title: EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

abstract: Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.


title: Multi-Level Intrusion Detection System (ML-IDS)

abstract: As the deployment of network-centric systems increases, network attacks are proportionally increasing in intensity as well as complexity. Attack detection techniques can be broadly classified as being signature-based, classification-based, or anomaly-based. In this paper we present a multi level intrusion detection system (ML-IDS) that uses autonomic computing to automate the control and management of ML-IDS. This automation allows ML-IDS to detect network attacks and proactively protect against them. ML-IDS inspects and analyzes network traffic using three levels of granularities (traffic flow, packet header, and payload), and employs an efficient fusion decision algorithm to improve the overall detection rate and minimize the occurrence of false alarms. We have individually evaluated each of our approaches against a wide range of network attacks, and then compared the results of these approaches with the results of the combined decision fusion algorithm.


title: A compressed domain change detection algorithm for RTP streams in video surveillance applications

abstract: This paper presents a novel change detection algorithm for the compressed domain. Many video surveillance systems in practical use transmit their video data over a network by using the Real-time Transport Protocol (RTP). Therefore, the presented algorithm concentrates on analyzing RTP streams to detect major changes within contained video content. The paper focuses on a reliable preselection for further analysis modules by decreasing the number of events to be investigated. The algorithm is designed to work on scenes with mainly static background, like in indoor video surveillance streams. The extracted stream elements are RTP timestamps and RTP packet sizes. Both values are directly accessible by efficient byte-reading operations without any further decoding of the video content. Hence, the proposed approach is codec-independent, while at the same time its very low complexity enables the use in extensive video surveillance systems. About 40,000 frames per second of a single RTP stream can be processed on an Intel® CoreTM 2 Duo CPU at 2 GHz and 2 GB RAM, without decreasing the efficiency of the algorithm.


title: The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation

abstract: BackgroundThere is increasing international interest in the concept of mental well-being and its contribution to all aspects of human life. Demand for instruments to monitor mental well-being at a population level and evaluate mental health promotion initiatives is growing. This article describes the development and validation of a new scale, comprised only of positively worded items relating to different aspects of positive mental health: the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS).MethodsWEMWBS was developed by an expert panel drawing on current academic literature, qualitative research with focus groups, and psychometric testing of an existing scale. It was validated on a student and representative population sample. Content validity was assessed by reviewing the frequency of complete responses and the distribution of responses to each item. Confirmatory factor analysis was used to test the hypothesis that the scale measured a single construct. Internal consistency was assessed using Cronbach's alpha. Criterion validity was explored in terms of correlations between WEMWBS and other scales and by testing whether the scale discriminated between population groups in line with pre-specified hypotheses. Test-retest reliability was assessed at one week using intra-class correlation coefficients. Susceptibility to bias was measured using the Balanced Inventory of Desired Responding.ResultsWEMWBS showed good content validity. Confirmatory factor analysis supported the single factor hypothesis. A Cronbach's alpha score of 0.89 (student sample) and 0.91 (population sample) suggests some item redundancy in the scale. WEMWBS showed high correlations with other mental health and well-being scales and lower correlations with scales measuring overall health. Its distribution was near normal and the scale did not show ceiling effects in a population sample. It discriminated between population groups in a way that is largely consistent with the results of other population surveys. Test-retest reliability at one week was high (0.83). Social desirability bias was lower or similar to that of other comparable scales.ConclusionWEMWBS is a measure of mental well-being focusing entirely on positive aspects of mental health. As a short and psychometrically robust scale, with no ceiling effects in a population sample, it offers promise as a tool for monitoring mental well-being at a population level. Whilst WEMWBS should appeal to those evaluating mental health promotion initiatives, it is important that the scale's sensitivity to change is established before it is recommended in this context.


title: The optimal design of GaN-based Dual Active Bridge for bi-directional Plug-IN Hybrid Electric Vehicle (PHEV) charger

abstract: A high frequency, high efficiency bi-directional battery charger for Plug-in Hybrid Electric Vehicle (PHEV) is built with high voltage normally-off GaN-on-Si HFETs. This paper characterized the multi-chip-model both statically and dynamically. The optimal design of the isolated 500 kHz Dual Active Bridge DC/DC stage is detailed, taking account the wide battery voltage range and sinusoidal charging, to eliminate large DC link capacitor. Experimentally result shows a 500 kHz DAB converter with discrete inductor and transformer can achieved 97.2% efficiency at 1kW and 96.4% efficiency at 2.4 kW. By integrating the inductor into the transformer, 98.2% efficiency is achieved at 1 kW.


title: Phase statistics of interferograms with applications to synthetic aperture radar

abstract: Interferometric methods are well established in optics and radio astronomy. In recent years, interferometric concepts have been applied successfully to synthetic aperture radar (SAR) and have opened up new possibilities in the area of earth remote sensing. However interferometric SAR applications require thorough phase control through the imaging process. The phase accuracy of SAR images is affected by decorrelation effects between the individual surveys. We analyze quantitatively the influence of decorrelation on the phase statistics of SAR interferograms. In particular, phase aberrations as they occur in typical SAR processors are studied in detail. The dependence of the resulting phase bias and variance on processor parameters is presented in several diagrams.


title: Influencing factors on ERP system selection

abstract: ERP(Enterprise Resource Planning) system are implemented as a total business solution that gain significant advantages, including dramatic increases in productivity, on-time shipments and sales, as well as decreases in lead times, purchase costs, quality problems, and inventories. But many ERP system implementations are not success stories. Many failed REP system implementations may be attributed to the selection of an inappropriate ERP system. In this paper, we will introduce several influencing factors to help manufactory managers solve the ERP system selection problem.


title: Smart Grid DNP3 Vulnerability Analysis and Experimentation

abstract: This paper highlights different security threats and vulnerabilities that is being challenged in smart-grid utilizing Distributed Network Protocol (DNP3) as a real time communication protocol. Experimentally, we will demonstrate two scenarios of attacks, unsolicited message attack and data set injection. The experiments were run on a computer virtual environment and then simulated in DETER testbed platform. The use of intrusion detection system will be necessary to identify attackers targeting different part of the smart grid infrastructure. Therefore, mitigation techniques will be used to ensure a healthy check of the network and we will propose the use of host-based intrusion detection agent at each Intelligent Electronic Device (IED) for the purpose of detecting the intrusion and mitigating it. Performing attacks, attack detection, prevention and counter measures will be our primary goal to achieve in this research paper.


title: Swearing: A Biopsychosocial Perspective

abstract: Swearing, also known as cursing, can be best described as a form of linguistic activity utilizing taboo words to convey the expression of strong emotions. Although swearing and cursing are frequently occurring behaviors, the actual functions of swearing remain largely unknown. Since swearing typically includes taboo words, these words can be more powerful than non-swear words. Therefore, people who swear are often judged negatively, because the uttered swearwords can shock and disturb others, though the comments of others are strongly dependent on contextual factors. In this review, we provide an insight into the current state of the literature with respect to the interpersonal functions of swearing. In addition, we briefly discuss neurological, psychosocial and contextual factors that may contribute to person’s swearing behavior. Swearing is hypothesized to produce a catharsis-effect, which results in a relief of stress or pain. Swearing also influences the perceived credibility, intensity, and persuasiveness of the swearer. Additionally, swearing can have a variety of interpersonal consequences, including promoting group bonding and solidarity, inhibiting aggression, eliciting humor, and causing emotional pain to others. This paper further presents a hypothetical model of swearing that draws from basic emotion research in an attempt to provide a scaffolding for future research.


SANE : Scalable Attribute-aware Network Embedding


title: Performance on tests of central auditory processing by individuals exposed to high-intensity blasts.

abstract: Thirty-six blast-exposed patients and twenty-nine non-blast-exposed control subjects were tested on a battery of behavioral and electrophysiological tests that have been shown to be sensitive to central auditory processing deficits. Abnormal performance among the blast-exposed patients was assessed with reference to normative values established as the mean performance on each test by the control subjects plus or minus two standard deviations. Blast-exposed patients performed abnormally at rates significantly above that which would occur by chance on three of the behavioral tests of central auditory processing: the Gaps-In-Noise, Masking Level Difference, and Staggered Spondaic Words tests. The proportion of blast-exposed patients performing abnormally on a speech-in-noise test (Quick Speech-In-Noise) was also significantly above that expected by chance. These results suggest that, for some patients, blast exposure may lead to difficulties with hearing in complex auditory environments, even when peripheral hearing sensitivity is near normal limits.


title: How do people manage their digital photographs?

abstract: In this paper we present and discuss the findings of a study that investigated how people manage their collections of digital photographs. The six-month, 13-participant study included interviews, questionnaires, and analysis of usage statistics gathered from an instrumented digital photograph management tool called Shoebox. Alongside simple browsing features such as folders, thumbnails and timelines, Shoebox has some advanced multimedia features: content-based image retrieval and speech recognition applied to voice annotations. Our results suggest that participants found their digital photos much easier to manage than their non-digital ones, but that this advantage was almost entirely due to the simple browsing features. The advanced features were not used very often and their perceived utility was low. These results should help to inform the design of improved tools for managing personal digital photographs.


Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction


The Cambrian faunas of north-eastern Australia. Part 3, The polymerid trilobites, with supplement no. 1


title: Harmonic algorithm for 3-dimensional strip packing problem

abstract: In the three dimensional strip packing problem, we are given a set of three-dimensional rectangular items I = {(x i , y i , z i ) : i = 1, ..., n} and a three dimensional box B. The goal is to pack all the items in the box B without any overlap, such that the height of the packing is minimized. We consider the most basic version of the problem, where the items must be packed with their edges parallel to the edges of B and cannot be rotated. Building upon Caprara's work [4] for the two dimensional bin packing problem we obtain an approximation algorithm with a similar performance guarantee of T ∞ a 1.69 where T ∞ is the well known Harmonic number that occurs naturally in the context of bin packing. The previously known approximation algorithms for this problem had worst case performance guarantees of 2 [7], 2.64 [14], 2.67 [15], 2.89 [10] and 3.25 [11]. Our second algorithm is an asymptotic PTAS for the case in which all items have square bases.


` Printed in Nigeria PERSONALITY IN ENGLISH LANGUAGE PROFICIENCY AND ACHIEVEMENT


title: Crossed Dipole Antennas: A review.

abstract: Crossed dipole antennas have been widely developed for current and future wireless communication systems. They can generate isotropic, omnidirectional, dual-polarized (DP), and circularly polarized (CP) radiation. Moreover, by incorporating a variety of primary radiation elements, they are suitable for single-band, multiband, and wideband operations. This article presents a review of the designs, characteristics, and applications of crossed dipole antennas along with the recent developments of single-feed CP configurations. The considerations of profile miniaturization, radiation pattern control, bandwidth enhancement, and multiband operation are emphasized.


title: Interrater Reliability of APACHE II Scores for Medical-Surgical Intensive Care Patients: A Prospective Blinded Study

abstract: BACKGROUND ::: Despite widespread use of the Acute Physiology and Chronic Health Evaluation II (APACHE II), its interrater reliability has not been well studied. ::: ::: ::: OBJECTIVE ::: To determine interrater reliability of APACHE II scores among 1 intensive care nurse and 2 research clerks. ::: ::: ::: METHODS ::: In a prospective, blinded, observational study, 3 raters collected APACHE II scores on 37 consecutive patients in a medical-surgical intensive care unit. One research clerk was blinded to the study's start date to minimize observer bias. The nurse and the other research clerk were blinded to each other's scores and did not communicate with the first research clerk about the study. The data analyst was blinded to the identity and source of all 3 raters' scores. Intraclass correlation coefficients and 95% confidence intervals were assessed. ::: ::: ::: RESULTS ::: Mean (standard deviation) APACHE II scores were 21.8 (9.2) for the nurse, 20.4 (7.7) for research clerk 1, and 20.5 (8.1) for research clerk 2. Among the 3 raters, the intraclass correlation coefficient (95% confidence interval) was 0.90 (0.84, 0.94) for the APACHE II total score. Within APACHE II score components, the highest reliability was for age (0.98 [0.97, 0.99]), with lower reliabilities for the Chronic Health Index (0.64 [0.50, 0.80]) and the verbal component of the Glasgow Coma Scale (0.40 [0.20, 0.60]). Results were similar between pairs of raters. ::: ::: ::: CONCLUSIONS ::: Use of trained nonmedical personnel to collect illness severity scores for clinical, research, and administrative purposes is reasonable. This method could be used to assess reliability of other illness severity scores.


title: Sensorless speed/position control of brushed DC motor

abstract: This paper presents a new and novel technique for speed control of a brushed DC motor without employing any direct shaft transducer in its feedback mechanism. This method uses the current/voltage variation produced on the motor main input power lines as the brushes move from collector blade to the next. The frequency of variation is proportional to the motor speed and the rotor position can also be detected by monitoring the changes for each collector blade. The performance of the motor torque speed characteristic using this system as its feedback has been evaluated through laboratory testing. This configuration is very suitable for harsh environment and places with not enough space for the motor with discrete sensors.


title: ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

abstract: Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Therefore, rigid body simulators and recently their differentiable variants are studied extensively. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and there-fore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects with collisions and can be seamlessly incorporated into soft robotic systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of inference, control and co-design tasks for soft robotics.


title: A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation

abstract: Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both types of vessels simultaneously by using a unified pixel-wise loss that treats all vessel pixels with equal importance. Due to the highly imbalanced ratio between thick vessels and thin vessels (namely the majority of vessel pixels belong to thick vessels), the pixel-wise loss would be dominantly guided by thick vessels and relatively little influence comes from thin vessels, often leading to low segmentation accuracy for thin vessels. To address the imbalance problem, in this paper, we explore to segment thick vessels and thin vessels separately by proposing a three-stage deep learning model. The vessel segmentation task is divided into three stages, namely thick vessel segmentation, thin vessel segmentation, and vessel fusion. As better discriminative features could be learned for separate segmentation of thick vessels and thin vessels, this process minimizes the negative influence caused by their highly imbalanced ratio. The final vessel fusion stage refines the results by further identifying nonvessel pixels and improving the overall vessel thickness consistency. The experiments on public datasets DRIVE, STARE, and CHASE_DB1 clearly demonstrate that the proposed three-stage deep learning model outperforms the current state-of-the-art vessel segmentation methods.


title: A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning

abstract: Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrow-clearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.


title: Appliance Flexibility Analysis Considering User Behavior in Home Energy Management System Using Smart Plugs

abstract: The home energy management system (HEMS) plays an important role in enabling residential customers to participate in demand response (DR) programs autonomously via appliance-level dispatch with smart plugs, and the flexibility analysis could help the HEMS to dispatch appliances efficiently without harming user's comfort level. This paper presents a bottom-up method to obtain the flexibility of appliances. With the use of smart plugs in the HEMS, the type of the appliance attached to each smart plug is inferred with consideration of measurement uncertainty as well as user behavior. Then, the flexibility of an appliance is analyzed based on the extrapolated power consumption of the appliance as well as the owner's usage behavior. In the case study, the types of appliances recognized for individual smart plugs as well as the corresponding flexibilities are used as reference for appliance dispatching in the HEMS under various DR applications. The case study demonstrates the performance of the proposed appliance load monitoring method and the application of the proposed flexibility analysis.


title: Structural health monitoring of wind turbine blades under fatigue loads

abstract: This paper presents the results of dynamic characterization and preparation of a full-scale fatigue test of a 9 m CX-100 blade. Sensors and actuators utilized include accelerometers and piezoelectric sensors. To dynamically characterize a 9 m CX-100 blade, full scale modal analyses were completed with varying boundary conditions and blade orientations. Also, multi-scale sensing damage detection techniques were explored; high frequency active-sensing was used in identifying fatigue damage initiation, while low frequency passive-sensing was used in assessing damage progression. Ultimately, high and low frequency response functions, wave propagations, and sensor diagnostic methods were utilized to monitor and analyze the condition of the wind turbine blade under fatigue loading.


Tables as Semi-structured Knowledge for Question Answering


title: Customer Churn in Mobile Markets A Comparison of Techniques

abstract: The high increase in the number of companies competing in mature markets makes customer retention an important factor for any company to survive. Thus, many methodologies (e.g., data mining and statistics) have been proposed to analyse and study customer retention. The validity of such methods is not yet proved though. This paper tries to fill this gap by empirically comparing two techniques: Customer churn - decision tree and logistic regression models. The paper proves the superiority of decision tree technique and stresses the needs for more advanced methods to churn modelling.


title: Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers

abstract: Abstract This paper presents a new approach for the treatment of uncertainty which is based on interval-valued fuzzy-rough numbers (IVFRN). It is shown that by integrating the rough approach with the traditional fuzzy approach, the subjectivity that exists when defining the borders of fuzzy sets is eliminated. IVFRN make decision making possible using only the internal knowledge in the operative data available to the decision makers. In this way objective uncertainties are used and there is no need to rely on models of assumptions. Instead of different external parameters in the application of IVFRN, the structure of the given data is used. On this basis an original multi-criteria model was developed based on an IVFRN approach. In this multi-criteria model the traditional steps of the BWM (Best–Worst method) and MABAC (Multi-Attributive Border Approximation area Comparison) methods are modified. The model was tested and validated on a study of the optimal selection of fire fighting helicopters. Testing demonstrated that the model based on IVFRN enabled more objective expert evaluation of the criteria in comparison with traditional fuzzy and rough approaches. A sensitivity analysis of the IVFRN BWM-MABAC model was carried out by means of 57 scenarios, the results of which showed a high degree of stability. The results of the IVFRN model were validated by comparing them with the results of the fuzzy and rough extension of the MABAC, COPRAS and VIKOR models.


