title: Time-series detection of perspiration as a liveness test in fingerprint devices

abstract: Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets.


title: 3D point cloud segmentation using topological persistence

abstract: In this paper, we present an approach to segment 3D point cloud data using ideas from persistent homology theory. The proposed algorithms first generate a simplicial complex representation of the point cloud dataset. Next, we compute the zeroth homology group of the complex which corresponds to the number of connected components. Finally, we extract the clusters of each connected component in the dataset. We show that this technique has several advantages over state of the art methods such as the ability to provide a stable segmentation of point cloud data under noisy or poor sampling conditions and its independence of a fixed distance metric.


title: Learning Compositional Visual Concepts with Mutual Consistency

abstract: Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.


title: Digital Beam-Forming reconfigurable Radar System demonstrator

abstract: Synthetic Aperture Radar (SAR) based on the Digital Beam-Forming (DBF) concept belongs to the family of Multi-Modal Radar Systems (MMRS) offering higher operational flexibility and improved performance compared to conventional radar systems using analog beam steering. DBF SAR, in particular, overcomes the fundamental limitation of classical SAR systems and can deliver high resolution and simultaneously wide swath images. The purpose of this paper is to present a laboratory prototype of such a MMRS architecture - a reconfigurable radar demonstrator based on the DBF architecture. A hardware configuration of the radar as well as advanced concepts to be experimentally verified using it are considered and discussed.


title: Celebratory technology: new directions for food research in HCI

abstract: Food is a central part of our lives. Fundamentally, we need food to survive. Socially, food is something that brings people together-individuals interact through and around it. Culturally, food practices reflect our ethnicities and nationalities. Given the importance of food in our daily lives, it is important to understand what role technology currently plays and the roles it can be imagined to play in the future. In this paper we describe the existing and potential design space for HCI in the area of human-food interaction. We present ideas for future work on designing technologies in the area of human-food interaction that celebrate the positive interactions that people have with food as they eat and prepare foods in their everyday lives.


title: Quantized Memory-Augmented Neural Networks

abstract: Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intriguing AI tasks that would otherwise be hard to address. This paper concerns the problem of quantizing MANNs. Quantization is known to be effective when we deploy deep models on embedded systems with limited resources. Furthermore, quantization can substantially reduce the energy consumption of the inference procedure. These benefits justify recent developments of quantized multi layer perceptrons, convolutional networks, and RNNs. However, no prior work has reported the successful quantization of MANNs. The in-depth analysis presented here reveals various challenges that do not appear in the quantization of the other networks. Without addressing them properly, quantized MANNs would normally suffer from excessive quantization error which leads to degraded performance. In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge. In our experiments, we achieved a computation-energy gain of 22x with 8-bit fixed-point and binary quantization compared to the floating-point implementation. Measured on the bAbI dataset, the resulting model, named the quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit fixed-point and binary quantization, respectively, compared to the MANN quantized using conventional techniques.


title: DC Microgrid Protection: Using the Coupled-Inductor Solid-State Circuit Breaker

abstract: Since the great debate between Thomas Edison and Nikola Tesla, our nation's power system has operated on alternating current (ac). This was chosen over direct current (dc) because of the need to increase voltage with ac transformers to a high value using transformers for long-distance power transmission. The system has served its purpose well, but now, many energy sources, such as solar panels, fuel cells, and batteries, supply dc voltage. Also, dc/dc power converters are commonly used to transform voltage and to interface these dc sources with a larger system. Because of this, local dc power systems (or microgrids) have become popular topics in research literature. It also turns out that interfacing a wind power generator to a dc system is simpler than interfacing it to an ac system because ac/dc conversion is needed for the former and ac/dc/ac conversion is needed for the latter. Although energy sources and power conversion are readily available for dc power systems, some highperformance applications require fast-acting dc circuit breakers, which are currently in the experimental phase. This article discusses options for high-performance dc circuit breakers and specifically details the coupled-inductor dc breaker. This breaker is demonstrated for fault protection in a notional dc microgrid.


title: Towards Scalable Multiprocessor Virtual Machines

abstract: A multiprocessor virtual machine benefits its guest operating system in supporting scalable job throughput and request latency-useful properties in server consolidation where servers require several of the system processors for steady state or to handle load bursts. ::: ::: Typical operating systems, optimized for multiprocessor systems in their use of spin-locks for critical sections, can defeat flexible virtual machine scheduling due to lock-holder preemption and misbalanced load. The virtual machine must assist the guest operating system to avoid lock-holder preemption and to schedule jobs with knowledge of asymmetric processor allocation. We want to support a virtual machine environment with flexible scheduling policies, while maximizing guest performance. ::: ::: This paper presents solutions to avoid lock-holder preemption for both fully virtualized and paravirtualized environments. Experiments show that we can nearly eliminate the effects of lock-holder preemption. Furthermore, the paper presents a scheduler feedback mechanism that despite the presence of asymmetric processor allocation achieves optimal and fair load balancing in the guest operating system.


title: Co-designing accelerators and SoC interfaces using gem5-Aladdin

abstract: Increasing demand for power-efficient, high-performance computing has spurred a growing number and diversity of hardware accelerators in mobile and server Systems on Chip (SoCs). This paper makes the case that the co-design of the accelerator microarchitecture with the system in which it belongs is critical to balanced, efficient accelerator microarchitectures. We find that data movement and coherence management for accelerators are significant yet often unaccounted components of total accelerator runtime, resulting in misleading performance predictions and inefficient accelerator designs. To explore the design space of accelerator-system co-design, we develop gem5-Aladdin, an SoC simulator that captures dynamic interactions between accelerators and the SoC platform, and validate it to within 6% against real hardware. Our co-design studies show that the optimal energy-delay-product (EDP) of an accelerator microarchitecture can improve by up to 7.4× when system-level effects are considered compared to optimizing accelerators in isolation.


