title: Steering with eyes closed: Mm-Wave beam steering without in-band measurement

abstract: Millimeter-wave communication achieves multi-Gbps data rates via highly directional beamforming to overcome pathloss and provide the desired SNR. Unfortunately, establishing communication with sufficiently narrow beamwidth to obtain the necessary link budget is a high overhead procedure in which the search space scales with device mobility and the product of the sender-receiver beam resolution. In this paper, we design, implement, and experimentally evaluate Blind Beam Steering (BBS) a novel architecture and algorithm that removes in-band overhead for directional mm-Wave link establishment. Our system architecture couples mm-Wave and legacy 2.4/5 GHz bands using out-of-band direction inference to establish (overhead-free) multi-Gbps mm-Wave communication. Further, BBS evaluates direction estimates retrieved from passively overheard 2.4/5 GHz frames to assure highest mm-Wave link quality on unobstructed direct paths. By removing in-band overhead, we leverage mm-Wave's very high throughput capabilities, beam-width scalability and provide robustness to mobility. We demonstrate that BBS achieves 97.8% accuracy estimating direction between pairing nodes using at least 5 detection band antennas. Further, BBS successfully detects unobstructed direct path conditions with an accuracy of 96.5% and reduces the IEEE 802.11ad beamforming training overhead by 81%.


title: Event-Based Mobile Social Networks: Services, Technologies, and Applications

abstract: Event-based mobile social networks (MSNs) are a special type of MSN that has an immanently temporal common feature, which allows any smart phone user to create events to share group messaging, locations, photos, and insights among participants. The emergence of Internet of Things and event-based social applications integrated with context-awareness ability can be helpful in planning and organizing social events like meetings, conferences, and tradeshows. This paper first provides review of the event-based social networks and the basic principles and architecture of event-based MSNs. Next, event-based MSNs with smartphone contained technology elements, such as context-aware mobility and multimedia sharing, are presented. By combining the feature of context-aware mobility with multimedia sharing in event-based MSNs, event organizers, and planners with the service providers optimize their capability to recognize value for the multimedia services they deliver. The unique features of the current event-based MSNs give rise to the major technology trends to watch for designing applications. These mobile applications and their main features are described. At the end, discussions on the evaluation of the event-based mobile applications based on their main features are presented. Some open research issues and challenges in this important area of research are also outlined.


title: Lateral Flow Assay with Near-Infrared Dye for Multiplex Detection

abstract: Background: Lateral flow assays (LFAs) are popular point-of-care diagnostic tools because they are rapid and easy to use. Nevertheless, they often lack analytical sensitivity and quantitative output and may be difficult to multiplex, limiting their usefulness in biomarker measurement. As a proof-of-concept study, we detail the design of a quantitative, multiplex LFA with readily available near-infrared (NIR) detection to improve analytical sensitivity. ::: ::: Methods: NIR dye was conjugated to selected antibodies and incorporated into LFAs. We used singleplex, optimized NIR-LFAs to measure interleukin (IL)-6 from 0 to 200 pg/mL and developed duplex assays to simultaneously measure IL-6 from 0 to 100 pg/mL (0 to 4.5 pmol/L) and C-reactive protein (CRP) from 50 to 2500 ng/mL (0.4 to 20 nmol/L) on a single test strip. Assays were tested on 60 different spiked samples and compared to ELISA results. ::: ::: Results: NIR-LFAs detected IL-6 in a 10% plasma matrix with a limit of detection of 4 pg/mL (182 fmol/L) and a CV <7%. Duplex NIR-LFAs quantitatively measured IL-6 and CRP concentrations simultaneously. Values strongly correlated to ELISA measurements, with R 2 values of 0.9825 and 0.9711 for IL-6 and CRP, respectively. ::: ::: Conclusions: NIR-LFAs exhibit quantitative measurement at pg/mL concentrations owing to a high signal-to-background ratio and robust detection antibody clearance through the test strip. Moreover, NIR-LFAs are able to detect molecules present at vastly different concentrations in multiplex format and compare favorably to ELISAs. LFAs with direct NIR detection may be a valuable tool for biomarker evaluation in the point-of-care setting.


title: BER comparison between Convolutional, Turbo, LDPC, and Polar codes

abstract: Channel coding is a fundamental building block in any communications system. High performance codes, with low complexity encoding and decoding are a must-have for future wireless systems, with requirements ranging from the operation in highly reliable scenarios, utilizing short information messages and low code rates, to high throughput scenarios, working with long messages, and high code rates. We investigate in this paper the performance of Convolutional, Turbo, Low-Density Parity-Check (LDPC), and Polar codes, in terms of the Bit-Error-Ratio (BER) for different information block lengths and code rates, spanning the multiple scenarios of reliability and high throughput. We further investigate their convergence behavior with respect to the number of iterations (turbo and LDPC), and list size (polar), as well as how their performance is impacted by the approximate decoding algorithms.


title: Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

abstract: Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.


title: Improving the efficiency of class-D audio amplifier systems using envelope tracking DC-DC power supplies

abstract: This paper presents the use of envelope tracking in class-D digital audio amplifiers supplied through step-up DC-DC converters as applied in automotive, battery-operated, and portable devices. It is shown that envelope tracking offers significant improvements in efficiency of up to 26% for the class-D stage of a 100 W amplifier in a typical usage scenario, with a corresponding overall system improvement of up to 27% including a boost converter. Detailed simulation results of the complete system are presented, as well as experimental results performed on an existing 100 W class-D amplifier design. Efficiency versus power results are combined with music power distribution profiles to simulate typical usage cases. Challenges relating to envelope tracking and DC-DC converter control are discussed. It is concluded that there is significant potential for envelope tracking to provide improvement over the standard fixed supply amplifier design — especially at low power, where the amplifier is normally used most.


Coordination mechanisms in human-robot collaboration


title: An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system

abstract: Abstract South Asian countries are amongst the largest producers of crops with favourable climate conditions and fertile soil. However, traditional agricultural mechanisms are in place and inadequate effort has been put into exploit the usage of technology. One of the main problems being faced by agriculture sector in Pakistan and other developing countries is that crop diseases are not diagnosed timely and efficiently. Conventional methods for disease diagnosis in crops lead to less accurate and inefficient diagnosis, consequently leading to low productivity. In this paper, an intelligent approach for the diagnosis of crop diseases is proposed which is capable of working over Android mobile devices using fuzzy inference system as the main decision making engine at the backend. The system is capable enough to communicate to the farmers in Pakistan in their local language Urdu and assist them in diagnosing diseases in their crops. Agriculture experts in government sector can get equal benefit from it in diagnosis and prevention of crops diseases. It takes symptoms of the crops as input with a provision of vague input and generates the output in the form of diagnosed disease using its inference engine. The proposed system caters two main crops of Pakistan, cotton and wheat and is capable to diagnose their main diseases. The proposed system has been tested on a pool of 100 real crop problems and its inference engine has shown excellent performance in prediction of the right disease which is up to 99% accurate.


Dynamic Asymmetric Cournot Oligopoly under Capacity Constraints


title: Smart meter based short-term load forecasting for residential customers

abstract: This paper examines the potential impact of automatic meter reading (AMR) on short-term load forecasting for a residential customer. Real-time measurement data from customers' smart meters provided by a utility company is modeled as the sum of a deterministic component and a Gaussian noise signal. The shaping filter for the Gaussian noise is calculated using spectral analysis. Kalman filtering is then used for load prediction. The accuracy of the proposed method is evaluated for different sampling periods and planning horizons. The results show that the availability of more real-time measurement data improves the accuracy of the load forecast significantly. However, the improved prediction accuracy can come at a high computational cost. Our results qualitatively demonstrate that achieving the desired prediction accuracy while avoiding a high computational load requires limiting the volume of data used for prediction. Consequently, the measurement sampling rate must be carefully selected as a compromise between these two conflicting requirements.


title: A high performance fingerprint liveness detection method based on quality related features

abstract: A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.


title: Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach

abstract: In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90\% and 98.01\% on the legal small and large test dataset.