title: Safe Planes for Injection Rhinoplasty: A Histological Analysis of Midline Longitudinal Sections of the Asian Nose

abstract: Background ::: Dorsal nasal augmentation is an essential part of injection rhinoplasty on the Asian nose. Aesthetic physicians require detailed knowledge of the nasal anatomy to accurately and safely inject filler.


title: Consequent-Pole Toroidal-Winding Outer-Rotor Vernier Permanent-Magnet Machines

abstract: A novel consequent-pole toroidal-winding outer-rotor (CPTW) Vernier permanent-magnet (VPM) machine is proposed in this paper. Different from a regular VPM machine, the stator and rotor of the proposed machine employ the consequent-pole and toroidal-winding configurations, which can significantly reduce magnet consumption, shorten the end-winding length, and improve the torque density. It is proved by a finite-element algorithm and prototype experiments that this machine can exhibit ∼20% higher back electromotive force and electromagnetic torque than that of a regular VPM machine, whereas its magnet consumption is only ∼60% of that in a regular VPM machine.


title: A 5-Gb/s Digital Clock and Data Recovery Circuit With Reduced DCO Supply Noise Sensitivity Utilizing Coupling Network

abstract: A digital clock and data recovery (CDR) is presented, which employs a low supply sensitivity scheme for a digitally controlled oscillator (DCO). A coupling network comprising capacitors, resistors, and coupling buffers enhances the supply variation immunity of the DCO and mitigates the jitter performance degradation. A supply variation-dependent bias generator produces the corresponding bias voltage to alleviate the supply variation with minimal area and power penalty. The proposed scheme improves 29.3 ps of peak-to-peak jitter and 11.5 dB of spur level, at 6 and 5 MHz 50 mVpp sinusoidal supply noise tone, respectively. Fabricated in a 65-nm CMOS process, the proposed CDR operates at 5-Gb/s data rate with BER $ for PRBS 31 and consumes 15.4 mW. The CDR occupies an active die area of 0.075 mm2.


title: WaP: Indoor localization and tracking using WiFi-Assisted Particle filter

abstract: High accurate indoor localization and tracking of smart phones is critical to pervasive applications. Most radio-based solutions either exploit some error prone power-distance models or require some labor-intensive process of site survey to construct RSS fingerprint database. This study offers a new perspective to exploit RSS readings by their contrast relationship rather than absolute values, leading to three observations and functions called turn verifying, room distinguishing and entrance discovering. On this basis, we design WaP (WiFi-Assisted Particle filter), an indoor localization and tracking system exploiting particle filters to combine dead reckoning, RSS-based analyzing and knowledge of floor plan together. All the prerequisites of WaP are the floor plan and the coarse locations on which room the APs reside. WaP prototype is realized on off-the-shelf smartphones with limited particle number typically 400, and validated in a college building covering 1362m2. Experiment results show that WaP can achieve average localization error of 0.71m for 100 trajectories by 8 pedestrians.


title: Answer-Type Prediction for Visual Question Answering

abstract: Recently, algorithms for object recognition and related tasks have become sufficiently proficient that new vision tasks can now be pursued. In this paper, we build a system capable of answering open-ended text-based questions about images, which is known as Visual Question Answering (VQA). Our approach's key insight is that we can predict the form of the answer from the question. We formulate our solution in a Bayesian framework. When our approach is combined with a discriminative model, the combined model achieves state-of-the-art results on four benchmark datasets for open-ended VQA: DAQUAR, COCO-QA, The VQA Dataset, and Visual7W.


title: Distraction Becomes Engagement in Automated Driving

abstract: As vehicle automation proliferates, the current emphasis on preventing driver distraction needs to transition to maintaining driver availability. During automated driving, vehicle operators are likely to use brought-in devices to access entertainment and information. Do these media devices need to be controlled by the vehicle in order to manage driver attention? In a driving simulation study (N=48) investigating driver performance shortly after transitions from automated to human control, we found that participants watching videos or reading on a tablet were far less likely (6% versus 27%) to exhibit behaviors indicative of drowsiness than when overseeing the automated driving system; irrespective of the pre-driving activity, post- transition driving performance after a five-second structured handoff was not impaired. There was not a significant difference in collision avoidance reaction time or minimum headway distance between supervision and media consumption conditions, irrespective of whether messages...


title: Automatic detection of Parkinsonism using significance measures and component analysis in DaTSCAN imaging

abstract: The study of neurodegenerative diseases has been based for some time on visual and semi-quantitative analysis of medical imaging. This is the case of Parkinsonian Syndrome (PS) or Parkinsonism, which is the second most common neurodegenerative disorder, where ^1^2^3I-ioflupane (better known by its tradename, DaTSCAN) images have been of great help. Recently, new developments in machine learning methods and statistics have been applied to the analysis of medical images, yielding to a more operator-independent, objective analysis of them, and thus, setting the Computer Aided Diagnosis (CAD) paradigm. In this work, a new CAD system based on preprocessing, voxel selection, feature extraction and classification of the images is proposed. After preprocessing the images, voxels are ranked by means of their significance in class discrimination, and the first N are selected. Then, these voxels are modelled using Independent Component Analysis (ICA), obtaining a few components that represent each image, which will be used later to train a classifier. The proposed system has been tested on two databases: a 208-DaTSCAN image database from the ''Virgen de la Victoria'' Hospital in Malaga (VV), Spain and a 289-DaTSCAN image database from the Parkinson Progression Markers Initiative (PPMI). Values of accuracy up to 94.7% and 91.3% for VV and PPMI databases are achieved by the proposed system, which has proved its robustness in PS pattern detection, and significantly improves the baseline Voxels-as-Features (VAF) approach, used as an approximation of the visual analysis.


title: Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand☆

abstract: In light of a growing interest in the use of social media marketing (SMM) among luxury fashion brands, this study set out to identify attributes of SMM activities and examine the relationships among those perceived activities, value equity, relationship equity, brand equity, customer equity, and purchase intention through a structural equation model. Five constructs of perceived SSM activities of luxury fashion brands are entertainment, interaction, trendiness, customization, and word of mouth. Their effects on value equity, relationship equity, and brand equity are significantly positive. For the relationship between customer equity drivers and customer equity, brand equity has significant negative effect on customer equity while value equity and relationship equity show no significant effect. As for purchase intention, value equity and relationship equity had significant positive effects, while relationship equity had no significant influence. Finally, the relationship between purchase intention and customer equity has significance. The findings of this study can enable luxury brands to forecast the future purchasing behavior of their customers more accurately and provide a guide to managing their assets and marketing activities as well.


title: Local line directional pattern for palmprint recognition

abstract: Local binary patterns (LBP) are one of the most important image representations. However, LBPs have not been as successful as other methods in palmprint recognition. Originally, the LBP descriptor methods construct feature vectors in the image intensity space, using pixel intensity differences to encode a local representation of the image. Recently, similar feature descriptors have been proposed which operate in the gradient space instead of the image intensity space, such as local directional patterns (LDP) and local directional number patterns (LDN). In this paper, we propose a new feature input space and define an LBP-like descriptor that operates in the local line-geometry space, thus proposing a new image descriptor, local line directional patterns (LLDP). Moreover, the purpose of this work is to show that different implementations of LLDP descriptors perform competitively in palmprint recognition. We evaluate variations to LLDPs, e.g., the modified finite radon transform (MFRAT) and the real part of Gabor filters are exploited to extract robust directional palmprint features. As is well-known, palm lines are the essential features of a palmprint. We are able to show that the proposed LLDP descriptors are suitable for robust palmprint recognition. Finally, we present a thorough performance comparison among different LBP-like and LLDP image descriptors. Based on experimental results, the proposed feature encoding of LLDPs using directional indexing can achieve better recognition performance than that of bit strings in the Gabor-based implementation of LLDPs. We used four databases for performance comparisons: the Hong Kong Polytechnic University Palmprint Database II, the blue band of the Hong Kong Polytechnic University Multispectral Palmprint Database, the Cross-Sensor palmprint database, and the IIT Delhi touchless palmprint database. Overall, LLDP descriptors achieve a performance that is competitive or better than other LBP descriptors. We propose LLDP descriptor, which uses the line feature to calculate the code.We show that line direction index number is better than bit strings for coding.LLDP achieves the best recognition performance among all LBP-structure descriptors.LLDP achieves promising recognition performance on four palmprint databases.