title: Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

abstract: Similarity-based layouts generated by multidimensional projections or other dimension reduction techniques are commonly used to visualize high-dimensional data. Many projection techniques have been recently proposed addressing different objectives and application domains. Nonetheless, very little is known about the effectiveness of the generated layouts from a user’s perspective, how distinct layouts from the same data compare regarding the typical visualization tasks they support, or how domain-specific issues affect the outcome of the techniques. Learning more about projection usage is an important step towards both consolidating their role inhigh-dimensional data analysis and taking informed decisions when choosing techniques. This work provides a contribution towards this goal. We describe the results of an investigation on the performance of layouts generated by projection techniques as perceived by their users. We conducted a controlled user study to test against the following hypotheses: (1) projection performance istask-dependent; (2) certain projections perform better on certain types of tasks; (3) projection performance depends on the nature of the data; and (4) subjects prefer projections with good segregation capability. We generated layouts of high-dimensional data with five techniques representative of different projection approaches. As application domains we investigated image and document data. We identified eight typical tasks, three of them related to segregation capability of the projection, three related to projection precision, and two related to incurred visual cluttering. Answers to questions were compared for correctness against ‘ground truth’ computed directly from the data. We also looked at subject confidence and task completion times. Statistical analysis of the collected data resulted in Hypotheses 1 and 3 being confirmed, Hypothesis 2 being confirmed partially and Hypotheses 4 could not be confirmed. We discuss our findings in comparison with some numerical measures of projection layout quality. Our results offer interesting insight on the use ofprojection layouts in data visualization tasks and provide a departing point for further systematic investigations.


title: Multi-task proximal support vector machine

abstract: With the explosive growth of the use of imagery, visual recognition plays an important role in many applications and attracts increasing research attention. Given several related tasks, single-task learning learns each task separately and ignores the relationships among these tasks. Different from single-task learning, multi-task learning can explore more information to learn all tasks jointly by using relationships among these tasks. In this paper, we propose a novel multi-task learning model based on the proximal support vector machine. The proximal support vector machine uses the large-margin idea as does the standard support vector machines but with looser constraints and much lower computational cost. Our multi-task proximal support vector machine inherits the merits of the proximal support vector machine and achieves better performance compared with other popular multi-task learning models. Experiments are conducted on several multi-task learning datasets, including two classification datasets and one regression dataset. All results demonstrate the effectiveness and efficiency of our proposed multi-task proximal support vector machine. HighlightsPropose highly efficient multi-task proximal support vector machine (MTPSVM).Develop a method to optimize the learning procedure of MTPSVM.Unbalanced MTPSVM is proposed to deal with the unbalanced sample problem.Propose proximal support vector regression (SVR) and multi-task proximal SVR.Extensive experiments demonstrate the effectiveness and efficiency of our MTPSVM.


title: Stability and change in generative concern: Evidence from a longitudinal survey

abstract: Longitudinal data taken at a ten-year interval from a large, nationally representative sample were used to examine stability and change in generative concern, as measured by a reduced form of the Loyola Generativity Scale (LGS). Rank-order stability over a ten year period was high (r > .6) among those respondents 30 or older at the time of first measurement. Mean scores on the LGS increased for men aged 24–29 and decreased for men and women aged 60–69, but the size of these changes was small. First marriage and childbearing were not associated with an increase in LGS scores. The evidence suggests both life-long stability and a small mid-life peak in generative concern.


title: Modern practical application and research on teleoperated excavators control, feedback and perception issues in post disaster recovery operation

abstract: This paper discusses the modern practical application and research on teleoperated excavators and related issues regarding the use of such system in high risk and dangerous post disaster recovery operation. We describe the issues of teleoperated excavator system, with respect to control strategy, feedback displays, perception, latency, master-slave technology and sustainability. Practical application in Japan for post disaster recovery operation and various research developments in unmanned and teleoperated construction system are also presented. The recent development in various disasters in Malaysia should enlighten the public and the government on the future direction in the advanced technology for disaster risk reduction and post disaster management in the country.


Computational Aesthetics in Graphics , Visualization , and Imaging EXPRESSIVE 2016


title: Strategies in treatment of suicidality: identification of common and treatment-specific interventions in empirically supported treatment manuals.

abstract: OBJECTIVE ::: Many reports of treatments for suicidal patients claim effectiveness in reducing suicidal behavior but fail to demonstrate which treatment interventions, or combinations thereof, diminish suicidality. In this study, treatment manuals for empirically supported psychological treatments for suicidal patients were examined to identify which interventions they had in common and which interventions were treatment-specific. ::: ::: ::: METHOD ::: Empirically supported treatments for suicidality were identified through a literature search of PsychLit and MEDLINE for the years 1970-2007, employing the following search strategy: [suicide OR parasuicide] AND [therapy OR psychotherapy OR treatment] AND [random OR randomized]. After identifying the reports on randomized controlled studies that tested effectiveness of different treatments, the reference list of each report was searched for further studies. Only reports published in English were included. To ensure that rated manuals actually correspond to the delivered and tested treatments, we included only treatment interventions with explicit adherence rating and scoring and with adequate adherence ratings in the published studies. Five manualized treatments demonstrating efficacy in reducing suicide risk were identified and were independently evaluated by raters using a list of treatment interventions. ::: ::: ::: RESULTS ::: The common interventions included a clear treatment framework; a defined strategy for managing suicide crises; close attention to affect; an active, participatory therapist style; and use of exploratory and change-oriented interventions. Some treatments encouraged a multimodal approach and identification of suicidality as an explicit target behavior, and some concentrated on the patient-therapist relationship. Emphasis on interpretation and supportive interventions varied. Not all methods encouraged systematic support for therapists. ::: ::: ::: CONCLUSION ::: This study identified candidate interventions for possible effectiveness in reducing suicidality. These interventions seem to address central characteristics of suicidal patients. Further studies are needed to confirm which interventions and which combinations thereof are most effective.


