must be made in fractions of a second after the potential purchaser is informed of what location (Internet site) has a spot available and who would see the advertisement. The entire transaction must be completed in near real-time to avoid delays loading the page and maintain a good users experience. This paper presents a bid-optimization approach that is implemented in production at Media6Degrees for bidding on these advertising opportunities at an appropriate price. The approach combines several supervised learning algorithms, as well as second price auction theory, to determine the correct price to ensure that the right message is delivered to the right person, at the right time.


Values of science portrayed in out-of-school Contexts


title: Decentralized Motion Planning and Scheduling of AGVs in an FMS

abstract: In this paper, decentralized motion planning and scheduling of automated guided vehicles (AGVs) in a flexible manufacturing system is proposed. A motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. The proposed strategy is based on two steps. The first step consists in planning a presumed trajectory to avoid collision conflicts previously detected by a central supervisor, enabling more appropriate decentralized scheduling by AGVs. The presumed trajectories, which represent the intentions of AGVs, are then exchanged with neighboring AGVs. The second step uses the presumed trajectories of neighbors to compute a collision-free trajectory according to the priority policy. Numerical and experimental results are provided to show the pertinence and the feasibility of the proposed strategy.


title: DATA MINING IN SOCIAL MEDIA

abstract: The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.


title: High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks

abstract: We propose a method for high-performance semantic image segmentation (or semantic pixel labelling) based on very deep residual networks, which achieves the state-of-the-art performance. A few design factors are carefully considered to this end. We make the following contributions. (i) First, we evaluate different variations of a fully convolutional residual network so as to find the best configuration, including the number of layers, the resolution of feature maps, and the size of field-of-view. Our experiments show that further enlarging the field-of-view and increasing the resolution of feature maps are typically beneficial, which however inevitably leads to a higher demand for GPU memories. To walk around the limitation, we propose a new method to simulate a high resolution network with a low resolution network, which can be applied during training and/or testing. (ii) Second, we propose an online bootstrapping method for training. We demonstrate that online bootstrapping is critically important for achieving good accuracy. (iii) Third we apply the traditional dropout to some of the residual blocks, which further improves the performance. (iv) Finally, our method achieves the currently best mean intersection-over-union 78.3% on the PASCAL VOC 2012 dataset, as well as on the recent dataset Cityscapes. ∗This research was in part supported by the Data to Decisions Cooperative Research Centre. C. Shen’s participation was in part supported by an ARC Future Fellowship (FT120100969). C. Shen is the corresponding author. 1 ar X iv :1 60 4. 04 33 9v 1 [ cs .C V ] 1 5 A pr 2 01 6


title: A Software Defined Networking architecture for the Internet-of-Things

abstract: The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.


title: Resilient Robots: Concept, Review, and Future Directions

abstract: This paper reviews recent developments in the emerging field of resilient robots and the related robots that share common concerns with them, such as self-reconfigurable robots. This paper addresses the identity of the resilient robots by distinguishing the concept of resilience from other similar concepts and summarizes the strategies used by robots to recover their original function. By illustrating some issues of current resilient robots in the design of control systems, physical architecture modules, and physical connection systems, this paper shows several of the possible solutions to facilitate the development of the new and improved robots with higher resilience. The conclusion outlines several directions for the future of this field.


title: Hybrid speech recognition with Deep Bidirectional LSTM

abstract: Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.


title: Optimisation of the SHA-2 family of hash functions on FPGAs

abstract: Hash functions play an important role in modern cryptography. This paper investigates optimisation techniques that have recently been proposed in the literature. A new VLSI architecture for the SHA-256 and SHA-512 hash functions is presented, which combines two popular hardware optimisation techniques, namely pipelining and unrolling. The SHA processors are developed for implementation on FPGAs, thereby allowing rapid prototyping of several designs. Speed/area results from these processors are analysed and are shown to compare favourably with other FPGA-based implementations, achieving the fastest data throughputs in the literature to date.


title: Ats: A language that combines programming with theorem proving

abstract: ATS is a language with a highly expressive type system that supports a restricted form of dependent types in which programs are not allowed to appear in type expressions. The language is separated into two components: a proof language in which (inductive) proofs can be encoded as (total recursive) functions that are erased before execution, and a programming language for constructing programs to be evaluated. This separation enables a paradigm that combines programming with theorem proving. In this paper, we illustrate by example how this programming paradigm is supported in ATS.