title: A rotary magnetorheological fluid damper for pathological tremor suppression

abstract: This study explores the development of a new configuration of rotary magnetorheological fluid damper (MR damper) for pathological tremor suppression. In order to be attached to portable rehabilitation robot developed for practical application in robot assisted therapy, the proposed MR damper aims for design of compact size, light weight and enough output torque (about 4.5 Nm). An approach to form serpentine magnetic path by employing an offset electromagnetic coil and an asymmetric rotor is utilized to obtain a large radius and high area utilization rate of the magnetorheological fluid layer (thickness<1 mm) which is exposed to external magnetic field. Electromagnetic analytical methods and FEM numerical simulation are conducted for dimensioning and optimization. Design methods for compact size and light weight are proposed and could be viewed as an effective way to expand the application fields of MR damper. Finally, the output torque characteristic of the damper is evaluated and the tremor suppression experiment is conducted to verify the proposed design. The experiment results prove the effectiveness of MR damper in pathological tremor suppression. Compared with an existed MR damper (axial length 90 mm) for pathological tremor suppression, our 58 mm shorter prototype MR damper can generate 2 times more torque and weigh 40% lighter.


title: There exists a neural network that does not make avoidable mistakes

abstract: The authors show that a multiple-input, single-output, single-hidden-layer feedforward network with (known) hardwired connections from input to hidden layer, monotone squashing at the hidden layer and no squashing at the output embeds as a special case a so-called Fourier network, which yields a Fourier series approximation properties of Fourier series representations. In particular, approximation to any desired accuracy of any square integrable function can be achieved by such a network, using sufficiently many hidden units. In this sense, such networks do not make avoidable mistakes. >


title: A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

abstract: In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating in a unifying discussion, and a forward-looking conclusion. An extensive survey of human-robot interactive communication is provided.Verbal as well as non-verbal aspects of human-robot interaction are covered.The paper starts with a historical introduction.Ten special desiderata that human-robot systems should fulfil are proposed.The desiderata are examined in detail, culminating to a forward-looking discussion.


title: AoT: Authentication and Access Control for the Entire IoT Device Life-Cycle

abstract: The consumer electronics industry is witnessing a surge in Internet of Things (IoT) devices, ranging from mundane artifacts to complex biosensors connected across disparate networks. As the demand for IoT devices grows, the need for stronger authentication and access control mechanisms is greater than ever. Legacy authentication and access control mechanisms do not meet the growing needs of IoT. In particular, there is a dire need for a holistic authentication mechanism throughout the IoT device life-cycle, namely from the manufacturing to the retirement of the device. As a plausible solution, we present Authentication of Things (AoT), a suite of protocols that incorporate authentication and access control during the entire IoT device life span. Primarily, AoT relies on Identity- and Attribute-Based Cryptography to cryptographically enforce Attribute-Based Access Control (ABAC). Additionally, AoT facilitates secure (in terms of stronger authentication) wireless interoperability of new and guest devices in a seamless manner. To validate our solution, we have developed AoT for Android smartphones like the LG G4 and evaluated all the cryptographic primitives over more constrained devices like the Intel Edison and the Arduino Due. This included the implementation of an Attribute-Based Signature (ABS) scheme. Our results indicate AoT ranges from highly efficient on resource-rich devices to affordable on resource-constrained IoT-like devices. Typically, an ABS generation takes around 27 ms on the LG G4, 282 ms on the Intel Edison, and 1.5 s on the Arduino Due.


title: PeerChooser: visual interactive recommendation

abstract: Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm.


title: Multi-unit Bayesian Auction with Demand or Budget Constraints

abstract: We consider the problem of revenue maximization on multi-unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount bi to compete for ad slots on a web page. The value of each ad slot corresponds to its click-through-rate, and each buyer has her own per-click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting.


title: Elasmobranch Phylogeny: A Mitochondrial Estimate Based on 595 Species

abstract: Interest in elasmobranch biodiversity and taxonomy has grown in recent years, catalyzed primarily by four influences: (1) the large number of new species that have been described over the past 30 years (e.g., Last and Stevens, 2009); (2) the recognition that many species of elasmobranchs, several of which have not yet been formally described, may be threatened with extinction from fishing pressures and habitat destruction (Stevens et al., 2000); (3) the growing interest in DNA “barcoding” as a tool to augment taxonomic description (e.g., Ward et al., 2007); and (4) an emerging recognition of the important role that elasmobranchs play as top predators in marine ecosystems (Heithaus et al., 2008). Increasingly, elasmobranch workers across a wide range of fields of science, both pure and applied, are recognizing the importance of an accurate species-level taxonomy. Fisheries scientists are ever more keenly aware of the need for accurate species-level assessments of catches to manage fisheries effectively. Ecologists have become more careful to ensure that the animals to which life history attributes are ascribed constitute distinct species rather than assemblages of closely related congeners with potentially different ecological roles and life history attributes. Finally, conservation biologists are beginning to recognize how critically important it is to have an accurate understanding of species compositions based on careful taxonomy to prioritize and manage units of biodiversity for conservation (Griffiths et al., 2010; Iglesias et al., 2009; White and Kyne, 2010). CONTENTS


title: Vision-based eye-gaze tracking for human computer interface

abstract: Eye-gaze is an input mode which has the potential for an efficient computer interface. Eye movement has been the focus of research in this area. Non-intrusive eye-gaze tracking that allows slight head movement is addressed in this paper. A small 2D mark is employed as a reference to compensate for this movement. The iris center has been chosen for the purposes of measuring eye movement. The gaze point is estimated after acquiring the eye movement data. Preliminary experimental results are given through a screen pointing application.


title: Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech

abstract: We introduce a new method to grade non-native spoken language tests automatically. Traditional automated response grading approaches use manually engineered time-aggregated features (such as mean length of pauses). We propose to incorporate general time-sequence features (such as pitch) which preserve more information than time-aggregated features and do not require human effort to design. We use a type of recurrent neural network to jointly optimize the learning of high level abstractions from time-sequence features with the time-aggregated features. We first automatically learn high level abstractions from time-sequence features with a Bidirectional Long Short Term Memory (BLSTM) and then combine the high level abstractions with time-aggregated features in a Multilayer Perceptron (MLP)/Linear Regression (LR). We optimize the BLSTM and the MLP/LR jointly. We find such models reach the best performance in terms of correlation with human raters. We also find that when there are limited time-aggregated features available, our model that incorporates time-sequence features improves performance drastically.


title: Robotic lower limb exoskeletons using proportional myoelectric control

abstract: Robotic lower limb exoskeletons have been built for augmenting human performance, assisting with disabilities, studying human physiology, and re-training motor deficiencies. At the University of Michigan Human Neuromechanics Laboratory, we have built pneumatically-powered lower limb exoskeletons for the last two purposes. Most of our prior research has focused on ankle joint exoskeletons because of the large contribution from plantar flexors to the mechanical work performed during gait. One way we control the exoskeletons is with proportional myoelectric control, effectively increasing the strength of the wearer with a physiological mode of control. Healthy human subjects quickly adapt to walking with the robotic ankle exoskeletons, reducing their overall energy expenditure. Individuals with incomplete spinal cord injury have demonstrated rapid modification of muscle recruitment patterns with practice walking with the ankle exoskeletons. Evidence suggests that proportional myoelectric control may have distinct advantages over other types of control for robotic exoskeletons in basic science and rehabilitation.


title: Missing piece of the puzzle in the science of consciousness: Resting state and endogenous correlates of consciousness

abstract: Abstract Consciousness still stands as one of the most interesting and the most elusive problems of neuroscience. Finding its correlates is the first step toward its satisfactory explanation. Several theories have proposed its correlates but none of them seem to be generally accepted even though most of them share some very similar elements. These elements are the activity of the thalamus, which is considered by some as the central region for consciousness, and gamma synchronization, which should be the general principal for the emergence of conscious experience. However, all of these proposed theories share one characteristic and that is that they do not take into consideration the recently discovered endogenous activity of the brain, which is generally associated with the default mode network. Although the activity of this large scale brain network is in correlation with various levels of consciousness it is still missing in discussions of consciousness. This review recognizes the importance of endogenous activity and points out the important discoveries of endogenous activity that could be an important step toward a satisfactory explanation of consciousness.


title: Comparative analysis of PWM/PFM controlled SMPS using Matlab Simulink

abstract: The design of PWM/PFM controller for SMPS is explained in this paper. An application concerning logic controller to manage is given. controller used here is Pulse frequency modulation (PFM)and pulse width modulation(PWM). In general for light load pulse width modulated buck converter operates with high efficiency since it has less switching losses, for heavy load operation pulse frequency modulation buck converter operates with less losses. Discontinuous conduction mode of pulse frequency modulated buck converter is considered in this paper for its concert in an application connecting a source with unstable voltage conditions with a load which requires constant voltage. Here, the pulses are apply to the semiconductor MOSFET control in step down converter is ended at some point and released at some point depending upon the load and threshold values of output voltage of the converter.