title: Generalization and Exploration via Randomized Value Functions

abstract: We propose randomized least-squares value iteration (RLSVI) -- a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized value functions. We explain why versions of least-squares value iteration that use Boltzmann or epsilon-greedy exploration can be highly inefficient, and we present computational results that demonstrate dramatic efficiency gains enjoyed by RLSVI. Further, we establish an upper bound on the expected regret of RLSVI that demonstrates near-optimality in a tabula rasa learning context. More broadly, our results suggest that randomized value functions offer a promising approach to tackling a critical challenge in reinforcement learning: synthesizing efficient exploration and effective generalization.


title: Math. Program., Ser. A 91: 239–269 (2002) Digital Object Identifier (DOI) 10.1007/s101070100244

abstract: In this paper the solution of nonlinear programming problems by a Sequential Quadratic Programming (SQP) trust-region algorithm is considered. The aim of the present work is to promote global convergence without the need to use a penalty function. Instead, a new concept of a “filter” is introduced which allows a step to be accepted if it reduces either the objective function or the constraint violation function. Numerical tests on a wide range of test problems are very encouraging and the new algorithm compares favourably with LANCELOT and an implementation of Sl1QP.


title: A 45nm Logic Technology with High-k+Metal Gate Transistors, Strained Silicon, 9 Cu Interconnect Layers, 193nm Dry Patterning, and 100% Pb-free Packaging

abstract: A 45 nm logic technology is described that for the first time incorporates high-k + metal gate transistors in a high volume manufacturing process. The transistors feature 1.0 nm EOT high-k gate dielectric, dual band edge workfunction metal gates and third generation strained silicon, resulting in the highest drive currents yet reported for NMOS and PMOS. The technology also features trench contact based local routing, 9 layers of copper interconnect with low-k ILD, low cost 193 nm dry patterning, and 100% Pb-free packaging. Process yield, performance and reliability are demonstrated on 153 Mb SRAM arrays with SRAM cell size of 0.346 mum2, and on multiple microprocessors.


title: A Web Scraping Methodology for Bypassing Twitter API Restrictions

abstract: Retrieving information from social networks is the first and primordial step many data analysis fields such as Natural Language Processing, Sentiment Analysis and Machine Learning. Important data science tasks relay on historical data gathering for further predictive results. Most of the recent works use Twitter API, a public platform for collecting public streams of information, which allows querying chronological tweets for no more than three weeks old. In this paper, we present a new methodology for collecting historical tweets within any date range using web scraping techniques bypassing for Twitter API restrictions.


title: Interactive memory systems in the human brain

abstract: Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates1,2. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies3,4 and neuroimaging5, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning6,7,8.


title: A human gut microbial gene catalogue established by metagenomic sequencing

abstract: To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, ~150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively


title: Self-Attentional Acoustic Models

abstract: Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities. These models have recently shown promising results for modeling discrete sequences, but they are non-trivial to apply to acoustic modeling due to computational and modeling issues. In this paper, we apply self-attention to acoustic modeling, proposing several improvements to mitigate these issues: First, self-attention memory grows quadratically in the sequence length, which we address through a downsampling technique. Second, we find that previous approaches to incorporate position information into the model are unsuitable and explore other representations and hybrid models to this end. Third, to stress the importance of local context in the acoustic signal, we propose a Gaussian biasing approach that allows explicit control over the context range. Experiments find that our model approaches a strong baseline based on LSTMs with network-in-network connections while being much faster to compute. Besides speed, we find that interpretability is a strength of self-attentional acoustic models, and demonstrate that self-attention heads learn a linguistically plausible division of labor.


title: 3D Reconstruction in the Presence of Glass and Mirrors by Acoustic and Visual Fusion

abstract: We present a practical and inexpensive method to reconstruct 3D scenes that include transparent and mirror objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, which commonly include glass. These large structures are often invisible to cameras or even to our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-sized reconstruction setting. Our simple hardware setup augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure the distance to any object, including transparent surfaces. The key technical challenge is the sparse sampling rate from the acoustic sensor, which only takes one point measurement per frame. To address this challenge, we take advantage of the fact that the large scale glass structures in indoor environments are usually either piece-wise planar or a simple parametric surface. Based on these assumptions, we have developed a novel sensor fusion algorithm that first segments the (hybrid) depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. We validated our algorithms with a number of challenging cases, including multiple panes of glass, mirrors, and even a curved glass cabinet.


title: Wireless Powered Communication: Opportunities and Challenges

abstract: The performance of wireless communication is fundamentally constrained by the limited battery life of wireless devices, the operations of which are frequently disrupted due to the need of manual battery replacement/recharging. The recent advance in RF-enabled wireless energy transfer (WET) technology provides an attractive solution named wireless powered communication (WPC), where the wireless devices are powered by dedicated wireless power transmitters to provide continuous and stable microwave energy over the air. As a key enabling technology for truly perpetual communications, WPC opens up the potential to build a network with larger throughput, higher robustness, and increased flexibility compared to its battery-powered counterpart. However, the combination of wireless energy and information transmissions also raises many new research problems and implementation issues that need to be addressed. In this article, we provide an overview of stateof- the-art RF-enabled WET technologies and their applications to wireless communications, highlighting the key design challenges, solutions, and opportunities ahead.