title: Publicly Verifiable Inner Product Evaluation over Outsourced Data Streams under Multiple Keys

abstract: Uploading data streams to a resource-rich cloud server for inner product evaluation, an essential building block in many popular stream applications (e.g., statistical monitoring), is appealing to many companies and individuals. On the other hand, verifying the result of the remote computation plays a crucial role in addressing the issue of trust. Since the outsourced data collection likely comes from multiple data sources, it is desired for the system to be able to pinpoint the originator of errors by allotting each data source a unique secret key, which requires the inner product verification to be performed under any two parties’ different keys. However, the present solutions either depend on a single key assumption or powerful yet practically-inefficient fully homomorphic cryptosystems. In this paper, we focus on the more challenging multi-key scenario where data streams are uploaded by multiple data sources with distinct keys. We first present a novel homomorphic verifiable tag technique to publicly verify the outsourced inner product computation on the dynamic data streams, and then extend it to support the verification of matrix product computation. We prove the security of our scheme in the random oracle model. Moreover, the experimental result also shows the practicability of our design.


The Pakistan National Emergency Department Surveillance Study (Pak-NEDS): Introducing a pilot surveillance


title: A Review on Supply Chain Coordination: Coordination Mechanisms, Managing Uncertainty and Research Directions

abstract: The Supply Chain (SC) members are dependent on each other for resources and information, and this dependency has been increasing in recent times due to outsourcing, globalization and rapid innovations in information technologies. This increase in dependency brings some extent of risk and uncertainty too along with benefits. To meet these challenges, SC members must work towards a unified system and coordinate with each other. There is a need to identify the coordination mechanisms which helps in addressing the uncertainty in supply chain and achieving supply chain coordination.


Running head: REDUCUING FALLS WITH HOURLY ROUNDING 1 Reducing Falls with Hourly Rounding


title: Confidence intervals for standardized effect sizes: Theory, application, and implementation

abstract: The behavioral, educational, and social sciences are undergoing a paradigmatic shift in methodology, from disciplines that focus on the dichotomous outcome of null hypothesis significance tests to disciplines that report and interpret effect sizes and their corresponding confidence intervals. Due to the arbitrariness of many measurement instruments used in the behavioral, educational, and social sciences, some of the most widely reported effect sizes are standardized. Although forming confidence intervals for standardized effect sizes can be very beneficial, such confidence interval procedures are generally difficult to implement because they depend on noncentral t, F, and x2 distributions. At present, no main-stream statistical package provides exact confidence intervals for standardized effects without the use of specialized programming scripts. Methods for the Behavioral, Educational, and Social Sciences (MBESS) is an R package that has routines for calculating confidence intervals for noncentral t, F, and x2 distributions, which are then used in the calculation of exact confidence intervals for standardized effect sizes by using the confidence interval transformation and inversion principles. The present article discusses the way in which confidence intervals are formed for standardized effect sizes and illustrates how such confidence intervals can be easily formed using MBESS in R.


VISUALLY INDUCED MOTION SICKNESS : AN INSIGHT FROM NEUROSCIENCE


title: A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology

abstract: Background ::: The relative roles of climate variability and population related effects in malaria transmission could be better understood if regional-scale dynamical malaria models could account for these factors.


title: Algorand: Scaling Byzantine Agreements for Cryptocurrencies

abstract: Algorand is a new cryptocurrency that confirms transactions with latency on the order of a minute while scaling to many users. Algorand ensures that users never have divergent views of confirmed transactions, even if some of the users are malicious and the network is temporarily partitioned. In contrast, existing cryptocurrencies allow for temporary forks and therefore require a long time, on the order of an hour, to confirm transactions with high confidence. Algorand uses a new Byzantine Agreement (BA) protocol to reach consensus among users on the next set of transactions. To scale the consensus to many users, Algorand uses a novel mechanism based on Verifiable Random Functions that allows users to privately check whether they are selected to participate in the BA to agree on the next set of transactions, and to include a proof of their selection in their network messages. In Algorand's BA protocol, users do not keep any private state except for their private keys, which allows Algorand to replace participants immediately after they send a message. This mitigates targeted attacks on chosen participants after their identity is revealed. We implement Algorand and evaluate its performance on 1,000 EC2 virtual machines, simulating up to 500,000 users. Experimental results show that Algorand confirms transactions in under a minute, achieves 125x Bitcoin's throughput, and incurs almost no penalty for scaling to more users.


