title: Algorithm 857: POLSYS_GLP—a parallel general linear product homotopy code for solving polynomial systems of equations

abstract: Globally convergent, probability-one homotopy methods have proven to be very effective for finding all the isolated solutions to polynomial systems of equations. After many years of development, homotopy path trackers based on probability-one homotopy methods are reliable and fast. Now, theoretical advances reducing the number of homotopy paths that must be tracked and handling singular solutions have made probability-one homotopy methods even more practical. POLSYS_GLP consists of Fortran 95 modules for finding all isolated solutions of a complex coefficient polynomial system of equations. The package is intended to be used on a distributed memory multiprocessor in conjunction with HOMPACK90 (Algorithm 777), and makes extensive use of Fortran 95-derived data types and MPI to support a general linear product (GLP) polynomial system structure. GLP structure is intermediate between the partitioned linear product structure used by POLSYS_PLP (Algorithm 801) and the BKK-based structure used by PHCPACK. The code requires a GLP structure as input, and although finding the optimal GLP structure is a difficult combinatorial problem, generally physical or engineering intuition about a problem yields a very good GLP structure. POLSYS_GLP employs a sophisticated power series end game for handling singular solutions, and provides support for problem definition both at a high level and via hand-crafted code. Different GLP structures and their corresponding Bezout numbers can be systematically explored before committing to root finding.


title: Detection in the stochastic block model with multiple clusters: proof of the achievability conjectures, acyclic BP, and the information-computation gap

abstract: In a paper that initiated the modern study of the stochastic block model, Decelle et al., backed by Mossel et al., made a fascinating conjecture: Denote by $k$ the number of balanced communities, $a/n$ the probability of connecting inside communities and $b/n$ across, and set $\mathrm{SNR}=(a-b)^2/(k(a+(k-1)b)$; for any $k \geq 2$, it is possible to detect communities efficiently whenever $\mathrm{SNR}>1$ (the KS threshold), whereas for $k\geq 4$, it is possible to detect communities information-theoretically for some $\mathrm{SNR}<1$. Massouli\'e, Mossel et al.\ and Bordenave et al.\ succeeded in proving that the KS threshold is efficiently achievable for $k=2$, while Mossel et al.\ proved that it cannot be crossed information-theoretically for $k=2$. The above conjecture remained open for $k \geq 3$. This paper proves this conjecture, and further extends the efficient detection to non-symmetrical SBMs with a generalized notion of detection and KS threshold. For the efficient part, a linearized acyclic belief propagation (ABP) algorithm is developed and proved to detect communities for any $k$ down to the KS threshold in time $O(n \log n)$. Achieving this requires showing optimality of ABP in the presence of cycles, a challenge in the realm of graphical models. The paper further connects ABP to a power iteration method with a nonbacktracking operator of generalized order, formalizing the interplay between message passing and spectral methods. For the information-theoretic (IT) part, a non-efficient algorithm sampling a typical clustering is shown to break down the KS threshold at $k=4$. The emerging gap is shown to be large in some cases; if $a=0$, the KS threshold reads $b \gtrsim k^2$ whereas the IT bound reads $b \gtrsim k \ln(k)$.


title: 1-800-OVERLAYS: using overlay networks to improve VoIP quality

abstract: The cost savings and novel features associated with Voice over IP (VoIP) are driving its adoption by service providers. Such a transition however can successfully happen only if the quality and reliability offered is comparable to the existing PSTN. Unfortunately, the Internet's best effort service model provides no inherent quality of service guarantees. Because low latency and jitter is the key requirement for supporting high quality interactive conversations, VoIP applications use UDP to transfer data, thereby subjecting themselves to performance degradations caused by packet loss and network failures.In this paper we describe two algorithms to improve the performance of such VoIP applications. These mechanisms are used for localized packet loss recovery and rapid rerouting in the event of network failures. The algorithms are deployed on the routers of an application-level overlay network and require no changes to the underlying infrastructure. Initial experimental results indicate that these two approaches can be composed to yield voice quality on par with the PSTN.


title: Parallel Computing for Machine Learning in Social Network Analysis

abstract: Machine learning, especially deep learning, is revolutionizing how many engineering problems are being solved. Three critical ingredients are needed to apply deep machine learning to significant real world problems: i.) large data sets; ii.) software to implement deep learning and; iii.) significant computing cycles. This paper discusses the state of each ingredient with a specific focus on: a.) how deep learning can apply to large-scale social network analysis and; b.) the computing resources required to make such analyses feasible.


title: GGAS: Global GPU address spaces for efficient communication in heterogeneous clusters

abstract: Modern GPUs are powerful high-core-count processors, which are no longer used solely for graphics applications, but are also employed to accelerate computationally intensive general-purpose tasks. For utmost performance, GPUs are distributed throughout the cluster to process parallel programs. In fact, many recent high-performance systems in the TOP500 list are heterogeneous architectures. Despite being highly effective processing units, GPUs on different hosts are incapable of communicating without assistance from a CPU. As a result, communication between distributed GPUs suffers from unnecessary overhead, introduced by switching control flow from GPUs to CPUs and vice versa. Most communication libraries even require intermediate copies from GPU memory to host memory. This overhead in particular penalizes small data movements and synchronization operations, reduces efficiency and limits scalability. In this work we introduce global address spaces to facilitate direct communication between distributed GPUs without CPU involvement. Avoiding context switches and unnecessary copying dramatically reduces communication overhead. We evaluate our approach using a variety of workloads including low-level latency and bandwidth benchmarks, basic synchronization primitives like barriers, and a stencil computation as an example application. We see performance benefits of up to 2× for basic benchmarks and up to 1.67× for stencil computations.


title: Deformable Convolutional Networks

abstract: Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/msracver/Deformable-ConvNets.


title: A cross-device matching fingerprint database from multi-type sensors

abstract: Databases play an important role in evaluating the performance of fingerprint identification algorithms. But which can be used to test the interoperability? That is to say, few of databases can test the performance of an algorithm on images acquired by different sensors. In order to solve the problem, we create the FingerPass cross-device matching fingerprint database which consists of almost 80 thousand fingerprint images from 90 subjects on nine different fingerprint sensors. We take both technology type and interaction type into consideration when choosing the sensors, totally different from other databases. It can test the interoperability of an algorithm at both the sensor level and the sensor type level. So we can use the FingerPass to test the performance of a cross-device matching algorithm for sensors of a specific type or different types. We apply the Ver-iFinger fingerprint recognition algorithm on it, and the experimental results indicate that the FingerPass cross-device matching database is a challenge for fingerprint algorithms.


title: Marama Bean (Tylosema esculentum, Fabaceae) seed crop in Texas

abstract: The marama bean, Tylosema esculentum, is a drought- tolerant legume native to southern Africa. Its seeds are comparable to soybeans in protein content and quality, its oil content approaches that of peanuts, and the plants might be desirable as a forage legume. Although the marama bean has great potential as an arid land crop in the United States, studies of the species are extremely limited. The current investigations have demonstrated that the plants can be grown successfully in an arid region of the United States, at least under experimental conditions, and that a healthy seed crop can be expected in about 4.5 years.


title: Mechanically Tunable Substrate Integrated Waveguide (SIW) Cavity Based Oscillator

abstract: This work presents the design of a tunable oscillator circuit based on a mechanically tuned resonant cavity that allows varying the oscillation frequency by 2%-3% by means of a flap. The proposed tuning method is validated by implementing a substrate integrated waveguide (SIW) resonant cavity which is used as the resonator of an oscillator circuit operating at 12.2 GHz. The mechanically tuned cavity can vary the frequency of the oscillator in the range of 12.1 GHz to 12.4 GHz allowing for a fine compensation of a potential resonance frequency variation due to fabrication and component tolerances. The proposed method can be used for fine tuning of oscillator circuits at the implementation phase.