title: Beyond transitional probability computations: Extracting word-like units when only statistical information is available

abstract: Endress and Mehler (2009) reported that when adult subjects are exposed to an unsegmented artificial language composed from trisyllabic words such as ABX, YBC, and AZC, they are unable to distinguish between these words and what they coined as the ‘‘phantomword’’ ABC in a subsequent test. This suggests that statistical learning generates knowledge about the transitional probabilities (TPs) within each pair of syllables (AB, BC, and A C), which are common to words and phantom-words, but, crucially, does not lead to the extraction of genuine word-like units. This conclusion is definitely inconsistent with chunk-based models of word segmentation, as confirmed by simulations run with the MDLChunker (Robinet, Lemaire, & Gordon, 2011) and PARSER (Perruchet & Vinter, 1998), which successfully discover the words without computing TPs. Null results, however, can be due to multiple causes, and notably, in the case of Endress and Mehler, to the reduced level of intelligibility of their synthesized speech flow. In three experiments, we observed positive results in conditions similar to Endress and Mehler after only 5 min of exposure to the language, hence providing strong evidence that statistical information is sufficient to extract word-like units.


title: Further Evidence That the Majority of Primary Nuclear RNA Transcripts in Mammalian Cells Do Not Contribute to mRNA

abstract: Nuclear RNA from Chinese hamster ovary cells was effectively separated into polyadenylic acid (poly(A))-containing (poly (A)/sup +/) and non-poly(A)-containing (poly(A)/sup -/) fractions so that --90% of the poly(A) was present in the (A)/sup +/ fraction. Only 25% of the 5'-terminal caps of the large nuclear molecules were present in the (A)/sup +/ class, but about 70% of the specific mRNA sequences (assayed with cDNA clones) were in the (A)/sup +/ class. It appears that many long capped heterogeneous nuclear RNA molecules are of a different sequence category from those molecules that are successfully processed into mRNA.


title: Automatic suggestion of query-rewrite rules for enterprise search

abstract: Enterprise search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the enterprise domain. This challenge is partly addressed by having domain experts maintaining the enterprise search engine and adapting it to the domain specifics. Those administrators commonly address user complaints about relevant documents missing from the top matches. For that, it has been proposed to allow administrators to influence search results by crafting query-rewrite rules, each specifying how queries of a certain pattern should be modified or augmented with additional queries. Upon a complaint, the administrator seeks a semantically coherent rule that is capable of pushing the desired documents up to the top matches. However, the creation and maintenance of rewrite rules is highly tedious and time consuming. Our goal in this work is to ease the burden on search administrators by automatically suggesting rewrite rules. This automation entails several challenges. One major challenge is to select, among many options, rules that are ``natural'' from a semantic perspective (e.g., corresponding to closely related and syntactically complete concepts). Towards that, we study a machine-learning classification approach. The second challenge is to accommodate the cross-query effect of rules---a rule introduced in the context of one query can eliminate the desired results for other queries and the desired effects of other rules. We present a formalization of this challenge as a generic computational problem. As we show that this problem is highly intractable in terms of complexity theory, we present heuristic approaches and optimization thereof. In an experimental study within IBM intranet search, those heuristics achieve near-optimal quality and well scale to large data sets.


title: Lightweight Memory Checkpointing

abstract: Memory check pointing is a pivotal technique in systems reliability, with applications ranging from crash recovery to replay debugging. Unfortunately, many traditional memory check pointing use-cases require high-frequency checkpoints, something for which existing application-level solutions are not well-suited. The problem is that they incur either substantial run-time performance overhead, or poor memory usage guarantees. As a result, their application in practice is hampered. This paper presents Lightweight Memory Check pointing (LMC), a new user-level memory check pointing technique that combines low performance overhead with strong memory usage guarantees for high check pointing frequencies. To this end, LMC relies on compiler-based instrumentation to shadow the entire memory address space of the running program and incrementally checkpoint modified memory bytes in a LMC-maintained shadow state. Our evaluation on popular server applications demonstrates the viability of our approach in practice, confirming that LMC imposes low performance overhead with strictly bounded memory usage at runtime.


title: Directed test generation using symbolic grammars

abstract: We present CESI, an algorithm that combines exhaustive enumeration of test inputs from a structured domain with symbolic execution driven test generation. CESI is a hybrid of two predominant techniques: specification-based enumerative test generation (which exhaustively generates all possible inputs satisfying some constraint) and symbolic directed test generation (which explores program paths based on symbolic path constraint solving). We target programs whose valid inputs are determined by some context free grammar. We introduce symbolic grammars, where the original tokens are replaced with symbolic constants, that link enumerative grammar-based input generation with symbolic directed testing. Symbolic grammars abstract the concrete input syntax, thus reducing the set of input strings that must be enumerated exhaustively. For each enumerated input string, which may contain symbolic constants, symbolic execution based test generation instantiates the constants based on program execution paths. The "template" generated by enumerating valid strings reduces the burden on the symbolic execution to generate syntactically valid inputs and hence exercise interesting code paths. Together, symbolic grammars provide a link between exhaustive enumeration of valid inputs and execution-directed symbolic test generation. In preliminary experiments, CESI is better than if both enumerative and symbolic techniques are used alone.