title: BeyondTouch: Extending the Input Language with Built-in Sensors on Commodity Smartphones

abstract: While most smartphones today have a rich set of sensors that could be used to infer input (.e.g., accelerometer, gyroscope, microphone), the primary mode of interaction is still limited to the front-facing touchscreen and several physical buttons on the case. To investigate the potential opportunities for interactions supported by built-in sensors, we present the implementation and evaluation of BeyondTouch, a family of interactions to extend and enrich the input experience of a smartphone. Using only existing sensing capabilities on a commodity smartphone, we offer the user a wide variety of additional tapping and sliding inputs on the case of and the surface adjacent to the smartphone. We outline the implementation of these interaction techniques and demonstrate empirical evidence of their effectiveness and usability. We also discuss the practicality of BeyondTouch for a variety of application scenarios.


title: Liver segmentation in MRI images based on whale optimization algorithm

abstract: This paper proposes an approach for liver segmentation in MRI images based on Whale optimization algorithm (WOA). It is used to extract the different clusters in the abdominal image to support the segmentation process. A statistical image is prepared to define the potential liver position in the abdominal image. Then, WOA divides the image into a predefined number of clusters. The prepared statistical image is converted into a binary image and multiplied by the image clustered by WOA. This multiplication process removes a great part of other organs from the image. It is followed by some points, picked up by user interaction, representing the required clusters which reside in the area of liver. The morphological operations enhance the initial segmented liver and produces the final image. The proposed approach is tested using a set of 70 MRI images, annotated and approved by radiology specialists. The resulting image is validated using structural similarity index measure (SSIM), similarity index (SI) and other five measures. The overall accuracy of the experimental result showed accuracy of 96.75% using SSIM and 97.5 using SI%.


title: 1 OUT-OF-BAG ESTIMATION

abstract: In bagging, predictors are constructed using bootstrap samples from the training set and then aggregated to form a bagged predictor. Each bootstrap sample leaves out about 37% of the examples. These left-out examples can be used to form accurate estimates of important quantities. For instance, they can be used to give much improved estimates of node probabilities and node error rates in decision trees. Using estimated outputs instead of the observed outputs improves accuracy in regression trees. They can also be used to give nearly optimal estimates of generalization errors for bagged predictors.


title: Type I Membranes, Phase Resetting Curves, and Synchrony

abstract: Type I membrane oscillators such as the Connor model (Connor et al. 1977) and the Morris-Lecar model (Morris and Lecar 1981) admit very low frequency oscillations near the critical applied current. Hansel et al. (1995) have numerically shown that synchrony is difficult to achieve with these models and that the phase resetting curve is strictly positive. We use singular perturbation methods and averaging to show that this is a general property of Type I membrane models. We show in a limited sense that so called Type II resetting occurs with models that obtain rhythmicity via a Hopf bifurcation. We also show the differences between synapses that act rapidly and those that act slowly and derive a canonical form for the phase interactions.


title: CETAD: Detecting evil twin access point attacks in wireless hotspots

abstract: Wireless hotspots allow users to use Internet via Wi-Fi interface, and many shops, cafés, parks, and airports provide free wireless hotspot services to attract customers. However, there is no authentication mechanism of Wi-Fi access points (APs) available in such hotspots, which makes them vulnerable to evil twin AP attacks. Such attacks are harmful because they allow to steal sensitive data from users. Today, there is no client-side mechanism that can effectively detect an evil twin AP attack without additional infrastructure supports. In this paper, we propose a mechanism CETAD leveraging public servers to detect such attacks. CETAD only requires installing an app at the client device and does not require to change the hotspot APs. CETAD explores the similarities between the legitimate APs and discrepancies between evil twin APs, and legitimate ones to detect an evil twin AP attack. Through our implementation and evaluation, we show that CETAD can detect evil twin AP attacks in various scenarios effectively.


title: Validation to the Requirement Elicitation Framework via Metrics

abstract: Requirement elicitation is the most significant factor in the requirement engineering process. It is already proven fact that improper elicitation of the requirements leads to the failure of the project, product. To improve the process quality and of that of industry we need a better elicitation technique or elicitation process (framework) to make it better. In this paper we have proposed a methodology that helps elicitor to validate the requirement elicitation using prioritization techniques and framework and further it will help the elicitor to effectively, efficiently select the elicitation technique.


title: Interleukin-17 in human inflammatory diseases

abstract: Human Th17 pro-inflammatory cells are currently defined as cells that produce IL-17A and F, tumor necrosis factor (TNF)-α, IL-6, IL-21, IL-22 and IL-23. Recently discovered related molecules are forming a family of cytokines, the IL-17 family, IL-17A, IL-17B, IL-17C, IL-17D, IL-17E and IL-17F. The associated receptors for the IL-17 family identified are IL-17R, IL-17RH1, IL-17RL (receptor like), IL-17RD and IL-17RE. This review introduces the roles of IL-17 and Th17 cells in human autoimmune diseases. Studies have shown that T cells with inflammatory effects on epithelial, endothelial and fibroblast cells express IL-17. Th17 cells are supposed to be involved in various autoimmune diseases, such as rheumatoid arthritis, psoriasis, multiple sclerosis, and inflammatory bowel diseases. Base on the biologic functions and regulation, IL-17 has regulatory roles in host defense and chronic inflammation which result in tissue damage and autoimmunity. So the IL-17 links links innate and adaptive immunity and has both beneficial and pathological effects on the immune system. This paper will focus on the possible roles of IL-17 in autoimmune diseases, a fundamental player in immune regulation.


title: Smooth kinematic controller vs. pure-pursuit for non-holonomic vehicles

abstract: The paper introduces a method for generating trajectories with curve sharpness and curvature constraints. Trajectories are based on continuous-curvature paths using Single & Double Continuous-Curvature paths. The paper also proposes a multi-rate control scheme by considering different dynamics with different sampling frequencies: global path planner at low frequency; target update based on Look-Ahead distance and path computation at medium frequency; curvature control at high frequency. An exhaustive analysis to evaluate the performance of the new method with respect to Pure-Pursuit method has been carried out, showing that the proposed method has better performance in terms of settling time, overshoot and robustness against design parameters. As a conclusion, we can say that our method introduces improvements in comfort and safety because the sharpness, normal jerk and mean abruptness are lower than with the Pure-Pursuit control.


title: I Know What You Want: Semantic Learning for Text Comprehension

abstract: Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL). Although SRL is naturally essential to text comprehension tasks, it is surprisingly ignored in previous work. This paper thus makes the first attempt to let SRL enhance text comprehension and inference through specifying verbal arguments and their corresponding semantic roles. In terms of deep learning models, our embeddings are enhanced by semantic role labels for more fine-grained semantics. We show that the salient labels can be conveniently added to existing models and significantly improve deep learning models in challenging text comprehension tasks. Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art.


title: Unity: A General Platform for Intelligent Agents

abstract: Recent advances in Deep Reinforcement Learning and Robotics have been driven by the presence of increasingly realistic and complex simulation environments. Many of the existing platforms, however, provide either unrealistic visuals, inaccurate physics, low task complexity, or a limited capacity for interaction among artificial agents. Furthermore, many platforms lack the ability to flexibly configure the simulation, hence turning the simulation environment into a black-box from the perspective of the learning system. Here we describe a new open source toolkit for creating and interacting with simulation environments using the Unity platform: Unity ML-Agents Toolkit. By taking advantage of Unity as a simulation platform, the toolkit enables the development of learning environments which are rich in sensory and physical complexity, provide compelling cognitive challenges, and support dynamic multi-agent interaction. We detail the platform design, communication protocol, set of example environments, and variety of training scenarios made possible via the toolkit.


title: Grafting in revision rhinoplasty

abstract: Rhinoplasty is one of the most difficult aesthetic surgery procedures with a high rate of revision. In revision rhinoplasty the surgeon should explore the patient's concerns and then verify the possibility to satisfy expectations after complete internal and external examination of the nose. For the vast majority of complex secondaries, an open approach is the only reasonable method. In fact, in secondary nasal surgery, because of the scarring process following the primary operation, dissection is tedious, and landmarks are lost. One of the main objectives for the surgeon who approaches secondary rhinoplasty is to restore the structural support of the nose and to replace the lost volume of soft tissues. To achieve this purpose, the surgeon must often rely on grafts. An ideal grafting material must be easy to sculpt, resistant to trauma, infection and extrusion, mechanically stable, inert and readily available. For all these reasons, autogenous cartilage grafts harvested from septum, auricular concha and rib represent the first choice in rhinoplasty. In order to obtain a camouflage graft that provides natural contouring to the nose, temporalis fascia can be used. All these carefully trimmed grafts are useful in tip revision surgery, in secondary surgery of the dorsum and to resolve or reduce functional problems.


title: CrackIT — An image processing toolbox for crack detection and characterization

abstract: This paper presents a comprehensive set of image processing algorithms for detection and characterization of road pavement surface crack distresses, which is being made available to the research community. The toolbox, in the Matlab environment, includes algorithms to preprocess images, to detect cracks and characterize them into types, based on image processing and pattern recognition techniques, as well as modules devoted to the performance evaluation of crack detection and characterization solutions. A sample database of 84 pavement surface images taken during a traditional road survey is provided with the toolbox, since no pavement image databases are publicly available for crack detection and characterization evaluation purposes. Results achieved applying the proposed toolbox to the sample database are discussed, illustrating the potential of the available algorithms.


title: Qualitatively exploring electronic portfolios: a text mining approach to measuring student emotion as an early warning indicator

abstract: The collection and analysis of student-level data is quickly becoming the norm across school campuses. More and more institutions are starting to use this resource as a window into better understanding the needs of their student population. In previous work, we described the use of electronic portfolio data as a proxy to measuring student engagement, and showed how it can be predictive of student retention. This paper highlights our ongoing efforts to explore and measure the valence of positive and negative emotions in student reflections and how they can serve as an early warning indicator of student disengagement.