title: Biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion

abstract: An antagonistic muscle mechanism that regulates joint compliance contributes enormously to human dynamic locomotion. Antagonism is considered to be the key for realizing more than one locomotion mode. In this paper, we demonstrate how antagonistic pneumatic actuators can be utilized to achieve three dynamic locomotion modes (walking, jumping, and running) in a biped robot. Firstly, we discuss the contribution of joint compliance to dynamic locomotion, which highlights the importance of tunable compliance. Secondly, we introduce the design of a biped robot powered by antagonistic pneumatic actuators. Lastly, we apply simple feedforward controllers for realizing walking, jumping, and running and confirm the contribution of joint compliance to such multimodal dynamic locomotion. Based on the results, we can conclude that the antagonistic pneumatic actuators are superior candidates for constructing a human-like dynamic locomotor.


title: A signaling game model for moving target defense

abstract: Incentive-driven advanced attacks have become a major concern to cyber-security. Traditional defense techniques that adopt a passive and static approach by assuming a fixed attack type are insufficient in the face of highly adaptive and stealthy attacks. In particular, a passive defense approach often creates information asymmetry where the attacker knows more about the defender. To this end, moving target defense (MTD) has emerged as a promising way to reverse this information asymmetry. The main idea of MTD is to (continuously) change certain aspects of the system under control to increase the attacker's uncertainty, which in turn increases attack cost/complexity and reduces the chance of a successful exploit in a given amount of time. In this paper, we go one step beyond and show that MTD can be further improved when combined with information disclosure. In particular, we consider that the defender adopts a MTD strategy to protect a critical resource across a network of nodes, and propose a Bayesian Stackelberg game model with the defender as the leader and the attacker as the follower. After fully characterizing the defender's optimal migration strategies, we show that the defender can design a signaling scheme to exploit the uncertainty created by MTD to further affect the attacker's behavior for its own advantage. We obtain conditions under which signaling is useful, and show that strategic information disclosure can be a promising way to further reverse the information asymmetry and achieve more efficient active defense.


title: Water-Pipe Tobacco Smoking: An Emerging Health Risk Behavior in Adolescents in the United States

abstract: Objective: To assess the attitude and practices of adolescents regarding water-pipe smoking (WPS) in a predefined 'perceived' high risk group of youth 12-18 years of age in the Detroit metropolitan area. Methods: This was a cross-sectional survey (conducted March1 st through June 30 th , 2008) in which participants completed a self-reported questionnaire, which included demographics, WPS, other types of smoking, and their perception about the hazards of WPS. Results: A total of 272 (85% response rate (272/320) surveys were completed. One hundred and sixty seven (61%) were WPS "users". Fifty-six (22%) used WPS at home, 34 (13%) at coffee shops, 74 (29%) at both locations, and 3 (1%) at other locations. Relative to non-users, WPS users were more likely to be cigarette smokers (OR = 1.7, unadjusted OR = 2.5, p < 0.05), to have someone else in the household who uses WPS (OR = 2.2), to believe that WPS is safe (OR = 1.4, unadjusted OR = 2.0) and that WPS is less harmful than cigarettes (OR = 1.2 unadjusted OR = 1.6, p<0.05). In addition WPS use was associated with less likelihood of believing that one may acquire an infection (OR = 0.9) and that the toxicity of the inhalants was equivalent to cigarette smoking (OR = 0.8). Conclusions: WPS represents a growing public health issue for adolescents in the US. Aggressive education of adolescents is essential in combating the misperception associated this form of smoking.


title: Design of power efficient hybrid flash-successive approximation register analog to digital converter

abstract: The need for a high speed and low power ADC is very essential for various applications. This paper presents a new hybrid analog-to-digital convertor (ADC) combining a capacitive digital-to-analog (CDAC) based multi-bit/cycle successive approximation (SAR) ADC with a flash ADC. As portable terminals demand long battery and high sampling rate. Handheld equipment's for wireless like WI-FI and WI-MAX or cellular communication systems like WCDMA, CDMA 2000 and TDSCDMA requires power efficient, moderate resolution and high sampling rate ADC's. By using the proposed scheme, we achieved not only high-speed conversion, but also more efficient digital-error-correction (DEC). The flash-SAR ADC is simulated in 90nm technology using CMOS process.


title: Artificial Intelligence and Big Data

abstract: AI Innovation in Industry is a new department for IEEE Intelligent Systems, and this paper examines some of the basic concerns and uses of AI for big data (AI has been used in several different ways to facilitate capturing and structuring big data, and it has been used to analyze big data for key insights).


title: Hybrid sampling for imbalanced data

abstract: Decision tree learning in the presence of imbalanced data is an issue of great practical importance, as such data is ubiquitous in a wide variety of application domains. We propose hybrid data sampling, which uses a combination of two sampling techniques such as random oversampling and random undersampling, to create a balanced dataset for use in the construction of decision tree classification models. The results demonstrate that our methodology is often able to improve the performance of a C4.5 decision tree learner in the context of imbalanced data.


title: C2E2: A Verification Tool for Stateflow Models

abstract: Mathworks' Stateflow is a predominant environment for modeling embedded and cyber-physical systems where control software interacts with physical processes. We present Compare-Execute-Check-Engine C2E2--a verification tool for continuous and hybrid Stateflow models. It checks bounded time invariant properties of models with nonlinear dynamics, and discrete transitions with guards and resets. C2E2 transforms the model, generates simulations using a validated numerical solver, and then computes reachtube over-approximations with increasing precision. For this last step it uses annotations that have to be added to the model. These annotations are extensions of proof certificates studied in Control Theory and can be automatically obtained for linear dynamics. The C2E2 algorithm is sound and it is guaranteed to terminate if the system is robustly safe or unsafe with respect to perturbations of guards and invariants of the model. We present the architecture of C2E2, its workflow, and examples illustrating its potential role in model-based design, verification, and validation.


title: BENEFITS REALIZATION FROM ERP SYSTEMS: THE ROLE OF CUSTOMIZATION

abstract: Organizations are making large investments in package based Enterprise Resource Planning (ERP) systems. While some organizations have achieved business improvement from their ERP systems, many still fail to realize the benefits identified at the project outset. One recommended approach to improve the likelihood of ERP system success and thereby delivery of benefits, is to avoid package customization. However, it appears that implementing a truly ‘vanilla’ system, although desirable, is rarely achieved. The type of customization may also be important in influencing the level of benefits realized from ERP systems. A more tailored system may provide a better fit with organizational processes and increased chances of benefit realization. Research that explores the relationship between ERP system customization and benefits realization is lacking. This study helps to fill this gap using an existing benefits management framework explicitly examining the role of customization in ERP projects. The research method will be exploratory research interviews followed by two in-depth case studies. It is anticipated that investigating the role of customization will significantly advance our understanding of the benefits realization process from ERP system projects.


title: Task Allocation for Undependable Multiagent Systems in Social Networks

abstract: Task execution of multiagent systems in social networks (MAS-SN) can be described through agents' operations when accessing necessary resources distributed in the social networks; thus, task allocation can be implemented based on the agents' access to the resources required for each task and aimed to minimize this resource access time. Currently, in undependable MAS-SN, there are deceptive agents that may fabricate their resource status information during task allocation but not really contribute resources to task execution; although there are some game theory-based solutions for undependable MAS, but which do not consider minimizing resource access time that is crucial to the performance of task execution in social networks. To achieve dependable resources with the least access time to execute tasks in undependable MAS-SN, this paper presents a novel task allocation model based on the negotiation reputation mechanism, where an agent's past behaviors in the resource negotiation of task execution can influence its probability to be allocated new tasks in the future. In this model, the agent that contributes more dependable resources with less access time during task execution is rewarded with a higher negotiation reputation, and may receive preferential allocation of new tasks. Through experiments, we determine that our task allocation model is superior to the traditional resources-based allocation approaches and game theory-based allocation approaches in terms of both the task allocation success rate and task execution time and that it usually performs close to the ideal approach (in which deceptive agents are fully detected) in terms of task execution time.


title: Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

abstract: Purpose ::: Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step.


title: Identifying risk issues and research advancements in supply chain risk management

abstract: The purpose of this paper is to investigate the research development in supply chain risk management (SCRM), which has shown an increasing global attention in recent years. Literature survey and citation/co-citation analysis are used to fulfil the research task. Literature survey has undertaken a thorough search of articles on selected journals relevant to supply chain operations management. Meanwhile, citation/co-citation analysis uses Web of Sciences database to disclose SCRM development between 1995 and 2009. Both the approaches show similar trends of rising publications over the past 15 years. This review has piloted us to identify and classify the potential risk associated with different flows, namely material, cash and information flows. Consequently, we identify some research gaps. Even though there is a pressing need and awareness of SCRM from industrial aspect, quantitative models in the field are relatively lacking and information flow risk has received less attention. It is also interesting to observe the evolutions and advancements of SCRM discipline. One finding is that the intellectual structure of the field made statistically significant increase during 2000-2005 and evolved from passively reacting to vague general issues of disruptions towards more proactively managing supply chain risk from system perspectives.


