title: Modality and Component Aware Feature Fusion for RGB-D Scene Classification

abstract: While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically meant that the standard full-image CNN features are suboptimal for scene classification. In this paper, we investigate a framework allowing greater spatial flexibility, in which the Fisher vector (FV) encoded distribution of local CNN features, obtained from a multitude of region proposals per image, is considered instead. The CNN features are computed from an augmented pixel-wise representation comprising multiple modalities of RGB, HHA and surface normals, as extracted from RGB-D data. More significantly, we make two postulates: (1) component sparsity — that only a small variety of region proposals and their corresponding FV GMM components contribute to scene discriminability, and (2) modal non-sparsity — within these discriminative components, all modalities have important contribution. In our framework, these are implemented through regularization terms applying group lasso to GMM components and exclusive group lasso across modalities. By learning and combining regressors for both proposal-based FV features and global CNN features, we were able to achieve state-of-the-art scene classification performance on the SUNRGBD Dataset and NYU Depth Dataset V2.


title: Recurrent Neural Network Based Prediction of Epileptic Seizures in Intra- and Extracranial EEG

abstract: Abstract Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN training/testing, as opposed to specific signal features extracted from EEG. To the best of our knowledge this approach has never been attempted before. The data used included both scalp and intracranial EEG recordings obtained from two epileptic patients. We demonstrate that the existence of a “preictal” stage (immediately preceding seizure) of some minutes duration is quite feasible.


title: Cluster Forests

abstract: With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain"good local clusterings"and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure kappa. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis reveals that the kappa measure makes it possible to grow the local clustering in a desirable way---it is"noise-resistant". A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.


title: Experimental Evaluation of Energy Efficiency for a Soft Wearable Robotic Suit

abstract: This paper presents a new soft wearable robotic suit for energy-efficient walking in daily activities for elderly persons. The presented robotic suit provides a small yet effective assistive force for hip flexion through winding belts that include elastic elements. In addition, it does not restrict the range of movement in the lower limbs. Moreover, its structure is simple and lightweight, and thus wearers can easily take the device on and off by themselves. Experimental results on nine elderly subjects (age = 74.2±3.7 years) show that the robotic suit worn and powered on (PON) significantly reduced energy expenditure by an average of 5.9% compared with the condition of worn but powered off (POFF). Furthermore, compared with the POFF condition, there was a significant improvement in gait characteristics in the PON condition for all subjects.


title: Cycle detection using a stack

abstract: We present an algorithm for detecting periodicity in sequences produced by repeated application of a given function. Our algorithm uses logarithmic memory with high probability, runs in linear time, and is guaranteed to stop within the second loop through the cycle. We also present a partitioning technique that offers a time/memory tradeoff. Our algorithm is especially well suited for sequences where the cycle length is typically small compared to the length of the acyclic prefix.


title: Predators of Diaprepes Abbreviatus (Coleoptera: Curculionidae) Larvae

abstract: Larvae of Diaprepes abbreviatus (L.) in petri dishes were susceptible to predation on the soil surface in a citrus grove. An average of 47% were removed by predators in the first 20 minutes in a 1979 study to determine rates of predation. Predators (2,613 recorded events) were exclusively ants in the grove studied, with the exceptions of 2 predation events by the spider Corythalia canosa Walckenaer (Salticidae) and 3 by Labidura sp. earwig juveniles. Of the 9 observed ant predator species, Tetramorium simillimum Roger, Paratrechina bourbonica (Forel), Pheidole floridana Emery, and Pheidole dentata Mayr were considered important because of their consistency in rates and presence in predation on Diaprepes larvae. Two other species, Solenopsis invicta Buren and Pheidole morrisi Forel, were found commonly. Predation rates by the various species differed over the course of the summer and with the time of day.


Contextualized Bilinear Attention Networks


title: A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-Based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing

abstract: The scene text interpretation is a critical part of the natural scene interpretation. Currently, most of the existing work is based on high-end graphics processing units (GPUs) implementation, which is commonly used on the server side. However, in Internet of Things (IoT) application scenarios, the communication overhead from the edge device to the server is quite large, which sometimes even dominates the total processing time. Hence, the edge-computing oriented design is needed to solve this problem. In this paper, we present an architectural design and implementation of a natural scene text interpretation (NSTI) accelerator, which can classify and localize the text region on pixel-level efficiently in real-time on mobile devices. To target the real-time and low-latency processing, the binary convolutional encoder–decoder network is adopted as the core architecture to enable massive parallelism due to its binary feature. Massively parallelized computations and a highly pipelined data flow control enhance its latency and throughput performance. In addition, all the binarized intermediate results and parameters are stored on chip to eliminate the power consumption and latency overhead of the off-chip communication. The NSTI accelerator is implemented in a 40 nm CMOS technology, which can process scene text images (size of 128 × 32) at 34 fps and latency of 40 ms for pixelwise interpretation with the pixelwise classification accuracy over 90% on ICDAR-03 and ICDAR-13 dataset. The real energy-efficiency is 698 GOP/s/W and the peak energy-efficiency can get up to 7825 GOP/s/W. The proposed accelerator is 7 times more energy efficient than its optimized GPU-based implementation counterpart, while maintaining a real-time throughput with latency of 40 ms.


title: PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

abstract: Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.


title: An OFDMA based concurrent multiuser MAC for upcoming IEEE 802.11ax

abstract: Recently, IEEE drew up a new task group named TGax to draft out the standard IEEE 802.11ax for next generation WLANs. However, the average throughput is very low due to the current medium access control (MAC) protocol. A promising solution for this problem is to draw Orthogonal Frequency Division Multiple Access (OFDMA) into IEEE 802.11ax to enable multiuser access. The key challenges of adopting OFDMA are synchronization and overhead reduction. In this paper, we propose an OFDMA based Multiple Access for IEEE 802.11ax (OMAX) protocol to solve both two challenges above. The whole channel physical channel sensing and fast backoff are adopted to ensure synchronization, while enhanced RTS/CTS mechanism and new frame structure are designed to reduce overhead. Moreover, the mathematic model of OMAX is formulated, and the performance of OMAX is analyzed. The analysis and simulation result indicate that the proposed OMAX protocol increases the throughput to 160%.


Autoencoder based multi-stream combination for noise robust speech recognition


title: Social networks and context-aware spam

abstract: Social networks are popular for online communities. This paper evaluates the risk of sophisticated context-aware spam that could result from information sharing on social networks and discusses potential mitigation strategies. Unlike normal spam, context-aware spam would likely have a high click-through rate due to exploitation of authentic social connections. Context-aware spam could lead to more insidious attacks that try to install malware or steal passwords. In this paper, we analyzed Facebook, a popular social networking website. Our goal was to determine how many users were vulnerable to context-aware attack email and understand aspects of Facebook's design that make such attacks possible. We also classified different kinds of email attacks based on certain pieces of data such as birthdays, lists of friends, wall posts, and user news feeds. We analyzed Facebook starting from a single university e-mail address to calculate the number of users who would be vulnerable to each type of attack. We found that a hacker could send sophisticated context-aware email to approximately 85% of users. Furthermore, our analysis shows that people with private profiles are almost equally vulnerable to a subset of attacks. Finally, we discuss defense strategies. Some strategies would require users to coordinate their privacy policies with each other. We also suggest design improvements for social networks that may help reduce exposure to context-aware attack email.


title: Database Servers on Chip Multiprocessors: Limitations and Opportunities

abstract: Prior research shows that database system performance is dominated by off-chip data stalls, resulting in a concerted effort to bring data into on-chip caches. At the same time, high levels of integration have enabled the advent of chip multiprocessors and increasingly large (and slow) on-chip caches. These two trends pose the imminent technical and research challenge of adapting high-performance data management software to a shifting hardware landscape. In this paper we characterize the performance of a commercial database server running on emerging chip multiprocessor technologies. We find that the major bottleneck of current software is data cache stalls, with L2 hit stalls rising from oblivion to become the dominant execution time component in some cases. We analyze the source of this shift and derive a list of features for future database designs to attain maximum performance.


YOLO Net on iOS


title: A parametric model and estimation techniques for the inharmonicity and tuning of the piano

abstract: Inharmonicity of piano tones is an essential property of their timbre that strongly influences the tuning, leading to the so-called octave stretching. It is proposed in this paper to jointly model the inharmonicity and tuning of pianos on the whole compass. While using a small number of parameters, these models are able to reflect both the specificities of instrument design and tuner's practice. An estimation algorithm is derived that can run either on a set of isolated note recordings, but also on chord recordings, assuming that the played notes are known. It is applied to extract parameters highlighting some tuner's choices on different piano types and to propose tuning curves for out-of-tune pianos or piano synthesizers.


