title: A Dual-Band Metasurface Antenna Using Characteristic Mode Analysis

abstract: A compact metasurface-based antenna is proposed for dual-band operations. The proposed metasurface is designed on a single-layered substrate including an array of modified $3 \times 3$ squared patches. Each of the four corner patches is split into four fractional patches while the four edge patches are evolved into Malta crosses and the center patch is scaled. A substrate integrated waveguide-based Y-junction cavity-fed dual slot drives the metasurface with multiple impedance resonances. Based on the predicted modal behaviors of metasurface using a characteristic mode analysis (CMA), as an example, an antenna operating at three resonant modes at 28, 33, and 36 GHz, respectively is designed for the dual-band operation for the coming 5G. The proposed design shows that the measured impedance bandwidths (return loss larger than 10 dB) are 23.7–29.2 GHz and 36.7–41.1 GHz with the achieved gain of 4.8–7.2 dBi and 8.9–10.9 dBi, respectively. The proposed dual-band antenna features the advantages of low profile and wideband, suitable for the coming dual-band 5G applications.


title: Fast and Robust Triangle-Triangle Overlap Test Using Orientation Predicates

abstract: Abstract This paper presents an algorithm for determining whether two triangles in three dimensions intersect. The general scheme is identical to the one proposed by Moller [Moller 97]. The main difference is that our algorithm relies exclusively on the sign of 4 × 4 determinants and does not need any intermediate explicit constructions which are the source of numerical errors. Besides the fact that the resulting code is more reliable than existing methods, it is also more efficient. The source code is available online.


title: Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability

abstract: Digital humanities scholars strongly need a corpus exploration method that provides topics easier ::: to interpret than standard LDA topic models. To move towards this goal, here we propose a ::: combination of two techniques, called Entity Linking and Labeled LDA. Our method identifies ::: in an ontology a series of descriptive labels for each document in a corpus. Then it generates a specific topic for each label. Having a direct relation between topics and labels makes interpretation ::: easier; using an ontology as background knowledge limits label ambiguity. As our topics are described with a limited number of clear-cut labels, they promote interpretability and support ::: the quantitative evaluation of the obtained results. We illustrate the potential of the approach by ::: applying it to three datasets, namely the transcription of speeches from the European Parliament ::: fifth mandate, the Enron Corpus and the Hillary Clinton Email Dataset. While some of these ::: resources have already been adopted by the natural language processing community, they still ::: hold a large potential for humanities scholars, part of which could be exploited in studies that ::: will adopt the fine-grained exploration method presented in this paper.


title: AppSec: A Safe Execution Environment for Security Sensitive Applications

abstract: Malicious OS kernel can easily access user's private data in main memory and pries human-machine interaction data, even one that employs privacy enforcement based on application level or OS level. This paper introduces AppSec, a hypervisor-based safe execution environment, to protect both the memory data and human-machine interaction data of security sensitive applications from the untrusted OS transparently. AppSec provides several security mechanisms on an untrusted OS. AppSec introduces a safe loader to check the code integrity of application and dynamic shared objects. During runtime, AppSec protects application and dynamic shared objects from being modified and verifies kernel memory accesses according to application's intention. AppSec provides a devices isolation mechanism to prevent the human-machine interaction devices being accessed by compromised kernel. On top of that, AppSec further provides a privileged-based window system to protect application's X resources. The major advantages of AppSec are threefold. First, AppSec verifies and protects all dynamic shared objects during runtime. Second, AppSec mediates kernel memory access according to application's intention but not encrypts all application's data roughly. Third, AppSec provides a trusted I/O path from end-user to application. A prototype of AppSec is implemented and shows that AppSec is efficient and practical.


title: Perception of Patient Safety Among Nurses at Teaching Hospital

abstract: Background: All healthcare professionals are susceptible to commit adverse events. Nurses are the key to safety improvements in many aspects. Aim of this study was to investigate perception of patient safety among nurses at Shebin ElKom Teaching Hospital. The Design of this study was descriptive research design. A convenient sample of 114 nurses was selected to carry out this study. Setting of this study was Shebin ElKom Teaching Hospital. Tool of this study was Nurse Questionnaire [1]. Results showed that there was significant difference in the nurses' perception of work environment (80.16± 15.8; P< 0.000). The majority of nurses (57.9%) perceived low patient safety. The majority of nurses (63.2, 52.6, 57.9, 57.9 & 52.6) perceived never occurrence of adverse events. The majority of nurses did not formally report adverse events. Conclusion: nurses perceive negative work environment, job dissatisfaction, low patient safety and underreporting trend of the adverse events. Recommendation: Strategies for improvement should include non-punitive adverse event reporting system, develop patient safety policy and set clear guidelines for improving the work environment dimensions.


title: Designing of coupled inductor in interleaved critical conduction mode boost PFC converter

abstract: Boost converter in critical conduction mode (CRM) can be used for power factor correction in low power level and it also can be used in medium or high power application by using interleaving technical which can reduce the input current ripple and output capacitor rms current. In this paper, the two inductors of two-phase interleaved Boost converter in CRM is inverse coupled for copper volume reduction. The coupling effect on switching frequency is analyzed, and the design procedure including inductance, turns number and air gap is introduced step by step. Based on minimum switching frequency larger than 30 kHz, the turns number of each winding can be reduced by coupling compare with the non-coupled case.


title: Accuracy of a Custom Physical Activity and Knee Angle Measurement Sensor System for Patients with Neuromuscular Disorders and Gait Abnormalities

abstract: Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.


title: A Low-Power 0.5–6.6 Gb/s Wireline Transceiver Embedded in Low-Cost 28 nm FPGAs

abstract: This paper describes the design of a 0.5-6.6 Gb/s fully-adaptive low-power quad transceiver embedded in low-leakage 28 nm CMOS FPGAs. Integration techniques enable the utilization of the transceiver in FPGAs with both wire-bond and flip-chip packages and resolve significant challenges with receiver input and transmitter output insertion loss, power integrity, ESD, and reliability. The transceiver clocking network provides continuous operation range up to the maximum speed and incorporates two wide-range ring-based PLLs for enhanced clocking flexibility. The receiver front-end utilizes a 3-stage CTLE with wide input common-mode to remove the post-cursor ISI. The CTLE is fully adaptive using an LMS algorithm and edge-based equalization. The transmitter utilizes a 3-tap FIR. The transceiver achieves BER 10-15 at 6.6 Gb/s over a 20 dB loss channel. Power consumption is 129 mW from 1.2 V and 1 V supplies.


title: Transfer Learning by Borrowing Examples for Multiclass Object Detection

abstract: Despite the recent trend of increasingly large datasets for object detection, there still exist many classes with few training examples. To overcome this lack of training data for certain classes, we propose a novel way of augmenting the training data for each class by borrowing and transforming examples from other classes. Our model learns which training instances from other classes to borrow and how to transform the borrowed examples so that they become more similar to instances from the target class. Our experimental results demonstrate that our new object detector, with borrowed and transformed examples, improves upon the current state-of-the-art detector on the challenging SUN09 object detection dataset.


title: MudPad: tactile feedback and haptic texture overlay for touch surfaces

abstract: We introduce MudPad, a system capable of localized active haptic feedback on multitouch screens. We use an array of electromagnets combined with an overlay containing magnetorheological (MR) fluid to actuate a tablet-sized area. As MudPad has a very low reaction time it is able to produce instant multi-point feedback for multitouch input, ranging from static levels of surface softness to a broad set of dynamically changeable textures. Our system does not only convey global confirmative feedback on user input but allows the UI designer to enrich the entire interface with a tactile layer conveying local semantic information. This also allows users to explore the interface haptically.


title: The Physical World as a Virtual Reality

abstract: This paper explores the idea that the universe is a virtual reality created by information processing, and relates this strange idea to the findings of modern physics about the physical world. The virtual reality concept is familiar to us from online worlds, but our world as a virtual reality is usually a subject for science fiction rather than science. Yet logically the world could be an information simulation running on a multi-dimensional space-time screen. Indeed, if the essence of the universe is information, matter, charge, energy and movement could be aspects of information, and the many conservation laws could be a single law of information conservation. If the universe were a virtual reality, its creation at the big bang would no longer be paradoxical, as every virtual system must be booted up. It is suggested that whether the world is an objective reality or a virtual reality is a matter for science to resolve. Modern information science can suggest how core physical properties like space, time, light, matter and movement could derive from information processing. Such an approach could reconcile relativity and quantum theories, with the former being how information processing creates space-time, and the latter how it creates energy and matter.