title: Sulfoxide, Sulfur, and Nitrogen Oxidation and Dealkylation by Cytochrome P450.

abstract: The oxidation and dealkylation of dimethylsulfoxide (DMSO), dimethylsulfide (DMS), and trimethylamine (TMA) by cytochrome P450 has been studied with density functional theory calculations. The results show that the oxidation reactions always occur on the doublet spin surface, whereas dealkylations can take place for both the doublet and quartet spin states. Moreover, DMS is more reactive than DMSO, and S-oxidation is more favorable than S-dealkylation, whereas N-dealkylation is more favorable than N-oxidation. This is in perfect agreement with experimental results, showing that density functional activation energies are reliable and comparable for widely different reactions with cytochrome P450.


title: Enlightenment from Australian Network Security Plan to Chinese Information Security

abstract: With the development of information technology, the network information security is closely linked with national security. In order to resist the harmful information to safeguard national security, Australian government enforces network security plan. Its network of legal norms and such practices as real-name system, national information security have a certain degree of enlightenment to China.


title: Multi-view Convolutional Neural Networks for 3D Shape Recognition

abstract: A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.


title: Graphene-based Recyclable Photo-Absorbers for High-Efficiency Seawater Desalination.

abstract: Today's scientific advances in water desalination dramatically increase our ability to transform seawater into fresh water. As an important source of renewable energy, solar power holds great potential to drive the desalination of seawater. Previously, solar assisted evaporation systems usually relied on highly concentrated sunlight or were not suitable to treat seawater or wastewater, severely limiting the large scale application of solar evaporation technology. Thus, a new strategy is urgently required in order to overcome these problems. In this study, we developed a solar thermal evaporation system based on reduced graphene oxide (rGO) decorated with magnetic nanoparticles (MNPs). Because this material can absorb over 95% of sunlight, we achieved high evaporation efficiency up to 70% under only 1 kW m(-2) irradiation. Moreover, it could be separated from seawater under the action of magnetic force by decorated with MNPs. Thus, this system provides an advantage of recyclability, which can significantly reduce the material consumptions. Additionally, by using photoabsorbing bulk or layer materials, the deposition of solutes offen occurs in pores of materials during seawater desalination, leading to the decrease of efficiency. However, this problem can be easily solved by using MNPs, which suggests this system can be used in not only pure water system but also high-salinity wastewater system. This study shows good prospects of graphene-based materials for seawater desalination and high-salinity wastewater treatment.


title: Outcomes of Single Stage Phalloplasty by Pedicled Anterolateral Thigh Flap versus Radial Forearm Free Flap in Gender Confirming Surgery

abstract: Purpose: Phalloplasty is a critical step in female-to-male (transmale) gender confirming genital surgery. We examined outcomes between transmales who underwent phalloplasty with vaginectomy and full-length urethroplasty using the anterolateral thigh pedicled flap or the radial forearm free flap.Materials and Methods: We performed a single center, retrospective study of patients who underwent phalloplasty with vaginectomy and full-length urethroplasty using an anterolateral thigh pedicled flap or a radial forearm free flap from April 2013 to July 2016. All patients had at least 6 months of followup. Urethral and nonurethral complications were recorded. Complication rates were assessed using the OR of the anterolateral thigh pedicled flap and the radial forearm free flap groups.Results: Of the 213 patients 149 and 64 underwent radial forearm free flap and anterolateral thigh pedicled flap phalloplasty, respectively. Patients with a radial forearm free flap had a significantly higher body mass index than tho...


title: A novel deterministic approach for aspect-based opinion mining in tourism products reviews

abstract: This work proposes an extension of Bing Liu's aspect-based opinion mining approach in order to apply it to the tourism domain. The extension concerns with the fact that users refer differently to different kinds of products when writing reviews on the Web. Since Liu's approach is focused on physical product reviews, it could not be directly applied to the tourism domain, which presents features that are not considered by the model. Through a detailed study of on-line tourism product reviews, we found these features and then model them in our extension, proposing the use of new and more complex NLP-based rules for the tasks of subjective and sentiment classification at the aspect-level. We also entail the task of opinion visualization and summarization and propose new methods to help users digest the vast availability of opinions in an easy manner. Our work also included the development of a generic architecture for an aspect-based opinion mining tool, which we then used to create a prototype and analyze opinions from TripAdvisor in the context of the tourism industry in Los Lagos, a Chilean administrative region also known as the Lake District. Results prove that our extension is able to perform better than Liu's model in the tourism domain, improving both Accuracy and Recall for the tasks of subjective and sentiment classification. Particularly, the approach is very effective in determining the sentiment orientation of opinions, achieving an F-measure of 92% for the task. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions, using a non-extended approach for this task. Finally, results also showed the effectiveness of our design when applied to solving the industry's specific issues in the Lake District, since almost 80% of the users that used our tool considered that our tool adds valuable information to their business.


title: Face recognition using Histograms of Oriented Gradients

abstract: Face recognition has been a long standing problem in computer vision. Recently, Histograms of Oriented Gradients (HOGs) have proven to be an effective descriptor for object recognition in general and face recognition in particular. In this paper, we investigate a simple but powerful approach to make robust use of HOG features for face recognition. The three main contributions of this work are: First, in order to compensate for errors in facial feature detection due to occlusions, pose and illumination changes, we propose to extract HOG descriptors from a regular grid. Second, fusion of HOG descriptors at different scales allows to capture important structure for face recognition. Third, we identify the necessity of performing dimensionality reduction to remove noise and make the classification process less prone to overfitting. This is particularly important if HOG features are extracted from overlapping cells. Finally, experimental results on four databases illustrate the benefits of our approach.


title: ABMA+: lightweight and efficient algorithm for HTTP adaptive streaming

abstract: Currently exploited adaptive video streaming algorithms mainly focus on maximizing video representation while they usually not assess the risk of video freezing and not care about the influence of video representation switching. We propose a new Adaptation & Buffer Management Algorithm, called ABMA+, which selects video representation based on the predicted probability of video freezing. The algorithm continuously estimates segment download time characteristics and use the pre-computed playout buffer map to select the maximum video representation which guarantee smooth content playout. Our algorithm, thanks to the buffer map, avoids heavy online computation so it could be widely deployed on different terminals. The performed simulation and trial experiments confirm the efficiency of our approach.


title: Semi-automatic 2D to 3D image conversion using scale-space Random Walks and a graph cuts based depth prior

abstract: In this paper, we present a semi-automated method for converting conventional 2D images into stereoscopic 3D. User-defined strokes corresponding to a rough estimate of the depth values in the scene are defined for the image of interest. With these, our system determines the depth values for the rest of the image, producing a depth map that can be used to create stereoscopic 3D image pairs. Our work is based on a similar scheme, using the Random Walks segmentation paradigm. However, the related work is quite complex, with many processing steps required to produce the final stereoscopic image pair. Combined with its evident shortcomings, but noting the merits, we propose a system employing Random Walks, while incorporating information from the popular Graph Cuts segmentation paradigm. Thus, a final cohesive depth map is produced, combining the merits of both. The results show that we can produce good quality stereoscopic image pairs, while using a much more simplified method in comparison to the related work.


title: A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

abstract: Just over a decade has passed since the concept of morphological profile was defined for the analysis of remote sensing images. Since then, the morphological profile has largely proved to be a powerful tool able to model spatial information (e.g., contextual relations) of the image. However, due to the shortcomings of using the morphological profiles, many variants, extensions, and refinements of its definition have appeared stating that the morphological profile is still under continuous development. In this case, recently introduced theoretically sound attribute profiles (APs) can be considered as a generalization of the morphological profile, which is a powerful tool to model spatial information existing in the scene. Although the concept of the AP has been introduced in remote sensing only recently, an extensive literature on its use in different applications and on different types of data has appeared. To that end, the great amount of contributions in the literature that address the application of the AP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, and hyperspectral) proves how the AP is an effective and modern tool. The main objective of this survey paper is to recall the concept of the APs along with all its modifications and generalizations with special emphasis on remote sensing image classification and summarize the important aspects of its efficient utilization while also listing potential future works.