title: Design of household appliances for a Dc-based nanogrid system: An induction heating cooktop study case

abstract: Efficient energy management is becoming an important issue when designing any electrical system. Recently, a significant effort has been devoted to the design of optimized micro and nanogrids comprising residential area subsystems. One of these approaches consists on the design of a dc-based nanogrid optimized for the interoperation of electric loads, sources, and storage elements. Home appliances are one of the main loads in such dc-based nanogrids. In this paper, the design and optimization of an appliance for operation in a dc-based nanogrid is detailed. An induction heating cooktop appliance will be considered as a reference example, being some of the design considerations generalizable to other appliances. The main design aspects, including the inductor system, power converter, EMC filter, and control are considered. Finally, some simulation results of the expected converter performance are shown.


title: Smoking prevalence among women in the European community 1950-1990.

abstract: The paper reviews trends in tobacco use among women in the European Community (EC) between 1950 and 1990. The data suggest that EC countries occupy different points on what appears to be a common prevalence curve. Southern EC countries are represented in the early phases of this curve, marked out by sharply rising prevalence. In northern EC countries, female smoking prevalence appears to have peaked. Across the EC, the commodification of tobacco use, and the production and promotion of manufactured cigarettes in particular, underlies this prevalence curve. Young women in higher socio-economic groups have led the way into cigarette smoking in both northern and southern Europe, with smoking prevalence declining first among women who are privileged in terms of their education, occupation and income. Because the decline in prevalence has yet to be repeated among women in more disadvantaged circumstances, cigarette smoking among women in the EC is likely to become a habit increasingly linked to low socio-economic status.


title: Decrypting Distributed Ledger Design -- Taxonomy, Classification and Blockchain Community Evaluation

abstract: More than 1000 distributed ledger technology (DLT) systems raising $600 billion in investment in 2016 feature the unprecedented and disruptive potential of blockchain technology. A systematic and data-driven analysis, comparison and rigorous evaluation of the different design choices of distributed ledgers and their implications is a challenge. The rapidly evolving nature of the blockchain landscape hinders reaching a common understanding of the techno-socio-economic design space of distributed ledgers and the cryptoeconomies they support. To fill this gap, this paper makes the following contributions: (i) A conceptual architecture of DLT systems with which (ii) a taxonomy is designed and (iii) a rigorous classification of DLT systems is made using real-world data and wisdom of the crowd. (iv) A DLT design guideline is the end result of applying machine learning methodologies on the classification data. Compared to related work and as defined in earlier taxonomy theory, the proposed taxonomy is highly comprehensive, robust, explanatory and extensible. The findings of this paper can provide new insights and better understanding of the key design choices evolving the modeling complexity of DLT systems, while identifying opportunities for new research contributions and business innovation.


title: Unsupervised Learning of 3D Structure from Images

abstract: A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.


title: Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks

abstract: Predicting passenger pickup/dropoff demands based on historical mobility trips has been of great importance towards better vehicle distribution for the emerging mobility-on-demand (MOD) services. Prior works focused on predicting next-step passenger demands at selected locations or hotspots. However, we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses, and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies. In this paper, we propose an end-to-end deep neural network solution to the prediction task. We employ the encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influences and pickup-dropoff interactions on citywide passenger demands. A novel attention model is incorporated to emphasize the effects of latent citywide mobility regularities. We evaluate our proposed method using real-word mobility trips (taxis and bikes) and the experimental results show that our method achieves higher prediction accuracy than the adaptations of the state-of-the-art approaches.


title: Learning Deep Energy Models

abstract: Deep generative models with multiple hidden layers have been shown to be able to learn meaningful and compact representations of data. In this work we propose deep energy models, which use deep feedforward neural networks to model the energy landscapes that define probabilistic models. We are able to efficiently train all layers of our model simultaneously, allowing the lower layers of the model to adapt to the training of the higher layers, and thereby producing better generative models. We evaluate the generative performance of our models on natural images and demonstrate that this joint training of multiple layers yields qualitative and quantitative improvements over greedy layerwise training. We further generalize our models beyond the commonly used sigmoidal neural networks and show how a deep extension of the product of Student-t distributions model achieves good generative performance. Finally, we introduce a discriminative extension of our model and demonstrate that it outperforms other fully-connected models on object recognition on the NORB dataset.


title: Brain regions involved in the recognition of happiness and sadness in music

abstract: Here, we used functional magnetic resonance imaging to test for the lateralization of the brain regions speci¢cally involved in the recognition of negatively and positively valenced musical emotions. The manipulation of two major musical features (mode and tempo), resulting in the variation of emotional perception along the happiness^ sadness axis, was shown to principally involve subcortical and neocortical brain structures, which are known to intervene in emotion processing in other modalities. In particular, the minor mode (sad excerpts) involved the left orbito and mid-dorsolateral frontal cortex, which does not con¢rm the valence lateralization model. We also show that the recognition of emotions elicited by variations of the two perceptual determinants rely on both common (BA 9) and distinct neural mechanisms. NeuroReport 16:1981^1984 � c 2005 Lippincott Williams & Wilkins.


title: Vehicle logo detection using convolutional neural network and pyramid of histogram of oriented gradients

abstract: This paper presents a new method for vehicle logo detection and recognition from images of front and back views of vehicle. The proposed method is a two-stage scheme which combines Convolutional Neural Network (CNN) and Pyramid of Histogram of Gradient (PHOG) features. CNN is applied as the first stage for candidate region detection and recognition of the vehicle logos. Then, PHOG with Support Vector Machine (SVM) classifier is employed in the second stage to verify the results from the first stage. Experiments are performed with dataset of vehicle images collected from internet. The results show that the proposed method can accurately locate and recognize the vehicle logos with higher robustness in comparison with the other conventional schemes. The proposed methods can provide up to 100% in recall, 96.96% in precision and 99.99% in recognition rate in dataset of 20 classes of the vehicle logo.


11 Sociocultural Theory and Second Language Learning


title: Learning scene-specific pedestrian detectors without real data

abstract: We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.


title: A flexible and miniaturized wireless ECG recording system with metal-skin contacts input for wearable personalized healthcare

abstract: This paper presents a miniaturized wireless finger-based ECG acquisition system with metal-skin contact input for wearable healthcare applications. This system is implemented using a novel low-noise analog front-end chip and is equipped with wireless transmission ability allowing the acquired ECG signals to be sent to end-devices such as smartphones or PCs through the Bluetooth V4.0 and Micro USB interface. Benefiting from the compact size of the chipset with many integrated features, which reduces many off-the-shelf components, we are able to achieve a small implementation size of only 1.4 cm by 2.4 cm on the flexible printed circuit board, which improves the wearability of the system. Moreover, we propose a new finger-based placement using dry metal-electrode, which has a much smaller area and is more comfortable than wearing the conventional chest based placement using wet electrode with conduction gel. Experimental results show that the proposed finger-based ECG acquisition system is able to achieve comparable ECG signal quality with the Nihon Kohden ECG-1550P non-portable ECG, which is widely used in hospitals. The good performance and compact size of the proposed system provides an attractive alternative to wearable ECG devices for enhancing the user experience and comfort.


title: New Measures of Mental State and Behavior Based on Data Collected From Sensors, Smartphones, and the Internet

abstract: With the rapid and ubiquitous acceptance of new technologies, algorithms will be used to estimate new measures of mental state and behavior based on digital data. The algorithms will analyze data collected from sensors in smartphones and wearable technology, and data collected from Internet and smartphone usage and activities. In the future, new medical measures that assist with the screening, diagnosis, and monitoring of psychiatric disorders will be available despite unresolved reliability, usability, and privacy issues. At the same time, similar non-medical commercial measures of mental state are being developed primarily for targeted advertising. There are societal and ethical implications related to the use of these measures of mental state and behavior for both medical and non-medical purposes.