title: Social robots as assistants for autism therapy in Iran: Research in progress

abstract: Autistic children are often impaired in initiating and responding to Joint Attention. In recent years, there has been an increase in the application of robots in diagnosis and treatment of autism. The purpose of the current research has been primarily to originate the proper therapeutic scenarios and to implement two interactive humanoid robots as therapy assistants in autism treatment in Iran. To this end, the humanoid robots were programmed and teleoperated via Microsoft Kinect Sensor and PhantomOmni Haptic Robot to elicit reactions consisting of imitation of humans by the humanoid robots and vice versa. In this paper, we elaborate on the therapeutic items that we have designed to improve joint attention and imitation in autistic children through using humanoid robots. Moreover, the fairly promising results of some interventions conducted in a pilot study on four autistic cases will be addressed and discussed. Our research target is to increase social interaction and involve autistic children in dyadic/triadic interactions which seems quite possible due to the findings of the pilot study conducted. i


title: Information Extraction: Methodologies and Applications

abstract: This chapter is concerned with the methodologies and applications of information extraction. Information is hidden in the large volume of web pages and thus it is necessary to extract useful information from the web content, called Information Extraction. In information extraction, given a sequence of instances, we identify and pull out a sub-sequence of the input that represents information we are interested in. In the past years, there was a rapid expansion of activities in the information extraction area. Many methods have been proposed for automating the process of extraction. However, due to the heterogeneity and the lack of structure of Web data, automated discovery of targeted or unexpected knowledge information still presents many challenging research problems. In this chapter, we will investigate the problems of information extraction and survey existing methodologies for solving these problems. Several real-world applications of information extraction will be introduced. Emerging challenges will be discussed.


title: Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms

abstract: Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy.


Automated calibration of an accelerometers , magnetometers and gyroscopes-A feasibility study


title: Pheromones: Convergence and contrasts in insects and vertebrates

abstract: The individuals in animal societies interact via a complex web of semiochemical signals. Eusocial species of social insects and social mammals are characterized by reproductive division of labour. In some species, group members fight to establish which animals will reproduce. Other species use pheromones that act as signals rather than as coercion. The mechanisms used in social insects and in mammals have many similarities.


title: Prioritizing collision avoidance and vehicle stabilization for autonomous vehicles

abstract: One approach to autonomous vehicle control is to generate and then track a desired trajectory without explicit consideration of vehicle stability. Stabilization is then entrusted to the vehicle's built-in production systems, such as electronic stability control, which constantly augment driving inputs to ensure stability. Other approaches explicitly consider stabilization criteria and implement permanently active constraints on the vehicle's actions. Situations exist, however, where enforcing stability constraints could lead to an otherwise avoidable collision. This paper presents an alternative paradigm for autonomous vehicle control that explicitly considers vehicle stability and environmental boundaries as it attempts to track a trajectory; such a mediator can choose to violate short term stability constraints in order to avoid a collision. Model predictive control provides an implementation framework, and an autonomous vehicle demonstrates the viability of the controller as it performs aggressive maneuvers. Driving around a turn at the vehicle's limits exhibits the importance of vehicle stability for autonomous vehicle control. Performing an emergency double lane change, however, highlights a situation where stability criteria must be temporarily violated to avoid a collision.


title: Rethinking Privacy Decisions: Pre-Existing Attitudes, Pre-Existing Emotional States, and a Situational Privacy Calculus

abstract: As a potential explanation to measured inconsistencies between stated privacy concerns and actual disclosing behavior, denoted as the „privacy paradox“, scholars have proposed a systematic distinction between situational privacy considerations and pre-existing, superordinate factors that shape the decisive situation without being directly connected to the situation itself. Deploying an experimental approach, we explored the dynamics of two types of such pre-existing factors, namely (1) pre-existing attitudes (such as general privacy concerns and general institutional trust) and (2) pre-existing emotional states (such as an individual’s current mood) in shaping situation-specific risk and benefit considerations (i.e., a situational privacy calculus). Compared to a negative emotional state, individuals in a positive emotional state were found to perceive lowered situation-specific privacy risks, even if the sources of this state were unrelated to the decisive situation at hand. Moreover, results indicated that pre-existing attitudes may be partially or even fully overridden by situational risk and benefit considerations. Adopting a differentiated view on privacy decision-making, these findings imply that the privacy paradox could be driven by a gap between pre-existing cognitive and affective factors on the one side, and situation-specific considerations and decisions on the other. Implications for researchers and practitioners are discussed.


title: Sensor technology for smart homes.

abstract: A smart home is a residence equipped with technology that observes the residents and provides proactive services. Most recently, it has been introduced as a potential solution to support independent living of people with disabilities and older adults, as well as to relieve the workload from family caregivers and health providers. One of the key supporting features of a smart home is its ability to monitor the activities of daily living and safety of residents, and in detecting changes in their daily routines. With the availability of inexpensive low-power sensors, radios, and embedded processors, current smart homes are typically equipped with a large amount of networked sensors which collaboratively process and make deductions from the acquired data on the state of the home as well as the activities and behaviors of its residents. This article reviews sensor technology used in smart homes with a focus on direct environment sensing and infrastructure mediated sensing. The article also points out the strengths and limitations of different sensor technologies, as well as discusses challenges and opportunities from clinical, technical, and ethical perspectives. It is recommended that sensor technologies for smart homes address actual needs of all stake holders including end users, their family members and caregivers, and their doctors and therapists. More evidence on the appropriateness, usefulness, and cost benefits analysis of sensor technologies for smart homes is necessary before these sensors should be widely deployed into real-world residential settings and successfully integrated into everyday life and health care services.


title: A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

abstract: Gated Recurrent Unit (GRU) is a recently-developed variation of the long short-term memory (LSTM) unit, both of which are variants of recurrent neural network (RNN). Through empirical evidence, both models have been proven to be effective in a wide variety of machine learning tasks such as natural language processing, speech recognition, and text classification. Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss. In this paper, we present an amendment to this norm by introducing linear support vector machine (SVM) as the replacement for Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. While there have been similar studies, this proposal is primarily intended for binary classification on intrusion detection using the 2013 network traffic data from the honeypot systems of Kyoto University. Results show that the GRU-SVM model performs relatively higher than the conventional GRU-Softmax model. The proposed model reached a training accuracy of a81.54% and a testing accuracy of a84.15%, while the latter was able to reach a training accuracy of a63.07% and a testing accuracy of a70.75%. In addition, the juxtaposition of these two final output layers indicate that the SVM would outperform Softmax in prediction time - a theoretical implication which was supported by the actual training and testing time in the study.


title: A gender-specific behavioral analysis of mobile device usage data

abstract: Mobile devices provide a continuous data stream of contextual behavioral information which can be leveraged for a variety of user services, such as in personalizing ads and customizing home screens. In this paper, we aim to better understand gender-related behavioral patterns found in application, Bluetooth, and Wi-Fi usage. Using a dataset which consists of approximately 19 months of data collected from 189 subjects, gender classification is performed using 1,000 features related to the frequency of events yielding up to 91.8% accuracy. Then, we present a behavioral analysis of application traffic using techniques commonly used for web browsing activity as an alternative data exploration approach. Finally, we conclude with a discussion on impersonation attacks, where we aim to determine if one gender is less vulnerable to unauthorized access on their mobile device.