Credit Card Fraud Detection Using HMM and K-Means Clustering Algorithm


title: Extended Pie Menus for Immersive Virtual Environments

abstract: Pie menus are a well-known technique for interacting with 2D environments and so far a large body of research documents their usage and optimizations. Yet, comparatively little research has been done on the usability of pie menus in immersive virtual environments (IVEs). In this paper we reduce this gap by presenting an implementation and evaluation of an extended hierarchical pie menu system for IVEs that can be operated with a six-degrees-of-freedom input device. Following an iterative development process, we first developed and evaluated a basic hierarchical pie menu system. To better understand how pie menus should be operated in IVEs, we tested this system in a pilot user study with 24 participants and focus on item selection. Regarding the results of the study, the system was tweaked and elements like check boxes, sliders, and color map editors were added to provide extended functionality. An expert review with five experts was performed with the extended pie menus being integrated into an existing VR application to identify potential design issues. Overall results indicated high performance and efficient design.


title: Composition and calibration of a custom made omnidirectional camera

abstract: Conventional perspective cameras have been used before to create 3D camera systems. However, these systems are hindered by the field of view of the cameras, which limit the angle of the scene that can be reconstructed. Augmenting these systems to larger reconstructed scene angle requires more cameras or increasing the field of view of the cameras. A 3D camera system is introduced which utilizes omnidirectional cameras. These cameras are capable of capturing the scene in 360 degrees, and only a limited number of cameras are required to reconstruct the entire surrounding. In this article the design, construction and calibration of omnidirectional cameras are discussed. The used camera model is presented which allows us to use an omnidirectional camera as a perspective camera, and the distortion model that is suitable for correcting precision problems of the assembly process.


title: Word Sense Disambiguation Using WordNet Domains

abstract: In this paper we present the methodology for word sense disambiguation based on domain information. Domain is a set of words in which there is a strong semantic relation among the words. The words in the sentence contribute to determine the domain of the sentence. The availability of WordNet domains makes the domain-oriented text analysis possible. The domain of the target word can be fixed based on the domains of the content words in the local context. This approach can be effectively used to disambiguate nouns. We present the unsupervised approach to word sense disambiguation using the WordNet domains. The model determines the domain of the target word and the sense corresponding to this domain is taken as the correct sense. We have used the WordNet domains 3.1 as lexical database.


title: A data-centric approach to synchronization

abstract: Concurrency-related errors, such as data races, are frustratingly difficult to track down and eliminate in large object-oriented programs. Traditional approaches to preventing data races rely on protecting instruction sequences with synchronization operations. Such control-centric approaches are inherently brittle, as the burden is on the programmer to ensure that all concurrently accessed memory locations are consistently protected. Data-centric synchronization is an alternative approach that offloads some of the work on the language implementation. Data-centric synchronization groups fields of objects into atomic sets to indicate that these fields must always be updated atomically. Each atomic set has associated units of work, that is, code fragments that preserve the consistency of that atomic set. Synchronization operations are added automatically by the compiler. We present an extension to the Java programming language that integrates annotations for data-centric concurrency control. The resulting language, called AJ, relies on a type system that enables separate compilation and supports atomic sets that span multiple objects and that also supports full encapsulation for more efficient code generation. We evaluate our proposal by refactoring classes from standard libraries, as well as a number of multithreaded benchmarks, to use atomic sets. Our results suggest that data-centric synchronization is easy to use and enjoys low annotation overhead, while successfully preventing data races. Moreover, experiments on the SPECjbb benchmark suggest that acceptable performance can be achieved with a modest amount of tuning.


title: Ringteraction: Coordinated Thumb-index Interaction Using a Ring

abstract: The thumb has the unique property of being opposable to the other fingers and is thus used to perform specific tasks such as grasping objects, which cannot be done otherwise. In this paper we present an interactive ring that takes advantage of this biomechanical advantage, by enabling thumb-index interaction. We propose a set of gestures involving the coordinated movement of the thumb against the proximal phalanx of the index finger that we call bi-digit interaction. Further, we present several scenarios where performing bi-digit interaction is quick, easy and advantageous for users.


title: Predicting Students Final GPA Using Decision Trees: A Case Study

abstract:  Abstract—Educational data mining is the process of applying data mining tools and techniques to analyze data at educational institutions. In this paper, we used educational data mining to predict students' final GPA based on their grades in previous courses. In our case study, we collected students' transcript data that included their final GPA and their grades in all courses. After pre-processing the data, we applied the J48 decision tree algorithm to discover classification rules. We extracted useful knowledge for final GPA, and identify the most important courses in the students' study plan based on their grades in the mandatory courses.


title: An application of automated reasoning in natural language question answering

abstract: The LogAnswer system is an application of automated reasoning to the field of open domain question answering. In order to find answers to natural language questions regarding arbitrary topics, the system integrates an automated theorem prover in a framework of natural language processing tools. The latter serve to construct an extensive knowledge base automatically from given textual sources, while the automated theorem prover makes it possible to derive answers by deductive reasoning. In the paper, we discuss the requirements to the prover that arise in this application, especially concerning efficiency and robustness. The proposed solution rests on incremental reasoning, relaxation of the query (if no proof of the full query is found), and other techniques. In order to improve the robustness of the approach to gaps of the background knowledge, the results of deductive processing are combined with shallow linguistic features by machine learning.


title: The impact of whole-of-diet interventions on depression and anxiety: a systematic review of randomised controlled trials

abstract: Objective Non-pharmacological approaches to the treatment of depression and anxiety are of increasing importance, with emerging evidence supporting a role for lifestyle factors in the development of these disorders. Observational evidence supports a relationship between habitual diet quality and depression. Less is known about the causative effects of diet on mental health outcomes. Therefore a systematic review was undertaken of randomised controlled trials of dietary interventions that used depression and/or anxiety outcomes and sought to identify characteristics of programme success. Design A systematic search of the Cochrane, MEDLINE, EMBASE, CINAHL, PubMed and PyscInfo databases was conducted for articles published between April 1971 and May 2014. Results Of the 1274 articles identified, seventeen met eligibility criteria and were included. All reported depression outcomes and ten reported anxiety or total mood disturbance. Compared with a control condition, almost half (47 %) of the studies observed significant effects on depression scores in favour of the treatment group. The remaining studies reported a null effect. Effective dietary interventions were based on a single delivery mode, employed a dietitian and were less likely to recommend reducing red meat intake, select leaner meat products or follow a low-cholesterol diet. Conclusions Although there was a high level of heterogeneity, we found some evidence for dietary interventions improving depression outcomes. However, as only one trial specifically investigated the impact of a dietary intervention in individuals with clinical depression, appropriately powered trials that examine the effects of dietary improvement on mental health outcomes in those with clinical disorders are required.


title: Z-BeEAM (Ibritumomab tiuxetan, Bendamustine, Etoposide, Cytarabine, Melphalan) before autologous stem cell transplantation is safe and efficient for refractory large B-cell lymphoma

abstract: BackgroundRefractory or relapsed large B-cells lymphoma are usually treated with a high dose chemotherapy regimen followed by an autolougous stem cells transplantation. BEAM (carmustine, etoposide, cytarabine, melphalan) or more recently Z-BEAM (ibritumomab tiuxetan and BEAM) are commonly used regimens, but recently carmustine availability became difficult. The purpose of this study was to evaluate the feasibility and the safety of replacing carmustine by bendamustine in a new Z-BeEAM regimen (ibritumomab tiuxetan, bendamustine, etoposide, cytarabine, melphalan) prior to autologous stem cell transplantation.FindingsThis study was a retrospective analyze of six patients, with a median age of 60, treated by Z-BeEAM before autologous stem cell transplantation. We did not put in evidence any additional toxicities compared to conventional induction chemotherapy. The main toxicities were mucositis (3 grade III among 6 patients), gastrointestinal (2 grade III vomiting and 2 grade III diarrhea) and neutropenia (6 grade IV). Engraftment was successfully achieved for all patients. At the time of analysis of this study all patients were alive and in complete response based on the PET-CT evaluation.ConclusionsBeEAM plus ibritumomab tiuxetan combined regimen before autologous stem cell transplantation is feasible and safe in aggressive relapsing large B-cell lymphoma.