Construction and Usage of the Semantic Interaction Pipeline


title: What factors lead to software project failure?

abstract: It has been suggested that there is more than one reason for a software development project to fail. However, most of the literature that discusses project failure tends to be rather general, supplying us with lists of risk and failure factors, and focusing on the negative business effects of the failure. Very little research has attempted an in-depth investigation of a number of failed projects to identify exactly what are the factors behind the failure. In this research we analyze data from 70 failed projects. This data provides us with practitionerspsila perspectives on 57 development and management factors for projects they considered were failures. Our results show that all projects we investigated suffered from numerous failure factors. For a single project the number of such factors ranges from 5 to 47. While there does not appear to be any overarching set of failure factors we discovered that all of the projects suffered from poor project management. Most projects additionally suffered from organizational factors outside the project managerpsilas control. We conclude with suggestions for minimizing the four most common failure factors.


title: EEG theta/beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits

abstract: Power density-ratios of fast and slow frequency spectrum-bands can be calculated from resting-state electroencephalography (EEG) recordings. A well-established phenomenon is that slow wave/fast wave ratios (SW/FW) are increased in attention-deficit/hyperactivity disorder. Several researchers have also begun to study relationships between SW/FW and affect. This work suggests that increased SW/FW may reflect reduced frontal cortical control over subcortical affective approach drive. The present study (n=28) aimed to further examine this notion by testing several predictions derived from it. In line with these predictions, SW/FW was found to correlate negatively with fearful modulation of response inhibition in an emotional go/no-go task and with self-reported attentional control. Results also suggested a positive relation between SW/FW and trait approach motivation and a negative relation to anxiety, as predicted. These results are consistent with previous studies and support the notion that SW/FW may provide a useful tool in the study of affect and emotion regulation. Language: en


title: Improving Fully Convolution Network for Semantic Segmentation

abstract: Fully Convolution Networks (FCN) have achieved great success in dense prediction tasks including semantic segmentation. In this paper, we start from discussing FCN by understanding its architecture limitations in building a strong segmentation network. Next, we present our Improved Fully Convolution Network (IFCN). In contrast to FCN, IFCN introduces a context network that progressively expands the receptive fields of feature maps. In addition, dense skip connections are added so that the context network can be effectively optimized. More importantly, these dense skip connections enable IFCN to fuse rich-scale context to make reliable predictions. Empirically, those architecture modifications are proven to be significant to enhance the segmentation performance. Without engaging any contextual post-processing, IFCN significantly advances the state-of-the-arts on ADE20K (ImageNet scene parsing), Pascal Context, Pascal VOC 2012 and SUN-RGBD segmentation datasets.


MINIATURIZATION OF BROADBAND 3DB BRANCHLINE COUPLER


Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach


title: A framework for semantic annotation of geospatial data for agriculture

abstract: The Web is a huge repository of geospatial information. Efficient retrieval of this information is a key factor in planning and decision-making in many domains, including agriculture. However, standards for data annotation and exchange enable only syntactic interoperability, while semantic heterogeneity presents challenges. This work describes a framework that tackles interoperability problems via semantic annotations, which are based on multiple ontologies. The framework is being developed within a project to support agricultural planning in Brazil. The paper discusses design and implementation issues using a real case study, provides an overview of annotation mechanisms and identifies requirements for annotating agricultural data.


title: Smart solid waste management

abstract: The rapid growth in the population automatically demands better infrastructure and more facilities. Employment and attaining balance in economy is an important concern for a nation having such rapid increase in its population, which finally results into evolution of new urban areas and cities. A smart city is created upon various particular components and strong waste administration is one of these crucial viewpoints. For example, today, to address the rising issue of carbon emissions in construction process, contractual workers are obligatorily made a request to use supplies according certain standards. Subsequently, to employ such operational standards we need dynamic investment and acknowledgment from the workers in using equipment according to the endorsed technologies. Essentially, the adequacy of strong waste administration framework relies on the involvement of the considerable number of stakeholders and natives. Strong waste administration is of grave significance to a urbanized locale which confronts the consistent growth in population, rising infrastructural requests and extending inflow of migrants. Understanding the idea and setting of waste isolation is additionally a key segment in the strong waste administration handle. This is the phase where India still lingers behind as against the universal partners. In a nation like Finland, just around 7 percent of the waste gets arranged into the dumping yard and the staying around 93 percent of the waste segment is reused. This level of adequacy in actualizing the strong waste administration framework is possible just because of subjective spread of civic sense, clear understanding and acknowledgment over the idea of waste segregation.


title: Sub-Baudrate Sampling at DAC and ADC: Toward 200G per Lane IM/DD Systems

abstract: As optical transceivers evolve toward 200G per lane, the comparatively low bandwidth and sampling speed of the commercially available high-speed digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) increasingly become the dominating obstacle toward success. On the other hand, DACs and ADCs are essential to make use of digital signal processing for impairment compensation and error correction; altogether driving systems’ engineers to a hard design dilemma. With the purpose of alleviating this compromise, we propose a transceiver structure—encompassing both hardware and software—enabling the generation, transmission, and detection of strongly over-filtered data signals whose baudrate exceeds the sampling rates of (potentially) both the DAC and ADC. We call this approach sub-baudrate sampling, a technique which does not increase the processing complexity at the transmitter side and does not even require the information about the channel state for pre-distortion or pre-coding purposes. We experimentally assess the performance of sub-baudrate sampled signals in two blocks of experiments: first the proof of concept in back-to-back configuration, including up to 112-Gbaud on–off keying at 0.785 samples per symbol in ∼25-GHz aggregate bandwidth. Then the C-band transmission demonstration, showing up to 100-Gbaud quaternary pulse-amplitude modulation (0.92 samples per symbol) and 125-Gbaud on–off keying (0.736 samples per symbol) over 1 and 80-km of single-mode fiber, respectively, with more than 3-dB receiver sensitivity margin at the 7% overhead hard-decision bit error rate limit.


title: Biobanks, consent and claims of consensus

abstract: Many scholars claim there is a consensus on broad consent for biobanking. We analyzed the literature in PubMed and found no evidence for consensus. Public perception studies report mixed findings on consent, but many biobanks adopt broad consent. A belief in consensus may stem from knowledge of biobank consent practices.


title: Big Data – Challenges for Computer Science Education

abstract: Data processing is a central topic of computer science and hence also in secondary computer science education, which includes strategies for storing, managing and retrieving data. In the context of Big Data, this field changes tremendously: established ideas, such as avoiding redundancies and storing data in a persistent and consistent way, are dropped in order to speed up the access to distributed stored data as well as its availability. Furthermore, with the rapidly growing impact of data processing on everyone’s daily life, computer science education needs to address these aspects as well as their social and ethical implications, such as privacy issues.


title: Design of 6–18-GHz High-Power Amplifier in GaAs pHEMT Technology

abstract: This paper presents a design procedure for a wideband 6–18-GHz monolithic microwave integrated circuit high-power amplifier (HPA) in 0.25- $\mu \text{m}$ AlGaAs-InGaAs pHEMT technology. The design is mainly focused on the realization of the passive circuits to provide the required low-loss and wideband impedance transformation networks. The two-stage GaAs HPA achieves an average output power of 39.6 dBm and a peak output power of 40.5 dBm at 11 GHz, in pulsed mode operation, with a small-signal gain of $S_{21}>10$ dB over the entire bandwidth. The average power added efficiency (PAE) is 22%, with a peak PAE of 29% at 11 GHz. The HPA chip occupies an area of $5\times 3.6~\mathrm {mm}^{2}$ . The achieved output power and the corresponding power density of 0.51 W/ $\mathrm {mm}^{2}$ are amongst the highest reported values in wideband GaAs HPAs.


