title: The influence of design aesthetics in usability testing: effects on user performance and perceived usability.

abstract: Abstract This article examined the effects of product aesthetics on several outcome variables in usability tests. Employing a computer simulation of a mobile phone, 60 adolescents (14–17 yrs) were asked to complete a number of typical tasks of mobile phone users. Two functionally identical mobile phones were manipulated with regard to their visual appearance (highly appealing vs not appealing) to determine the influence of appearance on perceived usability, performance measures and perceived attractiveness. The results showed that participants using the highly appealing phone rated their appliance as being more usable than participants operating the unappealing model. Furthermore, the visual appearance of the phone had a positive effect on performance, leading to reduced task completion times for the attractive model. The study discusses the implications for the use of adolescents in ergonomic research.


title: Human resources and the resource based view of the firm

abstract: The resource-based view (RBV) of the firm has influenced the field of strategic human resource management (SHRM) in a number of ways. This paper explores the impact of the RBV on the theoretical and empirical development of SHRM. It explores how the fields of strategy and SHRM are beginning to converge around a number of issues, and proposes a number of implications of this convergence.


title: Extracellular electron transfer via microbial nanowires

abstract: Microbes that can transfer electrons to extracellular electron acceptors, such as Fe(iii) oxides, are important in organic matter degradation and nutrient cycling in soils and sediments. Previous investigations on electron transfer to Fe(iii) have focused on the role of outer-membrane c-type cytochromes. However, some Fe(iii) reducers lack c-cytochromes. Geobacter species, which are the predominant Fe(iii) reducers in many environments, must directly contact Fe(iii) oxides to reduce them, and produce monolateral pili that were proposed, on the basis of the role of pili in other organisms, to aid in establishing contact with the Fe(iii) oxides. Here we report that a pilus-deficient mutant of Geobacter sulfurreducens could not reduce Fe(iii) oxides but could attach to them. Conducting-probe atomic force microscopy revealed that the pili were highly conductive. These results indicate that the pili of G. sulfurreducens might serve as biological nanowires, transferring electrons from the cell surface to the surface of Fe(iii) oxides. Electron transfer through pili indicates possibilities for other unique cell-surface and cell-cell interactions, and for bioengineering of novel conductive materials.


title: Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

abstract: We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody. We show that conditioning Tacotron on this learned embedding space results in synthesized audio that matches the prosody of the reference signal with fine time detail even when the reference and synthesis speakers are different. Additionally, we show that a reference prosody embedding can be used to synthesize text that is different from that of the reference utterance. We define several quantitative and subjective metrics for evaluating prosody transfer, and report results with accompanying audio samples from single-speaker and 44-speaker Tacotron models on a prosody transfer task.


title: Reliability of measurement of muscle tone and muscle power in stroke patients

abstract: OBJECTIVES ::: to establish the reliability of the modified Ashworth scale for measuring muscle tone in a range of muscle groups (elbow, wrist, knee and ankle; flexors and extensors) and of the Medical Research Council scale for measuring muscle power in the same muscle groups and their direct antagonists. ::: ::: ::: DESIGN ::: a cross-sectional study involving repeated measures by two raters. We estimated reliability using the kappa statistic with quadratic weights (Kw). ::: ::: ::: SETTING ::: an acute stroke ward, a stroke rehabilitation unit and a continuing care facility. ::: ::: ::: SUBJECTS ::: people admitted to hospital with an acute stroke-35 patients, median age 73 (interquartile range 65-80), 20 men and 15 women. ::: ::: ::: RESULTS ::: inter- and intra-rater agreement for the measurement of power was good to very good for all tested muscle groups (Kw = 0.84-0.96, Kw = 0.70-0.96). Inter- and intra-rater agreement for the measurement of tone in the elbow, wrist and knee flexors was good to very good (Kw = 0.73-0.96, Kw = 0.77-0.94). Inter- and intra-rater agreement for the measurement of tone in the ankle plantarflexors was moderate to good (Kw = 0.45-0.51, Kw = 0.59-0.64). ::: ::: ::: CONCLUSIONS ::: the Medical Research Council scale was reliable in the tested muscle groups. The modified Ashworth scale demonstrated reliability in all tested muscle groups except the ankle plantarflexors. If reliable measurement of tone at the ankle is required for a specific purpose (e.g. to measure the effect of therapeutic intervention), further work will be necessary.


title: THE COORDINATION CHEMISTRY OF MERCURY

abstract: The coordination chemistry of mercury has received rather less attention than that of the later transition elements, due in part to the fact that as a d10 metal ion Hg2+ exhibits neither paramagnetism nor ‘d-d’ spectra, the study of which have provided much of the impetus in other areas of coordination chemistry. However, the current interest in the biological properties of mercury clearly points to the need for a more complete understanding of the ability of mercury to bind various donor atoms and of the resulting stereochemistry.


title: An uncertainty principle for quaternion Fourier transform

abstract: We review the quaternionic Fourier transform (QFT). Using the properties of the QFT we establish an uncertainty principle for the right-sided QFT. This uncertainty principle prescribes a lower bound on the product of the effective widths of quaternion-valued signals in the spatial and frequency domains. It is shown that only a Gaussian quaternion signal minimizes the uncertainty.


title: Detection of Phishing Attacks: A Machine Learning Approach

abstract: Phishing is a form of identity theft that occurs when a malicious Web site impersonates a legitimate one in order to acquire sensitive information such as passwords, account details, or credit card numbers.Though there are several anti-phishing software and techniques for detecting potential phishing attempts in emails and detecting phishing contents on websites, phishers come up with new and hybrid techniques to circumvent the available software and techniques.


Identifying the KAT6B Mutation via Diagnostic Exome Sequencing to Diagnose Say-Barber-Biesecker-Young-Simpson Syndrome in Three Generations of a Family


title: Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

abstract: Abstract Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera (i.e CCD cameras, omnidirectional cameras, …), foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.


title: An Ontology of Meta-Level Categories

abstract: We focus in this paper on some meta-level ontological distinctions among unary predicates, like those between concepts and assertional properties. Three are the main contributions of this work, mostly based on a revisitation of philosophical (and linguistic) literature in the perspective of knowledge representation. The first is a formal notion of ontological commitment, based on a modal logic endowed with mereological and topological primitives. The second is a formal account of Strawson's distinction between sortal and non-sortal predicates. Assertional properties like red belong to the latter category, while the former category is further refined by distinguishing substantial predicates (corresponding to types like person ) from non-substantial predicates (corresponding to roles like student ). The third technical contribution is definition of countability which exploits the topological notion of connection to capture the intended semantics of unary predicates.


title: Dating Couples’ Attachment Styles and Patterns of Cortisol Reactivity and Recovery in Response to a Relationship Conflict

abstract: This study investigated theoretically predicted links between attachment style and a physiological indicator of stress, salivary cortisol levels, in 124 heterosexual dating couples. Cortisol was assessed at 7 points before and after an experimental conflict negotiation task, creating a trajectory of stress reactivity and recovery for each participant. Growth modeling of cortisol data tested hypotheses that (a) insecurely attached individuals show patterns of greater physiological stress reactions to interpersonal conflict than do securely attached individuals and (b) people with insecurely attached partners show patterns of greater stress in reaction to relationship conflict than those with securely attached partners. Hypothesis 1 was supported, but men and women differed in the type of insecure attachment that predicted stress trajectories. Hypothesis 2 was supported for men, but not for women. The discussion emphasizes the role of gender role norms and partner characteristics in understanding connections between adult attachment and patterns of cortisol responses to interpersonal stress. A fundamental assumption of attachment theory is that attachment figures serve the function of helping individuals regulate feelings of distress in the face of a threat (Bowlby, 1969, 1973, 1979, 1980; Sroufe & Waters, 1977). Bowlby’s original theory highlighted two ways in which the experience and regulation of affect are implicated in the infant–caregiver attachment bond. First, when infants experience distress in response to a threat, they seek proximity to their caregiver. Second, caregivers who are sensitive and responsive help infants regulate their feelings of distress, enabling them to experience an emotional sense of wellbeing or “felt security” (Sroufe & Waters, 1977). These ideas have been extended to adult romantic relationships, which embody many of the features of an attachment relationship (e.g., Hazan & Shaver, 1987; Mikulincer & Shaver, 2003). Like children, when adults become distressed in the face of a threat, they may turn to an attachment figure (e.g., their romantic partner) in an attempt to regain an emotional sense of felt security (Simpson & Rholes, 1994).