title: Enhancing student learning experience with technology-mediated gamification: An empirical study

abstract: Abstract We evaluated the use of gamification to facilitate a student-centered learning environment within an undergraduate Year 2 Personal and Professional Development (PPD) course. In addition to face-to-face classroom practices, an information technology-based gamified system with a range of online learning activities was presented to students as support material. The implementation of the gamified course lasted two academic terms. The subsequent evaluation from a cohort of 136 students indicated that student performance was significantly higher among those who participated in the gamified system than in those who engaged with the nongamified, traditional delivery, while behavioral engagement in online learning activities was positively related to course performance, after controlling for gender, attendance, and Year 1 PPD performance. Two interesting phenomena appeared when we examined the influence of student background: female students participated significantly more in online learning activities than male students, and students with jobs engaged significantly more in online learning activities than students without jobs. The gamified course design advocated in this work may have significant implications for educators who wish to develop engaging technology-mediated learning environments that enhance students’ learning, or for a broader base of professionals who wish to engage a population of potential users, such as managers engaging employees or marketers engaging customers.


title: New patients with Temple syndrome caused by 14q32 deletion: Genotype-phenotype correlations and risk of thyroid cancer.

abstract: Temple syndrome (TS) is caused by abnormal expression of genes at the imprinted locus 14q32. A subset of TS patients carry 14q32 deletions of paternal origin. We aimed to define possible genotype-phenotype correlations and to highlight the prevalence of thyroid dysfunction, which is a previously unreported feature of TS. We described four new patients who carry deletions of paternal origin at 14q32 detected by array-CGH and reviewed nine patients reported in the medical literature. We compared clinical features with respect to deletion size and position. Expression of DLK1 is altered in all the patients with TS, but intellectual disability (ID) is present only in patients with larger deletions extending proximally to the imprinted locus. This study led to the identification of an ID "critical region" containing four annotated genes including YY1 as the strongest candidate. Furthermore, we described three patients with thyroid dysfunction, which progressed to papillary carcinoma at a very young age in two of them. We conclude that DLK1 loss of function is likely to be responsible for the core features of TS, while haploinsufficiency of a gene outside the imprinted region causes ID. Thyroid cancer may be an unrecognized feature and monitoring for thyroid dysfunction should thus be considered in TS patients.


title: On-Line and Off-Line Handwriting Recognition : A Comprehensive Survey

abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.


ABF++: fast and robust angle based flattening


title: Taking Control of SDN-based Cloud Systems via the Data Plane

abstract: Virtual switches are a crucial component of SDN-based cloud systems, enabling the interconnection of virtual machines in a flexible and "software-defined" manner. This paper raises the alarm on the security implications of virtual switches. In particular, we show that virtual switches not only increase the attack surface of the cloud, but virtual switch vulnerabilities can also lead to attacks of much higher impact compared to traditional switches. We present a systematic security analysis and identify four design decisions which introduce vulnerabilities. Our findings motivate us to revisit existing threat models for SDN-based cloud setups, and introduce a new attacker model for SDN-based cloud systems using virtual switches. We demonstrate the practical relevance of our analysis using a case study with Open vSwitch and OpenStack. Employing a fuzzing methodology, we find several exploitable vulnerabilities in Open vSwitch. Using just one vulnerability we were able to create a worm that can compromise hundreds of servers in a matter of minutes. Our findings are applicable beyond virtual switches: NFV and high-performance fast path implementations face similar issues. This paper also studies various mitigation techniques and discusses how to redesign virtual switches for their integration.


title: Deep Visual-Semantic Hashing for Cross-Modal Retrieval

abstract: Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO.


title: Low-profile fully integrated 60 GHz 18 element phased array on multilayer liquid crystal polymer flip chip package

abstract: We present an ultra-thin, low cost, fully integrated 60GHz phased array antenna module supporting 18 TX/RX antenna elements for 60 GHz WiGig applications. The RFIC is assembled on a 15 mm × 12 mm liquid crystal polymer (LCP) package containing the antenna elements. All millimeter-wave signal routing was done on the same layer as the die pads to minimize the number of package substrate layers (4 total metal layers) and achieve very small total package thickness of 0.41 mm. The measurement results for the package showed good agreement with the simulation. The measured peak directivity of the steerable array was 17.4 dBi and routing losses were 0.5 – 1dB.


title: Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry

abstract: Due to the strong competition that exists today, most retailers are in a continuous effort for increasing profits and reducing their cost. An accurate sales forecasting system is an efficient way to achieve the aforementioned goals and lead to improve the customers' satisfaction, reduce destruction of products, increase sales revenue and make production plan efficiently. In this study, the Gray extreme learning machine (GELM) integrates Gray relation analysis and extreme learning machine with Taguchi method to support purchasing decisions. GRA can sieve out the more influential factors from raw data and transforms them as the input data in a novel neural network such as ELM. The proposed system evaluated the real sales data in the retail industry. The experimental results demonstrate that our proposed system outperform several sales forecasting methods which are based on back-propagation neural networks such as BPN and MFLN models.


title: Collaborative filtering with weighted opinion aspects

abstract: Collaborative filtering (CF) aims to produce recommendations based on other users' ratings to an item. Most existing CF methods rely on the overall ratings an item has received. However, these ratings alone sometimes cannot provide sufficient information to understand users' behaviors. For example, a user giving a high rating may indicate that he loves the item as a whole; however, it is still likely that he dislikes some particular aspects at the same time. In addition, users tend to place different emphases on different aspects when reaching an overall rating. This emphasis on aspects may even vary from users to items, and has a significant impact on a user's final decision. To make a better understanding of a user' behavior and generate a more accurate recommendation, we propose a framework that incorporates both user opinions and preferences on different aspects. This framework is composed of three components, namely, an opinion mining component, an aspect weighting computing component, and a rating inference component. The first component exploits opinion mining techniques to extract and summarize opinions on multiple aspects from reviews, and generates ratings on various aspects. The second component applies a tensor factorization strategy to automatically infer weights of different aspects in reaching an overall rating. The last one infers the overall rating of an item based on both aspect ratings and weights. Experiments on two real datasets prove that our model performs better compared with the baseline methods.


