title: Comparative genomics reveals insights into avian genome evolution and adaptation

abstract: Birds are the most species-rich class of tetrapod vertebrates and have wide relevance across many research fields. We explored bird macroevolution using full genomes from 48 avian species representing all major extant clades. The avian genome is principally characterized by its constrained size, which predominantly arose because of lineage-specific erosion of repetitive elements, large segmental deletions, and gene loss. Avian genomes furthermore show a remarkably high degree of evolutionary stasis at the levels of nucleotide sequence, gene synteny, and chromosomal structure. Despite this pattern of conservation, we detected many non-neutral evolutionary changes in protein-coding genes and noncoding regions. These analyses reveal that pan-avian genomic diversity covaries with adaptations to different lifestyles and convergent evolution of traits.


title: On The Creation of Automatic Identification and Data Capture Infrastructure via RFID

abstract: Automatic identification data capture technologies are becoming increasingly important in the management of supply chain, manufacturing flow management, mobile asset tracking, inventory management, warehousing, and any application where physical items move through location in time. Tracking these items has historically been done by the use of bar-code technologies, which suffer from lack of efficiency, robustness, difficulty in automation, inability to have secure or dynamic data, etc., whereas the electronic technology of Radio Frequency Identification or RFID has the ability to overcome several of these limitations over barcode. This paper presents a comparative basis for the creation of Automatic Identification and Data Capture (AIDC) infrastructure via RFID versus other technologies such as bar-code and sensor technologies.


title: DC/DC Buck Power Converter as a Smooth Starter for a DC Motor Based on a Hierarchical Control

abstract: In this paper a smooth starter, based on a dc/dc Buck power converter, for the angular velocity trajectory tracking task of a dc permanent magnet motor is presented. To this end, a hierarchical controller is designed, which is integrated by a control associated with the dc motor based on differential flatness at the high level, and a control related with the dc/dc Buck converter based on a cascade control scheme at the low level. The control at the high level allows the dc motor angular velocity to track a desired trajectory and also provides the desired voltage profile that must be tracked by the output voltage of the dc/dc Buck power converter. In order to assure the latter, a cascade control at the low level is designed, considering a sliding mode control for the inner current loop and a proportional-integral control for the outer voltage loop. The hierarchical controller is tested through experiments using MATLAB-Simulink and the DS1104 board from dSPACE. The obtained results show that the desired angular velocity trajectory is well tracked under abrupt variations in the system parameters and that the controller is robust in such operation conditions, confirming the validity of the proposed controller.


title: China Compared with the US: Cultural Differences and the Impacts on Advertising Appeals

abstract: An important first step to successful global marketing is to understand the similarities and dissimilarities of values between cultures. This task is particularly daunting for companies trying to do business with China because of the scarcity of research-based information. This study uses updated values of Hofstede’s (1980) cultural model to compare the effectiveness of Pollay’s advertising appeals between the U.S. and China. Nine of the twenty hypotheses predicting effective appeals based on cultural dimensions were supported. An additional hypothesis was significant, but in the opposite direction as predicted. These findings suggest that it would be unwise to use Hofstede’s cultural dimensions as a sole predictor for effective advertising appeals. The Hofstede dimensions may lack the currency and fine grain necessary to effectively predict the success of the various advertising appeals. Further, the effectiveness of advertising appeals may be moderated by other factors, such as age, societal trends, political-legal environment and product usage.


title: Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search

abstract: This paper presents a supervised deep hashing approach that constructs binary hash codes from labeled data for large-scale image search. We assume that semantic labels are governed by a set of latent attributes in which each attribute can be on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network in which binary codes are learned by the optimization of an objective function defined over classification error and other desirable properties of hash codes. With this design, SSDH has a nice property that classification and retrieval are unified in a single learning model, and the learned binary codes not only preserve the semantic similarity between images but also are efficient for image search. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a pointwised manner and thus is naturally scalable to large-scale datasets. SSDH is simple and can be easily realized by a slight modification of an existing deep architecture for classification; yet it is effective and outperforms other unsupervised and supervised hashing approaches on several benchmarks and one large dataset comprising more than 1 million images.


title: Shape matching and classification using height functions

abstract: We propose a novel shape descriptor for matching and recognizing 2D object silhouettes. The contour of each object is represented by a fixed number of sample points. For each sample point, a height function is defined based on the distances of the other sample points to its tangent line. One compact and robust shape descriptor is obtained by smoothing the height functions. The proposed descriptor is not only invariant to geometric transformations such as translation, rotation and scaling but also insensitive to nonlinear deformations due to noise and occlusion. In the matching stage, the Dynamic Programming (DP) algorithm is employed to find out the optimal correspondence between sample points of every two shapes. The height function provides an excellent discriminative power, which is demonstrated by excellent retrieval performances on several popular shape benchmarks, including MPEG-7 data set, Kimia's data set and ETH-80 data set.


title: A Hierarchical Compositional Model for Face Representation and Sketching

abstract: This paper presents a hierarchical-compositional model of human faces, as a three-layer AND-OR graph to account for the structural variabilities over multiple resolutions. In the AND-OR graph, an AND-node represents a decomposition of certain graphical structure, which expands to a set of OR-nodes with associated relations; an OR-node serves as a switch variable pointing to alternative AND-nodes. Faces are then represented hierarchically: The first layer treats each face as a whole, the second layer refines the local facial parts jointly as a set of individual templates, and the third layer further divides the face into 15 zones and models detail facial features such as eye corners, marks, or wrinkles. Transitions between the layers are realized by measuring the minimum description length (MDL) given the complexity of an input face image. Diverse face representations are formed by drawing from dictionaries of global faces, parts, and skin detail features. A sketch captures the most informative part of a face in a much more concise and potentially robust representation. However, generating good facial sketches is extremely challenging because of the rich facial details and large structural variations, especially in the high-resolution images. The representing power of our generative model is demonstrated by reconstructing high-resolution face images and generating the cartoon facial sketches. Our model is useful for a wide variety of applications, including recognition, nonphotorealisitc rendering, superresolution, and low-bit rate face coding.


title: A micro-UAV-borne system for radar imaging: A feasibility study

abstract: Radar mounted onboard micro-UAV is an early stage technology and its potentiality is far from being focused, even if radar sensors having costs compatible with micro-UAV are currently developed. As a contribution to this topic, this paper describes a radar-equipped hexacopter assembled thanks to complementary skills available at IREA and DII. In order to test the operation mode of the system as well as to investigate its target detection and localization capabilities, a feasibility experiment has been carried out in December 2016. The results of this flight campaign are presented, in terms of both raw data and images obtained by means of an ad-hoc data processing approach. These results provide an encouraging preliminary proof of the achievable outcomes.


title: Three-Dimensional Printing: Rapid Tooling and Prototypes Directly from a CAD Model

abstract: Summary Three Dimensional Printing is a process for the manufacture of tooling and functional prototype parts directly from computer models. Three Dimensional Printing functions by the deposition of powdered material in layers and the selective binding of the powder by “ink-jet” printing of a binder material. Following the sequential application of layers, the unbound powder is removed, resulting in a complex three-dimensional part. The process may be applied to the production of metal, ceramic, and metal/ceramic composite parts. An experiment employing continuous-jet ink-jet printing technology has produced a three-dimensional part comprising eight intersecting planes spaced 0.375 inches apart. Future research will be directed toward the direct fabrication of cores and shells for metal casting, and toward the fabrication of porous ceramic preforms for metal-ceramic composite parts.


title: Playing for Benchmarks

abstract: We present a benchmark suite for visual perception. The benchmark is based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level and high-level vision tasks, including optical flow, semantic instance segmentation, object detection and tracking, object-level 3D scene layout, and visual odometry. Ground-truth data for all tasks is available for every frame. The data was collected while driving, riding, and walking a total of 184 kilometers in diverse ambient conditions in a realistic virtual world. To create the benchmark, we have developed a new approach to collecting ground-truth data from simulated worlds without access to their source code or content. We conduct statistical analyses that show that the composition of the scenes in the benchmark closely matches the composition of corresponding physical environments. The realism of the collected data is further validated via perceptual experiments. We analyze the performance of state-of-the-art methods for multiple tasks, providing reference baselines and highlighting challenges for future research. The supplementary video can be viewed at this https URL


title: Patient preferences for oropharyngeal cancer treatment de-escalation.