title: Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network

abstract: Recursive neural network is one of the most successful deep learning models for natural language processing due to the compositional nature of text. The model recursively composes the vector of a parent phrase from those of child words or phrases, with a key component named composition function. Although a variety of composition functions have been proposed, the syntactic information has not been fully encoded in the composition process. We propose two models, Tag Guided RNN (TGRNN for short) which chooses a composition function according to the part-ofspeech tag of a phrase, and Tag Embedded RNN/RNTN (TE-RNN/RNTN for short) which learns tag embeddings and then combines tag and word embeddings together. In the fine-grained sentiment classification, experiment results show the proposed models obtain remarkable improvement: TG-RNN/TE-RNN obtain remarkable improvement over baselines, TE-RNTN obtains the second best result among all the top performing models, and all the proposed models have much less parameters/complexity than their counterparts.


title: A Study on Genetic Algorithm and Neural Network for Mini-Games

abstract: Physics simulation and character control are two important issues in computer games. In this paper, we propose two games which are tailored for investigating some aspects of these two issues. We study on the applications of neural network and the genetic algorithm techniques for building the controllers and the controllers should be able to finish the specific tasks in the two games. The goal of the first game is that the controller can shoot a ball so that the ball collides with the other two balls one after another. The challenge of this game is that the ball should be shot from the proper position and the goal is achieved every time. The second game is a duel game and two virtual characters are controlled to fight with each other. We develop a method for verifying whether or not the skill power of the two virtual characters is balanced. The controllers of both games are evolved based on neural network and genetic algorithm in an unsupervised learning manner. We perform a comprehensive study on the performance and weaknesses of the controllers.


title: Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction

abstract: In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is to connect two words (w1 and w2) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w1 + r = w2 in the low-dimensional space, where the multi-hop dependency paths are treated as a sequence of grammatical relations and modeled by a recurrent neural network. Then, we design the embedding features that consider linear context and dependency context information, for the conditional random field (CRF) based aspect term extraction. Experimental results on the SemEval datasets show that, (1) with only embedding features, we can achieve state-of-the-art results; (2) our embedding method which incorporates the syntactic information among words yields better performance than other representative ones in aspect term extraction.


title: Acquired Sinus Pericranii Due To Parasagittal Meningiomatous Invasion of the Superior Sagittal Sinus and Bilateral Transverse Sinuses.

abstract: BACKGROUND ::: Sinus pericranii is a rare vascular anomaly characterized by abnormal connections between the extra- and intracranial venous systems. Although the etiology still remains unclear, sinus pericranii is usually congenital in nature, or less likely may be secondary to spontaneous or traumatic causes. ::: ::: ::: CASE DESCRIPTION ::: We present a unique case of spontaneous sinus pericranii secondary to a parasagittal meningioma invading and occluding the superior sagittal sinus, torcula herophilii, and bilateral transverse sinuses. The resultant venous outlet obstruction and venous hypertension may have been the inciting factors for the development of the collateral channel connecting the intra- and extracranial venous systems. ::: ::: ::: CONCLUSIONS ::: This case highlights the effect of the reversal of normal venous outflow secondary to meningiomatous occlusion of intracranial dural sinuses. Sinus pericranii may, on rare occasions, represent the sole means for intracranial venous drainage in cases of intracranial venous thrombosis or incomplete development of the cerebral venous system. Therefore, this case also highlights the importance of pretreatment evaluation of the venous drainage system in patients with sinus pericranii and those with meningiomas.


title: The impact of inadequate and dysfunctional training on Agile transformation process: A Grounded Theory study

abstract: Context ::: Training is an essential facilitator in moving from traditional to Agile software development. ::: Objective ::: This paper addresses the importance of adequate and functional training in Agile transformation process, the causes of inadequate and dysfunctional training, and the heuristic strategies that can be used in software companies for dealing with this phenomenon. ::: Method ::: A Grounded Theory study was conducted with participation of 35 Agile experts from 13 different countries. ::: Results ::: This research discovered that inadequate and dysfunctional training was one of the critical issues that affected Agile transformation process. This study shows that comprehensive and functional training is not often provided to support Agile transformation. This paper shows the primary causes of inadequate and dysfunctional training, its adverse consequences on the transformation process, and the heuristic and ad-hoc treatments as the strategies used by Agile teams to cope with this challenge. ::: Conclusion ::: Comprehensive training is important in Agile transformation process. Inadequate and dysfunctional training causes several challenges and problems for software companies and development teams when moving to Agile. Several ad-hoc strategies identified by this study can be employed to help software teams and companies facing similar problems.


title: Mathematics Anxiety: Separating the Math from the Anxiety

abstract: Anxiety about math is tied to low math grades and standardized test scores, yet not all math-anxious individuals perform equally poorly in math. We used functional magnetic resonance imaging to separate neural activity during the anticipation of doing math from activity during math performance itself. For higher (but not lower) math-anxious individuals, increased activity in frontoparietal regions when simply anticipating doing math mitigated mathspecific performance deficits. This network included bilateral inferior frontal junction, a region involved in cognitive control and reappraisal of negative emotional responses. Furthermore, the relation between frontoparietal anticipatory activity and highly math-anxious individuals’ math deficits was fully mediated (or accounted for) by activity in caudate, nucleus accumbens, and hippocampus during math performance. These subcortical regions are important for coordinating task demands and motivational factors during skill execution. Individual differences in how mathanxious individuals recruit cognitive control resources prior to doing math and motivational resources during math performance predict the extent of their math deficits. This work suggests that educational interventions emphasizing control of negative emotional responses to math stimuli (rather than merely additional math training) will be most effective in revealing a population of mathematically competent individuals, who might otherwise go undiscovered.


title: And Lead Us (Not) into Persuasion…? Persuasive Technology and the Ethics of Communication

abstract: The paper develops ethical guidelines for the development and usage of persuasive technologies (PT) that can be derived from applying discourse ethics to this type of technologies. The application of discourse ethics is of particular interest for PT, since ‘persuasion’ refers to an act of communication that might be interpreted as holding the middle between ‘manipulation’ and ‘convincing’. One can distinguish two elements of discourse ethics that prove fruitful when applied to PT: the analysis of the inherent normativity of acts of communication (‘speech acts’) and the Habermasian distinction between ‘communicative’ and ‘strategic rationality’ and their broader societal interpretation. This essay investigates what consequences can be drawn if one applies these two elements of discourse ethics to PT.


title: Mitigating Data Sparsity Using Similarity Reinforcement-Enhanced Collaborative Filtering

abstract: The data sparsity problem has attracted significant attention in collaborative filtering-based recommender systems. To alleviate data sparsity, several previous efforts employed hybrid approaches that incorporate auxiliary data sources into recommendation techniques, like content, context, or social relationships. However, due to privacy and security concerns, it is generally difficult to collect such auxiliary information. In this article, we focus on the pure collaborative filtering methods without relying on any auxiliary data source. We propose an improved memory-based collaborative filtering approach enhanced by a novel similarity reinforcement mechanism. It can discover potential similarity relationships between users or items by making better use of known but limited user-item interactions, thus to extract plentiful historical rating information from similar neighbors to make more reliable and accurate rating predictions. This approach integrates user similarity reinforcement and item similarity reinforcement into a comprehensive framework and lets them enhance each other. Comprehensive experiments conducted on several public datasets demonstrate that, in the face of data sparsity, our approach achieves a significant improvement in prediction accuracy when compared with the state-of-the-art memory-based and model-based collaborative filtering algorithms.


title: Automatic Building of Synthetic Voices from Audio Books

abstract: Current state-of-the-art text-to-speech systems produce intelligible speech but lack the prosody of natural utterances. Building better models of prosody involves development of prosodically rich speech databases. However, development of such speech databases requires a large amount of effort and time. An alternative is to exploit story style monologues (long speech files) in audio books. These monologues already encapsulate rich prosody including varied intonation contours, pitch accents and phrasing patterns. Thus, audio books act as excellent candidates for building prosodic models and natural sounding synthetic voices. The processing of such audio books poses several challenges including segmentation of long speech files, detection of mispronunciations, extraction and evaluation of representations of prosody. In this thesis, we address the issues of segmentation of long speech files, capturing prosodic phrasing patterns of a speaker, and conversion of speaker characteristics. Techniques developed to address these issues include – text-driven and speech-driven methods for segmentation of long speech files; an unsupervised algorithm for learning speaker-specific phrasing patterns and a voice conversion method by modeling target speaker characteristics. The major conclusions of this thesis are – • Audio books can be used for building synthetic voices. Segmentation of such long speech files can be accomplished without the need for a speech recognition system. • The prosodic phrasing patterns are specific to a speaker. These can be learnt and incorporated to improve the quality of synthetic voices. • Conversion of speaker characteristics can be achieved by modeling speaker-specific features of a target speaker. Finally, the techniques developed in this thesis enable prosody research by leveraging a large number of audio books available in the public domain.