title: Index modulated OFDM with interleaved grouping for V2X communications

abstract: Vehicle-to-Vehicle and Vehicle-to-Infrastructure (V2X) communication brings about stringent demand for robust and efficient communication techniques under sever Doppler effect and frequency-selective channel environments. Index Modulated Orthogonal Frequency Division Multiplexing with Interleaved grouping (IIM-OFDM), which utilizes the indices of active subcarriers in frequency domain interleaved subcarrier groups to carry information, is considered as a competitive candidate for its robustness against both Inter-Carrier Interference (ICI) and channel correlation. In this paper, we propose a novel revision of IIM-OFDM designed for V2X, namely Quadrature Index Modulated OFDM with Interleaved grouping (IQIM-OFDM), which performs index modulation independently for the inphase and quadrature components of the constellation symbols. Thanks to the increased index bits, IQIM-OFDM can work at smaller subcarrier group and constellation sizes compared with IIM-OFDM at the same data rate, and therefore boosts the performance under V2X channels. Monte Carlo simulations under measured V2X channels are conducted to verify the superiority of the proposed scheme against both IIM-OFDM and classical OFDM schemes.


title: Interactive visual supports for children with autism

abstract: Interventions to support children with autism often include the use of visual supports, which are cognitive tools to enable learning and the production of language. Although visual supports are effective in helping to diminish many of the challenges of autism, they are difficult and time-consuming to create, distribute, and use. In this paper, we present the results of a qualitative study focused on uncovering design guidelines for interactive visual supports that would address the many challenges inherent to current tools and practices. We present three prototype systems that address these design challenges with the use of large group displays, mobile personal devices, and personal recording technologies. We also describe the interventions associated with these prototypes along with the results from two focus group discussions around the interventions. We present further design guidance for visual supports and discuss tensions inherent to their design.


title: Some Principles and Applications of Adaptive Mathematical Morphology for Range Imagery

abstract: We explore the application of adaptive (i.e., data-dependent) mathematical morphology techniques to range imagery, i.e., the use of structuring elements that automatically adjust to the gray-scale values in a range image to deal with (e.g., extract or eliminate) features of known physical sizes.


title: Performance optimization of return-to-zero DPSK modulation in inter-satellite optical wireless communication

abstract: Increased demand of multimedia services to the customer, lead to the development of mobile satellite communication. In this paper, inter-satellite optical wireless communication is implemented using RZ optical signal generated by cascaded mach-zehnder modulator. The pulse duty cycle of 33%, 50%, and 67% with the modulation technique used is DPSK The proposed system is designed and analyzed with the coverage distance of 6000 Km with the bit-rate of 10 Gbps and transmitted power of 30 dBm. The performance parameters of the system are Q factor, BER, this paper targets to achieve the value of greater than 7 and as low as 10−9 respectively. The proposed system will be highly useful for present and next generation inter-satellite optical communication.


title: Identification of Child Sexual Abuse Survivor Subgroups Based on Early Maladaptive Schemas: Implications for Understanding Differences in Posttraumatic Stress Disorder Symptom Severity

abstract: Cognitive models have informed understanding of the development, maintenance, and treatment of posttraumatic stress disorder (PTSD). Limited research, however, has examined the relationship of early maladaptive schemas (EMS; Young in Cognitive therapy for personality disorders: A schema-focused approach (rev. ed.). Professional Resource Press, Sarasota, 1994) to PTSD among trauma survivors. The current study, using a sample of 127 female child sexual abuse (CSA) survivors, applied a model-based clustering procedure (Mclust; Fraley and Raftery in MCLUST Version 3 for R: Normal Mixture Modeling and Model-based Clustering, Technical Report No. 504, Department of Statistics, University of Washington, 2006) to the 15 subscales of the Young Schema Questionnaire-Short Form (YSQ-S; Young and Brown in Young Schema Questionnaire- short form. Cognitive Therapy Center, New York, 1994) and revealed three clusters differentiated primarily by level of schema elevation. Women in the cluster characterized by the highest schema scores reported the most severe PTSD symptoms. A discriminant analysis indicated that schemas of Mistrust/Abuse, Vulnerability to Harm, and Emotional Deprivation contributed most to distinguishing women differentiated on the basis of presumptive PTSD diagnostic status. Results underscore the importance of cognitive factors in the development and/or maintenance of PTSD symptoms and suggest possible treatment targets for cognitive therapy with CSA survivors.


Positive Youth Development in the United States


title: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

abstract: We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.


title: AN EMPIRICAL INVESTIGATION OF THE PRIVACY CONCERNS OF SOCIAL NETWORK SITE USERS IN TAIWAN

abstract: The study has two purposes: (1) to investigate the impact of awareness and control on Social Network Sites (SNSs) users’ information privacy concerns, and (2) to understand how information privacy concerns and the notion of trust shape users’ risk perceptions. The study collects data using survey methodology. A total of 356 SNS users are analyzed via partial least squares. Users regard awareness of and direct control over personal information disclosed on SNSs to be relative to their information privacy concerns in a positive and a negative direction, respectively. Furthermore, information privacy concerns and trust may collectively influence their perceived risk towards SNS. It is therefore advisable for SNS service providers to pay more attention to their own understanding of how awareness and control principles influence users’ information privacy concerns. Further, SNS providers should also propose more effective means necessary to reduce users’ information privacy concerns overall.


The container shipping network design problem with empty container repositioning


title: iScape: A Collaborative Memory Palace for Digital Library Search Results

abstract: Massive amounts of data are available in today’s Digital Libraries (DLs). The challenge is to find relevant information quickly and easily, and to use it effectively. A standard way to access DLs is via a text-based query issued by a single user. Typically, the query results in a potentially very long ordered list of matching documents, that makes it hard for users to find what they are looking for. This paper presents iScape, a shared virtual desktop world dedicated to the collaborative exploration and management of information. Data mining and information visualization techniques are applied to extract and visualize semantic relationships in search results. A three-dimensional (3-D) online browser system is exploited to facilitate complex and sophisticated human-computer and human-human interaction. Informal user studies have been conducted to compare the iScape world with a text-based, a 2-D visual Web interface, and a 3-D non-collaborative CAVE interface. We conclude with a discussion.


title: Customization, immersion satisfaction, and online gamer loyalty

abstract: Software designers often attempt to increase the customizability of their products to facilitate human-computer interaction and improve user response. However, exactly how customizability affects online gaming is unclear. This study posits that customization enhances gamer immersion satisfaction and loyalty. The study sample consists of 865 online gamers who provided valid responses to an online survey. Three models are compared using structural equation modeling: a partial mediator model, in which customization increases gamer loyalty directly and indirectly via enhanced immersion satisfaction; a full mediator model, in which immersion satisfaction fully mediates how customization influences loyalty; and an independent variable model, in which customization and immersion satisfaction are independent variables impacting gamer loyalty. The results of this study demonstrate that the partial mediator model significantly outperforms the other two models, suggesting that online game providers that increase customization and satisfy gamer needs regarding immersion can better foster gamer loyalty.


A Geometric Interpretation of v-SVM Classifiers


title: ONTOLOGIES: STATE OF THE ART, BUSINESS POTENTIAL, AND GRAND CHALLENGES

abstract: Retainer grooves are formed widthwise at a constant pitch on the external surface of a timing belt to retain the lead terminals of an electronic component therein. A magnet is mounted to a rail for guiding the timing belt to attract the lead terminals of the electronic component to the retainer grooves. The electronic component can be prevented from falling off or being displaced due to an inertia force generated at the time of intermittent feed so as to be stably conveyed not only horizontally but also in vertical or circular arc directions.


title: Blockchain for IoT security and privacy: The case study of a smart home

abstract: Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight instantiation of a BC particularly geared for use in IoT by eliminating the Proof of Work (POW) and the concept of coins. Our approach was exemplified in a smart home setting and consists of three main tiers namely: cloud storage, overlay, and smart home. In this paper we delve deeper and outline the various core components and functions of the smart home tier. Each smart home is equipped with an always online, high resource device, known as “miner” that is responsible for handling all communication within and external to the home. The miner also preserves a private and secure BC, used for controlling and auditing communications. We show that our proposed BC-based smart home framework is secure by thoroughly analysing its security with respect to the fundamental security goals of confidentiality, integrity, and availability. Finally, we present simulation results to highlight that the overheads (in terms of traffic, processing time and energy consumption) introduced by our approach are insignificant relative to its security and privacy gains.