abstract: BACKGROUND ::: The excellent prognosis of human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinomas (SCCs) against severe chemoradiotherapy (CRT) toxicities has opened discussion of deintensification trials. The purpose of this study was to describe the perspective of patients with HPV-positive and HPV-negative disease toward such studies. ::: ::: ::: METHODS ::: Fifty-one patients with oropharyngeal SCC (post-CRT) underwent semistructured interviews contrasting toxicities of radiotherapy (RT) alone and CRT. Patients were asked what potential difference in cancer survival was acceptable to prefer RT over CRT. Initially, survival rate was the same for both treatments, then the RT rate was reduced until the preference switched. Treatment experience and preference for deintensified CRT were collected. ::: ::: ::: RESULTS ::: Ninety-percent of patients initially selected RT, but 69% switched to CRT after 0% to 5% reduction in survival. Patients that rated their treatment experience as mild would accept lower survival versus severe treatment (p = .02). Eighty-one percent of patients (33 of 40) indicated they preferred reduced chemotherapy in CRT. ::: ::: ::: CONCLUSION ::: Patients accept little difference in survival between treatments to avoid toxicity.


title: An Examination of Digital Forensic Models

abstract: Law enforcement is in a perpetual race with criminals in the application of digital technologies, and requires the development of tools to systematically search digital devices for pertinent evidence. Another part of this race, and perhaps more crucial, is the development of a methodology in digital forensics that encompasses the forensic analysis of all genres of digital crime scene investigations. This paper explores the development of the digital forensics process, compares and contrasts four particular forensic methodologies, and finally proposes an


title: Dissecting GPU Memory Hierarchy Through Microbenchmarking

abstract: Memory access efficiency is a key factor in fully utilizing the computational power of graphics processing units (GPUs). However, many details of the GPU memory hierarchy are not released by GPU vendors. In this paper, we propose a novel fine-grained microbenchmarking approach and apply it to three generations of NVIDIA GPUs, namely Fermi, Kepler, and Maxwell, to expose the previously unknown characteristics of their memory hierarchies. Specifically, we investigate the structures of different GPU cache systems, such as the data cache, the texture cache and the translation look-aside buffer (TLB). We also investigate the throughput and access latency of GPU global memory and shared memory. Our microbenchmark results offer a better understanding of the mysterious GPU memory hierarchy, which will facilitate the software optimization and modelling of GPU architectures. To the best of our knowledge, this is the first study to reveal the cache properties of Kepler and Maxwell GPUs, and the superiority of Maxwell in shared memory performance under bank conflict.


title: A knowledge‐based approach for polarity classification in Twitter

abstract: Until now, most of the methods published for polarity classification in Twitter have used a supervised approach. The differences between them are only the features selected and the method used for weighting them. In this article, we present an unsupervised method for polarity classification in Twitter. The method is based on the expansion of the concepts expressed in the tweets through the application of PageRank to WordNet. In addition, we integrate SentiWordNet to compute the final value of polarity. The synsets values are weighted with the PageRank scores obtained in the previous random walk process over WordNet. The results obtained show that disambiguation and expansion are good strategies for improving overall performance.


title: Tangible play: research and design for tangible and tabletop games

abstract: This workshop addresses questions related to the areas of tangible interaction, game design and emerging technologies for tangible and tabletop games. We bring together researchers and practitioners from diverse fields related to these topics, such as HCI, computer science, interaction design and game design. We seek collaborative ways to move forward the field of tangible and tabletop games.


title: Context-Aware Saliency Detection

abstract: We propose a new type of saliency—context-aware saliency—which aims at detecting the image regions that represent the scene. This definition differs from previous definitions whose goal is to either identify fixation points or detect the dominant object. In accordance with our saliency definition, we present a detection algorithm which is based on four principles observed in the psychological literature. The benefits of the proposed approach are evaluated in two applications where the context of the dominant objects is just as essential as the objects themselves. In image retargeting, we demonstrate that using our saliency prevents distortions in the important regions. In summarization, we show that our saliency helps to produce compact, appealing, and informative summaries.


title: Applying Tropos Methodology to a real case study: Complexity and Criticality Analysis

abstract: Currently in Requirements Engineering the attention is being focused more and more on the understanding of a problem by studying the existing organizational setting in which the system will operate. In this paper we present the application of the Tropos early requirements analysis to a real case study, the Ice Co. We introduce a new type of analysis for actor diagrams based on two different parameters, complexity and criticality, and we show the results we obtained during the case study.


title: Pupil Fluctuations Track Fast Switching of Cortical States during Quiet Wakefulness

abstract: Summary Neural responses are modulated by brain state, which varies with arousal, attention, and behavior. In mice, running and whisking desynchronize the cortex and enhance sensory responses, but the quiescent periods between bouts of exploratory behaviors have not been well studied. We found that these periods of "quiet wakefulness" were characterized by state fluctuations on a timescale of 1–2 s. Small fluctuations in pupil diameter tracked these state transitions in multiple cortical areas. During dilation, the intracellular membrane potential was desynchronized, sensory responses were enhanced, and population activity was less correlated. In contrast, constriction was characterized by increased low-frequency oscillations and higher ensemble correlations. Specific subtypes of cortical interneurons were differentially activated during dilation and constriction, consistent with their participation in the observed state changes. Pupillometry has been used to index attention and mental effort in humans, but the intracellular dynamics and differences in population activity underlying this phenomenon were previously unknown.


title: Face to face collaborative AR on mobile phones

abstract: Mobile phones are an ideal platform for augmented reality. In this paper we describe how they also can be used to support face to face collaborative AR applications. We have created a custom port of the ARToolKit library to the Symbian mobile phone operating system and then developed a sample collaborative AR game based on this. We describe the game in detail and user feedback from people who have played it. We also provide general design guidelines that could be useful for others who are developing mobile phone collaborative AR applications.


title: Reading emotional words within sentences: The impact of arousal and valence on event-related potentials

abstract: Effects of emotional word meaning have been studied exclusively for words in isolation but not in the context of sentences. We addressed this question within the framework of two-dimensional models of affect, conceiving emotion as a function of valence and arousal. Negative and neutral target verbs, embedded within sentences, were presented while event-related brain potentials (ERPs) and the activity of the Corrugator muscle were recorded. Twenty-one participants performed a semantic decision task on the target verbs. In contrast to single word studies no early posterior negativity was present. However, emotion effects in ERPs were evident in a late positive complex (LPC) for negative, high-arousal words in comparison to neutral words. Interestingly, the LPC was unaffected by pure arousal variation when valence was controlled for, indicating the importance of valence for this emotion-related ERP effect.


title: Text-Edge-Box: An Object Proposal Approach for Scene Texts Localization

abstract: Text proposal has been gaining interest in recent years due to the great success of object proposal in categoriesindependent object localization. In this paper, we present a novel text-specific proposal technique that provides superior bounding boxes for accurate text localization in scenes. The proposed technique, which we call Text Edge Box (TEB), uses a binary edge map, a gradient map and an orientation map of an image as inputs. Connected components are first found within the binary edge map, which are scored by two proposed low-cue text features that are extracted in the gradient map and the orientation map, respectively. These scores present text probability of connected components and are aggregated in a text edge image. Scene texts proposals are finally generated by grouping the connected components and estimating their likelihood of being words. The proposed TEB has been evaluated on the two public scene text datasets: the Robust Reading Competition 2013 dataset (ICDAR 2013) dataset and the Street View Text (SVT) dataset. Experiments show that the proposed TEB outperforms the state-of-the-art techniques greatly.


title: Genetic and biochemical studies with ataxia telangiectasia

abstract: This article summarizes the genetics and clinical features of ataxia telangiectasia (AT) and then reviews recent cytogenetic, cellular, and biochemical studies which support the hypothesis that a defect in DNA repair is responsible for the various manifestations of the disease. The biochemical evidence further indicates that the defect specifically reduces the cellular capacity to remove bases and nucleotides damaged by ionizing radiation, without affecting the cells' ability to scavenge free radicals or to rejoin breaks in the sugar-phosphate backbone of DNA. Suggestions for additional research to more precisely identify the repair defect will also be presented.


title: A review of semantic segmentation using deep neural networks

abstract: During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.


title: Text categorization based on improved Rocchio algorithm

abstract: Text categorization is used to assign each text document to predefined categories. This paper presents a new text classification method for classifying Chinese text based on Rocchio algorithm. We firstly use the TFIDF to extract document vectors from the training documents which have been correctly categorized, and then use those document vectors to generate codebooks as classification models using the LBG and Rocchio algorithm. The codebook is then used to categorize the target documents using vector scores. We tested this method in the experiment and the result shows that this method can achieve better performance.