title: Design of smart antenna array of 2×2 MIMO for 4G-LTE

abstract: A rectangular patch antenna resonating at 2.1GHz for the 4G Long Term Evolution (LTE) is designed and simulated. The rectangular patch antenna designed by using two strip feed line deposited on FR-4 substrate single substrate with thickness of 1.6milimeter, resonates at 1.86GHz. Spurious radiation increases and also limits the bandwidth level by using strip feed which is significant. The return loss achieved is −26dB. Further, the patch is designed by using coaxial feed line to increase resonating frequency at 2.1GHz and also the patch is deposited on FR-4 substrate with triple layer substrate. Bandwidth level and desirable return loss can be achieved with the help of coaxial feed line which results in reduction of spurious radiation. The gain, directivity, return loss, efficiency and radiation pattern are analyzed. Here in this paper MIMO is focused because the activity of multipath technique is attained for a given frequency band. The proposed antenna is designed to adopt 2×2 Multiple Input Multiple Output (MIMO) structure for 4G LTE network.


title: New mechanism for a 3 DOF exoskeleton hip joint with five revolute and two prismatic joints

abstract: We present a new exoskeleton hip design representing the human hip ball joint by five revolute and two prismatic joints. The goal of the design is to increase wearing comfort and torque transmission by reducing misalignments between the human's and the exoskeleton's joint with a self-aligning yaw axis in the transverse plane. The required ranges of motions and joint velocities for the hip joint design were identified based on motion capture data consisting of 828 motion recordings of 26 different subjects. We present the kinematics of a new hip joint with five revolute and two prismatic joints and its first 3D printed prototype. For the experimental evaluation we focused on measuring interaction forces between the subject and the exoskeleton at the back, pelvis and thigh with different kinematic configurations. The results indicate that interaction forces between exoskeleton and user are reduced by the new hip exoskeleton.


title: Where's @wally?: a classification approach to geolocating users based on their social ties

abstract: This paper presents an approach to geolocating users of online social networks, based solely on their 'friendship' connections. We observe that users interact more regularly with those closer to themselves and hypothesise that, in many cases, a person's social network is sufficient to reveal their location. The geolocation problem is formulated as a classification task, where the most likely city for a user without an explicit location is chosen amongst the known locations of their social ties. Our method uses an SVM classifier and a number of features that reflect different aspects and characteristics of Twitter user networks. The SVM classifier is trained and evaluated on a dataset of Twitter users with known locations. Our method outperforms a state-of-the-art method for geolocating users based on their social ties.


title: ACT-R/PM and menu selection: applying a cognitive architecture to HCI

abstract: Understanding the interaction of a user with a designed device such as a GUI requires clear understanding of three components: the cognitive, perceptual and motor capabilities of the user, the task to be accomplished and the artefact used to accomplish the task. Computational modeling systems which enable serious consideration of all these constraints have only recently begun to emerge. One such system is ACT-R/PM, which is described in detail. ACT-R/PM is a production system architecture that has been augmented with a set of perceptual-motor modules designed to enable the detailed modeling of interactive tasks. Nilsen's (1991) random menu selection task serves two goals: to illustrate the promise of this system and to help further our understanding of the processes underlying menu selection and visual search. Nilsen's original study, two earlier models of the task, and recent eye-tracking data are all considered. Drawing from the best properties of the previous models considered and guided by information from the eye-tracking experiment, a series of new models of random menu selection were constructed using ACT-R/PM. The final model provides a zero-parameter fit to the data that does an excellent, though not perfect, job of capturing the data.


title: Training a Support Vector Machine in the Primal

abstract: Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.


title: Vehicle detection and tracking with 2D laser range finders

abstract: Dynamic environment perception is an important component for unmanned ground vehicle navigation. In this paper, we focus on the vehicle detection and tracking with two low cost 2D laser range finders. Firstly, a foreground segmentation method is proposed based on a combination of the time cue and motion cue. The modified laser beam model is proposed for the tilted 2D laser range finder in the foreground segmentation. Then, the vehicle detection and preliminary tracking is realized by the modified measurement model. Finally, a new precise tracking method is proposed to refine the preliminary tracking results. Qualitative and quantitative experiments are carried out in real dynamic environment, and the result shows that the proposed method can detect and track the vehicle accurately.


title: The Impact Of Natural Language Processing-Based Textual Analysis Of Social Media Interactions On Decision Making

abstract: Organizations typically use sentiment analysis-based systems, or even resort to simple manual analysis, to try to derive useful meaning from the public digital “chatter” of their customers. Motivated by the need for a more accurate way to qualitatively mine valuable productand brandoriented consumer-generated text, this paper experimentally tests the ability of an NLP-based analytics approach to extracting knowledge from highly unstructured text. Results indicate that for detecting problems from social media data, natural language processing outperforms sentiment analysis. Surprisingly, the experiment indicates that sentiment analysis is not only no better than manual analysis of social media data toward the goal of supporting organizational decision-making, but may even prove disadvantageous to such efforts.


title: Analyzing key influences of tourists' acceptance of online reviews in travel decisions

abstract: The purpose of this paper is to examine what influence travelers’ adoption of online reviews, and whether the online reviews will influence their travel planning decisions.,Data were collected from 193 respondents from eWOM websites and analyzed using structural equation modeling.,The results revealed that eWOM has a significant influence on travel decisions. Furthermore, travelers were willing to adopt information from eWOM and this information was useful in their travel planning and decisions. Gender and time spent on online reviews were found to affect travel planning and decisions. Travelers also found that the reviews and issues raised in eWOM had credibility and were of good quality.,The study was not able to incorporate all factors which may be relevant to this study and so further theoretical development may be necessary to develop the conceptual model. The sample size, while adequate, can be expanded further.,Operators and administrators of eWOM can use these findings to develop more user-friendly interfaces so that more positive reviews and sales can be generated.,The results showed that travelers who adopt the information in eWOM will, in turn, use eWOM in their travel planning. This confirms the importance of eWOM and travelers in general will translate their pre-travel decisions into actual travel planning.,This research extended existing eWOM and information system adoption studies and focused on the travel planning context. This research validated the significant roles of eWOM argument quality and credibility in predicting the information usefulness of eWOM.


title: Algorithms for large scale markov blanket discovery

abstract: This paper presents a number of new algorithms for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a low-order polynomial algorithm and several variants that soundly induce the Markov Blanket under certain broad conditions in datasets with thousands of variables and compare them to other state-of-the-art local and global methods with excellent results.


Cortico-Striatal-Thalamic Loop Circuits of the Salience Network: A Central Pathway in Psychiatric Disease and Treatment


title: Human-like Autonomous Vehicle Speed Control by Deep Reinforcement Learning with Double Q-Learning

abstract: Autonomous driving has become a popular research project. How to control vehicle speed is a core problem in autonomous driving. Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to control the vehicle speed. However, the popular Q-learning algorithm is unstable in some games in the Atari 2600 domain. In this paper, a reinforcement learning approach called Double Q-learning is used to control a vehicle’s speed based on the environment constructed by naturalistic driving data. Depending on the concept of the direct perception approach, we propose a new method called integrated perception approach to construct the environment. The input of the model is made up of high dimensional data including road information processed from the video data and the low dimensional data processed from the sensors. During experiment, compared with deep Q-learning algorithm, double deep Q-learning has improvements both in terms of value accuracy and policy quality. Our model’s score is 271.73% times that of deep Q-learning.


title: ReasoNet: Learning to Stop Reading in Machine Comprehension

abstract: Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.


title: A Grounded Theory Of Open Government Data: A Case Study In The UK

abstract: This paper explores the emergence and implementation of Open Government Data (OGD) as a part of e-government systems in public organisations. OGD has become a new approach and phenomenon among developed countries to increase the citizen’s trust and confidence in government. Several studies have shown the importance of implementing open data systems by public organisations and the potentials of open government data systems for better management. Grounded theory approach is used to build a theory by using semi-structured interviews: 5 interviews in the UK as a pilot study comprising 4 interviewees from local governments and 1 interviewee from a large national organisation in London. We found that OGD could optimise the performance of government’s administration by using potential opportunities that OGD presents to them despite challenges like data sharing, standardization in OGD, government responsibility and public awareness. We envisage that as OGD evolves over time, participation and responses from public organisations, especially from large organisations, would represent the practice of OGD as a whole. This could positively contribute to the transparency and openness of the government and consequently increase the confidence level and trust of the people.