COGNITIONS ABOUT COGNITIONS: THE THEORY OF METACOGNITION


title: Evolving Boids: Using a Genetic Algorithm to Develop Boid

abstract: The detection and analysis of clusters has become commonplace within geographic information science and has been applied in epidemiology, crime prevention, ecology, demography and other fields. One of the many methods for detecting and analyzing these clusters involves searching the dataset with a flock of boids (bird objects). While boids are effective at searching the dataset once their behaviors are properly configured, it can be difficult to find the proper configuration. Since genetic algorithms have been successfully used to configure neural networks, they may also be useful for configuring parameters guiding boid behaviors. In this paper, we develop a genetic algorithm to evolve the ideal boid behaviors. Preliminary results indicate that, even though the genetic algorithm does not return the same configuration each time, it does converge on configurations that improve over the parameters used when boids were initially proposed for geographic cluster detection. Also, once configured, the boids perform as well as other cluster detection methods. Continued work with this system could determine which parameters have a greater effect on the results of the boid system and could also discover rules for configuring a flock of boids directly from properties of the dataset, such as point density, rather than requiring the time-consuming process of optimizing the parameters for each new dataset.


title: A Broadband Dual-Polarized Planar Antenna for 2G/3G/LTE Base Stations

abstract: A broadband dual-polarized planar antenna is proposed for 2G/3G/LTE base stations. The dual-polarized antenna is composed of two perpendicularly crossed bow-tie dipoles. Each bow-tie dipole is excited by a microstrip stub that is directly fed by a coaxial line, making the dual-polarized antenna full planar. Due to the coupling between two crossed bow-tie dipoles, a broad bandwidth is achieved. It is shown that the dual-polarized antenna has a bandwidth of 45% (1.7-2.7 GHz) for return loss >15 dB with an isolation of higher than 30 dB between two polarization input ports. The dual-polarized antenna has a half-power beam width (HPBW) of around 65 ° and an average gain of 8.5 dBi for slant ±45° polarizations. An 8-element dual-polarized planar antenna array is developed for base station applications. A bandwidth of 56% (1.63-2.9 GHz) is obtained for the antenna array. The antenna gain of the array is about 16 dBi and the HPBW is 65±8° for each polarization.


title: Adaptive object tracking by learning background context

abstract: One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we present a solution that is based on our earlier approach for object tracking using particle filters and component-based descriptors. We extend the approach to deal with changing backgrounds by using a quick training phase with user interaction at the beginning of an image sequence. During this phase, some background clusters are learned along with object representations for those clusters. Next, for the rest of the sequence the best fitting background cluster is determined for each frame and the corresponding object representation is used for tracking. Experiments show a particle filter adapting to background changes can efficiently track objects and persons in natural scenes and results in higher tracking results than the basic approach. Additionally, using an object tracker to follow the main character in video games, we were able to explain a large amount of eye fixations higher than other saliency models in terms of NSS score proving that tracking is an important top-down attention component.


title: Active One-shot Learning

abstract: Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks. This paper combines reinforcement learning with one-shot learning, allowing the model to decide, during classification, which examples are worth labeling. We introduce a classification task in which a stream of images are presented and, on each time step, a decision must be made to either predict a label or pay to receive the correct label. We present a recurrent neural network based action-value function, and demonstrate its ability to learn how and when to request labels. Through the choice of reward function, the model can achieve a higher prediction accuracy than a similar model on a purely supervised task, or trade prediction accuracy for fewer label requests.


Three Decades of Iron Age Research in South Africa: Some Personal Reflections


title: Kernel Sparse Representation-Based Classifier

abstract: Sparse representation-based classifier (SRC), a combined result of machine learning and compressed sensing, shows its good classification performance on face image data. However, SRC could not well classify the data with the same direction distribution. The same direction distribution means that the sample vectors belonging to different classes distribute on the same vector direction. This paper presents a new classifier, kernel sparse representation-based classifier (KSRC), based on SRC and the kernel trick which is a usual technique in machine learning. KSRC is a nonlinear extension of SRC and can remedy the drawback of SRC. To make the data in an input space separable, we implicitly map these data into a high-dimensional kernel feature space by using some nonlinear mapping associated with a kernel function. Since this kernel feature space has a very high (or possibly infinite) dimensionality, or is unknown, we have to avoid working in this space explicitly. Fortunately, we can indeed reduce the dimensionality of the kernel feature space by exploiting kernel-based dimensionality reduction methods. In the reduced subspace, we need to find sparse combination coefficients for a test sample and assign a class label to it. Similar to SRC, KSRC is also cast into an l1-minimization problem or a quadratically constrained l1 -minimization problem. Extensive experimental results on UCI and face data sets show KSRC improves the performance of SRC.


title: Heidi matrix: nearest neighbor driven high dimensional data visualization

abstract: Identifying patterns in large high dimensional data sets is a challenge. As the number of dimensions increases, the patterns in the data sets tend to be more prominent in the subspaces than the original dimensional space. A system to facilitate presentation of such subspace oriented patterns in high dimensional data sets is required to understand the data. Heidi is a high dimensional data visualization system that captures and visualizes the closeness of points across various subspaces of the dimensions; thus, helping to understand the data. The core concept behind Heidi is based on prominence of patterns within the nearest neighbor relations between pairs of points across the subspaces. Given a d-dimensional data set as input, Heidi system generates a 2-D matrix represented as a color image. This representation gives insight into (i) how the clusters are placed with respect to each other, (ii) characteristics of placement of points within a cluster in all the subspaces and (iii) characteristics of overlapping clusters in various subspaces. A sample of results displayed and discussed in this paper illustrate how Heidi Visualization can be interpreted.


title: Mirror neurons, the representation of word meaning, and the foot of the third left frontal convolution

abstract: Previous neuroimaging research has attempted to demonstrate a preferential involvement of the human mirror neuron system (MNS) in the comprehension of effector-related action word (verb) meanings. These studies have assumed that Broca's area (or Brodmann's area 44) is the homologue of a monkey premotor area (F5) containing mouth and hand mirror neurons, and that action word meanings are shared with the mirror system due to a proposed link between speech and gestural communication. In an fMRI experiment, we investigated whether Broca's area shows mirror activity solely for effectors implicated in the MNS. Next, we examined the responses of empirically determined mirror areas during a language perception task comprising effector-specific action words, unrelated words and nonwords. We found overlapping activity for observation and execution of actions with all effectors studied, i.e., including the foot, despite there being no evidence of foot mirror neurons in the monkey or human brain. These "mirror" areas showed equivalent responses for action words, unrelated words and nonwords, with all of these stimuli showing increased responses relative to visual character strings. Our results support alternative explanations attributing mirror activity in Broca's area to covert verbalisation or hierarchical linearisation, and provide no evidence that the MNS makes a preferential contribution to comprehending action word meanings.


title: A vehicle license plate detection method using region and edge based methods

abstract: In this paper, a license plate detection system is implemented. For this purpose, we improve the contrast at possible locations where there might be a license plate, we propose a filtering method called ''region-based'' in order to smooth the uniform and background areas of an image, we apply the Sobel operator and morphological filtering to extract the vertical edges and the candidate regions respectively, and finally, we segment the plate region by considering some geometrical features. In fact the novelty and the strength of our license plate detection system is in applying the region-based filtering that decreases the run time and increases the accuracy in the two final stages: morphological filtering and using the geometrical features. The experimental results show that our proposed method achieves appropriate performance in different scenarios. We should mention that our system is reliable because the accuracy is above 92% in average for different scenarios and it is also practical because of the low computational cost.


title: Considerations on downlink non-orthogonal multiple access (NOMA) combined with closed-loop SU-MIMO

abstract: As a promising downlink multiple access scheme for further cellular enhancements toward 5G, non-orthogonal multiple access (NOMA) improves both cell average and cell-edge user throughput compared to orthogonal multiple access (OMA) in the cellular downlink. When NOMA is combined with multi-antenna technology in practice, some aspects are of great interest, such as error propagation (EP) of successive interference cancellation (SIC) receiver and user velocity. This paper investigates the system-level throughput of NOMA with closed-loop single-user multiple-input multiple-output (SU-MIMO) in the cellular downlink and clarifies the potential gains over OMA with SU-MIMO taking into account these practical aspects. Two EP models are presented into the NOMA with closed-loop (CL) SU-MIMO. Their impacts on performance of NOMA with CL SU-MIMO are investigated by system-level simulations with practical assumptions. Furthermore, impact of user velocity on performance gain of NOMA with CL SU-MIMO is also evaluated. Simulation results show that even with the worst case EP model, NOMA with CL SU-MIMO can still provide large performance gains for both low and high mobility scenarios.


title: What works and doesn't work with instructional video

abstract: Abstract In this commentary, we examine the papers in a special issue on “Developments and Trends in Learning with Instructional Video”. In particular, we focus on basic findings concerning which instructional features improve learning with instructional video (i.e., breaking the lesson into segments paced by the learner; recording from both first- and third-person perspectives) and which features or learner attributes do not (i.e., matching the instructor's gender to the learner's gender; having the instructor's face on the screen; adding practice without feedback; inserting pauses throughout the video; and spatial ability). In addition, we offer recommendations for future work on designing effective video lessons.