title: Textual Paralanguage and Its Implications for Marketing Communications

abstract: Both face-to-face communication and communication in online environments convey information beyond the actual verbal message. In a traditional face-to-face conversation, paralanguage, or the ancillary meaning- and emotion-laden aspects of speech that are not actual verbal prose, gives contextual information that allows interactors to more appropriately understand the message being conveyed. In this paper, we conceptualize textual paralanguage (TPL), which we define as written manifestations of nonverbal audible, tactile, and visual elements that supplement or replace written language and that can be expressed through words, symbols, images, punctuation, demarcations, or any combination of these elements. We develop a typology of textual paralanguage using data from Twitter, Facebook, and Instagram. We present a conceptual framework of antecedents and consequences of brands' use of textual paralanguage. Implications for theory and practice are discussed.


title: Cyber Bullying - A New Social Menace

abstract: Objective: Cyber bullying is a combination of the word cyber and bullying where cyber basically means the Internet or on-line. In this case, cyber bullying will focus on getting in action with bullying by using the Internet or modern technologies such as on-line chats, online media and short messaging texts through social media. The current review aims to compile and summarize the results of relevant publications related to “cyber bullying." The review also includes discussing on relevant variables related to cyber bullying. Methods: Information from relevant publications addresses the demographics, prevalence, differences between cyber bullying and traditional bullying, bullying motivation, avenues to overcome it, preventions, coping mechanisms in relation to “cyber bullying” were retrieved and summarized. Results: The prevalence of cyber bullying ranges from 30% - 55% and the contributing risk factors include positive association with perpetration, non-supportive school environment, and Internet risky behaviors. Both males and females have been equal weigh on being perpetrators and victims. The older groups with more technology exposures are more prone to be exposed to cyber bullying. With respect to individual components of bullying, repetition is less evident in cyber bullying and power imbalance is not measured by physicality but in terms of popularity and technical knowledge of the perpetrator. Conclusion: Due to the limited efforts centralized on the intervention, future researchers should focus on testing the efficacy of possible interventional programs and the effects of different roles in the intervention in order to curb the problem and prevent more deleterious effects of cyber bullying. Please click PDF below to download the full paper...


title: Q-MAC: QoS and mobility aware optimal resource allocation for dynamic application offloading in mobile cloud computing

abstract: Smartphone applications are getting more multi-farious and demanding of increased energy and computing resources. Mobile Cloud Computing (MCC) made a novel platform which allows personal Smartphones to execute heavy computing tasks with the assistance of powerful cloudlet servers attached to numerous wireless access points (APs). Furthermore, due to users' mobility in anywhere, ensuring the continuous connectivity of mobile devices in given wireless network access point is quite difficult because the signal strength becomes sporadic at that time. In this paper, we develop a QoS and mobility aware optimal resource allocation architecture, namely Q-MAC, for remote code execution in MCC that offers higher efficiency in timeliness and reliability domains. By carrying continuous track of user, our proposed architecture performs the offloading process. Our test-bed implementation results show that the Q-MAC outperforms the state-of-the-art methods in terms of success percentage, execution time and workload distribution.


title: FACE AND FACIAL FEATURE DETECTION EVALUATION Performance Evaluation of Public Domain Haar Detectors for Face and Facial Feature Detection

abstract: Fast and reliable face and facial feature detection are required abilities for any Human Computer Interaction approach based on Computer Vision. Since the publication of the Viola-Jones object detection framework and the more recent open source implementation, an increasing number of applications have appeared, particularly in the context of facial processing. In this respect, the OpenCV community shares a collection of public domain classifiers for this scenario. However, as far as we know these classifiers have never been evaluated and/or compared. In this paper we analyze the individual performance of all those public classifiers getting the best performance for each target. These results are valid to define a baseline for future approaches. Additionally we propose a simple hierarchical combination of those classifiers to increase the facial feature detection rate while reducing the face false detection rate.


title: Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes

abstract: Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.


title: Semi-Supervised QA with Generative Domain-Adaptive Nets

abstract: We study the problem of semi-supervised question answering----utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text.


title: An improved threshold estimation technique for partial discharge signal denoising using Wavelet Transform

abstract: Recent research have shown that the Wavelet Transform (WT) can potentially be used to extract Partial Discharge (PD) signals from severe noise like White noise, Random noise and Discrete Spectral Interferences (DSI). It is important to define that noise is a significant problem in PD detection. Accordingly, the paper mainly deals with denoising of PD signals, based on improved WT techniques namely Translation Invariant Wavelet Transform (TIWT). The improved WT method is distinct from other traditional method called as Fast Fourier Transform (FFT). The TIWT not only remain the edge of the original signal efficiently but also reduce impulsive noise to some extent. Additionally Translation Invariant (TI) Wavelet Transform denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the partial discharge signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like FFT, wavelet hard thresholding, wavelet soft thresholding, by evaluating five different parameters like, Signal to noise ratio, Cross correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.


title: Neural correlates of sexual arousal in homosexual and heterosexual men.

abstract: Men exhibit much higher levels of genital and subjective arousal to sexual stimuli containing their preferred sex than they do to stimuli containing only the nonpreferred sex. This study used event-related functional magnetic resonance imaging to investigate how this category-specific pattern would be reflected in the brains of homosexual (n = 11) and heterosexual (n = 11) men. Comparisons of activation to preferred sexual stimuli, nonpreferred sexual stimuli, and sports stimuli revealed large networks correlated with sexual arousal, spanning multiple cortical and subcortical areas. Both homosexual and heterosexual men exhibited category-specific arousal in brain activity. Within the amygdala, greater preference-related activity was observed in homosexual men, but it is unclear whether this is a cause or a consequence of their sexuality. In a subsequent analysis of regions hypothesized to support arousal, both participant groups demonstrated widespread increases in evoked activity for preferred stimuli. Aggregate data from these regions produced significant differences between stimulus types in 16 out of 22 participants. Significant activational differences matched reported sexual orientation in 15 of these 16 participants, representing an advance in psychophysiological measures of arousal.


title: How dogs scan familiar and inverted faces: an eye movement study

abstract: Faces play an important role in communication and identity recognition in social animals. Domestic dogs often respond to human facial cues, but their face processing is weakly understood. In this study, facial inversion effect (deficits in face processing when the image is turned upside down) and responses to personal familiarity were tested using eye movement tracking. A total of 23 pet dogs and eight kennel dogs were compared to establish the effects of life experiences on their scanning behavior. All dogs preferred conspecific faces and showed great interest in the eye area, suggesting that they perceived images representing faces. Dogs fixated at the upright faces as long as the inverted faces, but the eye area of upright faces gathered longer total duration and greater relative fixation duration than the eye area of inverted stimuli, regardless of the species (dog or human) shown in the image. Personally, familiar faces and eyes attracted more fixations than the strange ones, suggesting that dogs are likely to recognize conspecific and human faces in photographs. The results imply that face scanning in dogs is guided not only by the physical properties of images, but also by semantic factors. In conclusion, in a free-viewing task, dogs seem to target their fixations at naturally salient and familiar items. Facial images were generally more attractive for pet dogs than kennel dogs, but living environment did not affect conspecific preference or inversion and familiarity responses, suggesting that the basic mechanisms of face processing in dogs could be hardwired or might develop under limited exposure.


title: Mining stock market tendency using GA-based support vector machines

abstract: In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e.g., statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration.


title: Normal Bone Anatomy and Physiology

abstract: This review describes normal bone anatomy and physiology as an introduction to the subsequent articles in this section that discuss clinical applications of iliac crest bone biopsy. The normal anatomy and functions of the skeleton are reviewed first, followed by a general description of the processes of bone modeling and remodeling. The bone remodeling process regulates the gain and loss of bone mineral density in the adult skeleton and directly influences bone strength. Thorough understanding of the bone remodeling process is critical to appreciation of the value of and interpretation of the results of iliac crest bone histomorphometry. Osteoclast recruitment, activation, and bone resorption is discussed in some detail, followed by a review of osteoblast recruitment and the process of new bone formation. Next, the collagenous and noncollagenous protein components and function of bone extracellular matrix are summarized, followed by a description of the process of mineralization of newly formed bone matrix. The actions of biomechanical forces on bone are sensed by the osteocyte syncytium within bone via the canalicular network and intercellular gap junctions. Finally, concepts regarding bone remodeling, osteoclast and osteoblast function, extracellular matrix, matrix mineralization, and osteocyte function are synthesized in a summary of the currently understood functional determinants of bone strength. This information lays the groundwork for understanding the utility and clinical applications of iliac crest bone biopsy.