OPEN ACCESS Design of Efficient ZVS Half-Bridge Series Resonant Inverter with Suitable Control Technique


title: Meshed Patch Antennas Integrated on Solar Cells

abstract: This letter presents the study of integrating meshed patch antennas directly onto the solar cells of a small satellite to save valuable surface real estate. The cover glass of the solar cell is used as the substrate for the antennas. The integrated patch antennas are designed to have sufficient optical transparency to ensure the proper functionality of the solar cells. A prototype meshed patch antenna is designed and integrated on after-market solar cells. The antenna has an optical transparency of 93%, and the measurements agree well with the design.


title: Fast and Accurate Characterization of MOS and Interconnect Capacitance Using Direct Charge Measurement (DCM)

abstract: A fast and accurate capacitance measurement technique, direct charge measurement (DCM), is introduced to improve productivity of semiconductor parametric testing. The approach is simpler and much faster compared with conventional method using charge-based capacitance measurement (CBCM) or LCR meter. On-chip active device is not an essential necessity for DCM test structure and it is easy to implement parallel measurements. The basic theory, parallel measurement method and mathematical analysis on non-linear MOS capacitance measurement are explained. For interconnect capacitance measurement, an extension of DCM, degenerated exhaustive direct charge measurement (DEDCM) is presented as a faster, more accurate and thorough characterization technique. Experimental results show good data matching and significant throughput improvement over conventional LCR meter measurements.


title: PREFER: Using a Graph-Based Approach to Generate Paraphrases for Language Learning

abstract: Paraphrasing is an important aspect of language competence; however, EFL learners have long had difficulty paraphrasing in their writing owing to their limited language proficiency. Therefore, automatic paraphrase suggestion systems can be useful for writers. In this paper, we present PREFER, a paraphrase reference tool for helping language learners improve their writing skills. In this paper, we attempt to transform the paraphrase generation problem into a graphical problem in which the phrases are treated as nodes and translation similarities as edges. We adopt the PageRank algorithm to rank and filter the paraphrases generated by the pivot-based paraphrase generation method. We manually evaluate the performance of our method and assess the effectiveness of PREFER in language learning. The results show that our method successfully preserves both the semantic meaning and syntactic structure of the query phrase. Moreover, the students' writing performance improve most with the assistance of PREFER.


title: A Survey on Image-Based Approaches of Synthesizing Objects

abstract: Synthesizing virtual objects into real scenes is an important process in augmented reality. Illumination consistency is the key problem of expressing the photorealistic rendering result. Consequently, a wide range of solutions for illumination estimation are proposed, such as capturing real scenes and estimation from single image. Because of its convenience, image based methods have become a popular way to synthesize virtual objects gradually. This paper provides a survey on state-of-art image-based methods for illumination estimation. As for a special case, outdoor illumination is discussed separately because of different assumptions and process. Through discussion of advantages and shortcoming of existing methods, main challenges and future work are explained at last.


title: Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme

abstract: In 1996, Hoffstein, Pipher and Silverman introduced an efficient lattice based encryption scheme dubbed NTRUEncrypt . Unfortunately, this scheme lacks a proof of security. However, in 2011, Stehle and Steinfeld showed how to modify NTRUEncrypt to reduce security to standard problems in ideal lattices. In 2012, Lopez-Alt, Tromer and Vaikuntanathan proposed a fully homomorphic scheme based on this modified system. However, to allow homomorphic operations and prove security, a non-standard assumption is required. In this paper, we show how to remove this non-standard assumption via techniques introduced by Brakerski and construct a new fully homomorphic encryption scheme from the Stehle and Steinfeld version based on standard lattice assumptions and a circular security assumption. The scheme is scale-invariant and therefore avoids modulus switching and the size of ciphertexts is one ring element. Moreover, we present a practical variant of our scheme, which is secure under stronger assumptions, along with parameter recommendations and promising implementation results. Finally, we present an approach for encrypting larger input sizes by extending ciphertexts to several ring elements via the CRT on the message space.


GPS Navigation for Outdoor and Indoor Environment


Speech Recognition for Mobile Devices at


title: Causal Bandits: Learning Good Interventions via Causal Inference

abstract: We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that is strictly better (in all quantities) than algorithms that do not use the additional causal information.


title: PROMPTDIFF: A Fixed-Point Algorithm for Comparing Ontology Versions

abstract: As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files--an approach that does not work for comparing ontologies. Two ontologies can be identical but have different text representation. We have developed the PROMPTDIFF algorithm, which integrates different heuristic matchers for comparing ontology versions. We combine these matchers in a fixed-point manner, using the results of one matcher as an input for others until the matchers produce no more changes. The current implementation includes ten matchers but the approach is easily extendable to an arbitrary number of matchers. Our evaluation showed that PROMPTDIFF correctly identified 96% of the matches in ontology versions from large projects.


title: Recent Research on Mobile Phones Effects

abstract: The use of mobile phones always accompanied by the issue of health implications for human. Previous research studies show no evidence of the impacts of mobile phones to human health. New studies suggest that mobile radiation might double the risk of developing cancer on the side of the head used, increase brain activity, can cause damage to nerves around ears and, more importantly, damages the BBB. Also new research shows that biological effects are possible without any warming of tissues which impose of the current radiation exposure levels.


title: Spacetime faces: high resolution capture for modeling and animation

abstract: We present an end-to-end system that goes from video sequences to high resolution, editable, dynamically controllable face models. The capture system employs synchronized video cameras and structured light projectors to record videos of a moving face from multiple viewpoints. A novel spacetime stereo algorithm is introduced to compute depth maps accurately and overcome over-fitting deficiencies in prior work. A new template fitting and tracking procedure fills in missing data and yields point correspondence across the entire sequence without using markers. We demonstrate a data-driven, interactive method for inverse kinematics that draws on the large set of fitted templates and allows for posing new expressions by dragging surface points directly. Finally, we describe new tools that model the dynamics in the input sequence to enable new animations, created via key-framing or texture-synthesis techniques.


A Comparison of Back Propagation Implementations


title: Towards a Secure Agile Software Development Process

abstract: Agile methodologies such as scrum and Extreme Programming (XP) are efficient development processes by accepting changes at any phase and delivering software quickly to customers. However, these methodologies have been criticized because of the unavailability of security as an important quality goal of software systems. Although, there are pre-existing research results on this topic, there is no established approach for identifying security challenges of agile practices. Specifically, we analyze agile practices to find the security challenges in customer-and developer activities during software development. Identifying these challenges helps the secure development of software using agile practices. Our case study based results show that a number of developer-and customer activities result in security flaws and vulnerabilities for the software.


title: The Agenda of Wearable Healthcare

abstract: Driven by cost and quality issues, the health system in the developed countries will undergo a fundamental change in this decade, from a physician-operated and hospital centred health system to consumer operated personal prevention, early risk detection and wellness system. This paper sketches the vision of a ‘Personal Health Assistant’ PHA, opening up new vistas in patient centred healthcare. The PHA comprises a wearable sensing and communicating system, seamlessly embedded in our daily outfit. Several onbody sensors identify the biometric and contextual status of the wearer continuously. The embedded computer generates the ‘Life Balance Factor’ LBF as an individual feedback to the user and to the surroundings affording an effective prevention, disease management and rehabilitation also in telemedicine. The state-of-the-art enabling technologies – mainly miniaturization of electronics and sensors combined with wireless communication and recent developments in wearable and pervasive computing are presented and assessed concerning multiparameter health monitoring.


title: The Insecurity of Home Digital Voice Assistants -- Amazon Alexa as a Case Study

abstract: Home Digital Voice Assistants (HDVAs) are getting popular in recent years. Users can control smart devices and get living assistance through those HDVAs (e.g., Amazon Alexa, Google Home) using voice. In this work, we study the insecurity of HDVA service by using Amazon Alexa as a case study. We disclose three security vulnerabilities which root in the insecure access control of Alexa services. We then exploit them to devise two proof-of-concept attacks, home burglary and fake order, where the adversary can remotely command the victim's Alexa device to open a door or place an order from Amazon.com. The insecure access control is that the Alexa device not only relies on a single-factor authentication but also takes voice commands even if no people are around. We thus argue that HDVAs should have another authentication factor, a physical presence based access control; that is, they can accept voice commands only when any person is detected nearby. To this end, we devise a Virtual Security Button (VSButton), which leverages the WiFi technology to detect indoor human motions. Once any indoor human motion is detected, the Alexa device is enabled to accept voice commands. Our evaluation results show that it can effectively differentiate indoor motions from the cases of no motion and outdoor motions in both the laboratory and real world settings.


title: Robust Document Image Dewarping Method Using Text-Lines and Line Segments

abstract: Conventional text-line based document dewarping methods have problems when handling complex layout and/or very few text-lines. When there are few aligned text-lines in the image, this usually means that photos, graphics and/or tables take large portion of the input instead. Hence, for the robust document dewarping, we propose to use line segments in the image in addition to the aligned text-lines. Based on the assumption and observation that many of the line segments in the image are horizontally or vertically aligned in the well-rectified images, we encode this property into the cost function in addition to the text-line alignment cost. By minimizing the function, we can obtain transformation parameters for camera pose, page curve, etc., which are used for document rectification. Considering that there are many outliers in line segment directions and missed text-lines in some cases, the overall algorithm is designed in an iterative manner. At each step, we remove text components and line segments that are not well aligned, and then minimize the cost function with the updated information. Experimental results show that the proposed method is robust to the variety of page layouts.