title: Active Learning for Example-Based Dialog Systems

abstract: While example-based dialog is a popular option for the construction of dialog systems, creating example bases for a specific task or domain requires significant human effort. To reduce this human effort, in this paper, we propose an active learning framework to construct example-based dialog systems efficiently. Specifically, we propose two uncertainty sampling strategies for selecting inputs to present to human annotators who create system responses for the selected inputs. We compare performance of these proposed strategies with a random selection strategy in simulation-based evaluation on 6 different domains. Evaluation results show that the proposed strategies are good alternatives to random selection in domains where the complexity of system utterances is low.


title: Search-based structured prediction

abstract: We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem.


title: The visual N1 component as an index of a discrimination process

abstract: Many previous studies have demonstrated that the visual N I component is larger for attended-location stimuli than for unattended-location stimuli. This difference is observed typically only for tasks involving a discrimination of the attended-location stimuli, suggesting that the N1 wave reflects a discrimination process that is applied to the attended location. The present study tested this hypothesis by examining the N1 component elicited by attended stimuli under conditions that either required or did not require the subject to perform a discrimination. Specifically, the N1 elicited by foveal stimuli during choice-reaction time (RT) tasks was compared with the N1 elicited by identical stimuli during simple-RT tasks. In three experiments, a larger posterior N1 was observed in choice-RT tasks than in simple-RT tasks, even when several potential confounds were eliminated (e.g., arousal and motor preparation). This N1 discrimination effect was observed even when no motor response was required and was present for both color- and form-based discriminations. Moreover, this discrimination effect was equally large for easy and difficult discriminations, arguing against a simple resource-based explanation of the present results. Instead, the results of this study are consistent with the hypothesis that the visual N1 component reflects the operation of a discrimination process within the focus of attention.


title: Flow Theory, Evolution & Creativity: or, 'Fun & Games'

abstract: In this paper videogames and transmedia are examined from the perspectives of both creation (game design) and audience reception (gameplay experience), in light of the theories of the DPFi (Domain, Person Field interaction) systems model of creativity (Csikszentmihalyi 1988, 1996, 2006, 2014); its herein contended theoretical equivalent, evolutionary epistemology (Popper 1963, DT Campbell 1974, Simonton 2010) and the inherent biocultural evolutionary creative algorithm of selection, variation and transmission-with-heredity; 'flow' theory in creativity (Csikszentmihalyi 1975, 1990, 1996) as a determinant of 'fun factor' in games; 'narrative transportation' theory in fiction (Gerrig 1993, Green & Brock 2000, Van Laer et al 2014) as an additional (necessary but not sufficient) determinant of 'fun-factor' in 'story' videogames; and Boyd's (2009) general theory of creativity in the arts as 'cognitive play with pattern' - ultimately arguing that game play of any kind may potentially enhance animal intelligence, and therefore that videogames as an art form may potentially enhance human intelligence.


of ASBBS Volume 16 Number 1 E-Commerce and Inventory Management


title: LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets

abstract: For a transaction database, a frequent itemset is an itemset included in at least a specied number of transactions. A frequent itemset P is maximal if P is included in no other frequent itemset, and closed if P is included in no other itemset included in the exactly same transactions as P. The problems of nding these frequent itemsets are fundamental in data mining, and from the applications, fast implementations for solving the problems are needed. In this paper, we propose ecien t algorithms LCM (Linear time Closed itemset Miner), LCMfreq and LCMmax for these problems. We show the eciency of our algorithms by computational experiments compared with existing algorithms.


title: A novel iterative calibration approach for thermal infrared cameras

abstract: The accurate geometric calibration of thermal infrared (IR) cameras is of vital importance in many computer vision applications. In general, the calibration procedure consists of localizing a set of calibration points within a calibration image. This set is subsequently used to solve for the camera parameters. However, due to the physical limitations of the IR acquisition process, the localization of the calibration points often poses difficulties, subsequently leading to unsatisfying calibration results. In this work a novel IR camera calibration approach is introduced. It is able to localize the calibration points within the images of a conventional calibration board consisting of miniature light-bulbs with improved accuracy. Our algorithm models the radiation pattern of each light bulb as an ellipse and considers the center of mass of the extracted ellipsoidal region as the starting calibration point, which is refined iteratively using alternating mappings to and from an undistorted grid model. The proposed processing chain leads to a significantly reduced calibration error when compared to the state-of-the-art. Furthermore, the proposed methodology can also be used to calibrate visible-light cameras thus being suitable for the calibration of multiple camera rigs involving both visible-light and IR cameras.


title: Image Completion Using Global Optimization

abstract: A new exemplar-based framework unifying image completion, texture synthesis and image inpainting is presented in this work. Contrary to existing greedy techniques, these tasks are posed in the form of a discrete global optimization problem with a well defined objective function. For solving this problem a novel optimization scheme, called Priority- BP, is proposed which carries two very important extensions over standard belief propagation (BP): "prioritybased message scheduling" and "dynamic label pruning". These two extensions work in cooperation to deal with the intolerable computational cost of BP caused by the huge number of existing labels. Moreover, both extensions are generic and can therefore be applied to any MRF energy function as well. The effectiveness of our method is demonstrated on a wide variety of image completion examples.


title: IOT Based Smart Agriculture System

abstract: Smart agriculture is an emerging concept, because IOT sensors are capable of providing information about agriculture fields and then act upon based on the user input. In this Paper, it is proposed to develop a Smart agriculture System that uses advantages of cutting edge technologies such as Arduino, IOT and Wireless Sensor Network. The paper aims at making use of evolving technology i.e. IOT and smart agriculture using automation. Monitoring environmental conditions is the major factor to improve yield of the efficient crops. The feature of this paper includes development of a system which can monitor temperature, humidity, moisture and even the movement of animals which may destroy the crops in agricultural field through sensors using Arduino board and in case of any discrepancy send a SMS notification as well as a notification on the application developed for the same to the farmer’s smartphone using Wi-Fi/3G/4G. The system has a duplex communication link based on a cellularInternet interface that allows for data inspection and irrigation scheduling to be programmed through an android application. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas.


DETERMINATION OF MEASUREMENT ACCURACY OF OPTICAL 3D SCANNERS


title: TFE3 Expression in Tumors of the Microphthalmia-Associated Transcription Factor (MiTF) Family

abstract: The DNA-binding factor TFE3 is closely related to microphthalmia-associated transcription factor (MiTF) and is over-expressed in alveolar soft part sarcoma (ASPS) and select renal cell carcinomas. Reports of TFE3 expression in PEComa prompted investigation into TFE3 expression among other members of the putative MiTF group of neoplasms. The authors examined cases of PEComa (n = 6), conventional angiomyolipoma (AML; n = 22), metastatic melanoma (n = 16), and clear cell sarcoma (CCS; n = 9) for TFE3 expression. Nuclear immunostaining was observed in 74% (39/53) of cases, as follows: 5/6 PEComas, 18/22 AMLs, 10/16 metastatic melanomas, and 6/9 CCSs. However, with the exception of PEComas, compared with ASPS controls, TFE3 staining was significantly less intense in the tumors examined. These results illustrate that TFE3 immunoreactivity is detectable in other members of the MiTF family of neoplasms. For this reason, such neoplasms warrant consideration in the differential diagnosis with nuclear TFE3 immunoreactivity, particularly when staining is focal and less intense.


title: Predicting the Big Five personality traits from handwriting

abstract: We propose the first non-invasive three-layer architecture in literature based on neural networks that aims to determine the Big Five personality traits of an individual by analyzing offline handwriting. We also present the first database in literature that links the Big Five personality type with the handwriting features collected from 128 subjects containing both predefined and random texts. Testing our novel architecture on this database, we show that the predefined texts add more value if enforced on writers in the training stage, offering accuracies of 84.4% in intra-subject tests and 80.5% in inter-subject tests when the random dataset is used for testing purposes, up to 7% higher than when random datasets are used in the training phase. We obtain the highest prediction accuracy for Openness to Experience, Extraversion, and Neuroticism (over 84%), while for Conscientiousness and Agreeableness, the prediction accuracy is around 77%. Overall, our approach offers the highest accuracy compared with other state-of-the-art methods and results are computed in maximum 90 s, making the approach faster than the questionnaire or psychological interviews currently used for determining the Big Five personality traits. Our research also shows there are relationships between specific handwriting features and prediction with high accuracy of specific personality traits and this can be further exploited for improving, even more, the prediction accuracy of the proposed architecture.


title: A 77-GHz CMOS Power Amplifier With a Parallel Power Combiner Based on Transmission-Line Transformer

abstract: This paper presents a 77-GHz CMOS power amplifier (PA) with a parallel power combiner based on a transmission-line transformer (TLT). An inter-stage matched cascode power cell structure is applied to obtain high output power and efficiency, where a simple matching network between the common-gate and common-source stage is introduced. The parallel power combiner based on a broadside-coupled TLT is analyzed and compared with a series power combiner. The PA is fabricated using a 65-nm RF CMOS process. It achieves the saturated output power of 15.8 dBm, the power-added efficiency of 15.2%, and the power gain of 20.9 dB with a supply voltage of 2.0 V at 77 GHz.


title: Fuzzy c-means clustering with spatial information for image segmentation

abstract: A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.