title: Retrieving Lexical Semantics from Multilingual Corpora

abstract: This paper presents a technique to build a lexical resource used for annotation of parallel corpora where the tags can be seen as multilingual 'synsets'. The approach can be extended to add relationships between these synsets that are akin to WordNet relationships of synonymy and hypernymy. The paper also discusses how the success of this approach can be measured. The reported results are for English, German, French, and Greek using the Europarl parallel corpus.


title: Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering [Extended Version]

abstract: WPA2-Personal is widely used to protect Wi-Fi networks against illicit access. While attackers typically use GPUs to speed up the discovery of weak network passwords, attacking random passwords is considered to quickly become infeasible with increasing password length. Professional attackers may thus turn to commercial high-end FPGA-based cluster solutions to significantly increase the speed of those attacks. Well known manufacturers such as Elcomsoft have succeeded in creating world's fastest commercial FPGA-based WPA2 password recovery system, but since they rely on high-performance FPGAs the costs of these systems are well beyond the reach of amateurs. In this paper, we present a highly optimized low-cost FPGA cluster-based WPA-2 Personal password recovery system that can not only achieve similar performance at a cost affordable by amateurs, but in comparison our implementation would also be more than 5 times as fast on the original hardware. Since the currently fastest system is not only significantly slower but proprietary as well, we believe that we are the first to present the internals of a highly optimized and fully pipelined FPGA WPA2 password recovery system. In addition, we evaluated our approach with respect to performance and power usage and compare it to GPU-based systems. To assess the real-world impact of our system, we utilized the well known Wigle Wi-Fi network dataset to conduct a case study within the country and its border regions. Our results indicate that our system could be used to break into each of more than 160,000 existing Wi-Fi networks requiring 3 days per network on our low-cost FPGA cluster in the worst case.


title: Synthetic Aperture Radar Signal Processing with MATLAB Algorithms

abstract: Range Imaging Cross-Range Imaging SAR Radiation Pattern Generic Synthetic Aperture Radar Spotlight Synthetic Aperture Radar Stripmap Synthetic Aperture Radar Circular Synthetic Aperture Radar Monopulse Synthetic Aperture Radar Bibliography Index.


title: Acoustic levitator for structure measurements on low temperature liquid droplets.

abstract: A single-axis acoustic levitator was constructed and used to levitate liquid and solid drops of 1-3 mm in diameter at temperatures in the range -40 to +40 degrees C. The levitator comprised (i) two acoustic transducers mounted on a rigid vertical support that was bolted to an optical breadboard, (ii) an acoustic power supply that controlled acoustic intensity, relative phase of the drive to the transducers, and could modulate the acoustic forces at frequencies up to 1 kHz, (iii) a video camera, and (iv) a system for providing a stream of controlled temperature gas flow over the sample. The acoustic transducers were operated at their resonant frequency of approximately 22 kHz and could produce sound pressure levels of up to 160 dB. The force applied by the acoustic field could be modulated to excite oscillations in the sample. Sample temperature was controlled using a modified Cryostream Plus and measured using thermocouples and an infrared thermal imager. The levitator was installed at x-ray beamline 11 ID-C at the Advanced Photon Source and used to investigate the structure of supercooled liquids.


title: Patterned photostimulation via visible-wavelength photonic probes for deep brain optogenetics

abstract: Abstract. Optogenetic methods developed over the past decade enable unprecedented optical activation and silencing of specific neuronal cell types. However, light scattering in neural tissue precludes illuminating areas deep within the brain via free-space optics; this has impeded employing optogenetics universally. Here, we report an approach surmounting this significant limitation. We realize implantable, ultranarrow, silicon-based photonic probes enabling the delivery of complex illumination patterns deep within brain tissue. Our approach combines methods from integrated nanophotonics and microelectromechanical systems, to yield photonic probes that are robust, scalable, and readily producible en masse. Their minute cross sections minimize tissue displacement upon probe implantation. We functionally validate one probe design in vivo with mice expressing channelrhodopsin-2. Highly local optogenetic neural activation is demonstrated by recording the induced response—both by extracellular electrical recordings in the hippocampus and by two-photon functional imaging in the cortex of mice coexpressing GCaMP6.