title: Variolite: Supporting Exploratory Programming by Data Scientists

abstract: How do people ideate through code? Using semi-structured interviews and a survey, we studied data scientists who program, often with small scripts, to experiment with data. These studies show that data scientists frequently code new analysis ideas by building off of their code from a previous idea. They often rely on informal versioning interactions like copying code, keeping unused code, and commenting out code to repurpose older analysis code while attempting to keep those older analyses intact. Unlike conventional version control, these informal practices allow for fast versioning of any size code snippet, and quick comparisons by interchanging which versions are run. However, data scientists must maintain a strong mental map of their code in order to distinguish versions, leading to errors and confusion. We explore the needs for improving version control tools for exploratory tasks, and demonstrate a tool for lightweight local versioning, called Variolite, which programmers found usable and desirable in a preliminary usability study.


title: Botnet-based Distributed Denial of Service (DDoS) Attacks on Web Servers: Classification and Art

abstract: Botnets are prevailing mechanisms for the facilitation of the distributed denial of service (DDoS) attacks on computer networks or applications. Currently, Botnet-based DDoS attacks on the application layer are latest and most problematic trends in network security threats. Botnet-based DDoS attacks on the application layer limits resources, curtails revenue, and yields customer dissatisfaction, among others. DDoS attacks are among the most difficult problems to resolve online, especially, when the target is the Web server. In this paper, we present a comprehensive study to show the danger of Botnet-based DDoS attacks on application layer, especially on the Web server and the increased incidents of such attacks that has evidently increased recently. Botnetbased DDoS attacks incidents and revenue losses of famous companies and government websites are also described. This provides better understanding of the problem, current solution space, and future research scope to defend against such attacks efficiently.


title: Neuroevolution on the edge of chaos

abstract: Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Furthermore, the echo state networks are compared to networks evolved via neuroevolution. The evolved networks outperform the echo state networks, however, the evolution consumes significant computational resources. It is demonstrated that echo state networks with local connections combine the best of both worlds, the simplicity of random echo state networks and the performance of evolved networks. Finally, it is shown that evolution tends to stay close to the ordered side of the edge of chaos.


title: Broadband circular polarized antenna for 915MHz RFID systems using miniaturized bow-tie loop elements

abstract: In this work a new broadband circularly-polarized antenna is presented for ultrahigh frequency (UHF) RFID systems. The antenna is designed to simultaneously cover the European and American RFID/ISM band (860–868 MHz and 902–928 MHz, respectively). The simulated data shows a 10 dB return loss bandwidth covering the frequency range from 846 MHz up to 959 MHz, making the antenna suitable for RFID systems. Furthermore, reductions in length of 31% and occupied footprint of 53% for a single antenna element is achieved. The bowtie element was implemented in a four element circular polarized (CP) design for operation in the 915 MHZ band, fed by a 180 power splitter and 90 degree phase shifters to achieve circularly polarized radiation. Initial measurements show good performance in the 902–928 MHz band, with an axial ratio of 0.72 dB at 915 MHz. Due to the broadband performance of the designed antenna, operation in the European ISM band can be easily achieved by simply replacing the feed and matching network components.


title: A new hope on ARM Cortex-M

abstract: Security, Privacy, and Advanced Cryptography Engineering: 6th International Conference, SPACE 2016, Hyderabad, India, December 14-18, 2016


title: A Visual Analytics Approach to Multiscale Exploration of Environmental Time Series

abstract: We present a Visual Analytics approach that addresses the detection of interesting patterns in numerical time series, specifically from environmental sciences. Crucial for the detection of interesting temporal patterns are the time scale and the starting points one is looking at. Our approach makes no assumption about time scale and starting position of temporal patterns and consists of three main steps: an algorithm to compute statistical values for all possible time scales and starting positions of intervals, visual identification of potentially interesting patterns in a matrix visualization, and interactive exploration of detected patterns. We demonstrate the utility of this approach in two scientific scenarios and explain how it allowed scientists to gain new insight into the dynamics of environmental systems.


title: Trainable frontend for robust and far-field keyword spotting

abstract: Robust and far-field speech recognition is critical to enable true hands-free communication. In far-field conditions, signals are attenuated due to distance. To improve robustness to loudness variation, we introduce a novel frontend called per-channel energy normalization (PCEN). The key ingredient of PCEN is the use of an automatic gain control based dynamic compression to replace the widely used static (such as log or root) compression. We evaluate PCEN on the keyword spotting task. On our large rerecorded noisy and far-field eval sets, we show that PCEN significantly improves recognition performance. Furthermore, we model PCEN as neural network layers and optimize high-dimensional PCEN parameters jointly with the keyword spotting acoustic model. The trained PCEN frontend demonstrates significant further improvements without increasing model complexity or inference-time cost.


title: Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction

abstract: Recently, many e-commerce websites have encouraged their users to rate shopping items and write review texts. This review information has been very useful for understanding user preferences and item properties, as well as enhancing the capability to make personalized recommendations of these websites. In this paper, we propose to model user preferences and item properties using convolutional neural networks (CNNs) with dual local and global attention, motivated by the superiority of CNNs to extract complex features. By using aggregated review texts from a user and aggregated review text for an item, our model can learn the unique features (embedding) of each user and each item. These features are then used to predict ratings. We train these user and item networks jointly which enable the interaction between users and items in a similar way as matrix factorization. The local attention provides us insight on a user's preferences or an item's properties. The global attention helps CNNs focus on the semantic meaning of the whole review text. Thus, the combined local and global attentions enable an interpretable and better-learned representation of users and items. We validate the proposed models by testing on popular review datasets in Yelp and Amazon and compare the results with matrix factorization (MF), the hidden factor and topical (HFT) model, and the recently proposed convolutional matrix factorization (ConvMF+). Our proposed CNNs with dual attention model outperforms HFT and ConvMF+ in terms of mean square errors (MSE). In addition, we compare the user/item embeddings learned from these models for classification and recommendation. These results also confirm the superior quality of user/item embeddings learned from our model.


AUC Maximizing Support Vector Learning


title: Complete font generation of Chinese characters in personal handwriting style

abstract: Since a complete Chinese font has typically several thousand or more Chinese characters and symbols, and most of them are much more complicated than English alphabets, it takes a lot of time and efforts for even professional font engineers to create a Chinese font. Although several attempts had been made to synthesize Chinese characters from strokes and components, it is still not easy to synthesize so many Chinese characters at one time. In this paper, we present an easy and fast solution for an ordinary user to create a Chinese font of his or her handwriting style. We adopt the approach: to synthesize Chinese characters using components extracted from the user's handwritings. In the preprocessing phase, we built a Web interface for crowds to label the positions and sizes of components of every Chinese character in the target character set. The standard Kai font was selected as a reference. We also devised an algorithm to find a small subset of Chinese characters having all required components to synthesize other Chinese characters. To create a personal handwriting font, with commonly-used 3,914 traditional Chinese characters, a user only has to handwrite 400 or so Chinese characters on a pad. One character by one character, our system can track every stroke, recognize and extract components from the user's handwritings. Then, every target Chinese character is synthesized from the extracted components, by placing them properly according to their position and size information. The experiment results show that although manually fine-tune is still required for few synthesized Chinese characters, users can create a Chinese font of their personal handwriting styles more easily and quickly.


title: OrigamiBot-I: A thread-actuated origami robot for manipulation and locomotion

abstract: This paper presents OrigamiBot-I, a thread- actuated origami robot, to demonstrate a physical application of an origami design for robotic manipulation and locomotion. The selected design can generate twisting and bending motions by pulling, pushing, or torsional force applied to the origami structure. Thread-based actuation also enables various shapes and motions by using different numbers of threads and routing them through different paths. The kinematics for each twisting and bending motions based on estimated parameters is derived. To evaluate potential use of origami for real-world applications and identify structural weaknesses, preliminary stiffness and durability testing was conducted. For physical demonstrations of robotic manipulation and locomotion, OrigamiBot-I was equipped with four independently-routed threads, where each thread is controlled by a geared DC motor. The robot suc- cessfully demonstrated its simple manipulation and locomotion capabilities.


title: A complete formalized knowledge representation model for advanced digital forensics timeline analysis

abstract: Having a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to understand the timeline of a crime, is one of the most important step of a DI process. This complex task requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. For this purpose, we propose a new methodology, supported by theoretical concepts, that can assist investigators through the whole process including the construction and the interpretation of the events describing the case. The proposed approach is based on a model which integrates knowledge of experts from the fields of digital forensics and software development to allow a semantically rich representation of events related to the incident. The main purpose of this model is to allow the analysis of these events in an automatic and efficient way. This paper describes the approach and then focuses on the main conceptual and formal aspects: a formal incident modelization and operators for timeline reconstruction and analysis.