title: Congruity Effects in Time and Space: Behavioral and ERP Measures

abstract: Two experiments investigated whether motion metaphors for time affected the perception of spatial motion. Participants read sentences either about literal motion through space or metaphorical motion through time written from either the ego-moving or object-moving perspective. Each sentence was followed by a cartoon clip. Smiley-moving clips showed an iconic happy face moving toward a polygon, and shape-moving clips showed a polygon moving toward a happy face. In Experiment 1, using an explicit judgment task, participants judged smiley-moving cartoons as related to ego-moving sentences about space and about time, and shape-moving cartoons as related to object-moving sentences. In Experiment 2, participants viewed the same stimuli, but the cartoons were task-irrelevant. Event-related brain potentials revealed an early attentional effect of congruity on cartoons following sentences about space, and a later semantic effect on cartoons following sentences about time. Results are most consistent with accounts that posit differences in the processing of novel and conventional metaphors.


title: Naturally acid freshwater ecosystems are diverse and functional: evidence from boreal streams

abstract: Acidification has strongly affected natural ecosystems due to the deposition of acidifying pollutants in geographical areas with low buffering capacity. Here we show both that taxonomic richness of benthic invertebrates (as a measure of stream health) and breakdown rate of leaf litter (a major process in stream ecosystems) in three naturally acid streams were not significantly different from richness and breakdown rates of three circumneutral control streams in the same area in northern Sweden. In contrast, a comparative data-set representing a gradient from circumneutral to acidified streams in France showed decreasing richness and litter breakdown rates (by 60% and 70% between pH 6.5 and 4.5, respectively) typical of acidified streams. The strong negative effects found in many freshwater systems may be a consequence of low capacity to adjust to rapidly changed conditions. In contrast, organisms in naturally acidic systems, such as streams of the boreal region in catchments with accumulating organic material and a geology poor in buffering substances, seem better adapted as a consequence of protracted exposure to low pH over evolutionary time. These results have implications for the management of streams and rivers in northern Sweden, where considerable efforts are spent on remediation without consideration of the fact that the natural state of many of these systems is acidic.


title: A small novel ultra wideband antenna with slotted ground plane

abstract: A small novel ultra wideband (UWB) antenna is presented in this letter. The proposed antenna has a small size of 15 x 12 mm and provides an ultra wide bandwidth from 3.2 to 10.5 GHz with VSWR level less than 2. The impedance bandwidth of the antenna is improved by adding two notches at the bottom of patch, T slot, and slotted ground plane. Simulation is performed by using Zeland FDTD software. The measured return loss shows agree well with the simulated one. The measured radiation pattern has shown an omnidirectional pattern for the whole frequency range.


title: TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter.

abstract: During large scale events, a large volume of content is posted on Twitter, but not all of this content is trustworthy. The presence of spam, advertisements, rumors and fake images reduces the value of information collected from Twitter, especially during sudden-onset crisis events where information from other sources is scarce. In this research work, we describe various facets of assessing the credibility of usergenerated content on Twitter during large scale events, and develop a novel real-time system to assess the credibility of tweets. Firstly, we develop a semi-supervised ranking model using SVM-rank for assessing credibility, based on training data obtained from six high-impact crisis events of 2013. An extensive set of forty-five features is used to determine the credibility score for each of the tweets. Secondly, we develop and deploy a system–TweetCred–in the form of a browser extension, a web application and an API at the link: http://twitdigest.iiitd.edu.in/TweetCred/. To the best of our knowledge, this is the first research work to develop a practical system for credibility on Twitter and evaluate it with real users. TweetCred was installed and used by 717 Twitter users within a span of three weeks. During this period, a credibility score was computed for more than 1.1 million unique tweets. Thirdly, we evaluated the real-time performance of TweetCred , observing that 84% of the credibility scores were displayed within 6 seconds. We report on the positive feedback that we received from the system’s users and the insights we gained into improving the system for future iterations.


title: Architecture of Qbox : A scalable first-principles molecular dynamics code

abstract: We describe the architecture of Qbox, a parallel, scalable first-principles molecular dynamics (FPMD) code. Qbox is a C++/ Message Passing Interface implementation of FPMD based on the plane-wave, pseudopotential method for electronic structure calculations. It is built upon well-optimized parallel numerical libraries, such as Basic Linear Algebra Communication Subprograms (BLACS) and Scalable Linear Algebra Package (ScaLAPACK), and also features an Extensible Markup Language (XML) interface built on the Apache Xerces-C library. We describe various choices made in the design of Qbox that led to excellent scalability on large parallel computers. In particular, we discuss the case of the IBM Blue Gene/L™ platform on which Qbox was run using up to 65,536 nodes. Future design challenges for upcoming petascale computers are also discussed. Examples of applications of Qbox to a variety of first-principles simulations of solid, liquids, and nanostructures are briefly described.


title: Structured Receptive Fields in CNNs

abstract: Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain. Another option is to design priors into the model, which can range from tuned hyperparameters to fully engineered representations like Scattering Networks. We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs. This flexibility is achieved by expressing receptive fields in CNNs as a weighted sum over a fixed basis which is similar in spirit to Scattering Networks. The key difference is that we learn arbitrary effective filter sets from the basis rather than modeling the filters. This approach explicitly connects classical multiscale image analysis with general CNNs. With structured receptive field networks, we improve considerably over unstructured CNNs for small and medium dataset scenarios as well as over Scattering for large datasets. We validate our findings on ILSVRC2012, Cifar-10, Cifar-100 and MNIST. As a realistic small dataset example, we show state-of-the-art classification results on popular 3D MRI brain-disease datasets where pre-training is difficult due to a lack of large public datasets in a similar domain.


title: 5.8-GHz CMOS T/R switches with high and low substrate resistances in a 0.18-μm CMOS process

abstract: Two single-pole, double-throw transmit/receive switches were designed and fabricated with different substrate resistances using a 0.18-/spl mu/m p/sup $/substrate CMOS process. The switch with low substrate resistances exhibits 0.8-dB insertion loss and 17-dBm P/sub 1dB/ at 5.825 GHz, whereas the switch with high substrate resistances has 1-dB insertion loss and 18-dBm P/sub 1dB/. These results suggest that the optimal insertion loss can be achieved with low substrate resistances and 5.8-GHz T/R switches with excellent insertion loss and reasonable power handling capability can be implemented in a 0.18-/spl mu/m CMOS process.