title: Learning Common Sense through Visual Abstraction

abstract: Common sense is essential for building intelligent machines. While some commonsense knowledge is explicitly stated in human-generated text and can be learnt by mining the web, much of it is unwritten. It is often unnecessary and even unnatural to write about commonsense facts. While unwritten, this commonsense knowledge is not unseen! The visual world around us is full of structure modeled by commonsense knowledge. Can machines learn common sense simply by observing our visual world? Unfortunately, this requires automatic and accurate detection of objects, their attributes, poses, and interactions between objects, which remain challenging problems. Our key insight is that while visual common sense is depicted in visual content, it is the semantic features that are relevant and not low-level pixel information. In other words, photorealism is not necessary to learn common sense. We explore the use of human-generated abstract scenes made from clipart for learning common sense. In particular, we reason about the plausibility of an interaction or relation between a pair of nouns by measuring the similarity of the relation and nouns with other relations and nouns we have seen in abstract scenes. We show that the commonsense knowledge we learn is complementary to what can be learnt from sources of text.


title: Quantum Plasmonic Circuits

abstract: Interactions between light and matter can be dramatically modified by concentrating light into a small volume for a long period of time. Gaining control over such interaction is critical for realizing many schemes for classical and quantum information processing, including optical and quantum computing, quantum cryptography, and metrology and sensing. Plasmonic structures are capable of confining light to nanometer scales far below the diffraction limit, thereby providing a promising route for strong coupling between light and matter, as well as miniaturization of photonic circuits. At the same time, however, the performance of plasmonic circuits is limited by losses and poor collection efficiency, presenting unique challenges that need to be overcome for quantum plasmonic circuits to become a reality. In this paper, we survey recent progress in controlling emission from quantum emitters using plasmonic structures, as well as efforts to engineer surface plasmon propagation and design plasmonic circuits using these elements.


title: A Logic-Based System for e-Tourism

abstract: In this paper we present a successful application of logic programming for e-tourism: the iTravel system. The system exploits two technologies that are based on the state-of-the-art computational logic system DLV: (i) a system for ontology representation and reasoning, called OntoDLV; and, (ii) H$\dotlessi$LeX a semantic information-extraction tool. The core of iTravel is an ontology which models the domain of tourism offers. The ontology is automatically populated by extracting the information contained in the tourism leaflets produced by tour operators. A set of specifically devised logic programs is used to reason on the information contained in the ontology for selecting the holiday packages that best fit the customer needs. An intuitive web-based user interface eases the task of interacting with the system for both the customers and the operators of a travel agency.


River and lake ice conditions as determined from AIRSAR imagery


title: Diffusion based projection method for distributed source localization in wireless sensor networks

abstract: Source localization based on energy measurements is an important problem in wireless sensor networks (WSNs). It is well known that the associated objective function is not convex and may have multiple local optima and saddle points. Most of the existing algorithms can not achieve global optima. In this paper, we formulate the source localization as a convex feasibility problem (CFP) and propose a diffusion based projection method as a solution. In the proposed method, no fusion center is required; the sensor nodes need to communicate only with their closest neighbors and all the sensors update their estimations simultaneously and finally they are able to achieve consensus on a possible minimizer asymptotically. The proposed method has low complexity and achieves global optimality. Theoretical analysis and simulation results show that the proposed method has good estimation performance whether or not the CFP is consistent or inconsistent.


title: Am I wasting my time organizing email?: a study of email refinding

abstract: We all spend time every day looking for information in our email, yet we know little about this refinding process. Some users expend considerable preparatory effort creating complex folder structures to promote effective refinding. However modern email clients provide alternative opportunistic methods for access, such as search and threading, that promise to reduce the need to manually prepare. To compare these different refinding strategies, we instrumented a modern email client that supports search, folders, tagging and threading. We carried out a field study of 345 long-term users who conducted over 85,000 refinding actions. Our data support opportunistic access. People who create complex folders indeed rely on these for retrieval, but these preparatory behaviors are inefficient and do not improve retrieval success. In contrast, both search and threading promote more effective finding. We present design implications: current search-based clients ignore scrolling, the most prevalent refinding behavior, and threading approaches need to be extended.


title: Classifying Very-High-Dimensional Data with Random Forests of Oblique Decision Trees

abstract: The random forests method is one of the most successful ensemble methods. However, random forests do not have high performance when dealing with very-high-dimensional data in presence of dependencies. In this case one can expect that there exist many combinations between the variables and unfortunately the usual random forests method does not effectively exploit this situation. We here investigate a new approach for supervised classification with a huge number of numerical attributes. We propose a random oblique decision trees method. It consists of randomly choosing a subset of predictive attributes and it uses SVM as a split function of these attributes.We compare, on 25 datasets, the effectiveness with classical measures (e.g. precision, recall, F1-measure and accuracy) of random forests of random oblique decision trees with SVMs and random forests of C4.5. Our proposal has significant better performance on very-high-dimensional datasets with slightly better results on lower dimensional datasets.


ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity


title: Heart rate variability biofeedback: how and why does it work?

abstract: In recent years there has been substantial support for Heart Rate Variability Biofeedback (HRVB) as a treatment for a variety of disorders and for performance enhancement (Gevirtz, 2013). Since conditions as widely varied as asthma and depression seem to respond to this form of cardiorespiratory feedback training, the issue of possible mechanisms becomes more salient. The most supported possible mechanism is the strengthening of homeostasis in the barorecptor (Vashillo, et al, 2002; Lehrer, et al, 2003). Recently, the effect on the vagal afferent pathway to the frontal cortical areas has been proposed. In this article, we review these and other possible mechanisms that might explain the positive effects of HRVB.


title: Helicopter radar return analysis: Estimation and blade number selection

abstract: The problem of helicopter parameter estimation and blade number selection is addressed. The parameters are estimated based on Maximum Likelihood methods and the corresponding Cramer-Rao bounds are derived. To avoid ambiguities in blade number estimation, we apply information theoretic criteria for blade number selection incorporating alternate penalty functions. We assume that a clutter filter essentially removes all of the clutter and also part of the signal returns, lowering the effective signal-to-noise-ratio. The fractional Fourier transform is used to separate the combined tail and main rotor signals into two different returns, allowing initial estimates of their corresponding rotor parameters. The proposed technique is validated by using returns from a helicopter observed experimentally with a pulse-Doppler radar.


title: Simulation of crack growth during fracture of heterogeneous materials

abstract: A cellular automaton model for describing the fracture of mechanically loaded heterogeneous materials has been constructed. Two extreme scenarios of the fracture process have been revealed, i.e., the dispersion (percolation) scenario, according to which defects accumulate uniformly throughout the volume of the material, and the correlated scenario (growth of predominantly a single source), which have been observed during the fracture of real materials. It has been shown that, in the case of the correlated fracture, a crack grows through the mechanism of ejection of double kinks of its front. In the intermediate case, the process occurs according to both scenarios: first, the slow accumulating (percolation) fracture and, then, the rapid correlated fracture; by the time the latter process begins, a self-organized critical state with a power-law size distribution of cracks typical of it has been formed.


Building Dynamic Knowledge Graphs


title: Compact Magnetic Antennas for Directional Excitation of Surface Plasmons

abstract: Plasmonics is considered as one of the most promising candidates for implementing the next generation of ultrafast and ultracompact photonic circuits. Considerable effort has been made to scale down individual plasmonic components into the nanometer regime. However, a compact plasmonic source that can efficiently generate surface plasmon polaritons (SPPs) and deliver SPPs to the region of interest is yet to be realized. Here, bridging the optical antenna theory and the recently developed concept of metamaterials, we demonstrate a subwavelength, highly efficient plasmonic source for directional generation of SPPs. The designed device consists of two nanomagnetic resonators with detuned resonant frequencies. At the operating wavelength, incident photons can be efficiently channeled into SPP waves modulated by the electric field polarization. By tailoring the relative phase at resonance and the separation between the two nanoresonators, SPPs can be steered to predominantly propagate along one specific direct...


title: Boosting up Scene Text Detectors with Guided CNN

abstract: Deep CNNs have achieved great success in text detection. Most of existing methods attempt to improve accuracy with sophisticated network design, while paying less attention on speed. In this paper, we propose a general framework for text detection called Guided CNN to achieve the two goals simultaneously. The proposed model consists of one guidance subnetwork, where a guidance mask is learned from the input image itself, and one primary text detector, where every convolution and non-linear operation are conducted only in the guidance mask. On the one hand, the guidance subnetwork filters out non-text regions coarsely, greatly reduces the computation complexity. On the other hand, the primary text detector focuses on distinguishing between text and hard non-text regions and regressing text bounding boxes, achieves a better detection accuracy. A training strategy, called background-aware block-wise random synthesis, is proposed to further boost up the performance. We demonstrate that the proposed Guided CNN is not only effective but also efficient with two state-of-the-art methods, CTPN and EAST, as backbones. On the challenging benchmark ICDAR 2013, it speeds up CTPN by 2.9 times on average, while improving the F-measure by 1.5%. On ICDAR 2015, it speeds up EAST by 2.0 times while improving the F-measure by 1.0%.


title: Perceiving an Object and Its Context in Different Cultures A Cultural Look at New Look

abstract: In two studies, a newly devised test (framed-line test) was used to examine the hypothesis that individuals engaging in Asian cultures are more capable of incorporating contextual information and those engaging in North American cultures are more capable of ignoring contextual information. On each trial, participants were presented with a square frame, within which was printed a vertical line. Participants were then shown another square frame of the same or different size and asked to draw a line that was identical to the first line in either absolute length (absolute task) or proportion to the height of the surrounding frame (relative task). The results supported the hypothesis: Whereas Japanese were more accurate in the relative task, Americans were more accurate in the absolute task. Moreover, when engaging in another culture, individuals tended to show the cognitive characteristic common in the host culture.


Mnemonics in education: Current research and applications.


A SCALABLE COMPACT WIDEBAND DUAL-POLARIZED PRINTED DIPOLE ANTENNA FOR BASE STATION APPLICATIONS


Human Computer Interaction Notes


title: Smart tag tracking for livestock farming

abstract: The problem of the outdoor farming of livestock are losing of the livestock or be stolen. Owing to such problem, we hereby study, research and develop a Smart Tag for livestock farming or STLiF. In this research, there are three processes: system design, implementation of STLiF, and testing. In the first step, we design the Smart Tag by assembling IoT (Internet of Thing) devices (GPS, 3G, and Arduino) within the livestock Tag. The GPS Module receive a value of a position from the nearest satellites. The 3G Module & SIM are used for sending data to Database Server by 3G signal. The Arduino Microcontroller board are used as a controller. Then an applications on smartphone gets the data from Database Server, analyzes and indicates results. Our application can track the latest position of Livestock, show its position with Smart Tag's position, history of its movement in specific period, and then warn us when it gets out of the allowed area. The values mentioned in this research comprise latitude, longitude, date time, and the ID of Smart Tag. Finally we evaluate the Smart Tag by to testing with goat at a farm in order to make sure that Smart Tag work properly.