Morphogenetic Aspects of Gynogenetic Embryoid and Callus in Ovary Culture of Oryza sativa L.


title: Paramedian vertical palatal bone height for mini-implant insertion: a systematic review

abstract: Paramedian insertion of orthodontic mini-implants is increasingly used to anchor molar distalizers. The aim of this review was to systematically examine the available measurements of vertical palatal bone height (VBH). PUBMED, MEDLINE and the Cochrane Controlled Trials Register were searched using specific search terms. Hand searches of bibliographies of articles were also performed to identify studies measuring VBH or bone thickness in the human palate. Sixteen studies were included, arising from 19 published articles. Repeat presentations were excluded. Ten of the 11 computed tomogram (CT)-based studies presented data from 956 orthodontic patients on average VBH and its variation at a range of palatal sites. Individual data were not available, and pooling of data was not feasible because of heterogeneity of subjects, different measurement sites, different CT methods and their associated software. The compilation of data did indicate that the region 3-4mm behind the incisive foramen and 3-9mm lateral to the midpalatal suture should normally provide sufficient VBH to anchor molar distalizers. The risks of unwanted effects during distalization should be small, but the limitations listed above and the small numbers of studies available impair the precision of the estimates and do not allow the results to be generalized. Paramedian anchorage in the anterior palate can be recommended for molar distalization but, given the great inter-individual variability of the palatal bone height, it must be preceded by reliable CT-based imaging in patients identified by routine investigations as being at risk.


title: Study of Text Segmentation and Recognition Using Leap Motion Sensor

abstract: Recognition of 3-D texts drawn by fingers using Leap motion sensor can be challenging for existing text recognition frameworks. The texts sensed by Leap motion device are different from traditional offline and on-line writing systems. This is because of frequent jitters and non-uniform character sizes while writing using Leap motion interface. Moreover, because of air writing, characters, words, and lines are usually connected by continuous stroke that makes it difficult to recognize. In this paper, we present a study of segmentation and recognition of text recorded using Leap motion sensor. The segmentation task of continuous text into words is performed using a heuristic analysis of stroke length between two successive words. Next, the recognition of each segmented word is performed using sequential classifiers. In this paper, we have performed 3-D text recognition using hidden Markov model (HMM) and bidirectional long short-term memory neural networks (BLSTM-NNs). We have created a data set consisting of 560 Latin sentences drawn by ten participants using Leap motion sensor for experiments. An accuracy of 78.2% has been obtained in word segmentation, whereas 86.88% and 81.25% accuracies have been recorded in word recognition using BLSTM-NN and HMM classifiers, respectively.


title: Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

abstract: A detailed and quantitative gait analysis can provide evidence of various gait impairments in elderly people. To provide an objective decision-making basis for gait analysis, simple applicable tests analyzing a high number of strides are required. A mobile gait analysis system, which is mounted on shoes, can fulfill these requirements. This paper presents a method for computing clinically relevant temporal and spatial gait parameters. Therefore, an accelerometer and a gyroscope were positioned laterally below each ankle joint. Temporal gait events were detected by searching for characteristic features in the signals. To calculate stride length , the gravity compensated accelerometer signal was double integrated, and sensor drift was modeled using a piece-wise defined linear function. The presented method was validated using GAITRite-based gait parameters from 101 patients (average age 82.1 years). Subjects performed a normal walking test with and without a wheeled walker. The parameters stride length and stride time showed a correlation of 0.93 and 0.95 between both systems. The absolute error of stride length was 6.26 cm on normal walking test. The developed system as well as the GAITRite showed an increased stride length, when using a four-wheeled walker as walking aid. However, the walking aid interfered with the automated analysis of the GAITRite system, but not with the inertial sensor-based approach. In summary, an algorithm for the calculation of clinically relevant gait parameters derived from inertial sensors is applicable in the diagnostic workup and also during long-term monitoring approaches in the elderly population.


title: Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts

abstract: To defend against multi-step intrusions in high-speed networks, efficient algorithms are needed to correlate isolated alerts into attack scenarios. Existing correlation methods usually employ an in-memory index for fast searches among received alerts. With finite memory, the index can only be built on a limited number of alerts inside a sliding window. Knowing this fact, an attacker can prevent two attack steps from both falling into the sliding window by either passively delaying the second step or actively injecting bogus alerts between the two steps. In either case, the correlation effort is defeated. In this paper, we first address the above issue with a novel queue graph (QG) approach. Instead of searching all the received alerts for those that prepare for a new alert, we only search for the latest alert of each type. The correlation between the new alert and other alerts is implicitly represented using the temporal order between alerts. Consequently, our approach can correlate alerts that are arbitrarily far away, and it has a linear (in the number of alert types) time complexity and quadratic memory requirement. Then, we extend the basic QG approach to a unified method to hypothesize missing alerts and to predict future alerts. Finally, we propose a compact representation for the result of alert correlation. Empirical results show that our method can fulfill correlation tasks faster than an IDS can report alerts. Hence, the method is a promising solution for administrators to monitor and predict the progress of intrusions and thus to take appropriate countermeasures in a timely manner.


title: Acute off-target effects of neural circuit manipulations

abstract: Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked behavioural changes reflect the function of the manipulated circuits. We show that this assumption is problematic because it disregards indirect effects on the independent functions of downstream circuits. Transient inactivations of motor cortex in rats and nucleus interface (Nif) in songbirds severely degraded task-specific movement patterns and courtship songs, respectively, which are learned skills that recover spontaneously after permanent lesions of the same areas. We resolve this discrepancy in songbirds, showing that Nif silencing acutely affects the function of HVC, a downstream song control nucleus. Paralleling song recovery, the off-target effects resolved within days of Nif lesions, a recovery consistent with homeostatic regulation of neural activity in HVC. These results have implications for interpreting transient circuit manipulations and for understanding recovery after brain lesions.


title: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation

abstract: We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network. In contrast to existing methods that either use geometry or RGB data as input for this task, we combine both data modalities in a joint, end-to-end network architecture. Rather than simply projecting color data into a volumetric grid and operating solely in 3D -- which would result in insufficient detail -- we first extract feature maps from associated RGB images. These features are then mapped into the volumetric feature grid of a 3D network using a differentiable backprojection layer. Since our target is 3D scanning scenarios with possibly many frames, we use a multi-view pooling approach in order to handle a varying number of RGB input views. This learned combination of RGB and geometric features with our joint 2D-3D architecture achieves significantly better results than existing baselines. For instance, our final result on the ScanNet 3D segmentation benchmark increases from 52.8\% to 75\% accuracy compared to existing volumetric architectures.


Poster : HoneyDroid-Creating a Smartphone Honeypot


title: Nonparametric Bayes Pachinko Allocation

abstract: Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and greater expressive power than previous models like latent Dirichlet allocation (LDA), it is also more difficult to determine the appropriate topic structure for a specific dataset. In this paper, we propose a nonparametric Bayesian prior for PAM based on a variant of the hierarchical Dirichlet process (HDP). Although the HDP can capture topic correlations defined by nested data structure, it does not automatically discover such correlations from unstructured data. By assuming an HDP-based prior for PAM, we are able to learn both the number of topics and how the topics are correlated. We evaluate our model on synthetic and real-world text datasets, and show that nonparametric PAM achieves performance matching the best of PAM without manually tuning the number of topics.


title: Are more data always better for factor analysis

abstract: Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and the composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors, and yet the resulting factors are less useful for forecasting, and the answer is yes. Such a problem tends to arise when the idiosyncratic errors are cross-correlated. It can also arise if forecasting power is provided by a factor that is dominant in a small dataset but is a dominated factor in a larger dataset. In a real time forecasting exercise, we find that factors extracted from as few as 40 pre-screened series often yield satisfactory or even better results than using all 147 series. Weighting the data by their properties when constructing the factors also lead to improved forecasts. Our simulation analysis is unique in that special attention is paid to cross-correlated idiosyncratic errors, and we also allow the factors to have stronger loadings on some groups of series than others. It thus allows us to better understand the properties of the principal components estimator in empirical applications.