title: Deep Learning for Continuous Electronic Fetal Monitoring in Labor

abstract: Continuous electronic fetal monitoring (EFM) is used worldwide to visually assess whether a fetus is exhibiting signs of distress during labor, and may benefit from an emergency operative delivery (e.g. Cesarean section). Previously, computerized EFM assessment that mimics clinical experts showed no benefit in randomized clinical trials. However, as an example of routinely collected `big’ data, EFM interpretation should benefit from data-driven computational approaches, such as deep learning, which allow automated evaluation based on large clinical datasets.Here we report our investigation of long short term memory (LSTM) and convolutional neural networks (CNN) in analyzing EFM traces from over 35,000 labors for the prediction of fetal compromise. Of these, 85% are used for training with crossvalidation and the remainder are set aside for testing. The results are compared with Clinical practice (reason for operative deliveryrecorded as fetal distress) and an earlier prototype system for computerized analysis of EFM (OxSys 1.5), developed on the same data. We demonstrate that CNN outperforms LSTM, Clinical practice, and OxSys 1.5 in predicting fetal compromise, with a sensitivity of 42% (30%, 34%, and 36% for the others, respectively), at comparable or lower false positive rates. We also show that increasing the size of the training set improves the sensitivity and stability of CNN’s performance on the testing set. When tested on a small open-access external database, CNN moderately improves on the performance of published feature extraction based methods.We conclude that CNN could play an important role in the field of automated EFM analysis, but requires further work.


title: Issues in User Authentication

abstract: In this brief paper we outline our recent work on u sability studies of user authentication systems. In particul ar, we have conducted studies on password managers and graphical password systems, using in-lab experiment s, interviews, and broader field studies. We then disc uss some of the important issues arising, including mental m odels of security, the role of persuasion, and the nature of identify theft.


title: Chemical Genetics of Acetyl-CoA Carboxylases

abstract: Chemical genetic studies on acetyl-CoA carboxylases (ACCs), rate-limiting enzymes in long chain fatty acid biosynthesis, have greatly advanced the understanding of their biochemistry and molecular biology and promoted the use of ACCs as targets for herbicides in agriculture and for development of drugs for diabetes, obesity and cancers. In mammals, ACCs have both biotin carboxylase (BC) and carboxyltransferase (CT) activity, catalyzing carboxylation of acetyl-CoA to malonyl-CoA. Several classes of small chemicals modulate ACC activity, including cellular metabolites, natural compounds, and chemically synthesized products. This article reviews chemical genetic studies of ACCs and the use of ACCs for targeted therapy of cancers.


title: Thermoelectric Converters of Human Warmth for Self-Powered Wireless Sensor Nodes

abstract: Solar cells are the most commonly used devices in customer products to achieve power autonomy. This paper discusses a complementary approach to provide power autonomy to devices on a human body, i.e., thermoelectric conversion of human heat. In indoor applications, thermoelectric converters on the skin can provide more power per square centimeter than solar cells, particularly in adverse illumination conditions. Moreover, they work day and night. The first sensor nodes powered by human heat have been demonstrated and tested on people in 2004-2005. They used the state-of-the-art 100-muW watch-size thermoelectric wrist generators fabricated at IMEC and based on custom-design small-size BiTe thermopiles. The sensor node is completed with a power conditioning module, a microcontroller, and a wireless transceiver mounted on a watchstrap


title: A 57dBOmega 1GHz CMOS Front-End Preamplifier for Optical Receivers

abstract: A front-end preamplifier consisted of transimpedance amplifier (TIA) core, single-to-differential converter (STDC) and output buffer(OB) is proposed in this paper. The goal is to realize a low cost and high performance preamplifier to be used in the optical receiver modules based on the standard 0.5um complementary metal oxide semiconductor (CMOS) process. The regulated cascade (RGC) input structure followed by a gain stage and a resistive feedback is employed in the TIA core. The measurement results show that the chip achieves a transimpedance gain of 57.56dBΩ, -3dB bandwidth of 1.04GHz, and 18.33pA/sqrt(Hz) input referred noise current. Under a 3.3V supply voltage, the front-end TIA consumes 73.38mW and the total chip area is 941.6 × 1042.2um2 including an output buffer and testing pads.


title: Smart-Phones Attacking Smart-Homes

abstract: The explosion in Internet-connected household devices, such as light-bulbs, smoke-alarms, power-switches, and webcams, is creating new vectors for attacking "smart-homes" at an unprecedented scale. Common perception is that smart-home IoT devices are protected from Internet attacks by the perimeter security offered by home routers. In this paper we demonstrate how an attacker can infiltrate the home network via a doctored smart-phone app. Unbeknownst to the user, this app scouts for vulnerable IoT devices within the home, reports them to an external entity, and modifies the firewall to allow the external entity to directly attack the IoT device. The ability to infiltrate smart-homes via doctored smart-phone apps demonstrates that home routers are poor protection against Internet attacks and highlights the need for increased security for IoT devices.


title: An Evaluation of Dynamic Web Service Composition Approaches ?

abstract: Web Services composition has received much interest from both the academic researchers and industry to support cross-enterprise application integration. Promising research projects and their prototypes are being developed. At the same time the web service environment is getting more dynamic as numerous web services are being published by the service providers in the Internet. To meet the users requirements regarding on-demand delivery of customized services, dynamic web service composition approaches have emerged. But still many compositional issues have to be overcome like dynamic discovery of services, compositional correctness, transactional supports etc. In this paper we discuss some of these issues and then investigate some of the representative dynamic web service composition approaches. We evaluate those approaches on the basis of the issues and present how the future research can benefit by addressing those issues of dynamic web service composition.


title: Feature-based surface parameterization and texture mapping

abstract: Surface parameterization is necessary for many graphics tasks: texture-preserving simplification, remeshing, surface painting, and precomputation of solid textures. The stretch caused by a given parameterization determines the sampling rate on the surface. In this article, we present an automatic parameterization method for segmenting a surface into patches that are then flattened with little stretch. Many objects consist of regions of relatively simple shapes, each of which has a natural parameterization. Based on this observation, we describe a three-stage feature-based patch creation method for manifold surfaces. The first two stages, genus reduction and feature identification, are performed with the help of distance-based surface functions. In the last stage, we create one or two patches for each feature region based on a covariance matrix of the feature's surface points. To reduce stretch during patch unfolding, we notice that stretch is a 2 × 2 tensor, which in ideal situations is the identity. Therefore, we use the Green-Lagrange tensor to measure and to guide the optimization process. Furthermore, we allow the boundary vertices of a patch to be optimized by adding scaffold triangles. We demonstrate our feature-based patch creation and patch unfolding methods for several textured models. Finally, to evaluate the quality of a given parameterization, we describe an image-based error measure that takes into account stretch, seams, smoothness, packing efficiency, and surface visibility.


title: Multiobjective optimization for project portfolio selection

abstract: This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of candidate projects, which is unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure. Results show evidence that the project selection technique can be used in large-scale scenarios and that GA presents better results than simpler search strategy.


title: An ultra-wideband absorber based on multiple resonators in 3-D frequency-selective structure

abstract: An ultra-wideband absorber based on employing multiple resonators in the unit cell of a 3-D frequency-selective structure is proposed. The original design consists of two-dimensional periodic array of three-dimensional unit cells and lumped components to provide matching with free space and absorb the incident electromagnetic wave. By introducing one more resonator in the unit cell, an ultra-wideband absorber can be constructed. It has a fractional bandwidth of 155.7 % for −10 dB of reflection coefficient under the normal incidence with an absorber thickness of 0.12 λ 0 at the lowest absorption frequency. This absorber works for TE polarization at which two quasi-TEM modes are excited in the air and substrate regions. The proposed absorber shows a stable performance under oblique angles of incidence.


SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS


title: Security Analysis of Voice-over-IP Protocols

abstract: The transmission of voice communications as datagram packets over IP networks, commonly known as voice-over-IP (VoIP) telephony, is rapidly gaining wide acceptance. With private phone conversations being conducted on insecure public networks, security of VoIP communications is increasingly important. We present a structured security analysis of the VoIP protocol stack, which consists of signaling (SIP), session description (SDP), key establishment (SDES, MIKEY, and ZRTP) and secure media transport (SRTP) protocols. Using a combination of manual and tool-supported formal analysis, we uncover several design flaws and attacks, most of which are caused by subtle inconsistencies between the assumptions that protocols at different layers of the VoIP stack make about each other. The most serious attack is a replay attack on SDES, which causes SRTP to repeat the keystream used for media encryption, thus completely breaking transport-layer security. We also demonstrate a man-in-the-middle attack on ZRTP, which allows the attacker to convince the communicating parties that they have lost their shared secret. If they are using VoIP devices without displays and thus cannot execute the "human authentication" procedure, they are forced to communicate insecurely, or not communicate at all, i.e., this becomes a denial of service attack. Finally, we show that the key derivation process used in MIKEY cannot be used to prove security of the derived key in the standard cryptographic model for secure key exchange.


title: Effect of High-Intensity Resistance Training on Performance of Competitive Distance Runners

abstract: In a recent study competitive road cyclists experienced substantial gains in sprint and endurance performance when sessions of high-intensity interval training were added to their usual training in the competitive phase of a season. The current study reports the effect of this type of training on performance of 20 distance runners randomized to an experimental or control group for 5 to 7 weeks of training. The experimental group replaced part of their usual competitive-phase training with 10 × 30-minute sessions consisting of 3 sets of explosive single-leg jumps (20 for each leg) alternating with 3 sets of resisted treadmill sprints (5 × 30-second efforts alternating with 30-second recovery). Before and after the training period all runners completed an incremental treadmill test for assessment of lactate threshold and maximum running speed, 2 treadmill runs to exhaustion for prediction of 800- and 1500-m times, and a 5-km outdoor time trial. Relative to the control group, the mean changes (±90% confidenc...


title: Design and evaluation of novel pH-sensitive ureido-conjugated chitosan/TPP nanoparticles targeted to Helicobacter pylori.

abstract: The covalently modified ureido-conjugated chitosan/TPP multifunctional nanoparticles have been developed as targeted nanomedicine delivery system for eradication of Helicobacter pylori. H. pylori can specifically express the urea transport protein on its membrane to transport urea into cytoplasm for urease to produce ammonia, which protects the bacterium in the acid milieu of stomach. The clinical applicability of topical antimicrobial agent is needed to eradicate H. pylori in the infected fundal area. In this study, we designed and synthesized two ureido-conjugated chitosan derivatives UCCs-1 and UCCs-2 for preparation of multifunctional nanoparticles. The process was optimized in order to prepare UCCs/TPP nanoparticles for encapsulation of amoxicillin. The results showed that the amoxicillin-UCCs/TPP nanoparticles exhibited favorable pH-sensitive characteristics, which could procrastinate the release of amoxicillin at gastric acids and enable the drug to deliver and target to H. pylori at its survival region effectively. Compared with unmodified amoxicillin-chitosan/TPP nanoparticles, a more specific and effective H. pylori growth inhibition was observed for amoxicillin-UCCs/TPP nanoparticles. Drug uptake analysis tested by flow cytometry and confocal laser scanning microscopy verified that the uptake of FITC-UCCs-2/TPP nanoparticles was associated with urea transport protein on the membrane of H. pylori and reduced with the addition of urea as competitive transport substrate. These findings suggest that the multifunctional amoxicillin-loaded nanoparticles have great potential for effective therapy of H. pylori infection. They may also serve as pharmacologically effective nanocarriers for oral targeted delivery of other therapeutic drugs to treat H. pylori.


Digital Activism in the Social Media Era: Critical Reflections on Emerging Trends in Sub-Saharan Africa


title: Neuroprotective Potential of Silymarin against CNS Disorders: Insight into the Pathways and Molecular Mechanisms of Action

abstract: Silymarin, a C25 containing flavonoid from the plant Silybum marianum, has been the gold standard drug to treat liver disorders associated with alcohol consumption, acute and chronic viral hepatitis, and toxin-induced hepatic failures since its discovery in 1960. Apart from the hepatoprotective nature, which is mainly due to its antioxidant and tissue regenerative properties, Silymarin has recently been reported to be a putative neuroprotective agent against many neurologic diseases including Alzheimer's and Parkinson's diseases, and cerebral ischemia. Although the underlying neuroprotective mechanism of Silymarin is believed to be due to its capacity to inhibit oxidative stress in the brain, it also confers additional advantages by influencing pathways such as β-amyloid aggregation, inflammatory mechanisms, cellular apoptotic machinery, and estrogenic receptor mediation. In this review, we have elucidated the possible neuroprotective effects of Silymarin and the underlying molecular events, and suggested future courses of action for its acceptance as a CNS drug for the treatment of neurodegenerative diseases.


title: Group-Sensitive Triplet Embedding for Vehicle Reidentification

abstract: The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation “group” between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database “PKU-Vehicle,” consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.


title: Scaling the Scattering Transform: Deep Hybrid Networks

abstract: We use the scattering network as a generic and fixed initialization of the first layers of a supervised hybrid deep network. We show that early layers do not necessarily need to be learned, providing the best results to-date with pre-defined representations while being competitive with Deep CNNs. Using a shallow cascade of 1 × 1 convolutions, which encodes scattering coefficients that correspond to spatial windows of very small sizes, permits to obtain AlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local encoding explicitly learns invariance w.r.t. rotations. Combining scattering networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing only 10 layers. We also find that hybrid architectures can yield excellent performance in the small sample regime, exceeding their end-to-end counterparts, through their ability to incorporate geometrical priors. We demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.


title: Deriving high-resolution urban air pollution maps using mobile sensor nodes

abstract: Up-to-date information on urban air pollution is of great importance for environmental protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 50 million measurements. We collected the measurements throughout more than two years using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100?m?i??100?m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with sub-weekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.


title: Security Testbed for the Internet of Things

abstract: The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time and in any place, to each other and to the Internet. One of the major problems associated with the IoT is maintaining security; the heterogeneous nature of such deployments poses a challenge to many aspects of security, including security testing and analysis. In addition, there is no existing mechanism that performs security testing for IoT devices in different contexts. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed supports both standard and context-based security testing, with a set of security tests conducted under the different environmental conditions in which IoT devices operate. The requirements and architectural design of the proposed testbed are discussed, and the testbed operation is demonstrated in several testing scenarios.


title: Unrealistic optimism about future life events: A cautionary note

abstract: A robust finding in social psychology is that people judge negative events as less likely to happen to themselves than to the average person, a behavior interpreted as showing that people are “unrealistically optimistic” in their judgments of risk concerning future life events. However, we demonstrate how unbiased responses can result in data patterns commonly interpreted as indicative of optimism for purely statistical reasons. Specifically, we show how extant data from unrealistic optimism studies investigating people's comparative risk judgments are plagued by the statistical consequences of sampling constraints and the response scales used, in combination with the comparative rarity of truly negative events. We conclude that the presence of such statistical artifacts raises questions over the very existence of an optimistic bias about risk and implies that to the extent that such a bias exists, we know considerably less about its magnitude, mechanisms, and moderators than previously assumed.


title: Lessons from Hereditary Colorectal Cancer

abstract: The authors are grateful to the members of their laboratories for their contributions to the reviewed studies and for their critical reading of the manuscript and to F. Giardiello and S. Hamilton for photographs of colorectal lesions. The authors are supported by Public Health Service grants CA 43460, CA 57345, and CA 62924. B. V. is an Investigator of the Howard Hughes Medical Institute.


title: LDA Analyzer: A Tool for Exploring Topic Models

abstract: Online technical forums are valuable sources for mining useful software engineering information. LDA (Latent Dirichlet Allocation) is an unsupervised machine learning method which can be used for extracting underlying topics out of such large forums. However, the main output of LDA forum learning are usually huge matrices that contain millions of numbers, which is impossible for researchers to directly scrutinize the numerical distribution and semantically evaluate the relationship between the extracted topics and large collection of unorganized documents. In this paper, we present LDAAnalyzer, an LDA visualization tool that makes the hidden topic-document structures rise to the surface. From the functionality point of view, LDA Analyzer consists of (1) LDA modeling (2) LDA output analysis and (3) new corpus training. With the help of LDAAnalyzer, our semantic topic-modeling evaluation based on large technical forums becomes feasible.


title: Multi-task Learning for Gender and Age Prediction on Chinese Microblog

abstract: The demographic attributes gender and age play an important role for social media applications. Previous studies on gender and age prediction mostly explore efficient features which are labor intensive. In this paper, we propose to use the multi-task convolutional neural network (MTCNN) model for predicting gender and age simultaneously on Chinese microblog. With MTCNN, we can effectively reduce the burden of feature engineering and explore common and unique representations for both tasks. Experimental results show that our method can significantly outperform the state-of-the-art baselines.