title: A Tool Kit to Generate 3D Animated CAESAR Bodies The Civilian American and European Surface

abstract: The Civilian American and European Surface Anthropometry Resource (CAESAR) database provides a comprehensive source for body measurement in numerous industries such as apparel, aerospace, and automobile. Generating animated CAESAR body sequences from still surface and landmark data will stimulate research and design in these areas. A tool kit has been developed to convert CAESAR bodies to models compliant with the Humanoid Animation specification (H-Anim). It will be helpful to set up a realistic motion capable humanoid library for application environment that can be reused in a wide variety of ergonomic applications. The process consists of preprocessing the mesh, building a skeleton structure, creating segments of the body, assigning weights for vertices, and integrating motion capture data. Publicly available software is adopted for mesh compression and hole filling. C programs were developed to implement the translation from CAESAR body data to H-Anim. The technical issues involved in the process are discussed, and experimental results are shown in the paper.


abstract: Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and P-against-Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.


title: End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

abstract: This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR could achieve a 66.3% Exact match and 74.7% F1 score on the Stanford Question Answering Dataset.


title: Chapter 23 Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

abstract: Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information presentintwodatatablesthatcollectmeasurementsonthesamesetofobservations. PLSmethodsproceedby derivinglatentvariableswhichare(optimal)linearcombinationsofthevariablesofadatatable.Whenthegoal is tofind the shared information between two tables, the approach is equivalent toa correlation problem and the technique is then called partial least square correlation (PLSC) (also sometimes called PLS-SVD). In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called partialleastsquareregression.Inthis casethereisonesetoflatentvariables(derivedfromthepredictor table) and these latent variables are required to give the best possible prediction. In this paper we present and illustratePLSCandPLSRandshowhowthesedescriptivemultivariateanalysistechniquescanbeextendedto deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.


title: Learning Compact Geometric Features

abstract: We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision, compactness, and robustness. We show that features with these properties can be learned from data, by optimizing deep networks that map high-dimensional histograms into low-dimensional Euclidean spaces. The presented approach yields a family of features, parameterized by dimension, that are both more compact and more accurate than existing descriptors.


Bag of Words Meets Bags of Popcorn


title: The design space of the number theoretic transform: A survey

abstract: The Number Theoretic Transform (NTT) is a necessary part of most Lattice-based cryptographic schemes. In particular, it offers an efficient means to achieve polynomial multiplication within the more efficient ring-based schemes. The NTT is also a crucial component which needs to be implemented in a critical way, since it is often the bottle-neck and the most resource consuming block of the whole design. As a result, the NTT is an appealing target for exploring different architectures and design trade-offs. In this paper, we compare various optimization strategies applied to maximize the performance or to reduce the resource utilization. Our analysis covers general purpose processors as well as dedicated hardware implemented on reconfigurable platforms and on ASIC. Previously explored design strategies range from the traditional computation where the multiplicative factors (called twiddle factors) are calculated on-the-fly versus memory trade-off exploration (using memory to store pre-computed twiddle factors), to the use of different butterfly designs for implementing the Fast Fourier Transform and its inverse in software, or the sharing of resources for hardware implementations of the forward and inverse NTT. The problem of side channel resistance is also addressed, discussing designs which are robust against power analysis attacks.


title: Adaptive Self-tuning Memory in DB2

abstract: DB2 for Linux, UNIX, and Windows Version 9.1 introduces the Self-Tuning Memory Manager (STMM), which provides adaptive self tuning of both database memory heaps and cumulative database memory allocation. This technology provides state-of-the-art memory tuning combining control theory, runtime simulation modeling, cost-benefit analysis, and operating system resource analysis. In particular, the nove use of cost-benefit analysis and control theory techniques makes STMM a breakthrough technology in database memory management. The cost-benefit analysis allows STMM to tune memory between radically different memory consumers such as compiled statement cache, sort, and buffer pools. These methods allow for the fast convergence of memory settings while also providing stability in the presence of system noise. The tuning mode has been found in numerous experiments to tune memory allocation as well as expert human administrators, including OLTP, DSS, and mixed environments. We believe this is the first known use of cost-benefit analysis and control theory in database memory tuning across heterogeneous memory consumers.