RGB-D to CAD Retrieval with ObjectNN Dataset


title: A comparison of MSA tools

abstract: Multiple sequence alignment (MSA) is essential in phylogenetic, evolutionary and functional analysis. Several MSA tools are available in the literature. Here, we use several MSA tools such as ClustalX, Align-m, T-Coffee, SAGA, ProbCons, MAFFT, MUSCLE and DIALIGN to illustrate comparative phylogenetic trees analysis for two datasets. Results show that there is no single MSA tool that consistently outperforms the rest in producing reliable phylogenetic trees.


Context-Aware End-To-End Relation Extracting from Clinical Texts with Attention-based Bi-Tree-GRU


title: A Survey on Machine Learning: Concept,Algorithms and Applications

abstract: Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational approaches, to an independent research discipline that has not only provided the necessary base for statistical-computational principles of learning procedures, but also has developedvarious algorithms that are regularly used for text interpretation, pattern recognition, and a many other commercial purposes and has led to a separate research interest in data mining to identify hidden regularities or irregularities in social data that growing by second. This paper focuses on explaining the concept and evolution of Machine Learning, some of the popular Machine Learning algorithms and try to compare three most popular algorithms based on some basic notions. Sentiment140 dataset was used and performance of each algorithm in terms of training time, prediction time and accuracy of prediction have been documented and compared.


title: Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

abstract: Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods.


title: Parsing Models for Identifying Multiword Expressions

abstract: Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics. ::: ::: We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.


title: Benchmarking Deep Reinforcement Learning for Continuous Control

abstract: Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at this https URL in order to facilitate experimental reproducibility and to encourage adoption by other researchers.


title: Ranking of machine learning algorithms based on the performance in classifying DDoS attacks

abstract: Network Security has become one of the most important factors to consider as the Internet evolves. The most important attack which affects the availability of service is Distributed Denial of Service. The service disruption may cause substantial financial loss as well as damage to the concerned network system. The traffic patterns exhibited by the DDoS affected traffic can be effectively captured by machine learning algorithms. This paper gives an evaluation and ranking of some of the supervised machine learning algorithms with the aim of reducing type I and type II errors, increasing precision and recall while maintaining detection accuracy. The performance evaluation is done using Multi Criteria Decision Aid software called Visual PROMETHEE. This work demonstrates the effectiveness of ensemble based classifiers especially the ensemble algorithm of Adaboost with Random Forest as the base classifier. Publicly available datasets such as DARPA scenario specific dataset, CAIDA DDoS Attack 2007 and CAIDA Conficker are used to evaluate the algorithms.


title: Using a knowledge graph and query click logs for unsupervised learning of relation detection

abstract: In this paper, we introduce a novel statistical language understanding paradigm inspired by the emerging semantic web: Instead of building models for the target application, we propose relying on the semantic space already defined and populated in the knowledge graph for the target domain. As a first step towards this direction, we present unsupervised methods for training relation detection models exploiting the semantic knowledge graphs of the semantic web. The detected relations are used to mine natural language queries against a back-end knowledge base. For each relation, we leverage the complete set of entities that are connected to each other in the graph with the specific relation, and search these entity pairs on the web. We use the snippets that the search engine returns to create natural language examples that can be used as the training data for each relation. We further refine the annotations of these examples using the knowledge graph itself and iterate using a bootstrap approach. Furthermore, we explot the URLs returned for these pairs by the search engine to mine additional examples from the search engine query click logs. In our experiments, we show that, we can achieve relation detection models that perform about 60% macro F-measure on the relations that are in the knowledge graph without any manual labeling, resulting in a comparable performance with supervised training.


title: Stylistic text classification using functional lexical features

abstract: Most text analysis and retrieval work to date has focused on the topic of a text; that is, what it is about. However, a text also contains much useful information in its style, or how it is written. This includes information about its author, its purpose, feelings it is meant to evoke, and more. This article develops a new type of lexical feature for use in stylistic text classification, based on taxonomies of various semantic functions of certain choice words or phrases. We demonstrate the usefulness of such features for the stylistic text classification tasks of determining author identity and nationality, the gender of literary characters, a text’s sentiment (positive/ negative evaluation), and the rhetorical character of scientific journal articles. We further show how the use of functional features aids in gaining insight about stylistic differences among different kinds of texts.


title: Lazy User Theory: A Dynamic Model to Understand User Selection of Products and Services

abstract: In this paper we suggest that a user will most often choose the solution that will fulfill her (information) needs with the least effort. We call this "lazy user behavior". We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user's detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. We present the lazy user theory of solution selection, two mBusiness case examples, and discuss the implications of lazy user behavior on user selection of products and services. Implications on the design of new products and services are also discussed.


title: [Effect of walking speed on pressure distribution of orthopedic shoe technology].

abstract: Lesions to the diabetic foot have various causes. However, there is broad consensus that excessive plantar pressure plays a major role in the chain of events leading to ulcerations and gangrenes. During walking, on the other hand, peak values of plantar pressure are likely to increase with velocity even in therapeutic shoes. Therefore, the question arises whether a moderate velocity should be recommended to diabetic patients to reduce the risk of foot lesions. In this study, two velocities were compared for different types of therapeutic footwear. The velocities selected were considered moderate (0.7 m/s) and normal (1.3 m/s) for diabetic patients. A specially designed mathematical algorithm (velocity normalization) provided the pressure distributions from a common set of measurements: seven trials at different velocities for each subject and each type of footwear. Ten test subjects with healthy feet were studied. The shoes were ready-made and all had a midfoot rocker. The following four conditions were tested: flexible or rigid outsole respectively in combination with a flat insole or molded foot bed respectively. Pressure distribution measurements were performed with the Pedar in-shoe system, and the Pedar software package was used for analysis. The foot was divided into six regions: first toe, second to fifth toes, metatarsal region, medial midfoot, lateral midfoot, and heel. Only peak pressures were taken into account. Gait velocity was found to have an effect on plantar pressure distribution, mainly in the toes and heel region. Peak pressure in the heels increased significantly by about 20%. In the toe region, the increase was about the same, but was not statistically significant. At a higher velocity, pressure even slightly decreased in the midfoot region. The percentage variation was similar for all four conditions. Thus, walking slowly prevented the foot from high peak pressures, and the combination of rigid outsole and molded foot bed was best suited for both slow and higher velocities.


title: Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning

abstract: Abstract This chapter defines analytics and traces its evolution from its origin in 1988 to its current stage—cognitive analytics. We discuss types of learning and describe classes of machine learning algorithms. Given this backdrop, we propose a reference architecture for cognitive analytics and indicate ways to implement the architecture. A few cognitive analytics applications are briefly described. The chapter concludes by indicating current trends and future research direction.


title: Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy.

abstract: BACKGROUND ::: Transmitted light microscopy is used in pathology to examine stained tissues. Digital image analysis is gaining importance as a means to quantify alterations in tissues. A prerequisite for accurate and reproducible quantification is the possibility to recognise stains in a standardised manner, independently of variations in the staining density. ::: ::: ::: METHODS ::: The usefulness of three colour models was studied using data from computer simulations and experimental data from an immuno-doublestained tissue section. Direct use of the three intensities obtained by a colour camera results in the red-green-blue (RGB) model. By decoupling the intensity from the RGB data, the hue-saturation-intensity (HSI) model is obtained. However, the major part of the variation in perceived intensities in transmitted light microscopy is caused by variations in staining density. Therefore, the hue-saturation-density (HSD) transform was defined as the RGB to HSI transform, applied to optical density values rather than intensities for the individual RGB channels. ::: ::: ::: RESULTS ::: In the RGB model, the mixture of chromatic and intensity information hampers standardisation of stain recognition. In the HSI model, mixtures of stains that could be distinguished from other stains in the RGB model could not be separated. The HSD model enabled all possible distinctions in a two-dimensional, standardised data space. ::: ::: ::: CONCLUSIONS ::: In the RGB model, standardised recognition is only possible by using complex and time-consuming algorithms. The HSI model is not suitable for stain recognition in transmitted light microscopy. The newly derived HSD model was found superior to the existing models for this purpose.