Ant colonies for the traveling salesman problem


title: The haptic display of complex graphical environments

abstract: Force feedback coupled with visual display allows people to interact intuitiv ely with complex virtual environments. For this synergy of haptics and graphics to flourish, however, haptic systems must be capable of modeling environments with the same richness, complexity and interactivity that can be found in existing graphic systems. To help meet this challenge, we have developed a haptic rendering system that allows for the efficient tactile display of graphical information. The system uses a common high-level framework to model contact constraints, surface shading, friction and tex ture. The multilevel control system also helps ensure that the haptic device will remain stable even as the limits of the renderer’s capabilities are reached.


title: Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction

abstract: Retinal images can be used in several applications, such as ocular fundus operations as well as human recognition. Also, they play important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. Due to the high ability of the curvelet transform in representing the edges, modification of curvelet transform coefficients to enhance the retinal image edges better prepares the image for the segmentation part. The directionality feature of the multistructure elements method makes it an effective tool in edge detection. Hence, morphology operators using multistructure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward, morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multistructure elements. A simple thresholding method along with connected components analysis (CCA) indicates the remained ridges belonging to vessels. In order to utilize CCA more efficiently, we locally applied the CCA and length filtering instead of considering the whole image. Experimental results on a known database, DRIVE, and achieving to more than 94% accuracy in about 50 s for blood vessel detection, proved that the blood vessels can be effectively detected by applying our method on the retinal images.


title: Study on the reduction of acrylamide in mixed rye bread by fermentation with bacteriocin-like inhibitory substances producing lactic acid bacteria in combination with Aspergillus niger glucoamylase

abstract: Abstract This study mainly focuses on a strategy for reducing of acrylamide in cereal products, predominantly in bakery products. The effect of mesophilic lactic acid bacteria (LAB) strains as well as a novel fermentation media on the bases of extruded rye wholemeal on the acrylamide formation in mixed rye bread of different weight of loaf was studied. The LC–MS/MS method for acrylamide determination in bread crumb has been applied. Addition of 15% low pH (pH 3.4–4.3) fermented product to bread dough, which were produced by using commercial strain Lactobacillus casei and bacteriocin-like inhibitory substances (BLIS) producing strains Lactobacillus sakei KTU05-6 , Pediococcus acidilactici KTU05-7 and Pediococcus pentosaceus KTU05-8 separately, caused significant reduction of acrylamide in mixed rye bread. All bread loafs of 1000 g contained less by 27% acrylamide concentrations versus loafs of 500 g. Acrylamide formation was affected ( r 2 = 0.7193) by total reducing sugar content in bread and slightly correlated ( r 2 = 0.5587) with reducing sugar content in sourdough. The treatment of extruded rye wholemeal with Aspergillus niger glucoamylase as compared to the control sample was found to have a positive effect on the acidification process lowering the acrylamide formation on an average by 59.4% and 40% in 500 g and 1000 g loafs of bread, respectively. This study demonstrates that acrylamide content could be reduced by using LAB excreting lower amylolytic activity in the medium, while the higher proteolytic activity is preferred.


title: Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies

abstract: Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.


title: A Hierarchy with, of, and for Preposition Supersenses

abstract: English prepositions are extremely frequent and extraordinarily polysemous. In some usages they contribute information about spatial, temporal, or causal roles/relations; in other cases they are institutionalized, somewhat arbitrarily, as case markers licensed by a particular governing verb, verb class, or syntactic construction. To facilitate automatic disambiguation, we propose a general-purpose, broadcoverage taxonomy of preposition functions that we call supersenses: these are coarse and unlexicalized so as to be tractable for efficient manual annotation, yet capture crucial semantic distinctions. Our resource, including extensive documentation of the supersenses, many example sentences, and mappings to other lexical resources, will be publicly released. Prepositions are perhaps the most beguiling yet pervasive lexicosyntactic class in English. They are everywhere; their functional versatility is dizzying and largely idiosyncratic (1). They are nearly invisible, yet indispensable for situating the where, when, why, and how of events. In a way, prepositions are the bastard children of lexicon and grammar, rising to the occasion almost whenever a noun-noun or verbnoun relation is needed and neither subject nor object is appropriate. Consider the many uses of the word to, just a few of which are illustrated in (1):1 (1) a. My cake is to die for. b. If you want I can treat you to some. c. How about this: you go to the store d. to buy ingredients. e. Then if you give the recipe to me f. I’m happy to make the batter g. and put it in the oven for 30 to 40 minutes h. so you’ll arrive to the sweet smell of chocolate. i. That sounds good to me. j. That’s all there is to it. 1Though infinitival to is traditionally not considered a preposition, we allow it to be labeled with a supersense if the infinitival clause serves as a PURPOSE (as in (1d)) or FUNCTION. See §2. Sometimes a preposition specifies a relationship between two entities or quantities, as in (1g). In other scenarios it serves a case-marking sort of function, marking a complement or adjunct—principally to a verb (1b–1e, 1h, 1i), but also to an argument-taking noun or adjective (1f). Further, it is not always possible to separate the semantic contribution of the preposition from that of other words in the sentence. As amply demonstrated in the literature, prepositions play a key role in multiword expressions (Baldwin and Kim, 2010), as in (1a, 1b, 1j). An adequate descriptive annotation scheme for prepositions must deal with these messy facts. Following a brief discussion of existing approaches to preposition semantics (§1), this paper offers a new approach to characterizing their functions at a coarsegrained level. Our scheme is intended to apply to almost all preposition tokens, though some are excluded on the grounds that they belong to a larger multiword expression or are purely syntactic (§2). The rest of the paper is devoted to our coarse semantic categories, supersenses (§3).2 Many of these categories are based on previous proposals—primarily, Srikumar and Roth (2013a) (so-called preposition relations) and VerbNet (thematic roles; Bonial et al., 2011; Hwang, 2014, appendix C)—but we organize them into a hierarchy and motivate a number of new or altered categories that make the scheme more robust. Because prepositions are so frequent, so polysemous, and so crucial in establishing relations, we believe that a wide variety of NLP applications (including knowledge base construction, reasoning about events, summarization, paraphrasing, and translation) stand to benefit from automatic disambiguation of preposition supersenses. 2Supersense inventories have also been described for nouns and verbs (Ciaramita and Altun, 2006; Schneider et al., 2012; Schneider and Smith, 2015) and adjectives (Tsvetkov et al., 2014). Other inventories characterize semantic functions expressed via morphosyntax: e.g., tense/aspect (Reichart and Rappoport, 2010), definiteness (Bhatia et al., 2014, also hierarchical). A wiki documenting our scheme in detail can be accessed at http://tiny.cc/prepwiki. It maps finegrained preposition senses to our supersenses, along with numerous examples. The wiki is conducive to browsing and to exporting the structure and examples for use elsewhere (e.g., in an annotation tool). From our experience with pilot annotations, we believe that the scheme is fairly stable and broadly applicable.


title: Digital signatures for flows and multicasts

abstract: We present chaining techniques for signing/verifying multiple packets using a single signing/verification operation. We then present flow signing and verification procedures based upon a tree chaining technique. Since a single signing/verification operation is amortized over many packets, these procedures improve signing and verification rates by one to two orders of magnitude compared to the approach of signing/verifying packets individually. Our procedures do not depend upon reliable delivery of packets, provide delay-bounded signing, and are thus suitable for delay-sensitive flows and multicast applications. To further improve our procedures, we propose several extensions to the Feige-Fiat-Shamir digital signature scheme to speed up both the signing and verification operations, as well as to allow "adjustable and incremental" verification. The extended scheme, called eFFS, is compared to four other digital signature schemes (RSA, DSA, ElGamal, Rabin). We compare their signing and verification times, as well as key and signature sizes. We observe that (i) the signing and verification operations of eFFS are highly efficient compared to the other schemes, (ii) eFFS allows a tradeoff between memory and signing/verification time, and (iii) eFFS allows adjustable and incremental verification by receivers.


title: Collaborative workflow for crowdsourcing translation

abstract: In this paper we explore the challenges in crowdsourcing the task of translation over the web in which remotely located translators work on providing translations independent of each other. We then propose a collaborative workflow for crowdsourcing translation to address some of these challenges. In our pipeline model, the translators are working in phases where output from earlier phases can be enhanced in the subsequent phases. We also highlight some of the novel contributions of the pipeline model like assistive translation and translation synthesis that can leverage monolingual and bilingual speakers alike. We evaluate our approach by eliciting translations for both a minority-to-majority language pair and a minority-to-minority language pair. We observe that in both scenarios, our workflow produces better quality translations in a cost-effective manner, when compared to the traditional crowdsourcing workflow.


Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data


title: A new practical guide to the Luria-Delbrück protocol.

abstract: Since 2000 several review papers have been published about the analysis of experimental data obtained using the Luria-Delbrück protocol. These timely papers cleared much of the confusion surrounding various methods for estimating or comparing mutation rates. As a result, today the fluctuation test is more widely applied with much improved accuracy. The present paper provides guidelines on a few remaining problems that continue to baffle mutation researchers. Among the issues addressed are incomplete plating, relative fitness, and comparison of experiments where average final cell population sizes differ. It also offers a fresh view on the estimation methods that are based on the sample median.


title: Active learning with statistical models

abstract: For many types of machine learning algorithms, one can compute the statistically "optimal" way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.


title: Procedural Content Generation: Goals, Challenges and Actionable Steps

abstract: This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multicontent PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately.


title: What do architects and students perceive in their design sketches? A protocol analysis

abstract: The present research aims at examining what information architects think of and read off from their own freehand sketches, and at revealing how they perceptually interact with and benefit from sketches. We explored this in a protocol analysis of retrospective reports; each participant worked on an architectural design task while drawing freehand sketches and later reported what she/he had been thinking of during the design task. This research lies within the scope of examinations of why freehand sketches as external representation are essential for crystallizing design ideas in early design processes.


title: Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors

abstract: Medical error reduction is an international issue, as is the implementation of patient care information systems (PCISs) as a potential means to achieving it. As researchers conducting separate studies in the United States, The Netherlands, and Australia, using similar qualitative methods to investigate implementing PCISs, the authors have encountered many instances in which PCIS applications seem to foster errors rather than reduce their likelihood. The authors describe the kinds of silent errors they have witnessed and, from their different social science perspectives (information science, sociology, and cognitive science), they interpret the nature of these errors. The errors fall into two main categories: those in the process of entering and retrieving information, and those in the communication and coordination process that the PCIS is supposed to support. The authors believe that with a heightened awareness of these issues, informaticians can educate, design systems, implement, and conduct research in such a way that they might be able to avoid the unintended consequences of these subtle silent errors.


Pirates! Using the physical world as a game board


title: On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech Recognition

abstract: DNN-based speech recognition greatly benefits from spectro-temporal Gabor features.Gabor filters with high temporal modulation encode the most relevant information.A measure of phoneme similarity is proposed to quantify class separability.This metric is used to explain the improved results on phoneme level. Display Omitted Previous studies support the idea of merging auditory-based Gabor features with deep learning architectures to achieve robust automatic speech recognition, however, the cause behind the gain of such combination is still unknown. We believe these representations provide the deep learning decoder with more discriminable cues. Our aim with this paper is to validate this hypothesis by performing experiments with three different recognition tasks (Aurora4, CHiME2 and CHiME3) and assess the discriminability of the information encoded by Gabor filterbank features. Additionally, to identify the contribution of low, medium and high temporal modulation frequencies subsets of the Gabor filterbank were used as features (dubbed LTM, MTM and HTM, respectively). With temporal modulation frequencies between 16 and 25Hz, HTM consistently outperformed the remaining ones in every condition, highlighting the robustness of these representations against channel distortions, low signal-to-noise ratios and acoustically challenging real-life scenarios with relative improvements from 11 to 56% against a Mel-filterbank-DNN baseline. To explain the results, a measure of similarity between phoneme classes from DNN activations is proposed and linked to their acoustic properties. We find this measure to be consistent with the observed error rates and highlight specific differences on phoneme level to pinpoint the benefit of the proposed features.


title: Mechanical design of the Wheel-Leg hybrid mobile robot to realize a large wheel diameter

abstract: In this paper, a new category of the wheel-leg hybrid robot is presented. The proposed mechanism can compose large wheel diameter compared with the previous hybrid robot to realize a greater ability to climb obstacles. A prototype model of one Wheel-Leg module of the proposed robot mechanism has been developed to illustrate the concept. Actual design and mode changing experiment with a test mechanical module is also presented. Basic movement tests and a test of the basic properties of the rotational fingertip are also shown. The Basic configurations of wheel-leg retractable is considered well. The integrated mode is also described.


title: Credit Cycles and Macro Fundamentals*

abstract: We study the relation between the credit cycle and macro economic fundamentals in an intensity based framework. Using rating transition and default data of U.S. corporates from Standard and Poor's over the period 1980-2005 we directly estimate the credit cycle from the micro rating data. We relate this cycle to the business cycle, bank lending conditions, and financial market variables. In line with earlier studies, these variables appear to explain part of the credit cycle. As our main contribution, we test for the correct dynamic specification of these models. In all cases, the hypothesis of correct dynamic specification is strongly rejected. Moreover, accounting for dynamic mis-specification, many of the variables thought to explain the credit cycle, turn out to be insignificant. The main exceptions are GDP growth, and to some extent stock returns and stock return volatilities. Their economic significance appears low, however. This raises the puzzle of what macro-economic fundamentals explain default and rating dynamics


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title: The Psychotherapy Relationship: Theory, Research, and Practice.

abstract: The highly complex process of psychotherapy can be viewed as consisting of two major parts: the relationship and the technique. The relationship part is hard to define, even harder to grasp clinically, and difficult to study empirically. Although there are different schools of psychotherapy, few would disagree that a good therapeutic relationship is a necessary condition for the favorable outcome of psychotherapy. The central focus of this book is, in the authors' words, “what that relationship consists of, how it develops during treatment, how it varies for different systems of psychotherapy, how it operates in and affects treatment.” As they have acknowledged, the book is an extension of the theoretical formulations on psychotherapy relationship originally developed by Gelso and Carter.1,2 The focus is on individual psychotherapy, although the discussions and the propositions can be generalizable to other formats like group or family work. ::: ::: In today's world of evidenced-based treatments and, in the absence of evidence, expert opinions, the authors have done a fairly good job of combining theory and practice with research. They start by defining a successful psychotherapy relationship and discuss how such a relationship is defined in four major schools of psychotherapy: psychoanalytic, cognitive-behavioral, humanistic-existential, and feminist. In each of these sections, the authors draw upon both the literature and their own clinical experience. ::: ::: The first half of the book is focused on the components of the psychotherapy relationship: 1) the working alliance, 2) the “unrealistic” relationship in psychotherapy (transference and countertransference), and 3) the “real” relationship. The authors see all psychotherapy relationships as consisting of these three components. After introducing the three components at length, the authors examine and explore how each of them operates in different types and schools of psychotherapy. They further discuss the interaction of these components with each other and with therapists' techniques as the therapy progresses. Throughout, they discuss how the propositions are informed by theory, practice, and research. ::: ::: This first section prepares the reader to delve into the second half of the book, in which the authors discuss and analyze the conceptualizations of psychotherapy relationship in the four major theoretical clusters. With each, they approach this on four dimensions: 1) whether the relationship is seen as central or otherwise (“as an end in itself or as a means to an end”); 2) whether the relationship is seen as curative in itself; 3) the relative emphases on “real” relationship versus transference and countertransference; and 4) the therapist's conceptualization and use of power in the relationship. ::: ::: A particular strength lies in the effort to bring in findings from research, when it exists. In the first section of the book the authors review the available empirical research findings on the definition of psychotherapy relationship, the importance of working alliance to treatment outcome, and the area of transference configuration. Along with the evidence, they present their own views, developed from their practice and supervision of psychotherapy, and provide case material to clarify certain points. This dual emphasis on science and practice is, I think, the real strength of this book. ::: ::: An area where Gelso and Hayes's book seems to differ from most others on psychotherapy is in the authors' presentation of feminist therapy as one of the four main clusters of psychotherapy. They do raise the question of whether feminist therapy is a unique theoretical approach to clinical work or a system that possesses tenets of its own but also shares attributes with other approaches and hence can be combined with them. After discussing related aspects, the authors conclude that feminist therapy “constitutes a singular approach to therapy that nonetheless may be integrated with other distinct theoretical systems.” This conclusion seems less than strongly supportive of their including feminist therapy as one of the four major clusters of psychotherapy. It also appears to be an all-accommodating conclusion, which is, perhaps, more reflective of the field than of one particular book or opinion. ::: ::: Overall, the book seems theoretically balanced and fair; there is no apparent leaning toward one cluster of psychotherapy or antagonism toward others. The authors' expressed hope in writing this book was “to benefit both science and practice in the field of psychotherapy.” I think they certainly have done that.