MiMaze, a 3D Multi-Player Game On The Internet


1-Point RANSAC for EKF Filtering. Application to Real-Time Structure from Motion and Visual


title: Survey of Large-Scale Data Management Systems for Big Data Applications

abstract: Today, data is flowing into various organizations at an unprecedented scale. The ability to scale out for processing an enhanced workload has become an important factor for the proliferation and popularization of database systems. Big data applications demand and consequently lead to the developments of diverse large-scale data management systems in different organizations, ranging from traditional database vendors to new emerging Internet-based enterprises. In this survey, we investigate, characterize, and analyze the large-scale data management systems in depth and develop comprehensive taxonomies for various critical aspects covering the data model, the system architecture, and the consistency model. We map the prevailing highly scalable data management systems to the proposed taxonomies, not only to classify the common techniques but also to provide a basis for analyzing current system scalability limitations. To overcome these limitations, we predicate and highlight the possible principles that future efforts need to be undertaken for the next generation large-scale data management systems.


title: Chrome Extension For Malicious URLs detection in Social Media Applications Using Artificial Neural Networks And Long Short Term Memory Networks

abstract: Social media applications have become an integral part of our life, business and society today. Due to their increasing number of users and growing popularity, many organisations use them as a medium to generate income. The businesses use social media analytics and advertisements to increase their revenue. Although social media applications were initially built to connect people across the globe, they have now turned into one of the most favoured ways of propagation of cyber crimes. Most users lack cyber awareness and fall prey to the malicious activities distributed via Uniform Resource Locators (URLs) and advertisements. When a user visits the malicious URL, it makes the hackers privy to a lot of personal and sensitive information of the user. To overcome the problem of malicious URLs victimising users we propose a tool deployed as a chrome extension. This tool, analyses URLs and classifies them using two different neural networks, Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks which is a specific type of Recurrent Neural Network (RNN). The major objective of the proposed model is to aid the users to avoid becoming a victim of malicious and fraudulent activities like malicious URLs, phishing and social engineering that favour social media as their target medium by detecting them accurately. The model proposed is scalable, easy to train and compatible with devices of varied hardware specifications. The proposed model gives excellent accuracy and overcomes several issues faced by the existing systems.


title: DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image

abstract: In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusion, and combines estimates from the color image and surface normals with learned attention maps to improve the depth accuracy especially for distant areas. Extensive experiments demonstrate that our model improves upon the state-of-the-art performance on KITTI depth completion benchmark. Ablation study shows the positive impact of each model components to the final performance, and comprehensive analysis shows that our model generalizes well to the input with higher sparsity or from indoor scenes.


title: Neural Abstractive Text Summarization with Sequence-to-Sequence Models: A Survey

abstract: In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve the seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this paper, we provide a comprehensive literature and technical survey on different seq2seq models for abstractive text summarization from viewpoint of network structures, training strategies, and summary generation algorithms. Many models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Therefore, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on two recently released datasets, i.e., Newsroom and Bytecup.


title: Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources

abstract: Objective: ::: To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, the phase lag index (PLI), and compare its performance to the well-known phase coherence (PC), and to the imaginary component of coherency (IC). ::: ::: Methods: ::: The PLI is a measure of the asymmetry of the distribution of phase differences between two signals. The performance of PLI, PC, and IC was examined in (i) a model of 64 globally coupled oscillators, (ii) an EEG with an absence seizure, (iii) an EEG data set of 15 Alzheimer patients and 13 control subjects, and (iv) two MEG data sets. ::: ::: Results: ::: PLI and PC were more sensitive than IC to increasing levels of true synchronization in the model. PC and IC were influenced stronger than PLI by spurious correlations because of common sources. All measures detected changes in synchronization during the absence seizure. In contrast to PC, PLI and IC were barely changed by the choice of different montages. PLI and IC were superior to PC in detecting changes in beta band connectivity in AD patients. Finally, PLI and IC revealed a different spatial pattern of functional connectivity in MEG data than PC. ::: ::: Conclusion: ::: The PLI performed at least as well as the PC in detecting true changes in synchronization in model and real data but, at the same token and like-wise the IC, it was much less affected by the influence of common sources and active reference electrodes. Hum Brain Mapp 2007. © 2007 Wiley-Liss, Inc.


title: An empirical assessment of the software design pattern concept

abstract: Context: The publication of the milestone textbook on design patterns by the ‘Gang of Four’ (GoF) in 1995, introduced a set of 23 design patterns that are largely concerned with improving the practices and products of software development. However, there has been no comprehensive assessment of the effectiveness of design patterns, nor is there any evidence about any claims and factors that are made for pattern reuse in software development. ::: ::: Aims: The aims of this thesis are to assess the design patterns systematically in a sequence of studies, and to identify the claims and factors to determine how well they reflect experiences of pattern reuse in practice. ::: ::: Method: This thesis describes four studies: a document survey to identify claims for patterns, a mapping study to identify empirical studies about patterns, an online survey, and a narrative synthesis. The mapping study and the online survey together provide quite comprehensive and thorough evidence for the narrative synthesis. In the narrative synthesis, we check whether there is any consistency or not in the evidence about specific patterns, and also to see how the claims and factors influence pattern reuse. ::: ::: Results: The mapping study found 20 primary studies, and the online survey had 206 usable responses. In the 20 primary study of the mapping study 17 design patterns were examined. In the online survey 175 respondents considered patterns were useful, and 155 respondents reported on patterns that they considered not to be useful. ::: ::: Conclusion: From the synthesis results, the specific patterns Composite and Observer are evaluated as being generally useful, but the Visitor and Singleton patterns, while useful, have possible negative aspects. And also four of the claims and the effect of one factor are demonstrated to be generally true. But the others are either unsupported or have no effect.


title: Access Map Pattern Matching for High Performance Data Cache Prefetch

abstract: Hardware data prefetching is widely adopted to hide long memory latency. A hardware data prefetcher predicts the memory address that will be accessed in the near future and fetches the data at the predicted address into the cache memory in advance. To detect memory access patterns such as a constant stride, most existing prefetchers use differences between addresses in a sequence of memory accesses. However, prefetching based on the differences often fail to detect memory access patterns when aggressive optimizations are applied. For example, out-of-order execution changes the memory access order. It causes inaccurate prediction because the sequence of memory addresses used to calculate the difference are changed by the optimization. To overcome the problems of existing prefetchers, we propose Access Map Pattern Matching (AMPM). The AMPM prefetcher has two key components: a memory access map and hardware pattern matching logic. The memory access map is a bitmap-like data structure for holding past memory accesses. The AMPM prefetcher divides the memory address space into memory regions of a fixed size. The memory access map is mapped to the memory region. Each entry in the bitmap-like data structure is mapped to each cache line in the region. Once the bitmap is mapped to the memory region, the entry records whether the corresponding line has already been accessed or not. The AMPM prefetcher detects memory access patterns from the bitmap-like data structure that is mapped to the accessed region. The hardware pattern matching logic is used to detect stride access patterns in the memory access map. The result of pattern matching is affected by neither the memory access order nor the instruction addresses because the bitmap-like data structure holds neither the information that reveals the memory access order of past memory accesses nor the instruction addresses. Therefore, the AMPM prefetcher achieves high performance even when such aggressive optimizations are applied. The AMPM prefetcher is evaluated by performing cycle-accurate simulations using the memory-intensive benchmarks in the SPEC CPU2006 and the NAS Parallel Benchmark. In an aggressively optimized environment, the AMPM prefetcher improves prefetch coverage, while the other state-of-the-art prefetcher degrades the prefetch coverage significantly. As a result, the AMPM prefetcher increases IPC by 32.4% compared to state-of-the-art prefetcher.


title: Characterizing and modeling internet traffic dynamics of cellular devices

abstract: Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.


title: Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts

abstract: Word embedding is a Natural Language Processing (NLP) technique that automatically maps words from a vocabulary to vectors of real numbers in an embedding space. It has been widely used in recent years to boost the performance of a vari-ety of NLP tasks such as Named Entity Recognition, Syntac-tic Parsing and Sentiment Analysis. Classic word embedding methods such as Word2Vec and GloVe work well when they are given a large text corpus. When the input texts are sparse as in many specialized domains (e.g., cybersecurity), these methods often fail to produce high-quality vectors. In this pa-per, we describe a novel method to train domain-specificword embeddings from sparse texts. In addition to domain texts, our method also leverages diverse types of domain knowledge such as domain vocabulary and semantic relations. Specifi-cally, we first propose a general framework to encode diverse types of domain knowledge as text annotations. Then we de-velop a novel Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text annotations in word em-bedding. We have evaluated our method on two cybersecurity text corpora: a malware description corpus and a Common Vulnerability and Exposure (CVE) corpus. Our evaluation re-sults have demonstrated the effectiveness of our method in learning domain-specific word embeddings.