title: Energy reduction for STT-RAM using early write termination

abstract: The emerging Spin Torque Transfer memory (STT-RAM) is a promising candidate for future on-chip caches due to STT-RAM's high density, low leakage, long endurance and high access speed. However, one of the major challenges of STT-RAM is its high write current, which is disadvantageous when used as an on-chip cache since the dynamic power generated is too high. In this paper, we propose Early Write Termination (EWT), a novel technique to significantly reduce write energy with no performance penalty. EWT can be implemented with low complexity and low energy overhead. Our evaluation shows that up to 80% of write energy reduction can be achieved through EWT, resulting 33% less total energy consumption, and 34% reduction in ED2. These results indicate that EWT is an effective and practical scheme to improve the energy efficiency of a STT-RAM cache.


years of biometric research : Accomplishments , challenges , and opportunities ✩


title: Additive manufacturing of waveguide for Ku-band satellite communications antenna

abstract: A metal waveguide feed chain in a Ku-band scanning lens array antenna comprises 5 parts. These are: two iris polarisers, two bend-feeds, and a central power combiner. The combiner comprises a pair of septum polarisers and tee junctions. The feed chain generates a controllable, linear polarised E-field at each of two hemispherical lenses. Additive manufacturing was used to produce a copy of the power combiner and each bend-feed in copper plated plastic. In satellite receive-only trials at 10.7–12 GHz the plastic parts and metal parts performed equivalently.


title: WorkloadCompactor: reducing datacenter cost while providing tail latency SLO guarantees

abstract: Service providers want to reduce datacenter costs by consolidating workloads onto fewer servers. At the same time, customers have performance goals, such as meeting tail latency Service Level Objectives (SLOs). Consolidating workloads while meeting tail latency goals is challenging, especially since workloads in production environments are often bursty. To limit the congestion when consolidating workloads, customers and service providers often agree upon rate limits. Ideally, rate limits are chosen to maximize the number of workloads that can be co-located while meeting each workload's SLO. In reality, neither the service provider nor customer knows how to choose rate limits. Customers end up selecting rate limits on their own in some ad hoc fashion, and service providers are left to optimize given the chosen rate limits. This paper describes WorkloadCompactor, a new system that uses workload traces to automatically choose rate limits simultaneously with selecting onto which server to place workloads. Our system meets customer tail latency SLOs while minimizing datacenter resource costs. Our experiments show that by optimizing the choice of rate limits, WorkloadCompactor reduces the number of required servers by 30--60% as compared to state-of-the-art approaches.


title: An Overview of Recommender Systems in Requirements Engineering

abstract: Requirements engineering (RE) is considered as one of the most critical phases in software development. Poorly implemented RE processes are still one of the major risks for project failure. As a consequence, we can observe an increasing demand for intelligent software components that support stakeholders in the completion of RE tasks. In this chapter, we give an overview of the research dedicated to the application of recommendation technologies in RE. On the basis of a literature analysis, we exemplify the application of recommendation technologies in different scenarios. In this context, the approaches of collaborative filtering, content-based filtering, clustering, knowledge-based recommendation, group-based recommendation, and social network analysis are discussed. With the goal to stimulate further related research, we conclude the chapter with a discussion of issues for future work.


title: Vehicle Routing Problem with Time Windows, Part II: Metaheuristics

abstract: This paper surveys the research on the metaheuristics for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from one depot to a set of geographically scattered points. The routes must be designed in such a way that each point is visited only once by exactly one vehicle within a given time interval; all routes start and end at the depot, and the total demands of all points on one particular route must not exceed the capacity of the vehicle. Metaheuristics are general solution procedures that explore the solution space to identify good solutions and often embed some of the standard route construction and improvement heuristics described in the first part of this article. In addition to describing basic features of each method, experimental results for Solomon's benchmark test problems are presented and analyzed.


title: Escaping Plato's Cave using Adversarial Training: 3D Shape From Unstructured 2D Image Collections.

abstract: We introduce PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i.e. neither any relation between the images is available nor additional information about the images is known. The key idea is to train a deep neural network to generate 3D shapes which rendered to images are indistinguishable from ground truth images (for a discriminator) under various camera models (i.e. rendering layers) and camera poses. Discriminating 2D images instead of 3D shapes allows tapping into unstructured 2D photo collections instead of relying on curated (e.g., aligned, annotated, etc.) 3D data sets. To establish constraints between 2D image observation and their 3D interpretation, we suggest a family of rendering layers that are effectively differentiable. This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption. We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PlatonicGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods. We can also show that our method with additional 3D supervision further improves result quality and even surpasses the performance of 3D supervised methods.


title: The role of cerebellar circuitry alterations in the pathophysiology of autism spectrum disorders

abstract: The cerebellum has been repeatedly implicated in gene expression, rodent model and post-mortem studies of autism spectrum disorder (ASD). How cellular and molecular anomalies of the cerebellum relate to clinical manifestations of ASD remains unclear. Separate circuits of the cerebellum control different sensorimotor behaviors, such as maintaining balance, walking, making eye movements, reaching and grasping. Each of these behaviors has been found to be impaired in ASD, suggesting that multiple distinct circuits of the cerebellum may be involved in the pathogenesis of patients’ sensorimotor impairments. We will review evidence that the development of these circuits is disrupted in individuals with ASD and that their study may help elucidate the pathophysiology of sensorimotor deficits and core symptoms of the disorder. Preclinical studies of monogenetic conditions associated with ASD also have identified selective defects of the cerebellum and documented behavioral rescues when the cerebellum is targeted. Based on these findings, we propose that cerebellar circuits may prove to be promising targets for therapeutic development aimed at rescuing sensorimotor and other clinical symptoms of different forms of ASD.


title: Spark SQL: Relational Data Processing in Spark

abstract: Spark SQL is a new module in Apache Spark that integrates relational processing with Spark's functional programming API. Built on our experience with Shark, Spark SQL lets Spark programmers leverage the benefits of relational processing (e.g. declarative queries and optimized storage), and lets SQL users call complex analytics libraries in Spark (e.g. machine learning). Compared to previous systems, Spark SQL makes two main additions. First, it offers much tighter integration between relational and procedural processing, through a declarative DataFrame API that integrates with procedural Spark code. Second, it includes a highly extensible optimizer, Catalyst, built using features of the Scala programming language, that makes it easy to add composable rules, control code generation, and define extension points. Using Catalyst, we have built a variety of features (e.g. schema inference for JSON, machine learning types, and query federation to external databases) tailored for the complex needs of modern data analysis. We see Spark SQL as an evolution of both SQL-on-Spark and of Spark itself, offering richer APIs and optimizations while keeping the benefits of the Spark programming model.


The Comparison of Forwarding Strategies between Best Route , Multicast , and Access on Named Data Networking ( NDN ) . Case Study : A Node Compromised by the Prefix Hijack


title: How to Run Turing Machines on Encrypted Data

abstract: Cryptographic schemes for computing on encrypted data promise to be a fundamental building block of cryptography. The way one models such algorithms has a crucial effect on the efficiency and usefulness of the resulting cryptographic schemes. As of today, almost all known schemes for fully homomorphic encryption, functional encryption, and garbling schemes work by modeling algorithms as circuits rather than as Turing machines.


title: How to diagnose and treat back pain in the horse.

abstract: Back pain results in decreased range of motion and limited flexibility of the thoracolumbar spine, and it leads to performance issues in the equine athlete. Ancillary therapeutics and alternative therapies are often administered to patients with back pain. Such treatments without an accurate diagnosis fail to provide sufficient pain management in the affected horses. It is critical to obtain a complete performance history and assess each horse’s back through detailed palpation and visual inspection at rest and in motion. Specialized imaging techniques such as nuclear scintigraphy with digital radiography are able to diagnose and direct management of equine back disorders. The treatment of affected horses aims to alleviate discomfort and muscle spasm to make the horse as comfortable as possible to perform while promoting muscle function and strength. Some of the most effective therapies performed in our clinic for successful management of back pain include extracorporeal shockwave therapy (ESWT), mesotherapy, injections with corticosteroids, and systemic tiludronate therapy. At our practice, these non-invasive to minimally invasive medical treatments have been favorable for return to athletic use. This manuscript discusses multiple therapeutic options for thoracolumbar inflammation after diagnosis by moving, clinical, and imaging evaluation.


title: Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios

abstract: The autonomous emergency brake (AEB) is an active safety function for vehicles which aims to reduce the severity of a collision. An AEB performs a full brake when an accident becomes unavoidable. Even if this system cannot, in general, avoid the accident, it reduces the energy of the crash impact and is therefore referred to as a collision mitigation system. A new approach for the calculation of the trigger time of an emergency brake will be presented. The algorithm simultaneously considers all physically possible trajectories of the object and host vehicle. It can be applied to all different scenarios including rear-end collisions, collisions at intersections, and collisions with oncoming vehicles. Thus, 63% of possible accidents are addressed. The approach accounts for the object and host vehicles' dimensions. Unlike previous work, the orientation of the vehicles is incorporated into the collision estimation.


title: Test vector generation based on correlation model for ratio-I/sub DDQ/

abstract: For ratio-Iddq testing, the test performance is significantly affected by the correlation between two currents of different input patterns as process parameters vary. In this paper we first study the reason for strong correlation between Iddq currents for different test vectors, then build a model to estimate the correlation. Based on this model, we propose three test vector selection methods to improve the fault detection ability of ratio-Iddq testing by selecting test vector pairs with the highest correlation. Hspice simulation showed that the fault detection ability can be improved by as much as an order of magnitude. We also describe a test vector partitioning technique to increase the correlation between Iddq currents of different test vectors.