title: Dynamic voltage scaling algorithm for dynamic-priority hard real-time systems using slack time analysis

abstract: Dynamic voltage scaling (DVS), which adjusts the clock speed and supply voltage dynamically, is an effective technique in reducing the energy consumption of embedded real-time systems. The energy efficiency of a DVS algorithm largely depends on the performance of the slack estimation method used in it. In this paper, we propose a novel DVS algorithm for periodic hard real-time tasks based on an improved slack estimation algorithm. Unlike the existing techniques, the proposed method takes full advantage of the periodic characteristics of the real-time tasks under priority-driven scheduling such as EDF. Experimental results show that the proposed algorithm reduces the energy consumption by 20/spl sim/40 % over the existing DVS algorithm. The experiment results also show that our algorithm based on the improved slack estimation method gives comparable energy savings to the DVS algorithm based on the theoretically optimal (but impractical) slack estimation method.


title: A complete solution for AGV SLAM integrated with navigation in modern warehouse environment

abstract: Automated Guided Vehicle (AGV) is a common handling equipment in modern logistics. The application of traditional AGV in warehouse requires designated infrastructure with readily available map which demands large amount of investment and warehouse conversion. Researches in AGV localization and mapping in SLAM overcome these disadvantages, however, are usually separated from navigation. The main purpose of this paper is to provide a complete solution including localization, mapping and navigation for AGV working in modern warehouse. EKF-SLAM (SLAM based on extended Kalman filter) is proposed for mapping and localization and pure pursuit algorithm is presented for navigation. Consequently, AGV is able to localize itself automatically and trace the path accurately in the whole handling process even in a dynamic and unstructured environment. Besides, it improves the working efficiency, system flexibility and reduces constructing cost.


title: Simulation study of small isolated power electronic converters for PV system

abstract: Power electronic converters, next to PV panels, are an important element of PV systems. They are responsible for converting electrical energy parameters such as: voltage, current and frequency values. This paper presents a detail performance analyze and simulation studies of small photovoltaic energy system. The proposed system consist: a step-up converter fed from the PV array, a high-frequency transformer, a full wave rectifier and output inverter provides power to the grid. Converter is designed for load of 3.5 kW. The one step method (square wave) and sinusoidal pulse width modulation (SPWM) are used for switching the inverters circuit. The discussed system has been simulated in PSIM and the selected results are presented.


title: The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics

abstract: Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data’s volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research. Keywords : big data, analytics, public health informatics, visual analytics, analytical reasoning, interactive visualizations, human-information interaction


title: Modeling and control of a three-port DC-DC converter for PV-battery systems

abstract: This paper presents modeling and decoupled control design for an isolated three-port DC-DC converter (TPC), which interfaces a photovoltaic (PV) panel, a rechargeable battery, and an isolated load. A small signal model is derived from the state-space equations of the TPC. Based on the model, two decoupled controllers are designed to control the power flows of the two sources and the load voltage independently. Experimental studies are performed to verify that the two controllers are capable of regulating the power flows of the PV panel and the battery while maintaining a constant output voltage for the TPC.


title: Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing

abstract: Vocal perception is particularly important for understanding a speaker's emotional state and intentions because, unlike facial perception, it is relatively independent of speaker distance and viewing conditions. The idea, derived from brain lesion studies, that vocal emotional comprehension is a special domain of the right hemisphere has failed to receive consistent support from neuroimaging. This conflict can be reconciled if vocal emotional comprehension is viewed as a multi-step process with individual neural representations. This view reveals a processing chain that proceeds from the ventral auditory pathway to brain structures implicated in cognition and emotion. Thus, vocal emotional comprehension appears to be mediated by bilateral mechanisms anchored within sensory, cognitive and emotional processing systems.


title: Electric bicycle using batteries and supercapacitors

abstract: In this paper, a traction system useful for an autonomous Electric Vehicle of individual use is described. The developed system is constituted in a first approach by two different power sources: one is constituted by batteries or by fuel cells, and the other by supercapacitors. This paper describes a technical solution joining and accomplishing the usage of two energy storage systems in the same traction system. In the developed system, the supercapacitors run as element that store energy temporarily and that can be used to retrieve energy. Starting from the functional characteristics of typical electrical vehicles and characterization of a typical routing profile, the energy consumption is obtained. In order to characterize and design the system, this is described in detail, namely the supercapacitors models, the battery, the power converters and the implemented strategy of control. According to the obtained results, a control strategy that allows an effective management of the stored energy in the system regarding the vehicle's optimal functioning and increasing its autonomy is also presented and discussed. Based on experimental and simulation results, the advantages and disadvantages of the proposed solution are presented.


title: Maintainability and safety indicators at design stage for mechanical products

abstract: With the industrial products competition, reliability, maintainability and safety are key characteristics for availability improvement. This is mainly true in industries like automotive, aeronautic, or NC machines tools. Therefore, it is crucial to predict these characteristics as soon as possible before the manufacturing starts. This paper presents an approach to provide indicators for maintainability and safety prediction at early stage of design. The assessment procedure uses the product CAD 3D model and an associated semantic matrix gathering information on the product components criticality and reliability. Using this information we calculate indicators for the product maintainability and safety. An academic application is developed to illustrate our approach and point out the interaction between maintainability and safety constraints to determine suitable solution. In conclusion, we state on possible extensions of this approach for evaluating other product lifecycle characteristics and we give guidelines on the implementation aspects.


title: ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

abstract: A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks. This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner. While the use of crossbar memory as an analog dot-product engine is well known, no prior work has designed or characterized a full-fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog-to-digital conversion (ADC). (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8×, 5.5×, and 7.5× in throughput, energy, and computational density (respectively), relative to the state-of-the-art DaDianNao architecture.


title: Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning

abstract: In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as nonplayer characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, “sixth sense” knowledge of human players' whereabouts and deterministic, scripted behaviors are some of the causes of this. We propose, however, that one of the biggest indicators of nonhumanlike behavior in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and “locking on” to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable nonplayer characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.


title: Fingerprint Verification by Fusion of Optical and Capacitive Sensors

abstract: A few works have been presented so far on information fusion for fingerprint verification. None. however, have explicitly investigated the use of multi-sensor fusion, in other words, the integration of the information provided by multiple devices to capture fingerprint images. In this paper, a multi-sensor fingerprint verification system based on the fusion of optical and capacitive sensors is presented. Reported results show that such a multi-sensor system can perform better than traditional fingerprint matchers based on a single sensor.


title: WYSIWYM - Integrated visualization, exploration and authoring of semantically enriched un-structured content

abstract: The Semantic Web and Linked Data gained traction in the last years. However, the majority of information still is contained in unstructured documents. This can also not be expected to change, since text, images and videos are the natural way how humans interact with information. Semantic structuring on the other hand enables the (semi-)automatic integration, repurposing, rearrangement of information. NLP technologies and formalisms for the integrated representation of unstructured and semantic content (such as RDFa and Microdata) aim at bridging this semantic gap. However, in order for humans to truly benefit from this integration, we need ways to author, visualize and explore unstructured and semantically enriched content in an integrated manner. In this paper, we present the WYSIWYM (What You See is What You Mean) concept, which addresses this issue and formalizes the binding between semantic representation models and UI elements for authoring, visualizing and exploration. With RDFaCE, Pharmer and conTEXT we present and evaluate three complementary showcases implementing the WYSIWYM concept for different application domains.


title: Interpreting Neural Networks With Nearest Neighbors

abstract: Local model interpretation methods explain individual predictions by assigning an importance value to each input feature. This value is often determined by measuring the change in confidence when a feature is removed. However, the confidence of neural networks is not a robust measure of model uncertainty. This issue makes reliably judging the importance of the input features difficult. We address this by changing the test-time behavior of neural networks using Deep k-Nearest Neighbors. Without harming text classification accuracy, this algorithm provides a more robust uncertainty metric which we use to generate feature importance values. The resulting interpretations better align with human perception than baseline methods. Finally, we use our interpretation method to analyze model predictions on dataset annotation artifacts.