title: Acoustic characteristics of American English vowels

abstract: The purpose of this study was to replicate and extend the classic study of vowel acoustics by Peterson and Barney (PB) [J. Acoust. Soc. Am. 24, 175-184 (1952)]. Recordings were made of 45 men, 48 women, and 46 children producing the vowels /i,I,e, epsilon,ae,a, [symbol: see text],O,U,u, lambda,3 iota/ in h-V-d syllables. Formant contours for F1-F4 were measured from LPC spectra using a custom interactive editing tool. For comparison with the PB data, formant patterns were sampled at a time that was judged by visual inspection to be maximally steady. Analysis of the formant data shows numerous differences between the present data and those of PB, both in terms of average frequencies of F1 and F2, and the degree of overlap among adjacent vowels. As with the original study, listening tests showed that the signals were nearly always identified as the vowel intended by the talker. Discriminant analysis showed that the vowels were more poorly separated than the PB data based on a static sample of the formant pattern. However, the vowels can be separated with a high degree of accuracy if duration and spectral change information is included.


title: Variable stiffness fabrics with embedded shape memory materials for wearable applications

abstract: Keywords: stiffness fabrics embedded shape memory materials wearable Reference EPFL-CONF-206392 Record created on 2015-03-12, modified on 2016-08-09


title: UMLtoCSP: a tool for the formal verification of UML/OCL models using constraint programming

abstract: We present UMLtoCSP, a tool for the formal verification of UML/OCL models. Given a UML class diagram annotated with OCL constraints, UMLtoCSP is able to automatically check several correctness properties, such as the strong and weak satisfiability of the model or the lack of redundant constraints. The tool uses Constraint Logic Programming as the underlying formalism and the constraint solver ECLiPSe as the verification engine.


title: Leadership in the NHS: what are the competencies and qualities needed and how can they be developed?

abstract: Several commentators have documented the organisational reforms that the NHS has passed through since its inception in 1948. Each has supposedly been introduced to enable the NHS to meet the increasing demands to become more efficient and effective in delivering health care (for example, Harrison et al., 1992; Paton, 1996; Stewart, 1996). In line with these demands, academics and practitioners in the field of management development have responded enthusiastically and suggested various interventions aimed at supporting the service with techniques and processes for developing managers’ skills and other capabilities to enable them to cope with the ever-increasing complexity of the environment in which they operate.


title: Attacks on privacy and deFinetti's theorem

abstract: In this paper we present a method for reasoning about privacy using the concepts of exchangeability and deFinetti's theorem. We illustrate the usefulness of this technique by using it to attack a popular data sanitization scheme known as Anatomy. We stress that Anatomy is not the only sanitization scheme that is vulnerable to this attack. In fact, any scheme that uses the random worlds model, i.i.d. model, or tuple-independent model needs to be re-evaluated. The difference between the attack presented here and others that have been proposedin the past is that we do not need extensive background knowledge. An attacker only needs to know the nonsensitive attributes of one individual in the data, and can carry out this attack just by building a machine learning model over the sanitized data. The reason this attack is successful is that it exploits a subtle flaw in the way prior work computed the probability of disclosure of a sensitive attribute. We demonstrate this theoretically, empirically, and with intuitive examples. We also discuss how this generalizes to many other privacy schemes.


title: Tracking Multiple Vehicles Using a Variational Radar Model

abstract: High-resolution radar sensors are able to resolve multiple measurements per object and therefore provide valuable information for vehicle environment perception. For instance, multiple measurements allow to infer the size of an object or to more precisely measure the object's motion. Yet, the increased amount of data raises the demands on tracking modules: measurement models that are able to process multiple measurements for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for vehicles and demonstrates how this model can be incorporated in a Random-Finite-Set-based multi-object tracker. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multi-object tracker, the entire process chain from the raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data and it is demonstrated that data-driven measurement model outperforms a manually designed model.