title: Rhythm-centred music making in community living elderly: a randomized pilot study

abstract: BACKGROUND ::: Quality of life has become an important aspect in the measurement of the health of an individual as the population ages. Rhythm-centred music making (RMM) has been shown to improve physical, psychological and social health. The purpose of this study was to explore the effects of RMM on quality of life, depressive mood, sleep quality and social isolation in the elderly. ::: ::: ::: METHODS ::: A randomised controlled trial with cross over was conducted. 54 participants were recruited with 27 participants in each arm. In phase 1, group A underwent the intervention with group B as the control. In phase 2, group B underwent the intervention with group A as the control. The intervention involved 10 weekly RMM sessions. Patient related outcome data which included European Quality of Life-5 Dimensions (EQ5D), Geriatric Depression Scale (GDS), Pittsburg Sleep Quality Index (PSQI) and Lubben Social Network Scale (LSNS) scores were collected before the intervention, at 11th and at the 22nd week. ::: ::: ::: RESULTS ::: A total of 31 participants were analyzed at the end of the study. The mean age was 74.65 ± 6.40 years. In analysing the change in patient related outcome variables as a continuous measure, participation in RMM resulted in a non-significant reduction in EQ5D by 0.004 (95% CI: -0.097,0.105), GDS score by 0.479 (95% CI:-0.329,1.287), PSQI score by 0.929 (95% CI:- 0.523,2.381) and an improvement in LSNS by 1.125 (95% CI:-2.381,0.523). In binary analysis, participation in RMM resulted in a 37% (OR = 1.370, 95% CI: 0.355,5.290), 55.3% (OR = 1.553, 95% CI: 0.438,5.501), 124.1% (OR = 2.241, 95% CI = 0.677,7.419) and 14.5% (OR = 1.145, 95% CI = 0.331,3.963) non-significant increase in odds of improvement in EQ5D, GDS, PSQI and LSNS scores respectively. ::: ::: ::: CONCLUSION ::: Participation in RMM did not show any statistically significant difference in the quality of life of the participants. It is however, an interesting alternative tool to use in the field of integrative medicine. Moving forward, a larger study could be performed to investigate the effects of RMM on the elderly with an inclusion of a qualitative component to evaluate effects of RMM that were not captured by quantitative indicators. ::: ::: ::: TRIAL REGISTRATION ::: This trial was retrospectively registered. This trial was registered in the Australian New Zealand Clinical Trials Registry under trial number ACTRN12616001281482 on 12 September 2016.


title: Multi-scale local binary pattern histograms for face recognition

abstract: A novel discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition. The face image is first partitioned into several non-overlapping regions. In each region, multi-scale local binary uniform pattern histograms1 are extracted and concatenated into a regional feature. The features are then projected on the LDA space to be used as a discriminative facial descriptor. The method is implemented and tested in face identification on the standard Feret database and in face verification on the XM2VTS database with very promising results.


title: Silkworm eggs detection and classification using image analysis

abstract: Machine vision has been applied to various material inspection processes in agricultural industry in order to achieve fast and accurate operation. In this paper, we propose an image processing technique for silkworm eggs analysis. The proposed technique is able to identify individual egg objects and their types. The proposed technique was implemented and developed into software interface. The software was installed at the Thailand Sericulture center and it is going to be used in the real practical scenario. To evaluate our proposed technique, the approach was evaluated on 60 sample images from 7 shade groups of silkworm's egg sample. The obtained accuracies are nearly to 90% for various kinds of eggs types in both detection and classification. The results of this study are useful and they are served as an important base for future development of practical quality control technique.


title: NAWMS: nonintrusive autonomous water monitoring system

abstract: Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users. The goal of our work is to furnish users with an easy-to-install self-calibrating system that provides information on when, where, and how much water they are using. The system uses wireless vibration sensors attached to pipes and, thus, neither plumbing nor special expertise is necessary for its installation. By implementing a non-intrusive, autonomous, and adaptive system using commodity hardware, we believe it is cost-effective and widely deployable. NAWMS makes use of the existing household water flow meter, which is considered accurate, but lacks spatial granularity, and adds vibration sensors on individual water pipes to estimate the water flow to each individual outlet. Compensating for manufacturing, installation, and material variabilities requires calibration of these low cost sensors to achieve a reasonable level of accuracy. We have devised an adaptive auto-calibration procedure, which attempts to solve a two phase linear programming and mixed linear geometric programming problem. We show through experiments on a three pipe testbed that such a system is indeed feasible and adapts well to minimize error in the water usage estimate. We report an accuracy, over likely domestic flow-rate scenarios, with long-term stability and a mean absolute error of 7%.


title: Very deep convolutional networks for end-to-end speech recognition

abstract: Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and convolutional LSTMs to build very deep recurrent and convolutional structures. Our models exploit the spectral structure in the feature space and add computational depth without overfitting issues. We experiment with the WSJ ASR task and achieve 10.5% word error rate without any dictionary or language model using a 15 layer deep network.


title: Cross-dimensional Weighting for Aggregated Deep Convolutional Features

abstract: We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific non-parametric schemes for both spatial- and channel-wise weighting that boost the effect of highly active spatial responses and at the same time regulate burstiness effects. We experiment on different public datasets for image search and show that our approach outperforms the current state-of-the-art for approaches based on pre-trained networks. We also provide an easy-to-use, open source implementation that reproduces our results.


title: Minding the Gap - An Expectation-Disconfirmation Approach to Reward-Based Crowdfunding

abstract: The remarkable growth of crowdfunding has sparked academic interest. By collecting small contributions from a large number of individuals, entrepreneurs, artists and other individuals and organizations can raise considerable amounts of money. Despite the increasing academic interest in this topic, there still appears to be a lack of understanding regarding the factors that determine individuals' intentions to contribute to projects. In this research-in-progress paper, we propose a research model that examines future funding intentions by using Expectation-Disconfirmation Theory (EDT). By applying our research model to reward-based crowdfunding, we intend to shed light on the ways in which previous funding experiences influence future funding intentions. Through the data collected by this study, our research will contribute to the body of literature on reward-based crowdfunding and EDT. It will also provide practical implications for crowdfunding platforms and project initiators, providing them indications for their management of expectations.


title: A review of multiobjective test problems and a scalable test problem toolkit

abstract: When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not


title: An Experimental Study on Speech Enhancement Based on Deep Neural Networks

abstract: This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be separated from the background noises successfully without the annoying musical artifact commonly observed in conventional speech enhancement algorithms. A series of pilot experiments were conducted under multi-condition training with more than 100 hours of simulated speech data, resulting in a good generalization capability even in mismatched testing conditions. When compared with the logarithmic minimum mean square error approach, the proposed DNN-based algorithm tends to achieve significant improvements in terms of various objective quality measures. Furthermore, in a subjective preference evaluation with 10 listeners, 76.35% of the subjects were found to prefer DNN-based enhanced speech to that obtained with other conventional technique.


title: Tracing for web 3.0: trace compilation for the next generation web applications

abstract: Today's web applications are pushing the limits of modern web browsers. The emergence of the browser as the platform of choice for rich client-side applications has shifted the use of in-browser JavaScript from small scripting programs to large computationally intensive application logic. For many web applications, JavaScript performance has become one of the bottlenecks preventing the development of even more interactive client side applications. While traditional just-in-time compilation is successful for statically typed virtual machine based languages like Java, compiling JavaScript turns out to be a challenging task. Many JavaScript programs and scripts are short-lived, and users expect a responsive browser during page loading. This leaves little time for compilation of JavaScript to generate machine code. We present a trace-based just-in-time compiler for JavaScript that uses run-time profiling to identify frequently executed code paths, which are compiled to executable machine code. Our approach increases execution performance by up to 116% by decomposing complex JavaScript instructions into a simple Forth-based representation, and then recording the actually executed code path through this low-level IR. Giving developers more computational horsepower enables a new generation of innovative web applications.


title: Discovering latent causes in reinforcement learning

abstract: Effective reinforcement learning hinges on having an appropriate state representation. But where does this representation come from? We argue that the brain discovers state representations by trying to infer the latent causal structure of the task at hand, and assigning each latent cause to a separate state. In this paper, we review several implications of this latent cause framework, with a focus on Pavlovian conditioning. The framework suggests that conditioning is not the acquisition of associations between cues and outcomes, but rather the acquisition of associations between latent causes and observable stimuli. A latent cause interpretation of conditioning enables us to begin answering questions that have frustrated classical theories: Why do extinguished responses sometimes return? Why do stimuli presented in compound sometimes summate and sometimes do not? Beyond conditioning, the principles of latent causal inference may provide a general theory of structure learning across cognitive domains.