title: Towards the Implementation of IoT for Environmental Condition Monitoring in Homes

abstract: In this paper, we have reported an effective implementation for Internet of Things used for monitoring regular domestic conditions by means of low cost ubiquitous sensing system. The description about the integrated network architecture and the interconnecting mechanisms for the reliable measurement of parameters by smart sensors and transmission of data via internet is being presented. The longitudinal learning system was able to provide a self-control mechanism for better operation of the devices in monitoring stage. The framework of the monitoring system is based on a combination of pervasive distributed sensing units, information system for data aggregation, and reasoning and context awareness. Results are encouraging as the reliability of sensing information transmission through the proposed integrated network architecture is 97%. The prototype was tested to generate real-time graphical information rather than a test bed scenario.


title: Sensorless and constraint based peg-in-hole task execution with a dual-arm robot

abstract: Fast and sensorless peg-in-hole insertion is a challenging task for a robotic manipulator. In order to deal with the peg-in-hole insertion problem without any need of an external force/torque sensor, this paper proposes to actively accomplish compliance in the insertion task relying on an admittance based control. This is combined with a real-time trajectory generator, by means of constraint based optimization, where a model-based sensorless observer of interaction forces is exploited. Experiments have been performed on an ABB dual-arm 7-DOF lightweight prototype robot to validate the proposed approach, with an insertion speed comparable to human manual execution and in presence of geometric uncertainty.


title: Submitted to Physical Review E Information-theoretic characterization of quantum chaos

abstract: Hypersensitivity to perturbation is a criterion for chaos based on the question of how much information about a perturbing environment is needed to keep the entropy of a Hamiltonian system from increasing. We demonstrate numerically that hypersensitivity to perturbation is present in the following quantum maps: the quantum kicked top, the quantum baker's map, the quantum lazy baker's map, and the quantum sawtooth and cat maps.


title: Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection

abstract: We present a matrix factorization model inspired by challenges we encountered while working on the Xbox movies recommendation system. The item catalog in a recommender system is typically equipped with meta-data features in the form of labels. However, only part of these features are informative or useful with regard to collaborative filtering. By incorporating a novel sparsity prior on feature parameters, the model automatically discerns and utilizes informative features while simultaneously pruning non-informative features. The model is designed for binary feedback, which is common in many real-world systems where numeric rating data is scarce or non-existent. However, the overall framework is applicable to any likelihood function. Model parameters are estimated with a Variational Bayes inference algorithm, which is robust to over-fitting and does not require cross-validation and fine tuning of regularization coefficients. The efficacy of our method is illustrated on a sample from the Xbox movies dataset as well as on the publicly available MovieLens dataset. In both cases, the proposed solution provides superior predictive accuracy, especially for long-tail items. We then demonstrate the feature selection capabilities and compare against the common case of simple Gaussian priors. Finally, we show that even without features, our model performs better than a baseline model trained with the popular stochastic gradient descent approach.


title: A fast string-searching algorithm for multiple patterns

abstract: Abstract The string-searching problem is to find all occurrences of pattern(s) in a text string. The Aho-Corasick string searching algorithm simultaneously finds all occurrences of multiple patterns in one pass through the text. On the other hand, the Boyer-Moore algorithm is understood to be the fastest algorithm for a single pattern. By combining the ideas of these two algorithms, we present an efficient string searching algorithm for multiple patterns. The algorithm runs in sublinear time, on the average, as the BM algorithm achieves, and its preprocessing time is linear proportional to the sum of the lengths of the patterns like the AC algorithm.


title: Improved SVPWM schemes for vienna rectifiers without current distortion

abstract: Vienna rectifier is one of the most popular topologies for three-phase PFC converter. Many control schemes were presented for Vienna rectifiers with unity power factor. Because of the small phase shift θ caused by the inductor or current detection delay, a zero-crossing detection error occurs, resulting in zero-crossing current spikes. This paper presents two SVPWM methods for three phase Vienna rectifiers without current distortion. Due to the current commutation characteristics in Vienna rectifiers, the available space voltage vectors are limited. When Vienna rectifiers work with the small phase shift 0, the limitation of the available space voltage vectors is changed. The proposed schemes choose suitable vectors according to the polarity of the input current, so that the Vienna rectifiers can work normally without current distortion. The feasibility of the proposed schemes is verified by simulation and experiment.


title: An Efficient Approach to Detect Concept Drifts in Data Streams

abstract: Due to the presence of data streams in many applications like banking, sensor networks, and telecommunication, data stream mining has gained increased attention. Data stream is continuous, ordered sequence of data instances arriving at a rapid rate. One of the key challenges while learning from data streams is the detection of concept drift, i.e., changes in data distribution underlying data streams, observed over time. Drifts being either gradual or sudden, several algorithms have been put forward for detection of different kinds of drift. However, most of them work on only one of these kinds of drift. These algorithms show hampered output if different types of drift are mixed. To solve this issue, there is need of a single system that can handle all drifts simultaneously. In this paper, we propose a system that detects both kinds of drift efficiently. Our system combines features of the online classifier as well as a blockbased classifier to achieve the goal. We further analyzed drifts to find out missing values of attributes to be the root cause. Our system handles missing values in different ways for more improved performance.


title: Total quality management and sustainable competitive advantage

abstract: Abstract Although it is generally accepted that Total Quality Management (TQM) can generate a sustainable competitive advantage, there is, surprisingly, little or no theory to underpin that belief. Therefore, the primary purpose of this paper is to explore the validity of the claim. By drawing on the market-based theory of competitive advantage, resource-based theory of the firm, and systems theory, we are able to conclude that the belief is warranted. We deduce that the content of TQM is capable of producing a cost- or differentiation-based advantage, and that the tacitness and complexity that are inherent in the process of TQM have the potential to generate the barriers to imitation that are necessary for sustainability.


title: A Two-Dimensional Model for the Study of Interpersonal Attraction

abstract: We describe a model for understanding interpersonal attraction in which attraction can be understood as a product of the initial evaluations we make about others. The model posits that targets are evaluated on two basic dimensions, capacity and willingness, such that affective and behavioral attraction result from evaluations of (a) a target’s capacity to facilitate the perceiver’s goals/needs and (b) a target’s potential willingness to facilitate those goals/needs. The plausibility of the two-dimensional model of attraction is evaluated vis-a-vis the extant literature on various attraction phenomena including the reciprocity of liking effect, pratfall effect, matching hypothesis, arousal effects, and similarity effect. We conclude that considerable evidence across a wide range of phenomena supports the idea that interpersonal attraction is principally determined by inferences about the target’s capacity and willingness.


title: Simple Modular Equalization System with Reduced Switch Count and Constant Current Characteristics for Series-Connected EDLC Modules

abstract: This paper proposes a modular equalization system integrating module- and cell-level voltage equalizers for energy storage systems consisting of series-connected multiple energy storage modules, each comprising multiple cells connected in series. Phase-shift switched capacitor converters (PS-SCCs) and tapped-inductor-based resonant voltage multiplier (TI-RVM) are employed as module- and cell-level equalizers, respectively. Since two kinds of equalizers are integrated with sharing active switches, the proposed system can halve the switch count compared to conventional modular systems, hence achieving the reduced circuit complexity and cost. In addition, equalization currents of both the PS-SCC and TI-RVM can be constant, regardless of voltage imbalance conditions, offering intrinsic current protection capability at both the module and cell levels. An experimental equalization test was performed using the prototype for three electric double-layer capacitor (EDLC) modules, each containing six cells connected in series. Module and cell voltages were equalized at different rates, demonstrating the equalization performance.