Static hand gesture recognition using neural networks


title: Ensuring High-Performance of Mission-Critical Java Applications in Multi-tenant Cloud Platforms

abstract: Cloud Computing promises a cost-effective and administration-effective solution to the traditional needs of computing resources. While bringing efficiency to the users thanks to the shared hardware and software, the multi-tenency characteristics also bring unique challenges to the backend cloud platforms. In particular, the JVM mechanisms used by Java applications, coupled with OS-level features, give rise to a set of problems that are not present in other deployment scenarios. In this work, we consider the problem of ensuring high-performance of mission-critical Java applications in multi-tenant cloud environments. Based on our experiences with Linkedin's platforms, we identify and solve a set of problems caused by multi-tenancy. We share the lessons and knowledge we learned during the course.


title: PWM control and experiment of modular multilevel converters

abstract: A modular multilevel converter (MMC) is one of the next-generation multilevel PWM converters intended for high- or medium-voltage power conversion without transformers. The MMC consists of cascade connection of multiple bidirectional PWM chopper-cells and floating dc capacitors per leg, thus requiring voltage-balancing control of their chopper-cells. However, no paper has been discussed explicitly on voltage-balancing control with theoretical and experimental verifications. This paper deals with two types of modular multilevel PWM converters with focus on their circuit configurations and voltage-balancing control. Combination of averaging and balancing controls enables the MMCs to achieve voltage balancing without any external circuit. The viability of the MMCs as well as the effectiveness of the PWM control method is confirmed by simulation and experiment.


title: Beyond the Roofline: Cache-Aware Power and Energy-Efficiency Modeling for Multi-Cores

abstract: To foster the energy-efficiency in current and future multi-core processors, the benefits and trade-offs of a large set of optimization solutions must be evaluated. For this purpose, it is often crucial to consider how key micro-architecture aspects, such as accessing different memory levels and functional units, affect the attainable power and energy consumption. To ease this process, we propose a set of insightful cache-aware models to characterize the upper-bounds for power, energy and energy-efficiency of modern multi-cores in three different domains of the processor chip: cores, uncore and package. The practical importance of the proposed models is illustrated when optimizing matrix multiplication and deriving a set of power envelopes and energy-efficiency ranges of the micro-architecture for different operating frequencies. The proposed models are experimentally validated on a computing platform with a quad-core Intel 3770K processor by using hardware counters, on-chip power monitoring facilities and assembly micro-benchmarks.


title: Expanding the mirror: vicarious activity for actions, emotions, and sensations

abstract: We often empathically share the states of others. The discovery of ‘mirror neurons’ suggested a neural mechanism for monkeys to share the actions of others. Here we expand this view by showing that mirror neurons for actions not only exist in the premotor cortex or in monkeys and that vicarious activity can also be measured for the emotions and sensations of others. Although we still need to empirically explore the function and development of these vicarious activations, we should stop thinking of vicarious brain activity as a peculiar property of the premotor cortex: instead it seems to be a very common phenomenon which leads social stimuli to recruit a wide range of seemingly private neural systems.


title: Multi-Task Learning of Gaussian Graphical Models

abstract: We present multi-task structure learning for Gaussian graphical models. We discuss uniqueness and boundedness of the optimal solution of the maximization problem. A block coordinate descent method leads to a provably convergent algorithm that generates a sequence of positive definite solutions. Thus, we reduce the original problem into a sequence of strictly convex l∞ regularized quadratic minimization subproblems. We further show that this subproblem leads to the continuous quadratic knapsack problem, for which very efficient methods exist. Finally we show promising results in a dataset that captures brain function of cocaine addicted and control subjects under conditions of monetary reward.


title: WEBee: Physical-Layer Cross-Technology Communication via Emulation

abstract: Recent advances in Cross-Technology Communication (CTC) have improved efficient coexistence and cooperation among heterogeneous wireless devices (e.g., WiFi, ZigBee, and Bluetooth) operating in the same ISM band. However, until now the effectiveness of existing CTCs, which rely on packet-level modulation, is limited due to their low throughput (e.g., tens of bps). Our work, named WEBee, opens a promising direction for high-throughput CTC via physical-level emulation. WEBee uses a high-speed wireless radio (e.g., WiFi OFDM) to emulate the desired signals of a low-speed radio (e.g., ZigBee). Our unique emulation technique manipulates only the payload of WiFi packets, requiring neither hardware nor firmware changes in commodity technologies -- a feature allowing zero-cost fast deployment on existing WiFi infrastructure. We designed and implemented WEBee with commodity devices (Atheros AR2425 WiFi card and MicaZ CC2420) and the USRP-N210 platform (for PHY layer evaluation). Our comprehensive evaluation reveals that WEBee can achieve a more than 99% reliable parallel CTC between WiFi and ZigBee with 126 Kbps in noisy environments, a throughput about 16,000x faster than current state-of-the-art CTCs.


title: The 4+1 Model of Architecture

abstract: The 4+1 View Model organizes a description of a software architecture using five concurrent views, each of which addresses a specific set of concerns. Architects capture their design decisions in four views and use the fifth view to illustrate and validate them. The logical view describes the design's object model when an object-oriented design method is used. To design an application that is very data driven, you can use an alternative approach to develop some other form of logical view, such as an entity-relationship diagram. The process view describes the design's concurrency and synchronization aspects. The physical view describes the mapping of the software onto the hardware and reflects its distributed aspect. The development view describes the software's static organization in its development environment. >


title: Bee-MMT: A load balancing method for power consumption management in cloud computing

abstract: Energy consumption management has become an essential concept in cloud computing. In this paper, we propose a new power aware load balancing, named Bee-MMT (artificial bee colony algorithm-Minimal migration time), to decline power consumption in cloud computing; as a result of this decline, CO2 production and operational cost will be decreased. According to this purpose, an algorithm based on artificial bee colony algorithm (ABC) has been proposed to detect over utilized hosts and then migrate one or more VMs from them to reduce their utilization; following that we detect underutilized hosts and, if it is possible, migrate all VMs which have been allocated to these hosts and then switch them to the sleep mode. However, there is a trade-off between energy consumption and providing high quality of service to the customers. Consequently, we consider SLA Violation as a metric to qualify the QOS that require to satisfy the customers. The results show that the proposed method can achieve greater power consumption saving than other methods like LR-MMT (local regression-Minimal migration time), DVFS (Dynamic Voltage Frequency Scaling), IQR-MMT (Interquartile Range-MMT), MAD-MMT (Median Absolute Deviation) and non-power aware.


title: CCF-LRU: a new buffer replacement algorithm for flash memory

abstract: NAND flash memory has been widely used for storage in embedded systems and recently in enterprise computing environment, owing to its shock-resistance, nonvolatile, low energy consumption, and high I/O speed. In addition, flash memory has the characteristics of not-in-place update and asymmetric I/O costs among read, write, and erase operations, in which the cost of write/erase operations is much higher than that of read operation. Hence, the buffer replacement algorithms in flash-based systems should take the asymmetric I/O costs into account. Previous solutions to this issue, such as CFLRU and LRU-WSR, used a clean-first scheme to first evict clean pages from the buffer. Although they outperform the traditional LRU policy in performance, they do not consider the access frequency of clean page, which will consequently result in poor I/O performance. In order to solve this problem, we present a new buffering algorithm in this paper, called CCF-LRU, which enhances the previous CFLRU and LRU-WSR methods by differentiating clean pages into cold and hot ones, and evicting cold clean pages first and delaying the eviction of hot clean pages. We conduct a trace-driven experiment on a flash memory simulation environment, and use six types of synthesized traces as well as a real OLTP trace. The results show that the CCF-LRU algorithm outperforms LRU, CFLRU and LRUWSR in write count and runtime in all experiments. In particular, it provides more than 20% improvement over its competitors both in write count and runtime, when running the real OLTP trace.