Connected domination in fuzzy graphs using strong arcs


title: Text readability and fraud detection

abstract: Researchers and practitioners have employed various analytical and empirical examinations of financial data in their attempts to detect fraud. Nonetheless, despite the various comprehensive analytical examinations and other supporting techniques, there are still fraud incidences that are not discovered. There have been scarce research works that examine the readability of qualitative text in company annual report, particularly in detecting fraud. There could have been hidden clues in the narrative disclosures that can be indicators of fraud and thus, examining the narrative sections of a company's annual report may lead to discovery of fraud. Management obfuscation theory predicts that the management of a badly performed company tends to hide bad news by decreasing the readability of financial reports. Therefore, there is an imperative need for a reliable technique in measuring text complexity to detect the likelihood of fraud and deception in financial statements. Such fraud detection technique is also crucial to auditors and other stakeholders in their decision-making. This study suggests the application of a readability index tool as a proactive means in identifying fraud. The methodology suggests by this study is developed by using the Coh Metrix version 2 indices — Flesch Reading Ease Score Formula. To the best of our knowledge, no empirical study has employed the Coh Metrix-Flesch Reading index to examine text complexity to detect fraud of Malaysian companies.


title: Design and analysis of high performance crypt-NoSQL

abstract: NoSQL databases have become popular with enterprises due to their scalable and flexible storage management of big data. Nevertheless, their popularity also brings up security concerns. Most NoSQL databases lacked secure data encryption, relying on developers to implement cryptographic methods at application level or middleware layer as a wrapper around the database. While this approach protects the integrity of data, it increases the difficulty of executing queries. We were motivated to design a system that not only provides NoSQL databases with the necessary data security, but also supports the execution of query over encrypted data. Furthermore, how to exploit the distributed fashion of NoSQL databases to deliver high performance and scalability with massive client accesses is another important challenge. In this research, we introduce Crypt-NoSQL, the first prototype to support execution of query over encrypted data on NoSQL databases with high performance. Three different models of Crypt-NoSQL were proposed and performance was evaluated with Yahoo! Cloud Service Benchmark (YCSB) considering an enormous number of clients. Our experimental results show that Crypt-NoSQL can process queries over encrypted data with high performance and scalability. A guidance of establishing service level agreement (SLA) for Crypt-NoSQL as a cloud service is also proposed.


Supplemental Information KLF 4 Is Essential for Induction of Cellular Identity Change and Acinar-to-Ductal Reprogramming during Early Pancreatic Carcinogenesis


title: Codesign of Unconventional Array Architectures and Antenna Elements for 5G Base Stations

abstract: An innovative methodological paradigm is proposed for the design of unconventional antenna systems for future 5G base stations. In such a codesign strategy, the antenna element (a spline-shaped patch embedded in a finite-array model ) and the overall irregularly clustered-array layout are simultaneously synthesized through a multiobjective antenna-shape optimization combined with a subarraying technique based on a new single-objective integer-coded genetic algorithm able to intrinsically handle constraints on the cluster shapes/types. Selected numerical examples, drawn from an exhaustive design process, are presented to assess the advantages and the effectiveness of the proposed co-design scheme in view of the final manufacturing of 5G base stations thanks to its capability to take into account the impact of mutual coupling, nonideal antenna patterns, and implementation limitations.


Prediction on Housing Price Based on Deep Learning


title: Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation

abstract: We propose a convolutional network with hierarchical classifiers for per-pixel semantic segmentation, which is able to be trained on multiple, heterogeneous datasets and exploit their semantic hierarchy. Our network is the first to be simultaneously trained on three different datasets from the intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and is able to handle different semantic levelof-detail, class imbalances, and different annotation types, i.e. dense per-pixel and sparse bounding-box labels. We assess our hierarchical approach, by comparing against flat, nonhierarchical classifiers and we show improvements in mean pixel accuracy of 13.0% for Cityscapes classes and 2.4% for Vistas classes and 32.3% for GTSDB classes. Our implementation achieves inference rates of 17 fps at a resolution of 520 x 706 for 108 classes running on a GPU.


title: Generating Adversarial Driving Scenarios in High-Fidelity Simulators

abstract: In recent years self-driving vehicles have become more commonplace on public roads, with the promise of bringing safety and efficiency to modern transportation systems. Increasing the reliability of these vehicles on the road requires an extensive suite of software tests, ideally performed on highfidelity simulators, where multiple vehicles and pedestrians interact with the self-driving vehicle. It is therefore of critical importance to ensure that self-driving software is assessed against a wide range of challenging simulated driving scenarios. The state of the art in driving scenario generation, as adopted by some of the front-runners of the self-driving car industry, still relies on human input [1]. In this paper we propose to automate the process using Bayesian optimization to generate adversarial self-driving scenarios that expose poorly-engineered or poorly-trained self-driving policies, and increase the risk of collision with simulated pedestrians and vehicles. We show that by incorporating the generated scenarios into the training set of the self-driving policy, and by fine-tuning the policy using vision-based imitation learning we obtain safer self-driving behavior.


title: An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model

abstract: This paper proposes a novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc. The proposed framework is built on a novel total-variation based formulation which uses l1 norm regularization to robustly suppress noisy texture pixels for the accurate iris localization. A series of novel and robust post processing operations are introduced to more accurately localize the limbic boundaries. Our experimental results on three publicly available databases, i.e., FRGC, UBIRIS.v2 and CASIA.v4-distance, achieve significant performance improvement in terms of iris segmentation accuracy over the state-of-the-art approaches in the literature. Besides, we have shown that using iris masks generated from the proposed approach helps to improve iris recognition performance as well. Unlike prior work, all the implementations in this paper are made publicly available to further advance research and applications in biometrics at-d-distance.


title: Harnessing the potential of induced pluripotent stem cells for regenerative medicine

abstract: The discovery of methods to convert somatic cells into induced pluripotent stem cells (iPSCs) through expression of a small combination of transcription factors has raised the possibility of producing custom-tailored cells for the study and treatment of numerous diseases. Indeed, iPSCs have already been derived from patients suffering from a large variety of disorders. Here we review recent progress that has been made in establishing iPSC-based disease models, discuss associated technical and biological challenges, and highlight possible solutions to overcome these barriers. We believe that a better understanding of the molecular basis of pluripotency, cellular reprogramming and lineage-specific differentiation of iPSCs is necessary for progress in regenerative medicine.


title: The advisor robot: tracing people's mental model from a robot's physical attributes

abstract: Humanoid robots offer many physical design choices such as voice frequency and head dimensions. We used hierarchical statistical mediation analysis to trace differences in people's mental model of robots from these choices. In an experiment, a humanoid robot gave participants online advice about their health. We used mediation analysis to identify the causal path from the robot's voice and head dimensions to the participants' mental model, and to their willingness to follow the robot's advice. The male robot voice predicted impressions of a knowledgeable robot, whose advice participants said they would follow. Increasing the voice's fundamental frequency reduced this effect. The robot's short chin length (but not its forehead dimensions) predicted impressions of a sociable robot, which also predicted intentions to take the robot's advice. We discuss the use of this approach for designing robots for different roles, when people's mental model of the robot matters.


title: DAG Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm

abstract: Among the numerous DAG scheduling heuristics suitable for heterogeneous systems, the Heterogeneous Earliest Finish Time (HEFT) heuristic is known to give good results in short time. In this paper, we propose an improvement of HEFT, where the locally optimal decisions made by the heuristic do not rely on estimates of a single task only, but also look ahead in the schedule and take into account information about the impact of this decision to the children of the task being allocated. Preliminary simulation results indicate that the lookahead variation of HEFT can effectively reduce the makespan of the schedule in most cases without making the algorithm’s execution time prohibitively high.