title: Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things

abstract: Current developments in ICTs such as in Internet-of-Things (IoT) and CyberPhysical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e.,gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such as mobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our system on addressing a medical case study. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability.


title: Graphene/Pentacene Barristor with Ion-Gel Gate Dielectric: Flexible Ambipolar Transistor with High Mobility and On/Off Ratio

abstract: High-quality channel layer is required for next-generation flexible electronic devices. Graphene is a good candidate due to its high carrier mobility and unique ambipolar transport characteristics but typically shows a low on/off ratio caused by gapless band structure. Popularly investigated organic semiconductors, such as pentacene, suffer from poor carrier mobility. Here, we propose a graphene/pentacene channel layer with high-k ion-gel gate dielectric. The graphene/pentacene device shows both high on/off ratio and carrier mobility as well as excellent mechanical flexibility. Most importantly, it reveals ambipolar behaviors and related negative differential resistance, which are controlled by external bias. Therefore, our graphene/pentacene barristor with ion-gel gate dielectric can offer various flexible device applications with high performances.


title: Musical expertise, bilingualism, and executive functioning.

abstract: The authors investigated whether intensive musical experience leads to enhancements in executive processing, as has been shown for bilingualism. Young adults who were bilinguals, musical performers (instrumentalists or vocalists), or neither completed 3 cognitive measures and 2 executive function tasks based on conflict. Both executive function tasks included control conditions that assessed performance in the absence of conflict. All participants performed equivalently for the cognitive measures and the control conditions of the executive function tasks, but performance diverged in the conflict conditions. In a version of the Simon task involving spatial conflict between a target cue and its position, bilinguals and musicians outperformed monolinguals, replicating earlier research with bilinguals. In a version of the Stroop task involving auditory and linguistic conflict between a word and its pitch, the musicians performed better than the other participants. Instrumentalists and vocalists did not differ on any measure. Results demonstrate that extended musical experience enhances executive control on a nonverbal spatial task, as previously shown for bilingualism, but also enhances control in a more specialized auditory task, although the effect of bilingualism did not extend to that domain.


title: Effectiveness of interventions to improve patient compliance: a meta-analysis.

abstract: OBJECTIVES ::: This article summarizes the results of 153 studies published between 1977 and 1994 that evaluated the effectiveness of interventions to improve patient compliance with medical regimens. ::: ::: ::: METHODS ::: The compliance interventions were classified by theoretical focus into educational, behavioral, and affective categories within which specific intervention strategies were further distinguished. The compliance indicators broadly represent five classes of compliance-related assessments: (1) health outcomes (eg, blood pressure and hospitalization), (2) direct indicators (eg, urine and blood tracers and weight change), (3) indirect indicators (eg, pill count and refill records), (4) subjective report (eg, patients' or others' reports), and (5) utilization (appointment making and keeping and use of preventive services). An effect size (ES) r, defined as Fisher's Z transformation of the Pearson correlation coefficient, representing the association between each intervention (intervention versus control) and compliance measure was calculated. Both an unweighted and weighted r were calculated because of large sample size variation, and a combined probability across studies was calculated. ::: ::: ::: RESULTS ::: The interventions produced significant effects for all the compliance indicators (combined Z values more than 5 and less than 32), with the magnitude of effects ranging from small to large. The largest effects (unweighted) were evident for refill records and pill counts and in blood/urine and weight change studies. Although smaller in magnitude, compliance effects were evident for improved health outcomes and utilization. Chronic disease patients, including those with diabetes and hypertension, as well as cancer patients and those with mental health problems especially benefited from interventions. ::: ::: ::: CONCLUSIONS ::: No single strategy or programmatic focus showed any clear advantage compared with another. Comprehensive interventions combining cognitive, behavioral, and affective components were more effective than single-focus interventions.


title: Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach

abstract: Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This paper presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task called the multi-armed bandit. To learn user preferences, it uses a Bayesian model, which accounts for both audio content and the novelty of recommendations. A piecewise-linear approximation to the model and a variational inference algorithm are employed to speed up Bayesian inference. One additional benefit of our approach is a single unified model for both music recommendation and playlist generation. Both simulation results and a user study indicate strong potential for the new approach.


title: The application of two-level attention models in deep convolutional neural network for fine-grained image classification

abstract: Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what).


title: A Privacy-Preserving Mobile Payment System for Mass Transit

abstract: In the near future, mobile payment systems based on smartphones are expected to be widely applied in various environments, including transit services. When passengers use mass transit, their private information, such as their identity and route, may be made available to some related organizations, such as transit agencies, financial institutions, mobile carriers, and providers of smart cards, among others, even when the passengers may not want their information to be revealed. To protect passenger privacy, this paper proposes a privacy-preserving transit payment system based on traceable signatures, identity-based signatures, and anonymous signatures. In addition to passenger privacy, the proposed system facilitates the proactive blocking of misbehaving passengers, free-transfer services (or transfer discount), and postpaid programs. We demonstrate that the performance of this system is good enough for immediate deployment based on various experiments.


title: A hybrid model and kinematic-free control framework for a low-cost deformable manipulator using in home service

abstract: Home service robot works in unstructured environments with various tasks, where a low-cost, dexterous, and intrinsically safe manipulator is important. Traditionally, a redundant manipulator with high degrees of freedom (DOFs) is employed. However, high DOFs bring problems like high cost, complex physical designing, and heavy weight. Here a novel low-cost deformable manipulator for home service robot, which is composed of rigid joints and deformable links, is introduced. The deformable manipulator can obtain relatively dexterous and extended workspace with fewer joints by bending its deformable links. Considering the typical hybrid behaviors caused by bending operation, a hybrid model and a practical kinematic-free control framework with no prior kinematic information are proposed to describe and control the deformable manipulator. The model and controller have been implemented on a 4-DOF deformable manipulator with two deformable links in simulation. The simulation results validate the proposed deformable manipulator and kinematic-free control.


abstract: The continued increase of senior population worldwide is causing challenges to existing healthcare and support systems in terms of infrastructure and trained professionals. Recently, smart home environments are being utilized for ubiquitous health monitoring, allowing patients to stay at the comfort of their homes. In this paper we presented a Cloud-Based Smart Home Environment (CoSHE) for home healthcare. Our system collects physiological signals through non-invasive wearable sensors and provides contextual information in terms of daily activities and location in the home. The body fluid intake level is also provided. This enables healthcare professionals to study daily activities, behavioral changes and monitor rehabilitation and recovery processes. A smart home environment is set up with ambient sensors to provide contextual information. The sensor data is processed on a smart home gateway and is sent to a private cloud server. The cloud infrastructure is developed to provide real time data access for remote care givers. Our case study shows that we can successfully integrate contextual information to health data and this comprehensive information can help understand caretaker's health status.


title: Phylogenetic Analyses of Some Melanoleuca Species (Agaricales, Tricholomataceae) in Northern China, With Descriptions of Two New Species and the Identification of Seven Species as a First Record

abstract: Two new species (Melanoleuca galerinaspora and Melanoleuca subgrammopodia) and seven new recorded species from northern China are described here using morphological and molecular methods. Melanoleuca galerinaspora is mainly characterized by its hygrophanous pileus, decurrent lamellae, fibrous stipe and spores with round warts. Key characteristics of M. subgrammopodia include its discolored pileus, fibrous stipe and urticiform cystidia. The divergence time of Melanoleuca fungi as well as the phylogenetic relationships within this genus were analyzed using DNA sequences of the internal transcribed spacer (ITS) and the nuclear large subunit rDNA (nrLSU) gene fragments. Analyses revealed that morphological identifications and phylogenetic relationships were consistent with the results of divergence time, thereby confirming that M. galerinaspora and M. subgrammopodia are new species.