Rule-Based Phishing Attack Detection


title: Digitalisation and employment in manufacturing Pace of the digitalisation process and impact on employment in advanced Italian manufacturing companies

abstract: The present work tackles the issue of the effects of digitalisation on employment. This issue has been attracting a growing interest, in particular because of the anxiety generated by the idea that digital technologies could cancel a large number of jobs. Although I agree with argument put forward in opposition to the existence of a causal link between technological innovation and increased productivity at the macroeconomic level, I believe that the novelty and pervasiveness of digital technologies require more in-depth micro-level analysis in order to understand the extent to which new digital technologies are currently employed by leading manufacturing companies and the ways new technologies are affecting employment. The empirical findings show that among the different technologies included under the umbrella of Industry 4.0, mainly robots have received a great deal of attention so far, while the current application and employment impact for other emerging technological opportunities such as 3D printing, Internet of Things, Augmented reality, Big data Analytics have not been studied yet. In relation to the qualitative changes of the labour market, our empirical research confirms that there are new types of skills that will be demanded in the future in manufacturing, in particular in relation to service provision and software development.


title: Design of efficient double tail comparator for low power

abstract: Comparator is an important part of Analog to Digital Converter (ADC), used to find out whether input signal is high or low at each clock signal. A conventional comparator consisting of two cross-coupled inverters is modified for fast operation and low power by adding few transistors with multi-threshold voltage technology. Analysis of power and delay of dynamic comparator are presented. The advantage of this comparator is rail to rail output swing, no static power consumption and robustness against influence of mismatch. Simulation result in 0.18µm confirms reduction in power in proposed double tail comparator.


title: Integrated Microwave Photonics for Radio Access Networks

abstract: We explore the advantages that integrated microwave photonics (IMWP) can bring to access networks. We first of all review the most common architectures of radio access networks (RANs) to identify the segments where microwave photonic components and radio over fiber links are located. Then, we provide a short description of the basic principles of IMWP with the aim of illustrating the current state of the art of this technology and its potentials. We discuss the possibilities of incorporating IMWP technology into the RAN front-haul. In particular, we first of all identify the required MWP functionalities and then discuss the feasibility of implementing these in light of the current and near future state of the art.


title: An SI-MISO Boost Converter With Deadbeat-Based Control for Electric Vehicle Applications

abstract: This paper presents a single-inductor multiple-input single-output (SI-MISO) boost converter with a deadbeat-based control method. The proposed deadbeat-based control has the capability to simultaneously conduct the power regulation from multiple inputs and the output voltage regulation for the SI-MISO boost converter. The advantages of the proposed method are less component count, lower system cost, compatibility for arbitrary number of inputs, fast response, and simplicity for implementation. Hence, the SI-MISO boost converter is a promising interface for the regulation of multiple energy sources as a standalone power system, which can be utilized in electric vehicle applications. To verify the performance of the proposed deadbeat-based control method, a laboratorial prototype of the SI-MISO boost converter is built. The steady-state and dynamic operations are investigated based on the prototype. The experimental results demonstrate that input power and output voltage of the SI-MISO boost converter can be effectively regulated by the proposed control method. The comprehensive comparison with the existing methods is conducted to further illustrate the advantages of the proposed deadbeat-based method.


title: A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation

abstract: We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate our loss function, we improve the attention U-Net model by incorporating an image pyramid to preserve contextual features. We experiment on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and 21.4% of the images area and improve segmentation accuracy when compared to the standard U-Net by 25.7% and 3.6%, respectively.


title: Adaptive Multi-Level Saliency Network in 3D Generation

abstract: Many CNNs with encoder-decoder structure are widely used in supervised 3D voxel generation. However, their convolutional encoders are usually too simple which causes some local features to degrade during convolution, so it is difficult to extract a good feature representation from an input image by using a simple encoder. Some CNNs apply skip-connection layer for the encoder to reduce the degradation, but general skip-connection layer such as residual layer is not good enough especially when the quantity of convolutional layers in the encoder is relatively small. In this paper, we propose a novel structure called adaptive multi-level saliency network (AMSN) to reduce the degradation of local features. The major innovations of AMSN are multi-level saliency convolution kernels (MSCK) and saliency fusion layer. Different from the kernels used in general skip-connection layer, MSCK are adaptively learned from multi-level salient feature maps rather than initialized randomly. The salient feature maps are sampled from multiple layers in the encoder. MSCK can acquire multi-level features more easily so that we utilize MSCK to acquire local features from low-level layer before the degradation. After that, the acquired local features are fused back into encoder through a saliency fusion layer to reduce the degradation. We evaluated our approach on the ShapeNet and ModelNet40 dataset. The results indicate that our AMSN performs better than related works.


title: A gut (microbiome) feeling about the brain.

abstract: PURPOSE OF REVIEW ::: There is an increasing realization that the microorganisms which reside within our gut form part of a complex multidirectional communication network with the brain known as the microbiome-gut-brain axis. In this review, we focus on recent findings which support a role for this axis in modulating neurodevelopment and behavior. ::: ::: ::: RECENT FINDINGS ::: A growing body of research is uncovering that under homeostatic conditions and in response to internal and external stressors, the bacterial commensals of our gut can signal to the brain through a variety of mechanisms to influence processes such neurotransmission, neurogenesis, microglia activation, and modulate behavior. Moreover, the mechanisms underlying the ability of stress to modulate the microbiota and also for microbiota to change the set point for stress sensitivity are being unraveled. Dysregulation of the gut microbiota composition has been identified in a number of psychiatric disorders, including depression. This has led to the concept of bacteria that have a beneficial effect upon behavior and mood (psychobiotics) being proposed for potential therapeutic interventions. ::: ::: ::: SUMMARY ::: Understanding the mechanisms by which the bacterial commensals of our gut are involved in brain function may lead to the development of novel microbiome-based therapies for these mood and behavioral disorders.


title: Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions

abstract: Identifying collision-free paths over long time windows in environments with stochastically moving obstacles is difficult, in part because long-term predictions of obstacle positions typically have low fidelity, and the region of possible obstacle occupancy is typically large. As a result, planning methods that are restricted to identifying paths with a low probability of collision may not be able to find a valid path. However, allowing paths with a higher probability of collision may limit detection of imminent collisions. In this paper, we present Dynamic Risk Tolerance (DRT), a framework that dynamically evaluates risk tolerance, a function which is formulated as a time-varying upper bound on the acceptable likelihood of collision for a given path. DRT is implemented with forward stochastic reachable sets to predict the exact distribution of obstacles in a scalable manner over an arbitrarily long time window. In effect, DRT identifies actions that balance risks posed by both near and far obstacles. We empirically compare DRT to other state of the art methods that are capable of generating real-time solutions in highly crowded environments, and demonstrate the success rates for DRT that is 46% higher than the best performing comparison method, in the most difficult problem tested.


title: Innovation drivers in retail industry

abstract: Abstract Although in recent years research in innovation management in retail industries have increased drastically, most of these industries have focused only on addressing consumers’ acceptance of the most effective novel systems. For these reasons, a deeper understanding is needed in three main areas: the innovation management approaches in retail industry, the degree of innovation heterogeneity, and the innovation drivers. Starting from these evidences, this paper identifies to what extent there are similarities and differences in the main innovation drivers in retailing if compared to other sectors (i.e. education, game, etc.).


title: Author ' s personal copy A new algorithm for age recognition from facial images

abstract: In this paper, a new algorithm for age-group recognition from frontal face image is presented. The algorithm classifies subjects into four different age categories in four key stages: Pre-processing, facial feature extraction by a novel geometric feature-based method, face feature analysis, and age classification. In order to apply the algorithm to the problem, a face image database focusing on people's age information is required. Because there were no such databases, we created a database for this purpose, which is called Iranian face database (IFDB). IFDB contains digital images of people from 1 to 85 years of age. After pre-processing, then primary features of the faces in the database will be accurately detected. Finally, a neural network is used to classify the face into age groups using computed facial feature ratios and wrinkle densities. Experimental results show that the algorithm identifies the age group with accuracy of 86.64%.


title: MemZip: Exploring unconventional benefits from memory compression

abstract: Memory compression has been proposed and deployed in the past to grow the capacity of a memory system and reduce page fault rates. Compression also has secondary benefits: it can reduce energy and bandwidth demands. However, most prior mechanisms have been designed to focus on the capacity metric and few prior works have attempted to explicitly reduce energy or bandwidth. Further, mechanisms that focus on the capacity metric also require complex logic to locate the requested data in memory. In this paper, we design a highly simple compressed memory architecture that does not target the capacity metric. Instead, it focuses on complexity, energy, bandwidth, and reliability. It relies on rank subsetting and a careful placement of compressed data and metadata to achieve these benefits. Further, the space made available via compression is used to boost other metrics - the space can be used to implement stronger error correction codes or energy-efficient data encodings. The best performing MemZip configuration yields a 45% performance improvement and 57% memory energy reduction, compared to an uncompressed non-sub-ranked baseline. Another energy-optimized configuration yields a 29.8% performance improvement and a 79% memory energy reduction, relative to the same baseline.