title: A Low-Power Robust Easily Cascaded PentaMTJ-Based Combinational and Sequential Circuits

abstract: Advanced computing systems embed spintronic devices to improve the leakage performance of conventional CMOS systems. High speed, low power, and infinite endurance are important properties of magnetic tunnel junction (MTJ), a spintronic device, which assures its use in memories and logic circuits. This paper presents a PentaMTJ-based logic gate, which provides easy cascading, self-referencing, less voltage headroom problem in precharge sense amplifier and low area overhead contrary to existing MTJ-based gates. PentaMTJ is used here because it provides guaranteed disturbance free reading and increased tolerance to process variations along with compatibility with CMOS process. The logic gate is validated by simulation at the 45-nm technology node using a VerilogA model of the PentaMTJ.


title: Colorectal cancer screening: discussions with first degree relatives.

abstract: Screening by faecal occult blood test and colonoscopy is recommended for first degree relatives of people with colorectal cancer. While it is known that screening participation among relatives is low, relatives' beliefs and attitudes towards screening have not been explored at an in-depth level. In this study, four focus group discussions with first degree relatives of people with colorectal cancer were held. Discussions were audio-taped, transcribed verbatim and the data were independently coded and analysed by the authors. Three main themes were identified: risk, understanding, and screening. Perceived risk was determined by family history, age and gender. Of concern, there was limited understanding of the asymptomatic nature of screening with most relatives initially 'screened' after consulting a doctor with colorectal symptoms. These findings need to be considered in screening programs.


title: FinFET – A self-aligned double-gate MOSFET scalable beyond 20nm

abstract: MOSFETs with gate length down to 17 nm are reported. To suppress the short channel effect, a novel self-aligned double-gate MOSFET, FinFET, is proposed. By using boron-doped Si/sub 0.4/Ge/sub 0.6/ as a gate material, the desired threshold voltage was achieved for the ultrathin body device. The quasiplanar nature of this new variant of the vertical double-gate MOSFETs can be fabricated relatively easily using the conventional planar MOSFET process technologies.


title: Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence

abstract: The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called 'peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.


UAV Collision Avoidance Using RRT * and LOS Maximization Technical Report # CSSE 1203


Fast Algorithms for Mining Association Rules in Large Databases


title: A New Gait-Based Identification Method Using Local Gauss Maps

abstract: We propose a new descriptor for human identification based on gait. The current and most prevailing trend in gait representation revolves around encoding body shapes as silhouettes averaged over gait cycles. Our method, however, captures geometric properties of the silhouettes boundaries. Namely, we evaluate contour curvatures locally using Gauss maps. This results in an improved shape representation, as contrasted to average silhouettes. In addition, our approach does not require prior training. We thoroughly demonstrate the superiority of our method in gait-based human identification compared to state-of-the-art approaches. We use the OU-ISIR Large Population dataset, with over 4000 subjects captured at different viewing angles, to provide statistically reliable results.


title: System Design of Internet-of-Things for Residential Smart Grid

abstract: The Internet of Things envisions integration, coordination, communication, and collaboration of real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this article studies the design of a large-scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a back-end server, a unified home gateway (UHG) at the end users, and a user interface for mobile devices. We discuss the features of such an IoT system to support a large-scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using XMPP and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.


title: Optimal sensor network design for early intrusion detection and identification in water distribution networks

abstract: Water Distribution Networks (WDNs) are an important part of our society. Contaminant intrusion into WDNs could potentially affect a large cross-section of the population. Deliberate intrusion is the most serious threat to a WDN and can result in chemical and/or biological substances spreading throughout the WDN causing serious consequences. A contamination warning system needs to be installed in a WDN through sensors to detect the presence of contaminants as early as possible in order to protect the public from exposure. In this work, we develop algorithms for sensor network design for contamination detection and identification for a specific level of service using observability and identifiability concepts. We also estimate the total population exposed to the intrusion before detection and identification of contaminants is realized. The proposed methodologies are illustrated on a fairly large-scale network.


title: Novel Molecular Targets for Cancer Drug Discovery

abstract: Conventional cancer chemotherapy leaves much to be desired because the drugs in current use are highly cytotoxic and lack selectivity between cancer cells and normal cells. Therefore, there is a need for novel cancer therapies that will selectively eliminate neoplastic cells without toxicity to normal tissues. Recent advances in cancer cell biology, made possible by advances in recombinant DNA technology, offer new hope for achieving selective cancer chemotherapy. Many molecular mechanisms and genes involved in neoplastic transformation, tumor progression, tumor angiogenesis, and metastasis have been elucidated, providing a plethora of potential molecular targets for specific anticancer therapy (1–6). In addition to the discovery of novel molecular targets, advances in recombinant DNA technology have also facilitated investigation of the potential of these targets for drug design and development, including the functional expression or production of the targets. Production of target molecules enables high-throughput screening assays of natural and synthetic molecule libraries. Production of sufficient quantities of target proteins facilitates X-ray crystallography, which provides the three-dimensional structure of the target molecule and reveals the nature of interactions between target molecules and their ligands/inhibitors at the atomic level. These advances potentially contributes valuable information for rational structure-based drug design (7). Interesting and creative approaches to selectively killing cancer cells while sparing normal cells are emerging, like the use of engineered adenoviruses such as ONYX-015, which is currently in clinical trials (Onyx Pharmaceuticals). This adenovirus selectively replicates in and kills cells that have lost p53 function (>50% of all human cancers). ONYX-015 cannot replicate in cells with normal p53 function and therefore lacks cytotoxicity against normal cells.


title: Genetic Improvement in Crop Phosphorus Efficiency: A Case Study on Purple Acid Phosphatases in Common Bean

abstract: Phosphorus (P) deficiency is one of the limiting factors in crop production. Large genetic variations in crop P efficiency have been evaluated, and thus their genetic improvement should be important to address this problem. However, 50–80% of the total P in agricultural soils exists as organic compounds. One strategy for improving crop P nutrition is to enhance root secretion of acid phosphatases (APases) to obtain or remobilize Pi from organic P sources. Purple acid phosphatase (PAP) is a special group of APase with distinct properties. However, the molecular properties and functions of PAPs have not been well studied. In the present study, we found that there are four PvPAPs in common bean, which contain five blocks of conserved amino acid motifs with seven invariant residues. Subsequently, functions of P starvation-induced PvPAP3 were elucidated. Phylogenetic analysis shows that PvPAP1 and PvPAP2, PvPAP3 and PvPAP4 separately belong to high and low molecular mass PAP groups. Expression patterns of the four PvPAPs are obviously different as related to Pi availability. Among them, strong induced expression of PvPAP3 was detected by Pi starvation in roots. Transgenic Arabidopsis plants overexpressing PvPAP3 have superior growth and P uptake when ATP was supplied as the sole external P source. Taken together, our study demonstrated that the P efficiency in common bean could be improved through overexpression of PvPAP3, thus might have great implications for improving crop production on soils lacking of available P which is a serious limitation worldwide.


title: Dynamic- and Static-priority Scheduling of Recurring Real-time Tasks

abstract: The recurring real-time task model for hard-real-time task is studied from a feasibility-analysis perspective. This model generalizes earlier models such as the sporadic task model and the generalized multiframe task model. Algorithms are presented for the static-priority and dynamic-priority feasibility-analysis of systems of independent recurring real-time tasks in a preemptive uniprocessor environment.


title: How Do Mobile ICTs Enable Organizational Fluidity: Toward a Theoretical Framework

abstract: Abstract The focus of this theoretical paper is to investigate how mobile information and communication technologies (ICTs) give rise to the notion of organizational fluidity. Drawing upon previous literature, five affordances of mobile ICTs − mobility, connectedness, interoperability, identifiability, and personalization − are discussed. Delving into the concept of organizational fluidity, the paper captures three dimensions of organizational fluidity, namely, team fluidity, task fluidity, and control fluidity. The paper then develops propositions on how different combinations of the mobile ICT affordances influence each of the dimensions of organizational fluidity. The contributions and implications of the paper are discussed.