title: IoT survey: An SDN and fog computing perspective

abstract: Abstract Recently, there has been an increasing interest in the Internet of Things (IoT). While some analysts disvalue the IoT hype, several technology leaders, governments, and researchers are putting serious efforts to develop solutions enabling wide IoT deployment. Thus, the huge amount of generated data, the high network scale, the security and privacy concerns, the new requirements in terms of QoS, and the heterogeneity in this ubiquitous network of networks make its implementation a very challenging task. SDN, a new networking paradigm, has revealed its usefulness in reducing the management complexities in today's networks. Additionally, SDN, having a global view of the network, has presented effective security solutions. On the other hand, fog computing, a new data service platform, consists of pushing the data to the network edge reducing the cost (in terms of bandwidth consumption and high latency) of “big data” transportation through the core network. In this paper, we critically review the SDN and fog computing-based solutions to overcome the IoT main challenges, highlighting their advantages, and exposing their weaknesses. Thus, we make recommendations at the end of this paper for the upcoming research work.


title: Prediction of Employee Promotion Based on Personal Basic Features and Post Features

abstract: Promotion is the focus of human resource management research. Because there are few researches about the mining of promotion features in existing studies, this paper uses the data of a Chinese state-owned enterprise, constructs a number of features and applies machine learning methods to predict employee promotion. Firstly, we build personal basic features and post features based on five strategies. Secondly, the correlation analysis is conducted to preliminarily explore the associations between some features and promotion. Then, the model learning and testing are carried out. Experimental results show that the random forest model performs best, which verifies the validity of features. Finally, we calculate the Gini importance of each feature to further analyze its influence on staff promotion. It is found that post features have a higher impact on promotion compared with personal basic features. Among all the features, the working years, the number of different positions and the highest department level greatly affect employee promotion.


A Multistage Training Framework for Acoustic-to-Word Model


Jabberwocky Parsing: Dependency Parsing with Lexical Noise


title: CdSe tetrapod interfacial layer for improving electron extraction in planar heterojunction perovskite solar cells

abstract: We demonstrate the improvement in the efficiency of planar heterojunction perovskite solar cells by employing cadmium selenide tetrapods (CdSe TPs) as an electron extraction layer. The insertion of the CdSe TP layer between the titanium oxide (TiO2) and perovskite film facilitates electron transfer at the TiO2/perovskite interface, as indicated by the significantly quenched steady-state photoluminescence of the perovskite film. Furthermore, we observed a conductivity enhancement of the perovskite film by introducing the CdSe TP layer. The combination of both effects induced by the TPs leads to enhancement in the carrier extraction as well as decreased recombination losses in the perovskite solar cells. As a result, an efficiency of 13.5% (1 sun condition) is achieved in the perovskite solar cells that incorporate the CdSe TP layer, which is 10% higher than that of the device without the CdSe TP layer.


Co-occurrence Histograms of Oriented Gradients for Human Detection


title: Post-quantum key exchange: a new hope

abstract: At IEEE Security & Privacy 2015, Bos, Costello, Naehrig, and Stebila proposed an instantiation of Peikert's ring-learning-with-errors-based (Ring-LWE) key-exchange protocol (PQCrypto 2014), together with an implementation integrated into OpenSSL, with the affirmed goal of providing post-quantum security for TLS. In this work we revisit their instantiation and stand-alone implementation. Specifically, we propose new parameters and a better suited error distribution, analyze the scheme's hardness against attacks by quantum computers in a conservative way, introduce a new and more efficient error-reconciliation mechanism, and propose a defense against backdoors and all-for-the-price-of-one attacks. By these measures and for the same lattice dimension, we more than double the security parameter, halve the communication overhead, and speed up computation by more than a factor of 8 in a portable C implementation and by more than a factor of 27 in an optimized implementation targeting current Intel CPUs. These speedups are achieved with comprehensive protection against timing attacks.


title: The grateful disposition: A conceptual and empirical topography

abstract: In four studies, the authors examined the correlates of the disposition toward gratitude. Study 1 revealed that self-ratings and observer ratings of the grateful disposition are associated with positive affect and well-being, prosocial behaviors and traits, and religiousness/spirituality. Study 2 replicated these findings in a large nonstudent sample. Study 3 yielded similar results to Studies 1 and 2 and provided evidence that gratitude is negatively associated with envy and materialistic attitudes. Study 4 yielded evidence that these associations persist after controlling for Extraversion/positive affectivity, Neuroticism/negative affectivity, and Agreeableness. The development of the Gratitude Questionnaire, a unidimensional measure with good psychometric properties, is also described.


title: Weight-Related Teasing of Adolescents Who Are Primarily Obese: Roles of Sociocultural Attitudes Towards Appearance and Physical Activity Self-Efficacy

abstract: Adolescents who are obese are at risk for being teased about their appearance with the concomitant negative psychological sequelae. Identifying modifiable variables associated with teasing could inform pediatric weight-management interventions. Characterizing society’s role in the victimization of these at-risk individuals could guide anti-bullying programs for schools and broader public health efforts. This study aims to examine novel societal and cognitive factors associated with weight-related teasing frequency. Participants were adolescents (N = 334) being evaluated for a hospital-affiliated weight-management program. The outcome was perceived weight-related teasing frequency. Predictors were sociocultural awareness and internalization of appearance-related attitudes, physical activity self-efficacy, and psychological functioning. Multivariate regressions controlled for demographics and body mass index (BMI) z-scores with separate regressions testing interactions of BMI z-scores with all predictors. In adjusted analyses, higher physical activity self-efficacy and fewer depressive symptoms related to lower teasing frequency. Interactions indicated that less awareness/internalization of sociocultural attitudes towards appearance, more positive body image, and higher self-esteem related to lower teasing frequency regardless of BMI. Targeted interventions and public health campaigns should be developed and tested for adolescents that improve body image with promotion of diverse views about attractiveness, bolster confidence in overcoming physical activity barriers, and identify and treat mood symptoms.


title: Formal Analysis of the FIDO 1.x Protocol

abstract: This paper presents a formal analysis of FIDO, a protocol developed by the FIDO Alliance project, and which aims to provide either a passwordless experience or an extra security layer for user authentication over the Internet. We model the protocol using the applied pi-calculus and run our analysis using ProVerif. Our analysis shows that ignoring some optional steps of the standard could lead to the implementation of a flawed authentication process. On the contrary, we prove that these steps are sufficient to ensure the expected security properties.


title: Parental Feeding Attitudes and Styles and Child Body Mass Index: Prospective Analysis of a Gene-Environment Interaction

abstract: Background. Parental feeding styles were linked to child weight in cross-sectional studies, which were unable to test the direction of effect. Prospective studies can best establish causal relationships among such variables. Objective. We tested the 2-year stability of parental feeding attitudes and styles and investigated whether these variables predict child body mass index (BMI) z scores 2 years later. We evaluated whether these associations were dependent on children’s predisposition to obesity. Methods. Participants were 57 families enrolled in an Infant Growth Study of children born at high risk or low risk for obesity, on the basis of maternal prepregnancy overweight or leanness. Children were evaluated for weight and height at 3, 5, and 7 years of age. Measures of parental feeding attitudes and styles were ascertained with the Child Feeding Questionnaire at 5 and 7 years of age. Correlation and multiple regression analyses tested whether parental feeding styles at age 5 predicted increased child BMI z scores 2 years later. Results. Parental feeding attitudes and styles were stable for child ages of 5 to 7 years. With respect to feeding attitudes, perceived responsibility at age 5 predicted reduced child BMI z scores at age 7 among low-risk families, whereas child weight concern and perceived child weight predicted increased child BMI z scores among high-risk families. With respect to feeding styles, monitoring predicted reduced child BMI z scores at age 7 among low-risk children. In contrast, restriction predicted higher BMI z scores and pressure to eat predicted reduced BMI z scores among high-risk children. These associations remained significant after controlling for child weight status at age 3. Conclusions. The relationship between parental feeding styles and child BMI z scores depends on child obesity predisposition, suggesting a gene-environment interaction. Among children predisposed to obesity, elevated child weight appears to elicit restrictive feeding practices, which in turn may produce additional weight gain. Parenting guidelines for overweight prevention may benefit from consideration of child characteristics such as vulnerability to obesity and current weight status.


title: Design and Analysis of a Grid-Connected Photovoltaic Power System

abstract: A grid-connected photovoltaic (PV) power system with high voltage gain is proposed, and the steady-state model analysis and the control strategy of the system are presented in this paper. For a typical PV array, the output voltage is relatively low, and a high voltage gain is obligatory to realize the grid-connected function. The proposed PV system employs a ZVT-interleaved boost converter with winding-coupled inductors and active-clamp circuits as the first power-processing stage, which can boost a low voltage of the PV array up to a high dc-bus voltage. Accordingly, an accurate steady-state model is obtained and verified by the simulation and experimental results, and a full-bridge inverter with bidirectional power flow is used as the second power-processing stage, which can stabilize the dc-bus voltage and shape the output current. Two compensation units are added to perform in the system control loops to achieve the low total harmonic distortion and fast dynamic response of the output current. Furthermore, a simple maximum-power-point-tracking method based on power balance is applied in the PV system to reduce the system complexity and cost with a high performance. At last, a 2-kW prototype has been built and tested to verify the theoretical analysis of the paper.