Automatic Tongue Diagnosis Using a Smart Phone


title: iRiS: Vetting Private API Abuse in iOS Applications

abstract: With the booming sale of iOS devices, the number of iOS applications has increased significantly in recent years. To protect the security of iOS users, Apple requires every iOS application to go through a vetting process called App Review to detect uses of private APIs that provide access to sensitive user information. However, recent attacks have shown the feasibility of using private APIs without being detected during App Review. To counter such attacks, we propose a new iOS application vetting system, called iRiS, in this paper. iRiS first applies fast static analysis to resolve API calls. For those that cannot be statically resolved, iRiS uses a novel iterative dynamic analysis approach, which is slower but more powerful compared to static analysis. We have ported Valgrind to iOS and implemented a prototype of iRiS on top of it. We evaluated iRiS with 2019 applications from the official App Store. From these, iRiS identified 146 (7%) applications that use a total number of 150 different private APIs, including 25 security-critical APIs that access sensitive user information, such as device serial number. By analyzing iOS applications using iRiS, we also identified a suspicious advertisement service provider which collects user privacy information in its advertisement serving library. Our results show that, contrary to popular belief, a nontrivial number of iOS applications that violate Apple's terms of service exist in the App Store. iRiS is effective in detecting private API abuse missed by App Review.


title: SGDR: Stochastic Gradient Descent with Restarts

abstract: Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on CIFAR-10 and CIFAR-100 datasets where we demonstrate new state-of-the-art results below 4\% and 19\%, respectively. Our source code is available at this https URL


title: Wideband 5.5W Ka-Band Low-Cost MMIC High Power Amplifier with 30dB of Gain

abstract: A Ka-band MMIC high power amplifier (HPA) is presented that supplies over 5W of output power with 30dB of gain over a 3.5GHz band and is produced on a low-cost, mature, commercially available 0.25um PHEMT process. The HPA has the largest power-bandwidth product at Ka-band reported to date [1, 2]. It is also the first Ka-band MMIC HPA in the 4W plus power range to have five stages of gain, and consequently has about 10dB more gain than comparable MMIC amplifiers [1]. The high gain eliminates the costs associated with using another amplifier to drive the HPA. The HPA uses less chip area than comparable amplifiers that have only three stages of gain [1]. Other amplifiers have been reported that have comparable power performance but were fabricated on more expensive 0.1um or 0.15um processes [1, 2]. The high output power and gain in a small chip area along with the inexpensive 0.25um process make the HPA low cost.


title: UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition

abstract: Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.


title: Creativity as a distinct trainable mental state: An EEG study of musical improvisation

abstract: Abstract Alpha-band EEG was used to index how creative mental states relate to the creation of artistic works in skilled musicians. We contrasted differences in frontal upper alpha-band activity between tasks with high and low creativity demands by recording EEGs while skilled musicians listened to, played back, and improvised jazz melodies. Neural responses were compared for skilled musicians with training in musical improvisation versus those who had no formal improvisation training. Consistent with our hypotheses, individuals showed increased frontal upper alpha-band activity during more creative tasks (i.e., improvisation) compared to during less creative tasks (i.e., rote playback). Moreover, this effect was greatest for musicians with formal improvisation training. The strength of this effect also appeared to modulate the quality of these improvisations, as evidenced by significant correlations between upper alpha EEG power and objective post-hoc ratings of individuals’ performances. These findings support a conceptualization of creativity as a distinct mental state and suggest spontaneous processing capacity is better nurtured through formal institutional training than informal.


title: Traffic Identification of Mobile Apps Based on Variational Autoencoder Network

abstract: Traffic identification is a fundamental issue in network security. Traditional methods, such as depth packet inspection (DPI) and flow-based classifiers, have difficulties in labeling massive samples and extracting features manually. Motivated by the achievements in computer vision, we focus on mobile app traffic, proposing a deep learning model based on variational autoencoder network (VEAN). Our contributions are two-fold. First, we propose a novel method of transforming mobile app traffic flows into vision-meaningful images, and thus enable the machine to identify the traffic in a human way. Then, based on the transformation method, we create an open dataset named IMTD17. Second, an improved network model is proposed, where variational autoencoder (VAE) algorithm is introduced into a two-stage learning. The model realizes the learning from massive unlabeled data, and the feasibility of the replacement for manual feature extraction is illustrated by the visualization analysis of the latent features. The experimental results show that the identification accuracy can reach 99.6%, which satisfies the practical requirement.


title: AppIntent: analyzing sensitive data transmission in android for privacy leakage detection

abstract: Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not.