title: Community-Based Recommender Systems: Analyzing Business Models from a Systems Operator's Perspective

abstract: Past research has shown that corporations benefit from using community-based recommender systems. Through them, they create digitized word-of-mouth that helps consumers make purchase decisions. While there exists much literature on the effects of recommender systems, to our knowledge, no prior studies have examined the underlying business models, nor have they considered the roles of system operators and the process for these recommender systems to achieve profitability. Based on our synthesis of relevant theory, we propose a framework for evaluating the value of recommender system business models from the viewpoint of system operators. We discuss patterns and situational characteristics that are associated with the business value of consumer-generated content and the concomitant profits of system operators.


title: Associated types and constraint propagation for mainstream object-oriented generics

abstract: Support for object-oriented programming has become an integral part of mainstream languages, and more recently generic programming has gained widespread acceptance as well. A natural question is how these two paradigms, and their underlying language mechanisms, should interact. One particular design option, that of using subtyping to constrain the type parameters of generic functions, has been chosen in the generics of Java and those planned for a future revision of C#.Certain shortcomings have previously been identified in using subtyping for constraining parametric polymorphism in the context of generic programming.To address these, we propose extending object-oriented interfaces and subtyping to include associated types and constraint propagation.Associated types are type members of interfaces and classes. Constraint propagation allows certain constraints on type parameters to be inferred from other constraints on those parameters and their use in base class type expressions.The paper demonstrates these extensions in the context of C# (with generics), describes a translation of the extended features to C#, and presents a formalism proving their safety. The formalism is applicable to other mainstream object-oriented languages supporting F-bounded polymorphism, such as Java.


A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences


title: SDStorage: A Software Defined Storage Experimental Framework

abstract: With the rapid growth of data centers and the unprecedented increase in storage demands, the traditional storage control techniques are considered unsuitable to deal with this large volume of data in an efficient manner. The Software Defined Storage (SDStore) comes as a solution for this issue by abstracting the storage control operations from the storage devices and set it inside a centralized controller in the software layer. Building a real SDStore system without any simulation and emulation is considered an expensive solution and may have a lot of risks. Thus, there is a need to simulate such systems before the real-life implementation and deployment. In this paper we present SDStorage, an experimental framework to provide a novel virtualized test bed environment for SDStore systems. The main idea of SDStorage is based on the Mininet Software Defined Network (SDN) Open Flow simulator and is built over of it. The main components of Mininet, which are the host, the switch and the controller, are customized to serve the needs of SDStore simulation environments.


title: Multi-Parametric Toolbox 3.0

abstract: The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. It features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry. The new version 3.0 is a complete rewrite of the original toolbox with a more flexible structure that offers faster integration of new algorithms. The numerical side of the toolbox has been improved by adding interfaces to state of the art solvers and by incorporation of a new parametric solver that relies on solving linear-complementarity problems. The toolbox provides algorithms for design and implementation of real-time model predictive controllers that have been extensively tested.


title: Classification of entities via their descriptive sentences

abstract: Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications. Current methods suffer from either low precision or low recall. To decrease the difficulty of this problem, we adopt a classification-based method. We pre-define a concept taxonomy and classify an entity to one of its leaf concept, based on the name and description information of the entity. A convolutional neural network classifier and a K-means clustering module are adopted for classification. We applied this system to 2.1 million Baidu Baike entities, and 1.1 million of them were successfully identified with a precision of 99.36%.


title: Comparison of Pinning Voltage Estimation Methods in Pinned Photodiode CMOS Image Sensors

abstract: The pinning voltage is a key design parameter of pinned photodiode CMOS image sensors which significantly affects the device performances and which is often used by manufacturers to monitor production lines and for the optimization of technological processes. This paper presents a comparative study of pinning voltage estimation methods, which are based on both electrical measurements performed on isolated test structures (or on test structures arrays) and in-pixel measurements. It is shown, with the support of simulations and experimental measurements, that not all the estimation methods provide an absolute value of the pinning voltage. Moreover, this paper demonstrates that the commonly accepted theoretical definition of the pinning voltage does not correspond to the physical parameter which is measured with the existing methods.


Learning causal models from existing randomized experiments : Meta-analysis using regularized instrumental variables


title: Millimeter-wave tapered slot array for automotive radar applications

abstract: To support advanced driving and vehicle safety applications such as semi-autonomous and autonomous vehicle modes, a growing variety of high performance sensors for object detection are under development. These sensors include millimeter-wave radars. To extend the performance of passenger vehicle radars to true volume scan capability, end fire antenna arrays are explored. In this paper, we present a planar 76–81 GHz corrugated tapered slot antenna array. Over all designs, return loss within 76–81 GHz is better than −15 dB, and gain is 19 dB with a beamwidth of 8.5° when used in an 8-element array. Results of simulation and pattern measurement in anechoic chamber are shown. Additionally, integration of this end fire design into possible packages for automotive radar with true volume scan are presented.


title: DARPA's explainable artificial intelligence (XAI) program

abstract: The DARPA's Explainable Artificial Intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. This talk will summarize the XAI program and present highlights from these Phase 1 evaluations.


title: A 77GHz Antenna-in-Package with Low-Cost Solution for mm Wave System Integration

abstract: This work provides Antenna in package (AiP) implemented on Advanced single sided substrates ($aS^{3}$ package), which utilizes low-cost substrate and low-cost assembly process. The dipole antenna implemented on $aS^{3}$ package results in a peak gain of 5 dBi at operating frequency band. The return loss of < -10 dB bandwidth is from 65 to 81GHz. The resonant rectangular series-fed patch array implemented on $aS^{3}$ package results in a peak gain of 8 dBi at operating frequency band. The return loss of < -10 dB bandwidth is from 76 to 81GHz. The antenna performance of antenna on $aS^{3}$ package meets the requirements of 77 GHz automotive radar systems. This work also discuss with the impact of fabrication inaccuracies on the antenna bandwidth and gain. For 77 GHz automotive radar application, the corner analysis of process variation supports correct antenna performance and a short time to market.


title: Genetic Impact on Clinical Features in Parkinson's Disease: A Study on SNCA-rs11931074

abstract: SNCA-rs11931074 had been demonstrated to be strongly correlated with PD risk. However, there was lack of comprehensive analysis of SNCA-rs11931074-related clinical features which may help explain clinical heterogeneity of PD. In our study, we performed association analyses on the relationship between SNCA-rs11931074 and motor symptoms, nonmotor symptoms, and comorbidities in PD. 611 rs11931074 carriers and 113 rs11931074 noncarriers were enrolled. In the clinical phenotype analyses, the Unified Parkinson’s Disease Rating Scale part II (UPDRS II) and part III (UPDRS III) scores of rs11931074 carriers were lower than those of noncarriers (SC: −0.083, ; SC: −0.140, ). The Charlson Comorbidity Index (CCI) score of carriers was lower than that of noncarriers (SC: −0.097, ). No significant statistical differences were found between the variant and other clinical features such as motor complications and nonmotor symptoms. The SNCA-rs11931074 carriers may present with more benign clinical profiles than noncarriers with less severe motor symptoms and comorbidity burden.


title: Body-to-Body Indoor Channel Modeling at 2.45 GHz

abstract: This paper presents an indoor Body-to-Body narrowband channel model based on experimental data, acquired through a real-time dynamic measurement campaign at 2.45 GHz. The radio channel was investigated under different communication conditions, according to the movement performed by some human subjects and to their mutual position. Several node locations were considered, and two antenna types were used to assess the impact of their radiation characteristics on channel properties. For each investigated link, the channel power transfer function was modeled as composed by a channel gain and a small-scale fading contribution, the latter arising from the multipaths due to the environment and the human's motion. The shadowing effect of the body was also evaluated considering that the body itself can act as an obstacle to the communication, according to its spatial orientation. Every model component was characterized for each scenario, highlighting how a specific movement results in different effects on channel dynamic properties.