RFID Tag Antenna Design For Vehicle Plate Number ( e-Plate )


title: Review Integrated microfluidic devices

abstract: “With the fundamentals of microscale flow and species transport well developed, the recent trend in microfluidics has been to work towards the development of integrated devices which incorporate multiple fluidic, electronic and mechanical components or chemical processes onto a single chip sized substrate. Along with this has been a major push towards portability and therefore a decreased reliance on external infrastructure (such as detection sensors, heaters or voltage sources).” In this review we provide an in-depth look at the “state-of-the-art” in integrated microfludic devices for a broad range of application areas from on-chip DNA analysis, immunoassays and cytometry to advances in integrated detection technologies for and miniaturized fuel processing devices. In each area a few representative devices are examined with the intent of introducing the operating procedure, construction materials and manufacturing technique, as well as any unique and interesting features.


title: Adaptive authentication based on analysis of user behavior

abstract: Authentication is a mechanism to verify identity of users. Those who can present valid credential are considered as authenticated identities. In this paper, we introduce an adaptive authentication system called Unified Authentication Platform (UAP) which incorporates adaptive control to identify high-risk and suspicious illegitimate login attempts. The system evaluates comprehensive set of known information about the users from the past login history to define their normal behavior profile. The system leverages this information that has been previously stored to determine the security risk and level of assurance of current login attempt.


title: How genetic is school myopia ?

abstract: Abstract Myopia is of diverse aetiology. A small proportion of myopia is clearly familial, generally early in onset and of high level, with defined chromosomal localisations and in some cases, causal genetic mutations. However, in economically developed societies, most myopia appears during childhood, particularly during the school years. The chromosomal localisations characterised so far for high familial myopia do not seem to be relevant to school myopia. Family correlations in refractive error and axial length are consistent with a genetic contribution to variations in school myopia, but potentially confound shared genes and shared environments. High heritability values are obtained from twin studies, but rest on contestable assumptions, and require further critical analysis, particularly in view of the low heritability values obtained from parent–offspring correlations where there has been rapid environmental change between generations. Since heritability is a population-specific parameter, the values obtained on twins cannot be extrapolated to define the genetic contribution to variation in the general population. In addition, high heritability sets no limit to the potential for environmentally induced change. There is in fact strong evidence for rapid, environmentally induced change in the prevalence of myopia, associated with increased education and urbanisation. These environmental impacts have been found in all major branches of the human family, defined in modern molecular terms, with the exception of the Pacific Islanders, where the evidence is too limited to draw conclusions. The idea that populations of East Asian origin have an intrinsically higher prevalence of myopia is not supported by the very low prevalence reported for them in rural areas, and by the high prevalence of myopia reported for Indians in Singapore. A propensity to develop myopia in “myopigenic” environments thus appears to be a common human characteristic. Overall, while there may be a small genetic contribution to school myopia, detectable under conditions of low environmental variation, environmental change appears to be the major factor increasing the prevalence of myopia around the world. There is, moreover, little evidence to support the idea that individuals or populations differ in their susceptibility to environmental risk factors.


title: Articulatory movement prediction using deep bidirectional long short-term memory based recurrent neural networks and word/phone embeddings.

abstract: Automatic prediction of articulatory movements from speech or text can be beneficial for many applications such as speech recognition and synthesis. A recent approach has reported stateof-the-art performance in speech-to-articulatory prediction using feed forward neural networks. In this paper, we investigate the feasibility of using bidirectional long short-term memory based recurrent neural networks (BLSTM-RNNs) in articulatory movement prediction because they have long-context trajectory modeling ability. We show on the MNGU0 dataset that BLSTM-RNN apparently outperforms feed forward networks and pushes the state-of-the-art RMSE from 0.885 mm to 0.565 mm. On the other hand, predicting articulatory information from text heavily relies on handcrafted linguistic and prosodic features, e.g., POS and TOBI labels. In this paper, we propose to use word and phone embeddings to substitute these manual features. Word/phone embedding features are automatically learned from unlabeled text data by a neural network language model. We show that word and phone embeddings can achieve comparable performance without using POS and TOBI features. More promisingly, combining the conventional full feature set with phone embedding, the lowest RMSE is achieved.


title: Research on power quality prediction for DG integrated smart grid based on neural network

abstract: In this paper, a new approach based on neural network is proposed to availably predict the power quality (PQ) for smart grid with distributed generation (DG) integration. We first analyze the various factors that have remarkable impact on the variation tendency of PQ, such as climatic variation, fluctuation of power loads, etc. Then we put forward a general prediction process for steady-state index of power quality. Three neural networks with different structures, including Back Propagation (BP), Radical Basis Function (RBF) and General Regression Neural Network (GRNN) whose feasibilities and features are contrastively analyzed are used to build the prediction models for PQ. Simulation study is carried out on the IEEE 13 node test feeder, and the results demonstrate that the proposed prediction approaches for the steady-state PQ indexes based on neural networks are practicable, in which the prediction model based on the GRNN has the best performance.


title: Using TMT ontology in trust based medical tourism recommender system

abstract: Medical tourism is a domain rich of data that are stored in many hundreds of data sources. Many of these sources should be used during the development of tourism information systems. That is why, it is very important to create a referential model that represents the medical tourism in Tunisia. The e-tourism ontology provides a way to achieve integration and interoperability through the use of shared vocabulary and meanings for terms with respect to other terms. In this paper, we will create an ontology as a very expressive hierarchy of categories, populating with information from Tunisian medical tourism providers' services that represent all concepts and relations of the medical tourism in Tunisia. This ontology will be integrated in a trust based recommender system to deal with the lack of semantic information in personalized recommendation in tourism domain.


title: Stable Neo-Hookean Flesh Simulation

abstract: We present a novel variant of Neo-Hookean elasticity that is well suited for flesh simulation in graphics due to its stability, simplicity, and ability to preserve volume well.


title: Improving feature-based object recognition for X-ray baggage security screening using primed visualwords

abstract: We present a novel Bag-of-Words (BoW) representation scheme for image classification tasks, where the separation of features distinctive of different classes is enforced via class-specific feature-clustering. We investigate the implementation of this approach for the detection of firearms in baggage security X-ray imagery. We implement our novel BoW model using the Speeded-Up Robust Features (SURF) detector and descriptor within a Support Vector Machine (SVM) classifier framework. Experimentation on a large, diverse data set yields a significant improvement in classification performance over previous works with an optimal true positive rate of 99.07% at a false positive rate of 4.31%. Our results indicate that class-specific clustering primes the feature space and ultimately simplifies the classification process. We further demonstrate the importance of using diverse, representative data and efficient training and testing procedures. The excellent performance of the classifier is a strong indication of the potential advantages of this technique in threat object detection in security screening settings.


title: A survey of new trends in symbolic execution for software testing and analysis

abstract: Symbolic execution is a well-known program analysis technique which represents program inputs with symbolic values instead of concrete, initialized, data and executes the program by manipulating program expressions involving the symbolic values. Symbolic execution has been proposed over three decades ago but recently it has found renewed interest in the research community, due in part to the progress in decision procedures, availability of powerful computers and new algorithmic developments. We provide here a survey of some of the new research trends in symbolic execution, with particular emphasis on applications to test generation and program analysis. We first describe an approach that handles complex programming constructs such as input recursive data structures, arrays, as well as multithreading. Furthermore, we describe recent hybrid techniques that combine concrete and symbolic execution to overcome some of the inherent limitations of symbolic execution, such as handling native code or availability of decision procedures for the application domain. We follow with a discussion of techniques that can be used to limit the (possibly infinite) number of symbolic configurations that need to be analyzed for the symbolic execution of looping programs. Finally, we give a short survey of interesting new applications, such as predictive testing, invariant inference, program repair, analysis of parallel numerical programs and differential symbolic execution.