title: Human sensing for smart cities

abstract: Smart cities are powered by the ability to self-monitor and respond to signals and data feeds from heterogeneous physical sensors. These physical sensors, however, are fraught with interoperability and dependability challenges. Moreover, they also cannot shed light on human emotions and factors that impact smart city initiatives. Yet everyday, millions of city dwellers share their observations, thoughts, feelings, and experiences about their city through social media updates. This paper describes how citizens can serve as human sensors in providing supplementary, alternate, and complementary sources of information for smart cities. It presents a methodology, based on a probabilistic language model, to extract the perceptions that may be relevant to smart city initiatives from social media updates. Geo-tagged tweets collected over a two-month period from New York City are used to illustrate the potential of social media powered human sensors.


title: Small Antennas: Miniaturization Techniques and Applications

abstract: The paper presents research results in the field of small antennas obtained at the Department of Wireless Communications, Faculty of Electrical Engineering and Computing, University of Zagreb. A study comparing the application of several miniaturization techniques on a shorted patch antenna is presented. Single and dual band shorted patch antennas with notches and/or slot are introduced. A PIFA designed for application in mobile GSM terminals is described. The application of stacked shorted patches as array elements for a mobile communication base station as well as for electromagnetic field sensor is presented. The design of single and dual band folded monopoles is described. Prototypes of the presented antennas have been manufactured and their characteristics were verified b y measurements.


title: Structure and encapsulation in distributed systems: the proxy principle

abstract: We present a novel view of the structuring of distributed systems, and a few examples of its utilization in an object-oriented context. In a distributed system, the structure of a service or subsystem may be complex, being implemented as a set of communicating server objects; however, this complexity of structure should not be apparent to the client. In our proposal, a client must rst acquire a local object, called a proxy, in order to use such a service. The proxy represents the whole set of servers. The client directs all its communication to the proxy. The proxy, and all the objects it represents, collectively form one distributed object, which is not decomposable by the client. Any higher-level communication protocols are internal to this distributed object. Such a view provides a powerful structuring framework for distributed systems; it can be implemented cheaply without sacricing much exibility. It subsumes may previous proposals, but encourages better information-hiding and encapsulation


title: Sustainable food production: constraints, challenges and choices by 2050

abstract: The way we grow and consume food is changing both landscapes and societies globally. The constraints and challenges we face in meeting the anticipated large increase in global food demand out to 2050 are examined to show that while they present significant difficulties on many fronts, we have a large range of choices in the way this food demand might be met. Meeting this future food demand has frequently been articulated as a crisis of supply alone by some dominant institutions and individuals with prior ideological commitments to a particular framing of the food security issue. Our analysis indicates that the crisis can be avoided by the choices we make. The food security debate will be enriched by a rigorous evaluation of all these choices and recognition that the eventual solution will reside in a mixture of these choices. We could shift from our current paradigm of productivity enhancement while reducing environmental impacts, to a paradigm where ecological sustainability constitutes the entry point for all agricultural development. If we embraced this new paradigm, sustainable governance and management of ecosystems, natural resources and earth system processes at large, could provide the framework for practical solutions towards an intensification of agriculture. Such a paradigm shift could reposition world food production from its current role as the world’s single largest driver of global environmental change, to becoming a critical part of a world transition to work within the boundaries of the safe operating space for humanity with respect to the planet’s biophysical processes and functions.


title: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography

abstract: In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).


title: Crime Data Mining: An Overview and Case Studies

abstract: The concern about national security has increased significantly since the 9/11 attacks. However, information overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problems. In this paper, we review crime data mining techniques and present four case studies done in our ongoing COPLINK project.


title: Space Transformation for Understanding Group Movement

abstract: We suggest a methodology for analyzing movement behaviors of individuals moving in a group. Group movement is analyzed at two levels of granularity: the group as a whole and the individuals it comprises. For analyzing the relative positions and movements of the individuals with respect to the rest of the group, we apply space transformation, in which the trajectories of the individuals are converted from geographical space to an abstract 'group space'. The group space reference system is defined by both the position of the group center, which is taken as the coordinate origin, and the direction of the group's movement. Based on the individuals' positions mapped onto the group space, we can compare the behaviors of different individuals, determine their roles and/or ranks within the groups, and, possibly, understand how group movement is organized. The utility of the methodology has been evaluated by applying it to a set of real data concerning movements of wild social animals and discussing the results with experts in animal ethology.