title: Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions

abstract: Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.


title: Yoga posture recognition by detecting human joint points in real time using microsoft kinect

abstract: Musculoskeletal disorder is increasing in humans due to accidents or aging which is a great concern for future world. Physical exercises can reduce this disorder. Yoga is a great medium of physical exercise. For doing yoga a trainer is important who can monitor the perfectness of different yoga poses. In this paper, we have proposed a system which can monitor human body parts movement and monitor the accuracy of different yoga poses which aids the user to practice yoga. We have used Microsoft Kinect to detect different joint points of human body in real time and from those joint points we calculate various angles to measure the accuracy of a certain yoga poses for a user. Our proposed system can successfully recognize different yoga poses in real time.


title: Prototype implementation of ambient RF energy harvesting wireless sensor networks

abstract: Energy harvesting is a key technique that can be used to overcome the barriers that prevent the real world deployment of wireless sensor networks (WSNs). In particular, solar energy harvesting has been commonly used to overcome this barrier. However, it should be noted that WSNs operating on solar power suffer form energy shortage during nighttimes. Therefore, to solve this problem, we exploit the use of TV broadcasts airwaves as energy sources to power wireless sensor nodes. We measured the output of a rectenna continuously for 7 days; from the results of this measurement, we showed that Radio Frequency (RF) energy can always be harvested. We developed an RF energy harvesting WSN prototype to show the effectiveness of RF energy harvesting for the usage of a WSN. We also proposed a duty cycle determination method for our system, and verified the validity of this method by implementing our system. This RF energy harvesting method is effective in a long period measurement application that do not require high power consumption.


title: A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction

abstract: Deep convolutional neural networks have led to breakthrough results in numerous practical machine learning tasks such as classification of images in the ImageNet data set, control-policy-learning to play Atari games or the board game Go, and image captioning. Many of these applications first perform feature extraction and then feed the results thereof into a trainable classifier. The mathematical analysis of deep convolutional neural networks for feature extraction was initiated by Mallat, 2012. Specifically, Mallat considered so-called scattering networks based on a wavelet transform followed by the modulus non-linearity in each network layer, and proved translation invariance (asymptotically in the wavelet scale parameter) and deformation stability of the corresponding feature extractor. This paper complements Mallat's results by developing a theory that encompasses general convolutional transforms, or in more technical parlance, general semi-discrete frames (including Weyl-Heisenberg filters, curvelets, shearlets, ridgelets, wavelets, and learned filters), general Lipschitz-continuous non-linearities (e.g., rectified linear units, shifted logistic sigmoids, hyperbolic tangents, and modulus functions), and general Lipschitz-continuous pooling operators emulating, e.g., sub-sampling and averaging. In addition, all of these elements can be different in different network layers. For the resulting feature extractor we prove a translation invariance result of vertical nature in the sense of the features becoming progressively more translation-invariant with increasing network depth, and we establish deformation sensitivity bounds that apply to signal classes such as, e.g., band-limited functions, cartoon functions, and Lipschitz functions.


Demonstrating the oxidation of ammonia


title: INTERPERSONAL RELATIONS AND GROUP PROCESSES Functional Projection: How Fundamental Social Motives Can Bias Interpersonal Perception

abstract: Results from 2 experimental studies suggest that self-protection and mate-search goals lead to the perception of functionally relevant emotional expressions in goal-relevant social targets. Activating a self-protection goal led participants to perceive greater anger in Black male faces (Study 1) and Arab faces (Study 2), both out-groups heuristically associated with physical threat. In Study 2, participants' level of implicit Arab-threat associations moderated this bias. Activating a mate-search goal led male, but not female, participants to perceive more sexual arousal in attractive opposite-sex targets (Study 1). Activating these goals did not influence perceptions of goal-irrelevant targets. Additionally, participants with chronic self-protective and mate-search goals exhibited similar biases. Findings are consistent with a functionalist, motivation-based account of interpersonal perception.


title: Kinetic Model for a Spherical Rolling Robot with Soft Shell in a Beeline Motion

abstract: A simplified kinetic model called Spring Pendulum is developed for a spherical rolling robot with soft shell in order to meet the needs of attitude stabilization and controlling for the robot. The elasticity and plasticity of soft shell is represented by some uniform springs connected to the bracket in this model. The expression of the kinetic model is deduced from Newtonian mechanics principles. Testing data of the driving angle acquired from a prototype built by authors indicate that testing data curve accords to the theoretic kinetic characteristic curve, so the kinetic model is validated


title: 1 Through the Eyes of Love: Reality and Illusion in Intimate Relationships

abstract: This article reviews the research literature and theory concerned with accuracy of judgments in romantic relationships. We initially propose a model of cognition in (romantic) relationships that distinguishes between 2 forms of accuracy: mean-level bias and tracking accuracy. We then report the results of meta-analyses of research on heterosexual, romantic relationships, which used external benchmarks and reported levels of tracking accuracy (98 studies) and/or mean-level bias (48 studies). The results revealed robust overall effect sizes for both tracking accuracy (r = .47) and positive mean-level bias (r = .09). As expected, the effects were substantial and positive for tracking accuracy across 6 judgmental categories, whereas signed mean-level bias was negative for the interaction attributions (e.g., love, communication). The results showed, as expected, that these 2 forms of accuracy were independent—the 2 kinds of effect size derived from the same set of 38 studies were uncorrelated. As expected, gender, relationship length, and relationship evaluations moderated mean-level bias across studies but (unex- pectedly) not for tracking accuracy. In the Discussion we evaluate the prior model in light of the findings, other research, moderating variables (such as self-esteem), the role of projection, the early stages of mate selection, metacognition, and the rationality and nature of motivated cognition. We conclude that our model, findings, and analyses help to resolve the apparent paradox that love is both riven with illusions and rooted in reality, and support both evolutionary and social psychological approaches to understanding cognition in romantic relationships.


title: Do Drinking Practices at University Differ among Students Coming from Urban and Rural Centers? A Preliminary Examination of Knowledge, Attitudes and Beliefs.

abstract: This exploratory study examined the influence of residential background and gender on university student drinking patterns. A questionnaire was distributed to 255 kinesiology and physical education students. Rural students (n=104) were more likely than urban students (n=151) to drink at outdoor parties or in vehicles during their first experience with alcohol. No significant differences were found between residential background and drinking motives, knowledge, or negative consequences concerning alcohol. Males, however, experienced significantly more negative ramifications, such as driving while under the influence, damaging property, breaking the law, injuring another party, and getting into a physical or verbal confrontation. This study supports the gender differences concerning the negative consequences of alcohol consumption, and adds to the information concerning rural-urban differences and alcohol use. Introduction The university years are a period of transition for young adults involving a myriad of changes, such as new living arrangements, increased academic pressures and changes in social norms and expectations. In recent literature, alcohol consumption among university-aged students has received considerable attention, primarily due to the negative consequences of its misuse/abuse. Statistics Canada (2003) reports that 76% of men and 61% of women between the ages of 20 and 24 are regular drinkers (consume one or more drinks per month). In a United States national survey of 14,000 college students, 19% were found to be abstainers, 44% binge drinkers (5{4} drinks in a row for males {females} during past 2 weeks), and 23% frequent binge drinkers (binged 3+ times in past 2 weeks) (Wechsler, Lee, Kuo & Lee, 2000). High rates of alcohol consumption have the potential to compromise the well-being of university students, resulting in negative consequences such as illness, unplanned or unsafe sex, injury, memory loss, trouble with the law, and academic repercussions (Wechsler, Dowdall, Davenport & Rimm, 1995; Haemmerlie, Montgomery & Saling, 1994). For example, in a sample of 1919 university students, alcohol consumption was significantly linked to number of sexual partners and to the frequency of sexual activity (Prince & Bernard, 1998). In addition, 70% of 210 college students admitted inconsistent condom use after the consumption of alcohol (Poulson, Eppler, Satterwhite, Wuensch & Bass, 1998). Further, Barr and MacKinnon (1998) found that a mere 6% of 544 university students consistently used a non-drinking designated driver. Additionally, Caldwell (2002) found that heavier drinkers (eight or more drinks per episode) suffered significantly more negative consequences than those who drank lesser amounts. Influence of Gender and Residential Background A. Consumption Rates In a study of 130 male and 130 female potential problem drinkers (15+ drinks/week), 59% of males were found to drink at least 5 or 6 drinks on nearly every occasion, while only 36% of females drank this amount. Males consumed almost twice as much as females on a weekly basis (Corbin, McNair & Carter, 1996). Further a survey of Ontario university students showed males to be 3 times more likely to consume 15 drinks weekly (Gliksman, Newton-Taylor, Adlaf & Giesbrecht, 1997) (see also, Robinson, Gloria, Roth & Schuetter, 1993; Humphrey & Friedman, 1986). When comparing urban and rural residents, inconsistent findings exist. Mitic (1989) found that rural female adolescents (aged 13-19) reported more alcohol abstinence than their urban counterparts, while Booth, Ross, and Rost (1999) found that 61% of randomly selected rural residents abstained from alcohol compared to 49% of urban residents. Cronk and Sarvela (1997) revealed that consumption varied by regions at different times, with alcohol use being more prevalent among rural youth and the age of consumption being younger among rural youth; however, it was noted that the gap appears to be decreasing as time progresses. …