title: Speeding up the Number Theoretic Transform for Faster Ideal Lattice-Based Cryptography

abstract: The Number Theoretic Transform (NTT) provides efficient algorithms for cyclic and nega-cyclic convolutions, which have many applications in computer arithmetic, e.g., for multiplying large integers and large degree polynomials. It is commonly used in cryptographic schemes that are based on the hardness of the Ring Learning With Errors (R-LWE) problem to efficiently implement modular polynomial multiplication.


title: BA*: an online complete coverage algorithm for cleaning robots

abstract: This paper presents a novel approach to solve the online complete coverage task of autonomous cleaning robots in unknown workspaces based on the boustrophedon motions and the A* search algorithm (BA*). In this approach, the robot performs a single boustrophedon motion to cover an unvisited region until it reaches a critical point. To continue covering the next unvisited region, the robot wisely detects backtracking points based on its accumulated knowledge, determines the best backtracking point as the starting point of the next boustrophedon motion, and applies an intelligent backtracking mechanism based on the proposed A* search with smoothed path on tiling so as to reach the starting point with the shortest collision-free path. The robot achieves complete coverage when no backtracking point is detected. Computer simulations and experiments in real workspaces prove that our proposed BA* is efficient for the complete coverage task of cleaning robots.


title: Challenges in emulating sensor and resource-based state changes for Android malware detection

abstract: The increasing prevalence of mobile malware has driven the need for emulated, dynamic analysis techniques. Unfortunately, emulating mobile devices is nontrivial because of the different types of hardware features onboard (e.g., sensors) and the manner in which users interact with their devices as compared to traditional computing platforms. To evaluate this, our research focuses on the enumeration and comparison of multiple attributes and event values from sensors and dynamic resources on Android runtime environments, both from physical devices and online analysis services. Utilizing our results from enumeration, we develop two different Android applications that are successful in detecting and evading the emulated environments utilized by those mobile analysis services during execution. When ran on physical devices, the same applications successfully perform a pseudo-malware action and send device identifying information to our server.


Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting


title: Let's (not) stick together: pairwise similarity biases cross-validation in activity recognition

abstract: The ability to generalise towards either new users or unforeseen behaviours is a key requirement for activity recognition systems in ubiquitous computing. Differences in recognition performance for the two application cases can be significant, and user-dependent performance is typically assumed to be an upper bound on performance. We demonstrate that this assumption does not hold for the widely used cross-validation evaluation scheme that is typically employed both during system bootstrapping and for reporting results. We describe how the characteristics of segmented time-series data render random cross-validation a poor fit, as adjacent segments are not statistically independent. We develop an alternative approach -- meta-segmented cross validation -- that explicitly circumvents this issue and evaluate it on two data-sets. Results indicate a significant drop in performance across a variety of feature extraction and classification methods if this bias is removed, and that prolonged, repetitive activities are particularly affected.


title: Approximation Algorithms for Cascading Prediction Models

abstract: We present an approximation algorithm that takes a pool of pre-trained models as input and produces from it a cascaded model with similar accuracy but lower average-case cost. Applied to state-of-the-art ImageNet classification models, this yields up to a 2x reduction in floating point multiplications, and up to a 6x reduction in average-case memory I/O. The auto-generated cascades exhibit intuitive properties, such as using lower-resolution input for easier images and requiring higher prediction confidence when using a computationally cheaper model.


title: A novel method for distributed generation and capacitor optimal placement considering voltage profiles

abstract: To ensure the quality of power supply in distribution systems, bus voltages should be maintained within limits. Shunt capacitor banks installed along distribution feeders can reduce voltage drops. Moreover, distributed generations (DGs) can improve system voltage profile as well as supply real power. The locations of DGs and capacitors play an important role in maintaining voltage profiles. In this paper, two optimization models are proposed to obtain the optimal placements of DGs and capacitor banks to maintain better voltage profiles in distribution systems. First, the optimal DG placement problem is formulated as a modified optimal power flow (OPF) problem, with an innovative mathematic representation of voltage profile optimization. Then the capacitor optimal placement problem is modeled and solved. Both models are tested on the IEEE 41 bus distribution system, which is a radial system. Discussions are provided based on the results of case studies.


title: Conceptualization of competency based curricula in pre-service nursing and midwifery education: A grounded theory approach.

abstract: In health professional education, the competency-based curriculum concept has been an important driver of reform in the training of competent graduates for the 21st century. In African countries, although there has been implementing it in pre-service nursing and midwifery education and the literature reports a lack of understanding of what is it on the part of the implementers. This article explores the meaning of competency based curriculum in pre-service nursing and midwifery education in Rwanda. A grounded theory approach, following Corbin and Strauss, was used. Following ethical clearance by the university ethical committee, data was collected from 17 participants through in-depth individual interviews of staff. Four categories emerged: (a) transformation, (b) tool for primary health care philosophy, (c) technological approach to education, (d) and modular system. Competency-based curriculum is confirmed as an appropriate educational tool in producing competent graduates for today and the future.


Virtual Repositories in Delay Tolerant Networks


title: Traceband: A fast, low overhead and accurate tool for available bandwidth estimation and monitoring

abstract: Available bandwidth estimation techniques are being used in network monitoring and management tools to provide information about the utilization of the network and verify the compliance of service level agreements. However, the use of these techniques in other applications and network environments is limited by the long convergence times, accuracy errors, and the amount of overhead that they introduce. In this paper, we introduce Traceband, a hidden Markov model-based technique for end-to-end available bandwidth estimation and monitoring that improves these performance metrics and therefore promises to expand the use of these techniques in other scenarios. Traceband is evaluated and compared with Spruce and Pathload using Poisson and self-similar cross-traffic. Experimental results in a controlled environment with Poisson cross-traffic demonstrate that Traceband is as accurate as Spruce and Pathload but considerably faster, and introduces less overhead. Traceband's convergence time is demonstrated using bursty cross-traffic, as it is the only tool that accurately reacts to zero-traffic periods, which may be particularly useful for those applications that need to make decisions in real time. Using self-similar traffic, Traceband's mean accuracy and variability degrade with the Hurst parameter but it still performs within reasonable limits. A general and optional moving average algorithm is also introduced to solve these issues.


title: Supraglottic airway devices.

abstract: Supraglottic airway devices (SADs) are used to keep the upper airway open to provide unobstructed ventilation. Early (first-generation) SADs rapidly replaced endotracheal intubation and face masks in > 40% of general anesthesia cases due to their versatility and ease of use. Second-generation devices have further improved efficacy and utility by incorporating design changes. Individual second-generation SADs have allowed more dependable positive-pressure ventilation, are made of disposable materials, have integrated bite blocks, are better able to act as conduits for tracheal tube placement, and have reduced risk of pulmonary aspiration of gastric contents. SADs now provide successful rescue ventilation in > 90% of patients in whom mask ventilation or tracheal intubation is found to be impossible. However, some concerns with these devices remain, including failing to adequately ventilate, causing airway damage, and increasing the likelihood of pulmonary aspiration of gastric contents. Careful patient selection and excellent technical skills are necessary for successful use of these devices.


title: Blue photoluminescence from chemically derived graphene oxide.

abstract: Blue photoluminescence from chemically derived graphene oxide Goki Eda, Yun-Yue Lin, Cecilia Mattevi, Hisato Yamaguchi, Hsin-An Chen, I-Sheng Chen, Chun-Wei Chen, and Manish Chhowalla 1 Department of Materials, Imperial College, Exhibition Road, London SW7 2AZ, UK. 2 Department of Materials Science and Engineering, Rutgers University 607 Taylor Road, Piscataway, NJ 08854, USA. 3 Department of Materials Science and Engineering, National Taiwan University No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan.


title: Leveraging the Zachman framework implementation using action – research methodology – a case study: aligning the enterprise architecture and the business goals

abstract: With the emergence of new enterprise models, such as technology-based enterprises, and the large quantity of information generated through technological advances, the Zachman framework continues to represent a modelling tool of great utility and value to construct an enterprise architecture EA that can integrate and align the IT infrastructure and business goals. Nevertheless, implementing an EA requires an important effort within an enterprise. Small technology-based enterprises and start-ups can take advantage of EAs and frameworks but, because these enterprises have limited resources to allocate for this task, an enterprise framework implementation is not feasible in most cases. This article proposes a new methodology based on action–research for the implementation of the business, system and technology models of the Zachman framework to assist and facilitate its implementation. Following the explanation of cycles of the proposed methodology, a case study is presented to illustrate the results of implementing the Zachman framework in a technology-based enterprise: PyME CREATIVA, using action–research approach.