title: BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).


title: A temperature compensated fully trimmable on-chip IC oscillator

abstract: The design of an IC on-chip oscillator, including temperature compensation circuitry, a robust spread reduction technique and digital trimming is described. The IC oscillator provides a 12.8 MHz clock signal with a frequency spread of /spl plusmn/25% before the 8 bit digital trimming. After centering the oscillator at the target frequency, a temperature compensated voltage and current reference circuit allows for less than /spl plusmn/5 % frequency variation when operating from 3 to 5 V power supply and from -40 to 125 /spl deg/C of temperature range. The oscillator is implemented in a 0.5 /spl mu/m CMOS technology, occupies an area of 420/spl times/440 /spl mu/m/sup 2/ and dissipates less than 400 /spl mu/W at 3 V supply without requiring any external reference or components.


Big Data Challenges


title: A dual-polarized base-station antenna For LTE communication system

abstract: A wideband dual-polarized antenna excited by T-shaped microstrip feedline for Long Term Evolution (LTE) communication system is presented. The VSWR less than 1.5, isolation between the two input ports more than 30dB, gain more than 9 dBi, and stable radiation patterns, low cross-polarization ratio, low backlobe radiation are obtained in the entire operating frequency band from 1.7 to 2.7 GHz.


title: Big Data's Big Unintended Consequences

abstract: Businesses and governments exploit big data without regard for issues of legality, data quality, disparate data meanings, and process quality. This often results in poor decisions, with individuals bearing the greatest risk. The threats harbored by big data extend far beyond the individual, however, and call for new legal structures, business processes, and concepts such as a Private Data Commons. The Web extra at http://youtu.be/TvXoQhrrGzg is a video in which author Marcus Wigan expands on his article "Big Data's Big Unintended Consequences" and discusses how businesses and governments exploit big data without regard for issues of legality, data quality, disparate data meanings, and process quality. This often results in poor decisions, with individuals bearing the greatest risk. The threats harbored by big data extend far beyond the individual, however, and call for new legal structures, business processes, and concepts such as a Private Data Commons.


title: Fingerprinting Wi-Fi Devices Using Software Defined Radios

abstract: Wi-Fi (IEEE 802.11), is emerging as the primary medium for wireless Internet access. Cellular carriers are increasingly offloading their traffic to Wi-Fi Access Points to overcome capacity challenges, limited RF spectrum availability, cost of deployment, and keep up with the traffic demands driven by user generated content. The ubiquity of Wi-Fi and its emergence as a universal wireless interface makes it the perfect tracking device. The Wi-Fi offloading trend provides ample opportunities for adversaries to collect samples (e.g., Wi-Fi probes) and track the mobility patterns and location of users. In this work, we show that RF fingerprinting of Wi-Fi devices is feasible using commodity software defined radio platforms. We developed a framework for reproducible RF fingerprinting analysis of Wi-Fi cards. We developed a set of techniques for distinguishing Wi-Fi cards, most are unique to the IEEE802.11a/g/p standard, including scrambling seed pattern, carrier frequency offset, sampling frequency offset, transient ramp-up/down periods, and a symmetric Kullback-Liebler divergence-based separation technique. We evaluated the performance of our techniques over a set of 93 Wi-Fi devices spanning 13 models of cards. In order to assess the potential of the proposed techniques on similar devices, we used 3 sets of 26 Wi-Fi devices of identical model. Our results, indicate that it is easy to distinguish between models with a success rate of 95%. It is also possible to uniquely identify a device with 47% success rate if the samples are collected within a 10s interval of time.


title: VIGvisual: A Visualization Tool for the Vigenère Cipher

abstract: This paper describes a visualization tool VIGvisual that helps students learn and instructors teach the Vigenere cipher. The software allows the user to visualize both encryption and decryption through a variety of cipher tools. The demo mode is useful and efficient for classroom presentation. The practice mode allows the user to practice encryption and decryption. VIGvisual is quite versatile, providing support for both beginners learning how to encrypt and decrypt, and also for the more advanced users wishing to practice cryptanalysis in the attack mode. Classroom evaluation of the tool was positive.