title: Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

abstract: Objective: Accurate classification methods are critical in computer-aided diagnosis (CADx) and other clinical decision support systems. Previous research has reported on methods for combining genetic algorithm (GA) feature selection with ensemble classifier systems in an effort to increase classification accuracy. In this study, we describe a CADx system for pulmonary nodules using a two-step supervised learning system combining a GA with the random subspace method (RSM), with the aim of exploring algorithm design parameters and demonstrating improved classification performance over either the GA or RSM-based ensembles alone. Methods and materials: We used a retrospective database of 125 pulmonary nodules (63 benign; 62 malignant) with CT volumes and clinical history. A total of 216 features were derived from the segmented image data and clinical history. Ensemble classifiers using RSM or GA-based feature selection were constructed and tested via leave-one-out validation with feature selection and classifier training executed within each iteration. We further tested a two-step approach using a GA ensemble to first assess the relevance of the features, and then using this information to control feature selection during a subsequent RSM step. The base classification was performed using linear discriminant analysis (LDA). Results: The RSM classifier alone achieved a maximum leave-one-out Az of 0.866 (95% confidence interval: 0.794-0.919) at a subset size of s=36 features. The GA ensemble yielded an Az of 0.851 (0.775-0.907). The proposed two-step algorithm produced a maximum Az value of 0.889 (0.823-0.936) when the GA ensemble was used to completely remove less relevant features from the second RSM step, with similar results obtained when the GA-LDA results were used to reduce but not eliminate the occurrence of certain features. After accounting for correlations in the data, the leave-one-out Az in the two-step method was significantly higher than in the RSM and the GA-LDA. Conclusions: We have developed a CADx system for evaluation of pulmonary nodule based on a two-step feature selection and ensemble classifier algorithm. We have shown that by combining classifier ensemble algorithms in this two-step manner, it is possible to predict the malignancy for solitary pulmonary nodules with a performance exceeding that of either of the individual steps.


Time series analysis, forecasting and control


title: LSD-induced entropic brain activity predicts subsequent personality change

abstract: Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc.


title: 12-Port 5G Massive MIMO Antenna Array in Sub-6GHz Mobile Handset for LTE Bands 42/43/46 Applications

abstract: A 12-port antenna array operating in the long term evolution (LTE) band 42 (3400–3600 MHz), LTE band 43 (3600–3800 MHz), and LTE band 46 (5150–5925 MHz) for 5G massive multiple-input multiple-output (MIMO) applications in mobile handsets is presented. The proposed MIMO antenna is composed of three different antenna element types, namely, inverted $\pi $ -shaped antenna, longer inverted L-shaped open slot antenna, and shorter inverted L-shaped open slot antenna. In total, eight antenna elements are used for the $8 \times 8$ MIMO in LTE bands 42/43, and six antenna elements are designed for the $6 \times 6$ MIMO in LTE band 46. The proposed antenna was simulated, and a prototype was fabricated and tested. The measured results show that the LTE bands 42/43/46 are satisfied with reflection coefficient better than −6 dB, isolation lower than −12 dB, and total efficiencies of higher than 40%. In addition to that, the proposed antenna array has also shown good MIMO performances with an envelope correlation coefficient lower than 0.15, and ergodic channel capacities higher than 34 and 26.5 b/s/Hz in the LTE bands 42/43 and LTE band 46, respectively. The hand phantom effects are also investigated, and the results show that the proposed antenna array can still exhibit good radiation and MIMO performances when operating under data mode and read mode conditions.


title: Fall Detection Using Smartphone Audio Features

abstract: An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k -nearest neighbor classifier ( k -NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above $98\%$ . The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.


title: Reactive Reinforcement Learning in Asynchronous Environments

abstract: The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored. Frequently used models of the interaction between an agent and its environment, such as Markov Decision Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the fact that, in an asynchronous environment, the state of the environment may change during computation performed by the agent. In an asynchronous environment, minimizing reaction time—the time it takes for an agent to react to an observation—also minimizes the time in which the state of the environment may change following observation. In many environments, the reaction time of an agent directly impacts task performance by permitting the environment to transition into either an undesirable terminal state or a state where performing the chosen action is inappropriate. We propose a class of reactive reinforcement learning algorithms that address this problem of asynchronous environments by immediately acting after observing new state information. We compare a reactive SARSA learning algorithm with the conventional SARSA learning algorithm on two asynchronous robotic tasks (emergency stopping and impact prevention), and show that the reactive RL algorithm reduces the reaction time of the agent by approximately the duration of the algorithm's learning update. This new class of reactive algorithms may facilitate safer control and faster decision making without any change to standard learning guarantees.


title: Nonlinear Distortion Mitigation by Machine Learning of SVM Classification for PAM-4 and PAM-8 Modulated Optical Interconnection

abstract: We demonstrated a support vector machine (SVM) based machine learning method to mitigate modulation nonlinearity distortion for PAM-4 and PAM-8 vertical cavity surface emitter laser multi-mode fiber (VCSEL-MMF) optical link. Simulations at 100 Gb/s data rate and experimental work at 60 Gb/s data rate were carried out. We achieved a significant improvement in bit error rate (BER) when complete binary tree SVMs (CBT-SVMs) are applied for both PAM-4 and PAM-8 signals. Quantitative analysis of the sensitivity gain versus modulation nonlinearity distortion is presented with experimentally verification. The results indicate that CBT-SVMs have better performance for PAM-8 compared to PAM-4. The sensitivity gain increases almost linearly with the increase of eye-linearity (increase of modulation nonlinearity distortion). Up to 2.5-dB sensitivity improvement is achieved by the proposed CBT-SVMs at eye-linearity of 1.72 for PAM-4.


title: Mechanisms of Disease: pathogenesis of Crohn's disease and ulcerative colitis

abstract: Crohn's disease and ulcerative colitis are idiopathic, chronic, relapsing, inflammatory conditions that are immunologically mediated. Although their exact etiologies remain uncertain, results from research in animal models, human genetics, basic science and clinical trials have provided important new insights into the pathogenesis of chronic, immune-mediated, intestinal inflammation. These studies indicate that Crohn's disease and ulcerative colitis are heterogeneous diseases characterized by various genetic abnormalities that lead to overly aggressive T-cell responses to a subset of commensal enteric bacteria. The onset and reactivation of disease are triggered by environmental factors that transiently break the mucosal barrier, stimulate immune responses or alter the balance between beneficial and pathogenic enteric bacteria. Different genetic abnormalities can lead to similar disease phenotypes; these genetic changes can be broadly characterized as causing defects in mucosal barrier function, immunoregulation or bacterial clearance. These new insights will help develop better diagnostic approaches that identify clinically important subsets of patients for whom the natural history of disease and response to treatment are predictable.


title: ABLE: an Adaptive Block Lanczos Method for Non-Hermitian Eigenvalue Problems

abstract: This work presents an adaptive block Lanczos method for large-scale non-Hermitian Eigenvalue problems (henceforth the ABLE method). The ABLE method is a block version of the non-Hermitian Lanczos algorithm. There are three innovations. First, an adaptive blocksize scheme cures (near) breakdown and adapts the blocksize to the order of multiple or clustered eigenvalues. Second, stopping criteria are developed that exploit the semiquadratic convergence property of the method. Third, a well-known technique from the Hermitian Lanczos algorithm is generalized to monitor the loss of biorthogonality and maintain semibiorthogonality among the computed Lanczos vectors. Each innovation is theoretically justified. Academic model problems and real application problems are solved to demonstrate the numerical behaviors of the method.


title: Improving Distant Supervision with Maxpooled Attention and Sentence-Level Supervision.

abstract: We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FB-NYT dataset.


title: Screening and Evaluation of Antibacterial Activity of Bacteriocin Producing Lab against some Selected Bacteria Causing Food Spoilage

abstract: Technologies on preservation of food products help in maintaining its nutritional values and ensures its safety issues. These are the current areas of food research. For the preservation of food products many chemicals are being used for the activation of food borne pathogens. Chemical preservatives have undesirable side effects like alteration in the constituents, nutritional, and organoleptic properties of the food and toxic effects on human health. Despite all these measures, food borne diseases by pathogenic bacteria do occur frequently. The ever increasing demands of ready to eat food products without chemical preservation have led to development of new techniques of using biopreservatives. Biopreservatives extends the storage time of high quality foodstuff without using chemical preservatives. Hence with the increasing demand for biopreservatives, bacteriocin has caught the attention of food researchers in industries. [19] Lactic acid bacteria (LAB) produce lactic acid either through homofermentive or heterofermentive pathway and are wide spread in nature and ISSN: 2319-7706 Volume 3 Number 8 (2014) pp. 658-665 http://www.ijcmas.com


title: Role of adrenal stress hormones in forming lasting memories in the brain

abstract: Recent experiments investigating the effects of adrenal stress hormones on memory provide extensive evidence that epinephrine and glucocorticoids modulate long-term memory consolidation in animals and human subjects. Release of norepinephrine and activation of beta-adrenoceptors within the basolateral amygdala is critical in mediating adrenal stress hormone regulation of memory consolidation.