Feature Reduction with Linear Discriminant Analysis


title: Gamification for Word Sense Labeling

abstract: Obtaining gold standard data for word sense disambiguation is important but costly. We show how it can be done using a “Game with a Purpose” (GWAP) called Wordrobe. This game consists of a large set of multiple-choice questions on word senses generated from the Groningen Meaning Bank. The players need to answer these questions, scoring points depending on the agreement with fellow players. The working assumption is that the right sense for a word can be determined by the answers given by the players. To evaluate our method, we gold-standard tagged a portion of the data that was also used in the GWAP. A comparison yielded promising results, ranging from a precision of 0.88 and recall of 0.83 for relative majority agreement, to a precision of 0.98 and recall of 0.35 for questions that were answered unanimously.


title: Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images

abstract: Automated whole breast ultrasound (ABUS) is an emerging screening tool for detecting breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor likelihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect breast tumors in ABUS images.


title: Popularity of arXiv.org within Computer Science

abstract: It may seem surprising that, out of all areas of science, computer scientists have been slow to post electronic versions of papers on sites like arXiv.org. Instead, computer scientists have tended to place papers on our individual home pages, but this loses the benefits of aggregation, namely notification and browsing. ::: But this is changing. More and more computer scientists are now using the arXiv. At the same time, there is ongoing discussion and controversy about how prepublication affects peer review, especially for double-blind conferences. This discussion is often carried out with precious little evidence of how popular prepublication is. ::: We measure what percentage of papers in computer science are placed on the arXiv, by cross-referencing published papers in DBLP with e-prints on arXiv. We found: ::: * Usage of arXiv.org has risen dramatically among the most selective conferences in computer science. In 2017, fully 23% of papers had e-prints on arXiv, compared to only 1% ten years ago. ::: * Areas of computer science vary widely in e-print prevalence. In theoretical computer science and machine learning, over 60% of published papers are on arXiv, while other areas are essentially zero. In most areas, arXiv usage is rising. ::: * Many researchers use arXiv for posting preprints. Of the 2017 published papers with arXiv e-prints, 56% were preprints that were posted before or during peer review. ::: Our paper describes these results as well as policy implications for researchers and practitioners.


title: Hierarchy of Alternating Specialists for Scene Recognition

abstract: We introduce a method for improving convolutional neural networks (CNNs) for scene classification. We present a hierarchy of specialist networks, which disentangles the intra-class variation and inter-class similarity in a coarse to fine manner. Our key insight is that each subset within a class is often associated with different types of inter-class similarity. This suggests that existing network of experts approaches that organize classes into coarse categories are suboptimal. In contrast, we group images based on high-level appearance features rather than their class membership and dedicate a specialist model per group. In addition, we propose an alternating architecture with a global ordered- and a global orderless-representation to account for both the coarse layout of the scene and the transient objects. We demonstrate that it leads to better performance than using a single type of representation as well as the fused features. We also introduce a mini-batch soft k-means that allows end-to-end fine-tuning, as well as a novel routing function for assigning images to specialists. Experimental results show that the proposed approach achieves a significant improvement over baselines including the existing tree-structured CNNs with class-based grouping.


title: Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

abstract: In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.


title: Inhibition of β-Amyloid Formation by Haloperidol: A Possible Mechanism for Reduced Frequency of Alzheimer's Disease Pathology in Schizophrenia

abstract: Several reports have suggested that the frequency of Alzheimer's disease (AD) neuropathology is significantly reduced in elderly individuals with schizophrenia (SZ), and it has been proposed that medications used for treatment of SZ may be responsible. A central event in AD pathology is the formation of beta-amyloid (A beta) peptide, which is derived by enzymatic processing of its precursor protein. Haloperidol, an antipsychotic medication commonly used in the treatment of SZ, can act as an inhibitor of select proteinases; hence, we examined the ability of this compound to inhibit A beta formation by cultured cells. Haloperidol and, to a lesser extent, droperidol inhibited A beta in a dose-dependent manner. These results may explain the apparent reduction of AD neuropathological changes in elderly patients with SZ as well as provide a possible mechanism for this difference.


title: The physiological effects of Shinrin-yoku (taking in the forest atmosphere or forest bathing): evidence from field experiments in 24 forests across Japan.

abstract: This paper reviews previous research on the physiological effects of Shinrin-yoku (taking in the forest atmosphere or forest bathing), and presents new results from field experiments conducted in 24 forests across Japan. The term Shinrin-yoku was coined by the Japanese Ministry of Agriculture, Forestry, and Fisheries in 1982, and can be defined as making contact with and taking in the atmosphere of the forest. In order to clarify the physiological effects of Shinrin-yoku, we conducted field experiments in 24 forests across Japan. In each experiment, 12 subjects (280 total; ages 21.7 +/- 1.5 year) walked in and viewed a forest or city area. On the first day, six subjects were sent to a forest area, and the others to a city area. On the second day, each group was sent to the other area as a cross-check. Salivary cortisol, blood pressure, pulse rate, and heart rate variability were used as indices. These indices were measured in the morning at the accommodation facility before breakfast and also both before and after the walking (for 16 +/- 5 min) and viewing (for 14 +/- 2 min). The R-R interval was also measured during the walking and viewing periods. The results show that forest environments promote lower concentrations of cortisol, lower pulse rate, lower blood pressure, greater parasympathetic nerve activity, and lower sympathetic nerve activity than do city environments. These results will contribute to the development of a research field dedicated to forest medicine, which may be used as a strategy for preventive medicine.


title: A multifrontal QR factorization approach to distributed inference applied to multi-robot localization and mapping

abstract: QR factorization is most often used as a "black box" algorithm, but is in fact an elegant computation on a factor graph. By computing a rooted clique tree on this graph, the computation can be parallelized across subtrees, which forms the basis of so-called multifrontal QR methods. By judiciously choosing the order in which variables are eliminated in the clique tree computation, we show that one straightforwardly obtains a method for performing inference in distributed sensor networks. One obvious application is distributed localization and mapping with a team of robots. We phrase the problem as inference on a large-scale Gaussian Markov Random Field induced by the measurement factor graph, and show how multifrontal QR on this graph solves for the global map and all the robot poses in a distributed fashion. The method is illustrated using both small and large-scale simulations, and validated in practice through actual robot experiments.


title: From Complex Event Processing to Simple Event Processing

abstract: Many problems in Computer Science can be framed as the computation of queries over sequences, or "streams" of data units called events. The field of Complex Event Processing (CEP) relates to the techniques and tools developed to efficiently process these queries. However, most CEP systems developed so far have concentrated on relatively narrow types of queries, which consist of sliding windows, aggregation functions, and simple sequential patterns computed over events that have a fixed tuple structure. Many of them boast throughput, but in counterpart, they are difficult to setup and cumbersome to extend with user-defined elements. ::: This paper describes a variety of use cases taken from real-world scenarios that present features seldom considered in classical CEP problems. It also provides a broad review of current solutions, that includes tools and techniques going beyond typical surveys on CEP. From a critical analysis of these solutions, design principles for a new type of event stream processing system are exposed. The paper proposes a simple, generic and extensible framework for the processing of event streams of diverse types; it describes in detail a stream processing engine, called BeepBeep, that implements these principles. BeepBeep's modular architecture, which borrows concepts from many other systems, is complemented with an extensible query language, called eSQL. The end result is an open, versatile, and reasonably efficient query engine that can be used in situations that go beyond the capabilities of existing systems.


title: Preprocessing and analyzing educational data set using X-API for improving student's performance

abstract: Educational data mining concerns of developing methods to discover hidden patterns from educational data. The quality of data mining techniques depends on the collected data and features. In this paper, we proposed a new student performance model with a new category of features, which called behavioral features. This type of features is related to the learner interactivity with e-learning system. We collect the data from an e-Learning system called Kalboard 360 using Experience API web service (XAPI). After that, we use some data mining techniques such as Artificial Neural Network, Naive Bayesian, and Decision Tree classifiers to evaluate the impact of such features on student's academic performance. The results reveal that there is a strong relationship between learner behaviors and its academic achievement. Results with different classification methods using behavioral features achieved up to 29% improvement in the classification accuracy compared to the same data set when removing such features.


Inducing a Lexicon of Abusive Words – a Feature-Based Approach