title: An IoT Based Real-Time Environmental Monitoring System Using Arduino and Cloud Service

abstract: Internet of Things (IoT) is expected to play a major role in our lives through pervasive systems of sensor networks encompassing our environment. These systems are designed to monitor vital physical phenomena generating data which can be transmitted and saved at cloud from where this information can be accessed through applications and further actions can be taken. This paper presents the implementation and results of an environmental monitoring system which employs sensors for temperature and humidity of the surrounding area. This data can be used to trigger short term actions such as remotely controlling heating or cooling devices or long term statistics. The sensed data is uploaded to cloud storage and an Android application accesses the cloud and presents the results to the end users. The system employs Arduino UNO board, DHT11 sensor, ESP8266 Wi-Fi module, which transmits data to open IoT API service ThingSpeak where it is analyzed and stored. An Android application is developed which accesses the cloud and displays results for end users via REST API Web service. The experimental results show the usefulness of the system.


title: An End-to-End Deep Neural Network for Facial Emotion Classification

abstract: Facial emotional expression is a nonverbal communication medium in human-human communication. Facial expression recognition (FER) is a significantly challenging task in computer vision. With the advent of deep neural networks, facial expression recognition has transitioned from lab-controlled settings to more neutral environments. However, deep neural networks (DNNs) suffer from overfitting the data and biases towards specific categorical distribution. The number of samples in each category is heavily imbalanced, and overall the number of samples is much less than the full number of samples representing all emotions. In this paper, we propose an end-to-end convolutional-self attention framework for classifying facial emotions. The convolutional neural network (CNN) layers can capture the spatial features in a given frame. Here we apply a convolutional-self-attention mechanism to obtain the spatiotemporal features and perform context modelling. The AffectNet database is used to validate the framework. The AffectNet database has a large number of image samples in the wild settings, which makes this database very challenging. The result shows a 30% improvement in accuracy from the CNN baseline.


title: AlGaN/GaN HEMT with over 110 W Output Power for X-Band

abstract: AlGaN/GaN HEMT using field plate and recessed gate for X-band application was developed on SiC substrate. Internal matching circuits were designed to achieve high gain at 8 GHz for the developed device with single chip and four chips combining, respectively. The internally matched 5.52 mm single chip AlGaN/GaN HEMT exhibited 36.5 W CW output power with a power added efficiency (PAE) of 40.1% and power density of 6.6 W/mm at 35 V drain bias voltage (Vds). The device with four chips combining demonstrated a CW over 100 W across the band of 7.7-8.2 GHz, and an maximum CW output power of 119.1 W with PAE of 38.2% at Vds =31.5 V. This is the highest output power for AlGaN/GaN HEMT operated at X-band to the best of our knowledge.


title: Dissociable correlates of recollection and familiarity within the medial temporal lobes

abstract: Abstract Regions in the medial temporal lobes (MTL) have long been implicated in the formation of new memories for events, however, it is unclear whether different MTL subregions support different memory processes. Here, we used event-related functional magnetic resonance imaging (fMRI) to examine the degree to which two recognition memory processes—recollection and familiarity—were supported by different MTL subregions. Results showed that encoding activity in the rhinal cortex selectively predicted familiarity-based recognition, whereas, activity in the hippocampus and posterior parahippocampal cortex selectively predicted recollection. Collectively, these results support the view that different subregions within the MTL memory system implement unique encoding processes that differentially support familiarity and recollection.


title: SEVA 3 D : Autonomous Vehicles Parking Simulator in a three-dimensional environment

abstract: This paper describes a simulation system proposed in order to study and to implement intelli- gent autonomous vehicle control. The developed system can automatically drive a vehicle, implementing a robust control system capable of simulating in a realistic way autonomous parking in a parallel park- ing space. The system controls the vehicles based on the reading of sonar sensors and automatically generates acceleration and steering commands, parking it in a parallel parking space. The controller was implemented using a rule-based nite state automaton, and the results obtained in our simulations demonstrated that the proposed controller is perfectly able to correctly park the vehicle in different situ- ations.


title: Design of a class-E inverter for piezoelectric ultrasound generation against load variation

abstract: Piezoelectric transducers are used for generating ultrasound around their resonant frequencies. Single-ended class-E resonant inverter has a simple configuration and high conversion efficiency; therefore, was investigated for driving piezoelectric transducer for ultrasound generation. However, both piezoelectric transducer and class-E circuit are sensitive to load variation. This paper studies the susceptibility of output power and conversion efficiency of a class-E based piezoelectric ultrasound generator under different load conditions. By using the iteration-free extended impedance method, the integration of class-E inverter and piezoelectric transducer can be efficiently analyzed; parameters can be properly designed and optimized. An in-situ adapting approach, which tunes the duty ratio and dc supply voltage for maximizing the conversion efficiency at constant output power against load variation, is investigated. Simulation results show that, by taking this parameter tuning, the susceptibility of output power and conversion efficiency can be alleviated within a wide range of load.


title: Device-To-Device (D2D) Communication in Cellular Network - Performance Analysis of Optimum and Practical Communication Mode Selection

abstract: In a cellular network system one way to increase its capacity is to allow direct communication between closely located user devices when they are communicating with each other instead of conveying data from one device to the other via the radio and core network. The problem is then when the network shall assign direct communication mode and when not. In previous works the decision has been done individually per communicating device pair not taking into account other devices and the current state of the network. We derive means for getting optimal communication mode for all devices in the system in terms of system equations. The system equations capture information of the network such as link gains, noise levels, signal-to-interference-and-noise-ratios, etc., as well as communication mode selection for the devices. Using the derived equations performance bounds for the cellular system where D2D communication is an additional communication mode are illustrated via simulations. Further, practical communication mode selection algorithms are used to evaluate their system performance against the achievable bounds. Analysis show the usability of the system equations and the potential of having D2D operation integrated into a cellular system when there is enough local communication occurring.


Alana: Social Dialogue using an Ensemble Model and a Ranker trained on User Feedback


title: Memory Representations in Natural Tasks

abstract: The very limited capacity of short-term or working memory is one of the most prominent features of human cognition. Most studies have stressed delimiting the upper bounds of this memory in memorization tasks rather than the performance of everyday tasks. We designed a series of experiments to test the use of short-term memory in the course of a natural hand-eye task where subjects have the freedom to choose their own task parameters. In this case subjects choose not to operate at the maximum capacity of short-term memory but instead seek to minimize its use. In particular, reducing the instantaneous memory required to perform the task can be done by serializing the task with eye movements. These eye movements allow subjects to postpone the gathering of task-relevant information until just before it is required. The reluctance to use short-term memory can be explained if such memory is expensive to use with respect to the cost of the serializing strategy.


title: Control structure for a car-like robot using artificial neural networks and genetic algorithms

abstract: The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work.


title: Error Bounds for Piecewise Smooth and Switching Regression

abstract: This paper deals with regression problems, in which the nonsmooth target is assumed to switch between different operating modes. Specifically, piecewise smooth (PWS) regression considers target functions switching deterministically via a partition of the input space, while switching regression considers arbitrary switching laws. This paper derives generalization error bounds in these two settings by following the approach based on Rademacher complexities. For PWS regression, our derivation involves a chaining argument and a decomposition of the covering numbers of PWS classes in terms of the ones of their component function classes and the capacity of the classifier partitioning the input space. This yields error bounds with a radical dependence on the number of modes. For switching regression, the decomposition can be performed directly at the level of the Rademacher complexities, which yields bounds with a linear dependence on the number of modes. By using once more chaining and decomposition at the level of covering numbers, we show how to recover a radical dependence. Examples of applications are given in particular for PWS and swichting regression with linear and kernel-based component functions.