title: Performance comparison between slotted IEEE 802.15.4 and IEEE 802.1 lah in IoT based applications

abstract: In this paper, we present a performance comparison between IEEE 802.15.4 which specifies the physical and media access control layers for low-rate wireless personal area networks (LR-WPANs) like ZigBee, and IEEE 802.11ah, a new global WLAN standard using sub-1 GHz frequency band, in terms of throughput and energy consumption. Both standards are targeting low power applications with relatively high number of nodes as in IoT and M2M applications. The simulations results demonstrate the better performance of IEEE 802.11ah throughput mainly in congested networks. However in particular cases, the IEEE 802.15.4 outperforms the IEEE 802.11ah from energy consumption point of view.


title: Making one object look like another: controlling appearance using a projector-camera system

abstract: We present a method for controlling the appearance of an arbitrary 3D object using a projector and a camera. Our goal is to make one object look like another by projecting a carefully determined compensation image onto the object. The determination of the appropriate compensation image requires accounting for spatial variation in the object's reflectance, the effects of environmental lighting, and the spectral responses, spatially varying fall-offs, and non-linear responses in the projector-camera system. Addressing each of these effects, we present a compensation method which calls for the estimation of only a small number of parameters, as part of a novel off-line radiometric calibration. This calibration is accomplished by projecting and acquiring a minimal set of 6 images, irrespective of the object. Results of the calibration are then used on-line to compensate each input image prior to projection. Several experimental results are shown that demonstrate the ability of this method to control the appearance of everyday objects. Our method has direct applications in several areas including smart environments, product design and presentation, adaptive camouflages, interactive education and entertainment.


The assessment process


title: Analysis of induction motor for electric vehicle application based on drive cycle analysis

abstract: For an electric vehicle, motor selection is very important as it mainly determine its efficiency, cost and reliability. A particular motor may behave differently under different driving conditions. The driving behavior for an urban and suburban region is represented by its drive cycle. Thus, motor must be analyzed based on different drive cycles. In this paper, analysis of an induction motor for different drive cycles is discussed. Based on the analysis, motor selection criterion has been explained. The whole analysis of different drive cycles are carried out using Excel Macro.


title: Voltage and Transient Stability Support by Wind Farms Complying With the E.ON Netz Grid Code

abstract: In this paper, the impact of the response of a wind farm (WF) on the operation of a nearby grid is investigated during network disturbances. Only modern variable speed wind turbines are treated in this work. The new E.ON Netz fault response code for WF is taken as the base case for the study. The results found in this paper are that the performance of the used Cigre 32-bus test system during disturbances is improved when the WF is complying with the E.ON code compared to the traditional unity power factor operation. Further improvements are found when the slope of the reactive current support line is increased from the E.ON specified value. In addition, a larger converter of a variable speed wind turbine is exploited that is to be used in order to improve the stability of a nearby grid by extending the reactive current support. By doing so, it is shown in this paper that the voltage profile at the point of common coupling (pcc) as well as the transient stability of the grid are improved compared to the original E.ON code, in addition to the improvements already achieved by using the E.ON code in its original form. Finally, regarding the utilization of a larger converter, it is important to point out that the possible reactive power in-feed into the pcc from an offshore WF decreases with increasing cable length during network faults, making it difficult to support the grid with extra reactive power during disturbances.


title: Journey to the centre of the cell: Virtual reality immersion into scientific data.

abstract: Visualization of scientific data is crucial not only for scientific discovery but also to communicate science and medicine to both experts and a general audience. Until recently, we have been limited to visualizing the three-dimensional (3D) world of biology in 2 dimensions. Renderings of 3D cells are still traditionally displayed using two-dimensional (2D) media, such as on a computer screen or paper. However, the advent of consumer grade virtual reality (VR) headsets such as Oculus Rift and HTC Vive means it is now possible to visualize and interact with scientific data in a 3D virtual world. In addition, new microscopic methods provide an unprecedented opportunity to obtain new 3D data sets. In this perspective article, we highlight how we have used cutting edge imaging techniques to build a 3D virtual model of a cell from serial block-face scanning electron microscope (SBEM) imaging data. This model allows scientists, students and members of the public to explore and interact with a "real" cell. Early testing of this immersive environment indicates a significant improvement in students' understanding of cellular processes and points to a new future of learning and public engagement. In addition, we speculate that VR can become a new tool for researchers studying cellular architecture and processes by populating VR models with molecular data.


title: "`They brought in the horrible key ring thing!"Analysing the Usability of Two-Factor Authentication in UK Online Banking

abstract: To prevent password breaches and guessing attacks, banks increasingly turn to two-factor authentication (2FA), requiring users to present at least one more factor, such as a one-time password generated by a hardware token or received via SMS, besides a password. We can expect some solutions -- especially those adding a token -- to create extra work for users, but little research has investigated usability, user acceptance, and perceived security of deployed 2FA. ::: This paper presents an in-depth study of 2FA usability with 21 UK online banking customers, 16 of whom had accounts with more than one bank. We collected a rich set of qualitative and quantitative data through two rounds of semi-structured interviews, and an authentication diary over an average of 11 days. Our participants reported a wide range of usability issues, especially with the use of hardware tokens, showing that the mental and physical workload involved shapes how they use online banking. Key targets for improvements are (i) the reduction in the number of authentication steps, and (ii) removing features that do not add any security but negatively affect the user experience.


title: It's not a bug, it's a feature: How misclassification impacts bug prediction

abstract: In a manual examination of more than 7,000 issue reports from the bug databases of five open-source projects, we found 33.8% of all bug reports to be misclassified - that is, rather than referring to a code fix, they resulted in a new feature, an update to documentation, or an internal refactoring. This misclassification introduces bias in bug prediction models, confusing bugs and features: On average, 39% of files marked as defective actually never had a bug. We discuss the impact of this misclassification on earlier studies and recommend manual data validation for future studies.


title: Digital media triage with bulk data analysis and bulk extractor

abstract: Bulk data analysis eschews file extraction and analysis, common in forensic practice today, and instead processes data in "bulk," recognizing and extracting salient details ("features") of use in the typical digital forensics investigation. This article presents the requirements, design and implementation of the bulk_extractor, a high-performance carving and feature extraction tool that uses bulk data analysis to allow the triage and rapid exploitation of digital media. Bulk data analysis and the bulk_extractor are designed to complement traditional forensic approaches, not replace them. The approach and implementation offer several important advances over today's forensic tools, including optimistic decompression of compressed data, context-based stop-lists, and the use of a "forensic path" to document both the physical location and forensic transformations necessary to reconstruct extracted evidence. The bulk_extractor is a stream-based forensic tool, meaning that it scans the entire media from beginning to end without seeking the disk head, and is fully parallelized, allowing it to work at the maximum I/O capabilities of the underlying hardware (provided that the system has sufficient CPU resources). Although bulk_extractor was developed as a research prototype, it has proved useful in actual police investigations, two of which this article recounts.


title: BLEB: Bluetooth Low Energy Botnet for large scale individual tracking

abstract: This paper consider the problem of tracking user through the wireless fingerprint of their network interfaces. Bluetooth Low Energy (BLE) is one of these network interfaces and becomes more and more popular because of its energy efficiency and its improved security. The use of random MAC addresses and deep changes in the bluetooth communication procedure were expected to prevent the tracking of individuals through the BLE devices they carries. We reviewed popular devices and found that privacy preserving MAC address are rarely used, even when considering ubiquitous devices that are carried by users all the time. Combined with the new advertising procedure introduced in BLE, this constitute a serious flaw that can be used to track individuals. We investigate its exploitation by BLEB, a BLE Botnet comprise of smartphone and portable devices compromised by a tracking adversary. We investigate the multiple ways to implement it and show that with limited effort, an adversary using BLEB can achieve large scale, high precision and stealthy tracking. A BLEB attack can be more privacy threatening than the ones carried through WiFi or classic bluetooth during their early deployment.


title: Exploiting LTE Signals for Navigation: Theory to Implementation

abstract: Exploiting cellular long-term evolution (LTE) downlink signals for navigation purposes is considered. First, the transmitted LTE signal model is presented and relevant positioning and timing information that can be extracted from these signals are identified. Second, a software-defined receiver (SDR) that is capable of acquiring, tracking, and producing pseudoranges from LTE signals is designed. Third, a threshold-based approach for detecting the first peak of the channel impulse response is proposed in which the threshold adapts to the environmental noise level. This method is demonstrated to be robust against noise and interference in the environment. Fourth, an approach for estimating pseudoranges of multiple base stations by tracking only one base station is proposed. Fifth, a navigation framework based on an extended Kalman filter is proposed to produce the navigation solution using the pseudorange measurements obtained by the proposed SDR. Finally, the proposed SDR is evaluated experimentally on an unmanned aerial vehicle (UAV) and a ground vehicle. The root mean squared-error (RMSE) between the GPS navigation solution and LTE signals from three base stations produced by the proposed SDR for the UAV is shown to be 8.15 m with a standard deviation of 2.83 m. The RMSE between the GPS navigation solution and LTE signals from six base stations in a severe multipath environment for the ground vehicle is shown to be 5.80 m with a standard deviation of 3.02 m.


title: All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask)

abstract: Dynamic taint analysis and forward symbolic execution are quickly becoming staple techniques in security analyses. Example applications of dynamic taint analysis and forward symbolic execution include malware analysis, input filter generation, test case generation, and vulnerability discovery. Despite the widespread usage of these two techniques, there has been little effort to formally define the algorithms and summarize the critical issues that arise when these techniques are used in typical security contexts. The contributions of this paper are two-fold. First, we precisely describe the algorithms for dynamic taint analysis and forward symbolic execution as extensions to the run-time semantics of a general language. Second, we highlight important implementation choices, common pitfalls, and considerations when using these techniques in a security context.


title: Adversarial Active Learning

abstract: Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify problems caused by the application of present active learning techniques in adversarial settings, and propose a framework for experimentation and implementation of active learning systems in adversarial contexts. More than other contexts, adversarial contexts particularly need active learning as ongoing attempts to evade and confuse classifiers necessitate constant generation of labels for new content to keep pace with adversarial activity. Just as traditional machine learning algorithms are vulnerable to adversarial manipulation, we discuss assumptions specific to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are amplified in the active learning setting. Lastly, we present a software architecture, Security-oriented Active Learning Testbed (SALT), for the research and implementation of active learning applications in adversarial contexts.


title: TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang

abstract: Slang is ubiquitous on the Internet. The emergence of new social contexts like micro-blogs, question-answering forums, and social networks has enabled slang and non-standard expressions to abound on the web. Despite this, slang has been traditionally viewed as a form of non-standard language -- a form of language that is not the focus of linguistic analysis and has largely been neglected. In this work, we use UrbanDictionary to conduct the first large-scale linguistic analysis of slang and its social aspects on the Internet to yield insights into this variety of language that is increasingly used all over the world online. ::: We begin by computationally analyzing the phonological, morphological and syntactic properties of slang. We then study linguistic patterns in four specific categories of slang namely alphabetisms, blends, clippings, and reduplicatives. Our analysis reveals that slang demonstrates extra-grammatical rules of phonological and morphological formation that markedly distinguish it from the standard form shedding insight into its generative patterns. Next, we analyze the social aspects of slang by studying subject restriction and stereotyping in slang usage. Analyzing tens of thousands of such slang words reveals that the majority of slang on the Internet belongs to two major categories: sex and drugs. We also noted that not only is slang usage not immune to prevalent social biases and prejudices but also reflects such biases and stereotypes more intensely than the standard variety.


title: Extending Spark Analytics through Tika-Based Information Extraction and Retrieval

abstract: In this paper, we focus on techniques to merge the parallelized data processing (i.e. map-reduce) capabilities of Apache Spark with the extensive file-type parsing support of Apache Tika. These two frameworks each have unique appeal for data scientists. Where Spark makes highly efficient the parallelized processing of very large, often text-based data sets, Tika makes consistent the information extraction of over 1,200 text and binary file types on a sequential file basis. The technical integration of these two frameworks is the subject of our investigation, and is relevant for data scientists pursuing two types of use cases: (1) analysis of numerous (1000x) un-partitioned small to medium sized Tika parse-able files, and (2) analysis of very large partition-able Tika parse-able files. Given Tika's niche specialization of file extraction and Spark's specialization of parallelized computing, there is a need to explore the benefits of integration. Thus, we investigate best practices so as to empower data scientists with tools to gain insight into a greater portion of data formats commonly in use.


title: Comparison of array antenna designs for 77GHz radar Applications

abstract: In this paper, two types of microstrip array antenna for radars operating at 77 GHz are presented. The first type of array antenna is 10×4 elements rectangular patch array antenna with overall size of 20.43mm×7.83 mm. Unit element of this antenna type is a rectangular patch. Series-fed column of 10 unit elements are arranged into 10 × 4 arrays by implementing simple power divider structure. The second type of antenna is 12 × 4 elements leaf array antenna with overall size of 16.72mm×10.75 mm. Unit element of the second antenna type is a rectangular patch that is inclined 45° from the microstrip line axis. Twelve patches are arranged on both sides of the microstrip line and are connected directly at their corners. One additional patch at the end of the structure acts as a matching element. Array consisting of 12×4 unit elements are created by implementing simple power divider structure. Both types of antenna are combined with different types of waveguide-to-microstrip transition consist of two metal layers printed on a single dielectric substrate. The waveguide-to-microstrip transitions are fed by WR10 standard waveguide. The performances of both antennas are verified via simulations and measurements. The rectangular patch array antenna has 19.8 dB gain and 20° × 10° beam width at 77 GHz, while the leaf array antenna has 19.6 dB gain and 19° × 11.6° beam width at 77 GHz. Both types of antenna can be further developed as a reference for designing antenna for radar systems operating at higher frequencies, such as 94 GHz.


title: Measuring Online Social Bubbles

abstract: Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed—Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information.


title: E-Government and the Transformation of Service Delivery and Citizen Attitudes

abstract: The impact of new technology on public-sector service delivery and citizens' attitudes about government has long been debated by political observers. This article assesses the consequences ofe-government for service delivery, democratic responsiveness, and public attitudes over the last three years. Research examines the content of e-government to investigate whether it is taking advantage of the interactive features of the World Wide Web to improve service delivery, democratic responsiveness, and public outreach. In addition, a national public opinion survey examines the ability of e-government to influence citizens' views about government and their confidence in the effectiveness of service delivery. Using both Web site content as well as public assessments, I argue that, in some respects, the e-government revolution has fallen short of its potential to transform service delivery and public trust in government. It does, however, have the possibility of enhancing democratic responsiveness and boosting beliefs that government is effective.


title: A 6-DOF exoskeleton for head and neck motion assist with parallel manipulator and sEMG based control

abstract: This paper develops a novel parallel manipulator based exoskeleton for head and neck motion assist. Electromyography signal is used to extract feature and estimated neck force during motion. Then the estimated force is combined with the inverse dynamic controller to construct an interaction controller, which is used to reduce resultant contact force and moment between manipulator and human head. Simulation results show the proposed control scheme can help relieve the head burden and generate extra head motion assist.


title: GPx4, Lipid Peroxidation, and Cell Death: Discoveries, Rediscoveries, and Open Issues.

abstract: SIGNIFICANCE ::: Iron-dependent lipid peroxidation is a complex oxidative process where phospholipid hydroperoxides (PLOOH) are produced in membranes and finally transformed into a series of decomposition products, some of which are endowed with biological activity. It is specifically prevented by glutathione peroxidase 4 (GPx4), the selenoenzyme that reduces PLOOH by glutathione (GSH). PLOOH is both a product and the major initiator of peroxidative chain reactions, as well as an activator of lipoxygenases. α-Tocopherol both specifically breaks peroxidative chain propagation and inhibits lipoxygenases. Thus, GPx4, GSH, and α-tocopherol are integrated in a concerted anti-peroxidant mechanism. Recent Advances: Ferroptosis has been recently identified as a cell death subroutine that is specifically activated by missing GPx4 activity and inhibited by iron chelation or α-tocopherol supplementation. Ferroptosis induction may underlie spontaneous human diseases, such as major neurodegeneration and neuroinflammation, causing an excessive cell death. The basic mechanism of ferroptosis, therefore, fits the features of activation of lipid peroxidation. ::: ::: ::: CRITICAL ISSUES ::: Still lacking are convincing proofs that lipoxygenases are involved in ferroptosis. Also, unknown are the molecules eventually killing cells and the mechanisms underlying the drop of the cellular anti-peroxidant capacity. ::: ::: ::: FUTURE DIRECTIONS ::: Molecular events and mechanisms of ferroptosis to be unraveled and validated on animal models are GPx4 inactivation, role of GSH concentration, increased iron availability, and membrane structure and composition. This is expected to drive drug discovery that is aimed at halting cell death in degenerative diseases or boosting it in cancer cells. Antioxid. Redox Signal. 29, 61-74.