Facial Landmark Detection via Progressive Initialization Shengtao


title: Incognito: efficient full-domain K-anonymity

abstract: A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes that clearly identify individuals, such as Name and Social Security Number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zipcode, Sex, and Birthdate to re-identify individuals who were supposed to remain anonymous. "Joining" attacks are made easier by the availability of other, complementary, databases over the Internet.K-anonymization is a technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In this paper, we provide a practical framework for implementing one model of k-anonymization, called full-domain generalization. We introduce a set of algorithms for producing minimal full-domain generalizations, and show that these algorithms perform up to an order of magnitude faster than previous algorithms on two real-life databases.Besides full-domain generalization, numerous other models have also been proposed for k-anonymization. The second contribution in this paper is a single taxonomy that categorizes previous models and introduces some promising new alternatives.


Mining frequent patterns by pattern-growth: methodology and implications


title: Exact and Adaptive Signed Distance FieldsComputation for Rigid and DeformableModels on GPUs

abstract: Most techniques for real-time construction of a signed distance field, whether on a CPU or GPU, involve approximate distances. We use a GPU to build an exact adaptive distance field, constructed from an octree by using the Morton code. We use rectangle-swept spheres to construct a bounding volume hierarchy (BVH) around a triangulated model. To speed up BVH construction, we can use a multi-BVH structure to improve the workload balance between GPU processors. An upper bound on distance to the model provided by the octree itself allows us to reduce the number of BVHs involved in determining the distances from the centers of octree nodes at successively lower levels, prior to an exact distance query involving the remaining BVHs. Distance fields can be constructed 35-64 times as fast as a serial CPU implementation of a similar algorithm, allowing us to simulate a piece of fabric interacting with the Stanford Bunny at 20 frames per second.


title: Structured Probabilistic Pruning for Convolutional Neural Network Acceleration

abstract: Although deep Convolutional Neural Network (CNN) has shown better performance in various computer vision tasks, its application is restricted by a significant increase in storage and computation. Among CNN simplification techniques, parameter pruning is a promising approach which aims at reducing the number of weights of various layers without intensively reducing the original accuracy. In this paper, we propose a novel progressive parameter pruning method, named Structured Probabilistic Pruning (SPP), which effectively prunes weights of convolutional layers in a probabilistic manner. Specifically, unlike existing deterministic pruning approaches, where unimportant weights are permanently eliminated, SPP introduces a pruning probability for each weight, and pruning is guided by sampling from the pruning probabilities. A mechanism is designed to increase and decrease pruning probabilities based on importance criteria for the training process. Experiments show that, with 4x speedup, SPP can accelerate AlexNet with only 0.3% loss of top-5 accuracy and VGG-16 with 0.8% loss of top-5 accuracy in ImageNet classification. Moreover, SPP can be directly applied to accelerate multi-branch CNN networks, such as ResNet, without specific adaptations. Our 2x speedup ResNet-50 only suffers 0.8% loss of top-5 accuracy on ImageNet. We further prove the effectiveness of our method on transfer learning task on Flower-102 dataset with AlexNet.


title: Towards Evaluating the Quality of Experience of Remote Patient Monitoring Services: A Study Considering Usability Aspects

abstract: The applicability of advanced mobile technologies in the m-Health domain has led to a number of studies and (limited) commercial products supporting delivery of health services to remote users. A key issue regarding successful delivery and acceptance of such services is meeting their Quality of Service (QoS) and Quality of Experience (QoE) requirements, focusing on technical aspects and end user perceived quality, respectively. In this paper, the authors address the topic of evaluating QoE for non-emergency remote patient monitoring services. They identify relevant QoE influence factors and metrics, and present the results of a QoE evaluation study, whereby they focus on usability aspects. The study involves 26 users testing a prototype version of the Ericsson Mobile Health service, which is based on a smartphone application and measurement of vital signs via medical sensors. The results show a strong correlation between QoE and: perceived effectiveness of the mobile interface (regarding both adequacy of smartphone screen size and smartphone application navigational support), perceived ease of conducting a blood pressure measurement task, and user motivation for service usage.


title: Automatic detection of fruits in coffee crops from aerial images

abstract: A big challenge in the precision agriculture is the detection of fruits in coffee crops on agricultural environments. This paper presents a comparison of four features set to detect the red fruits (mature) in Coffee plants. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a coffee hall. The proposed methodology enables the extraction of visual features from image regions and uses supervised Machine Learning (ML) techniques to classify areas as coffee fruits and non-fruits (e.g. branches and leaves). Several ML methods were compared using the test data achieved from a Coffee plantation. Experimental results show that the ANN model is more reliable than other ML methods for accurately identifying coffee fruits.


title: A bluetooth based sophisticated home automation system using smartphone

abstract: This paper represents a reliable, compact, fast and low cost smart home automation system, based on Arduino (microcontroller) and Android app. Bluetooth chip has been used with Arduino, thus eliminating the use of personal computers (PCs). Various devices such as lights, DC Servomotors have been incorporated in the designed system to demonstrate the feasibility, reliability and quick operation of the proposed smart home system. The entire designed system has been tested and it is seen capable of running successfully and perform the desired operations, such as switching functionalities, position control of Servomotor, speed control of D.C motor and light intensity control (Via Voltage Regulation).


title: Trajectory clustering in road network environment

abstract: This paper proposes a new trajectory clustering scheme for objects moving on road networks. A trajectory on road networks can be defined as a sequence of road segments a moving object has passed by. We first propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments. Then, we propose a new clustering algorithm based on such similarity measurement criteria by modifying and adjusting the FastMap and hierarchical clustering schemes. To evaluate the performance of the proposed clustering scheme, we also develop a trajectory generator considering the fact that most objects tend to move from the starting point to the destination point along their shortest path. The performance result shows that our scheme has the accuracy of over 95%.


title: KineAssist: a robotic overground gait and balance training device

abstract: The KineAssist is a robotic device for gait and balance training. A user-needs analysis led us to focus on increasing the level of challenge to a patient's ability to maintain balance during gait training, and also on maintaining direct involvement of a physical therapist (rather than attempting robotic replacement.) The KineAssist provides partial body weight support and postural torques on the torso; allows many axes of motion of the trunk as well as of the pelvis; leaves the patient's legs accessible to a physical therapist during walking; servo-follows a patient's walking motions overground in forward, rotation, and sidestepping directions; and catches a patient who begins to fall. Design and development of the KineAssist proceeded more rapidly in the context of a small company than would have been possible in most research contexts. A prototype KineAssist has been constructed, and has received FDA approval and IRB clearance for initial human studies. We describe the KineAssist's motivation, design, and use.


title: Hybrid Data Hiding Scheme Using Right-Most Digit Replacement and Adaptive Least Significant Bit for Digital Images

abstract: The goal of image steganographic methods considers three main key issues: high embedding capacity, good visual symmetry/quality, and security. In this paper, a hybrid data hiding method combining the right-most digit replacement (RMDR) with an adaptive least significant bit (ALSB) is proposed to provide not only high embedding capacity but also maintain a good visual symmetry. The cover-image is divided into lower texture (symmetry patterns) and higher texture (asymmetry patterns) areas and these textures determine the selection of RMDR and ALSB methods, respectively, according to pixel symmetry. This paper has three major contributions. First, the proposed hybrid method enhanced the embedding capacity due to efficient ALSB utilization in the higher texture areas of cover images. Second, the proposed hybrid method maintains the high visual quality because RMDR has the closest selection process to generate the symmetry between stego and cover pixels. Finally, the proposed hybrid method is secure against statistical regular or singular (RS) steganalysis and pixel difference histogram steganalysis because RMDR is capable of evading the risk of RS detection attacks due to pixel digits replacement instead of bits. Extensive experimental tests (over 1500+ cover images) are conducted with recent least significant bit (LSB)-based hybrid methods and it is demonstrated that the proposed hybrid method has a high embedding capacity (800,019 bits) while maintaining good visual symmetry (39.00% peak signal-to-noise ratio (PSNR)).


Cloud-based Motion Plan Computation for Power-Constrained Robots


Categorical Data Clustering using Frequency and Tf-Idf based Cosine Similarity