title: Germination improvement and α-amylase and β-1,3-glucanase activity in dormant and non-dormant seeds of Oregano (Origanum vulgare).

abstract: Oregano plays a primary role among temperate culinary herbs in world trade. It is native of Southern Europe and is one of the most popular herbs in Mediterranean cooking. The germination response of this species to various germination improvement treatments including mechanical, physical and chemical scarification, were studied. Mechanical and chemical scarification of seed coat improved seed germination parameters and suggesting that O. vulgare seeds have an exogenous dormancy. Moreover, seed germination of oregano affected by other treatments such as moist chilling and chemicals, and proposing that oregano seeds also have endogenous dormancy. Standard germination test showed that germination percentage of untreated seeds is 25.8% while after chilling treatment at 4 C for 7 days germination percentage reached to 39%. Soaking seeds in 100 ppm of GA3 for 36 h resulted in 48% germination. The highest value of germination parameters detected by soaking seeds in -10 bar polyethylene glycol solution at 20 C for 72 h. Total germination percentage reached to 60% in this treatment. Combination of chilling and PEG improved seed germination significantly, but was not as effective as PEG solely. Enzyme activity of alpha-amylase and beta-1,3-glucanase in germinating seeds at 12 h after start of imbibition were assayed. Dormant seeds showed lower enzyme activity and enzyme activity in treated seeds increased significantly.


title: Algorithmic approaches to protein-protein interaction site prediction

abstract: Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.


title: The improvement of liposome-mediated transfection of pEGFP DNA into human prostate cancer cells by combining low-frequency and low-energy ultrasound with microbubbles.

abstract: The aim of this study was to explore the use of a contrast agent to study the effects of exposure to ultrasound, in combination with microbubbles, on liposome-mediated transfection of genes into human prostate cancer cells. A contrast agent was used to study the effects of ultrasound exposure in combination with microbubbles on liposomes, which transfect genes into human prostate cancer cells. The human prostate cancer cell line PC-3 in suspension was exposed to ultrasound with a 20% duty cycle (i.e., 2 sec 'on' time and 8 sec 'off' time) lasting 5 min, with and without ultrasound contrast agent (SonoVue™) using a digital sonifier at a frequency of 21 kHz and an intensity of 4.6 mW/cm2. Immediately after exposure to ultrasound, cell viability and membrane damage were measured. After exposure to ultrasound, the cell suspensions were put into 12‑well plates and cultured for 24 h. Fluorescence microscopy and flow cytometry were used to detect pEGFP transfection efficiency. Exposure to ultrasound alone and ultrasound combined with microbubbles resulted in minimal cell death and induced negligible cell membrane damage. Ultrasound combined with microbubbles had a greater effect on cell membrane damage in all groups: the average cell membrane damage was 41.87%, and it was approximately 42‑fold greater than in the control group. The average transfection efficiency of PC-3 cells was 20.30% for the liposome (Lipofectamine™)+pEGFP+ultrasound+ultrasound contrast agent (SonoVue) group; this was the highest rate of all groups measured and was approximately 81‑fold greater than that of the control group. The use of low-frequency and low-energy ultrasound, in combination with microbubbles, could be a potent physical method for increasing liposome gene delivery efficiency. This technique is a promising non-viral approach that can be used in prostate cancer gene therapy.


title: PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

abstract: We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, and can thus lower the end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain the low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations. Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.


title: MagicMeeting: A Collaborative Tangible Augmented Reality System

abstract: We describe an augmented reality (AR) system that allows multiple participants to interact with 2D and 3D data using tangible user interfaces. The system features face-to-face communication, collaborative viewing and manipulation of 3D models, and seamless access to 2D desktop applications within the shared 3D space. All virtual content, including 3D models and 2D desktop windows, is attached to tracked physical objects in order to leverage the efficiencies of natural two-handed manipulation. The presence of 2D desktop space within 3D facilitates data exchange between the two realms, enables control of 3D information by 2D applications, and generally increases productivity by providing access to familiar tools. We present a general concept for a collaborative tangible AR system, including a comprehensive set of interaction techniques, a distributed hardware setup, and a component-based software architecture that can be flexibly configured using XML. We show the validity of our concept with an implementation of an application scenario from the automotive industry.


title: Can a Decentralized Metadata Service Layer Benefit Parallel Filesystems?

abstract: The demand for scalable I/O continues to grow rapidly as computer clusters keep growing. Much of the research in storage systems has been focused on improving the scale and performance of I/O throughput. Scalable file systems do a good job of scaling large file access bandwidth by striping or sharing I/O resources across many servers or disks. However, the same cannot be said about scaling file metadata operation rates. Most existing parallel file systems choose to concentrate all the metadata processing load on a single server. This centralized processing can guarantee the correctness, but it severely hampers scalability. This downside is becoming more and more unacceptable as metadata throughput is critical for large scale applications. Distributing metadata processing load is critical to improve metadata scalability when handling huge number of client nodes. However, a solution to speed up metadata operations has to address two challenges simultaneously, namely the scalability and reliability. In this paper, we have designed a decentralized metadata service layer and evaluated its benefits and shortcomings that concern parallel file systems. The main aim of this service layer is to maintain reliability and consistency in a distributed metadata environment. At the same time we also focus on improving the scalability of the metadata operations, and in turn, the scalability of the underlying parallel file system. As demonstrated by experiments, the approach presented in this paper achieves significant improvements over native parallel file systems by large margin for all the major metadata operations. With 256 client processes, our decentralized metadata service outperforms Lustre and PVFS2 by a factor of 1.9 and 23, respectively, to create directories. With respect to stat() operation on files, our approach is 1.3 and 3.0 times faster than Lustre and PVFS.


title: Streaming Gibbs Sampling for LDA Model

abstract: Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an online extension of the collapsed Gibbs sampling (CGS). Our empirical study shows that SGS can reach similar perplexity as CGS, much better than SVB. Our distributed version of SGS, DSGS, is much more scalable than SVB mainly because the updates' communication complexity is small.


title: Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

abstract: Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.


title: Eye refractive error classification using machine learning techniques

abstract: Machine learning is a subdivision of Artificial Intelligence (AI) that is concerned with the design and development of intelligent algorithms that enables machines to learn from data without being programmed. Machine learning mainly focus on how to automatically recognize complex patterns among data and make intelligent decisions. In this paper, intelligent machine learning algorithms are used to classify the type of an eye disease based on ophthalmology data collected from patients of Mecca hospital in Sudan. Three machine-learning techniques are used to predict the severity of the eye that occurred during the investigation, which are Naive Bayesian, SVM, and J48 decision tree. The obtained result showed that J48 classifier outperforms both Naive Bayesian as well as SVM.


title: Scaling regression testing to large software systems

abstract: When software is modified, during development and maintenance, it is regression tested to provide confidence that the changes did not introduce unexpected errors and that new features behave as expected. One important problem in regression testing is how to select a subset of test cases, from the test suite used for the original version of the software, when testing a modified version of the software. Regression-test-selection techniques address this problem. Safe regression-test-selection techniques select every test case in the test suite that may behave differently in the original and modified versions of the software. Among existing safe regression testing techniques, efficient techniques are often too imprecise and achieve little savings in testing effort, whereas precise techniques are too expensive when used on large systems. This paper presents a new regression-test-selection technique for Java programs that is safe, precise, and yet scales to large systems. It also presents a tool that implements the technique and studies performed on a set of subjects ranging from 70 to over 500 KLOC. The studies show that our technique can efficiently reduce the regression testing effort and, thus, achieve considerable savings.


title: Kalman Filter based position estimation using "optical mouse movement sensor" and differential drive robot model

abstract: This article describes the creation of a mathematical model for the kinematics, dynamics and electronics of a two-wheel-steered robot. As a result, it is possible to use a previously created, potential field-based and fuzzy navigation-based robot control system ([1], [2], [3], [4]) with two-wheel-driven robots as well. Using the results presented in this article the current location and the driven path can be estimated more accurately. This can be achieved by using the Kalman Filter with the wheel encoder data and using Optical Flow-based movement measurement devices that are similar to the ones known from the optical mouse peripherals. The established equations define the bases for controlling and navigating robots in indoor environments (flat surface, no sliding). According to these, they reflect the kinematics of the ground unit, the mathematical model of the electronic motor, and also the models of the sensors installed on the robot (odometer and optical mouse movement sensor).


title: Emotion, Decision-Making and Substance Dependence: A Somatic-Marker Model of Addiction

abstract: Similar to patients with orbitofrontal cortex lesions, substance dependent individuals (SDI) show signs of impairments in decision-making, characterised by a tendency to choose the immediate reward at the expense of severe negative future consequences. The somatic-marker hypothesis proposes that decision-making depends in many important ways on neural substrates that regulate homeostasis, emotion and feeling. According to this model, there should be a link between abnormalities in experiencing emotions in SDI, and their severe impairments in decision-making in real-life. Growing evidence from neuroscientific studies suggests that core aspects of substance addiction may be explained in terms of abnormal emotional guidance of decision-making. Behavioural studies have revealed emotional processing and decision-making deficits in SDI. Combined neuropsychological and physiological assessment has demonstrated that the poorer decision-making of SDI is associated with altered reactions to reward and punishing events. Imaging studies have shown that impaired decision-making in addiction is associated with abnormal functioning of a distributed neural network critical for the processing of emotional information, including the ventromedial cortex, the amygdala, the striatum, the anterior cingulate cortex, and the insular/somato-sensory cortices, as well as non-specific neurotransmitter systems that modulate activities of neural processes involved in decision-making. The aim of this paper is to review this growing evidence, and to examine the extent of which these studies support a somatic-marker model of addiction.


title: Stress analysis of an infinite anisotropic elastic medium containing inclusions using the boundary point method

abstract: The purpose of this paper is to carry out the stress analysis of an infinite anisotropic elastic medium containing elastic inclusions using the boundary point method. The boundary point method avoids various singular integrals appearing in the boundary integral formulations. Its numerical implementation is simpler relative to the boundary element method since there is no interpolation and integral implementation in the boundary point method, and only the source points corresponding to the boundary points are needed to formulate the related fundamental solution matrices. Numerical results are satisfied compared with the available solutions.


title: Subword Encoding in Lattice LSTM for Chinese Word Segmentation

abstract: We investigate a lattice LSTM network for Chinese word segmentation (CWS) to utilize words or subwords. It integrates the character sequence features with all subsequences information matched from a lexicon. The matched subsequences serve as information shortcut tunnels which link their start and end characters directly. Gated units are used to control the contribution of multiple input links. Through formula derivation and comparison, we show that the lattice LSTM is an extension of the standard LSTM with the ability to take multiple inputs. Previous lattice LSTM model takes word embeddings as the lexicon input, we prove that subword encoding can give the comparable performance and has the benefit of not relying on any external segmentor. The contribution of lattice LSTM comes from both lexicon and pretrained embeddings information, we find that the lexicon information contributes more than the pretrained embeddings information through controlled experiments. Our experiments show that the lattice structure with subword encoding gives competitive or better results with previous state-of-the-art methods on four segmentation benchmarks. Detailed analyses are conducted to compare the performance of word encoding and subword encoding in lattice LSTM. We also investigate the performance of lattice LSTM structure under different circumstances and when this model works or fails.


title: Time Travel with One Click: Effects of Digital Filters on Perceptions of Photographs

abstract: Today's digital photographs are being heavily "filtered." By simple clicks on mobile apps like Hipstamatic and Instagram, users can easily apply digital filters to their pictures to create effects such as faux-vintage and light leaks. To understand the potential impacts of photo filters, we conducted an online experiment and investigated how the use of the black-and-white and film-style photo filters changed viewers' perceptions and descriptions of photographs. We found that photo filters substantially increased viewers' perceived temporal distances to photographs. Participants also tended to describe analogue-style photos more interpretively and tentatively than unfiltered ones, indicating an increase in construal levels. We suggest that the widely used photo filter is not just a tool to change aesthetics; it also adds a layer of history, meaning, and defamiliarization to photographs, allowing users to construct a mental distance in images that deviates from everyday experiences. We offer insights into the psychology of visual styles and implications for designing filter apps and photo-sharing platforms.


title: Smart Cities: A Survey on Security Concerns

abstract: A smart city is developed, deployed and maintained with the help of Internet of Things (IoT). The smart cities have become an emerging phenomena with rapid urban growth and boost in the field of information technology. However, the function and operation of a smart city is subject to the pivotal development of security architectures. The contribution made in this paper is twofold. Firstly, it aims to provide a detailed, categorized and comprehensive overview of the research on security problems and their existing solutions for smart cities. The categorization is based on several factors such as governance, socioeconomic and technological factors. This classification provides an easy and concise view of the security threats, vulnerabilities and available solutions for the respective technologies areas that are proposed over the period 2010-2015. Secondly, an IoT testbed for smart cities architecture, i.e., SmartSantander is also analyzed with respect to security threats and vulnerabilities to smart cities. The existing best practices regarding smart city security are discussed and analyzed with respect to their performance, which could be used by different stakeholders of the smart cities.


title: Microbes and mental health: A review

abstract: There is a growing emphasis on the relationship between the microorganisms inhabiting the gut (gastrointestinal microbiota) and human health. The emergence of a microbiota-gut-brain axis to describe the complex networks and relationship between the gastrointestinal microbiota and host reflects the major influence this environment may have in brain health and disorders of the central nervous system (CNS). Bidirectional communication between the microbiota and the CNS occurs through autonomic, neuroendocrine, enteric, and immune system pathways. Potential neurobiological mechanisms through which disruptions in this network may impact health and disease include hypothalamic-pituitary-adrenal (HPA)-axis activation, and altered activity of neurotransmitter and immune systems. Perturbations of the gut microbial community have already been implicated in multiple host diseases such as obesity, diabetes, and inflammation, while recent evidence suggests a potential role of the microbiota-gut-brain axis in neuropsychiatric disorders, such as depression and anxiety. Here, we review the current literature related to the influence of the gut microbial community on central nervous system function, with a specific focus on anxiety and depressive symptoms. The role of stress and stress-mediated changes in autonomic, neuroendocrine, immune, and neurotransmitter systems are examined, followed by a discussion of the role of the microbiota in novel gastrointestinal-based treatment options for the prevention and treatment of brain-based disorders such as anxiety and depression.


title: Topic Distillation with Knowledge Agents

abstract: This is the second year that our group participates in TREC’s Web track. Our experiments focused on the Topic distillation task. Our main goal was to experiment with the Knowledge Agent (KA) technology [1], previously developed at our Lab, for this particular task. The knowledge agent approach was designed to enhance Web search results by utilizing domain knowledge. We first describe the generic KA approach and then articulate on the use of this technology in the context of the topic distillation task. We focus mainly on the Knowledge Agent features that were used in this task. The rest of this paper is organized as follows: Section 2 describes KA in general. In Section 3 we describe how KA was used for the topic distillation experiment. Section 4 describes the obtained results. Section 5 concludes.


title: New multimedia archiving technique using multiple quick-response codes

abstract: Quick Response (QR) code is a two-dimensional bar-code, which is quite popular due to its excellent storage capacity and error resilience. Generally, QR codes are widely used to store text information such as URL links, geographical coordinates, name cards, inventory information, authorship, etc. To reach the current limit of QR codes, in this paper, we would like to propose an innovative multimedia archiving technique, which is built upon the advanced signal processing scheme to tackle the multiple QR codes all at once. The multimedia data often include texts, images, audio data, special-purpose codes, etc. The recently proposed software-defined multiplexing code (SDMC) can be applied to combine all of them even though each type of data would have the individual data format different from others. Our proposed new archiving technique involves two phases, namely multimedia-amalgamation (MA) and multimedia-detachment (MD). In the MA phase, the multimedia data, regardless of their data format, can be converted to the binary streams; then the SDMC will be employed to aggregate them together in a longer binary stream; such an ultimate long binary stream will be converted to the QR codes. The QR codes, protected by the inherent error-correction mechanism, will thus be placed on a sheet or multiple sheets with a uniform spacing. The sheet(s) containing multiple QR codes can thus be archived in soft (PDF) or hard (print-out) copies. In the MD (recovery) phase, one can scan these PDF files and employ our designed signal processing algorithms to separate the individual QR codes; then the corresponding QR decoder can convert these QR codes back to the original binary stream. Finally the unstuffing algorithm in the SDMC can detach the individual data from the long binary stream composed by the multimedia mixture.