title: Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data

abstract: Real-world sensors suffer from noise, blur, and other imperfections that make high-level computer vision tasks like scene segmentation, tracking, and scene understanding difficult. Making high-level computer vision networks robust is imperative for real-world applications like autonomous driving, robotics, and surveillance. We propose a novel end-to-end differentiable architecture for joint denoising, deblurring, and classification that makes classification robust to realistic noise and blur. The proposed architecture dramatically improves the accuracy of a classification network in low light and other challenging conditions, outperforming alternative approaches such as retraining the network on noisy and blurry images and preprocessing raw sensor inputs with conventional denoising and deblurring algorithms. The architecture learns denoising and deblurring pipelines optimized for classification whose outputs differ markedly from those of state-of-the-art denoising and deblurring methods, preserving fine detail at the cost of more noise and artifacts. Our results suggest that the best low-level image processing for computer vision is different from existing algorithms designed to produce visually pleasing images. The principles used to design the proposed architecture easily extend to other high-level computer vision tasks and image formation models, providing a general framework for integrating low-level and high-level image processing.


title: Semantic Role Labeling of Emotions in Tweets

abstract: Past work on emotion processing has focused solely on detecting emotions, and ignored questions such as ‘who is feeling the emotion (the experiencer)?’ and ‘towards whom is the emotion directed (the stimulus)?’. We automatically compile a large dataset of tweets pertaining to the 2012 US presidential elections, and annotate it not only for emotion but also for the experiencer and the stimulus. We then develop a classifier for detecting emotion that obtains an accuracy of 56.84 on an eight-way classification task. Finally, we show how the stimulus identification task can also be framed as a classification task, obtaining an F-score of 58.30.


title: High performance gradient driver for magnetic resonance imaging system

abstract: In high performance magnetic resonance imaging (MRI) systems, the gradient driver is required to supply the gradient coil with large amplitude (>600A) and high slew rate (>2A/µs) current waveform. In addition, extremely high fidelity for reproducing the current command from the central system is very critical for imaging quality. This paper presents an improved gradient driver structure and a new modulation scheme, with which the switching frequency will vary according to the slew rate of the current command. The corresponding output filter design is also introduced. With the proposed technique the switching loss can be reduced and evenly distributed in all the bridges, therefore the utilization ratio of the semiconductor devices is improved and the number of devices in parallel can be reduced. A 2 MVA platform has been built and fully tested. The experimental results proved the validity of the proposed structure and modulation technique.


title: Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

abstract: Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset.Capsule network consists of capsules which are a group of neurons representing the instantiating parameters of an object like the pose and orientation by using the dynamic routing and route by agreement algorithms.unlike the previous approaches of manual feature extraction,multiple deep neural networks with many parameters,our method eliminates the manual effort and provides resistance to the spatial variances.CNNs can be fooled easily using various adversary attacks and capsule networks can overcome such attacks from the intruders and can offer more reliability in traffic sign detection for autonomous vehicles.Capsule network have achieved the state-of-the-art accuracy of 97.6% on German Traffic Sign Recognition Benchmark dataset (GTSRB).


title: Pors: proofs of retrievability for large files

abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety. A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes. In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work. We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.


title: 3DOM: A 3 Degree of Freedom Manipulandum to Investigate Redundant Motor Control

abstract: This paper presents a novel robotic interface to investigate the neuromechanical control of redundant planar arm movements. A unique aspect of this device is the third axis by which the wrist, and hence the pose of the arm can be fully constrained. The topology is based on a 5R, closed loop pantograph, with a decoupled wrist flexion/extension cable actuated mechanism. The design and characterization (in terms of range of motion, impedance, friction and dynamics) are described in this paper. This device is lightweight, safe and has high force capabilities and low impedance. Simple experiments illustrate the advantages of this device for the investigation of redundant motor control in humans.


title: Forensic Investigation of Social Media and Instant Messaging Services in Firefox OS: Facebook, Twitter, Google+, Telegram, OpenWapp and Line as Case Studies

abstract: Mobile devices are increasingly utilized to access social media and instant messaging services, which allow users to communicate with others easily and quickly. However, the misuse of social media and instant messaging services facilitated conducting different cybercrimes such as cyber stalking, cyber bullying, slander spreading and sexual harassment. Therefore, mobile devices are an important evidentiary piece in digital investigation. In this chapter, we report the results of our investigation and analysis of social media and instant messaging services in Firefox OS. We examined three social media services (Facebook, Twitter and Google+) as well as three instant messaging services (Telegram, OpenWapp and Line). Our analysis may pave the way for future forensic investigators to trace and examine residual remnants of forensics value in FireFox OS.


title: Machine Learning Techniques in ADAS: A Review

abstract: What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.


title: Music Making as a Tool for Promoting Brain Plasticity across the Life Span

abstract: Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician’s lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce crossmodal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging.


title: MEMS actuators and sensors: observations on their performance and selection for purpose

abstract: This paper presents an exercise in comparing the performance of microelectromechanical systems (MEMS) actuators and sensors as a function of operating principle. Data have been obtained from the literature for the mechanical performance characteristics of actuators, force sensors and displacement sensors. On-chip and off-chip actuators and sensors are each sub-grouped into families, classes and members according to their principle of operation. The performance of MEMS sharing common operating principles is compared with each other and with equivalent macroscopic devices. The data are used to construct performance maps showing the capability of existing actuators and sensors in terms of maximum force and displacement capability, resolution and frequency. These can also be used as a preliminary design tool, as shown in a case study on the design of an on-chip tensile test machine for materials in thin-film form.


title: Adoption of CRM technology in multichannel environment: a review (2006‐2010)

abstract: Purpose – The purpose of this paper is to review state‐of‐art literature on adoption of CRM technology, including the CRM in multichannel environment and provide a comprehensive view of insights gained in this area of research through the bibliography.Design/methodology/approach – All together, nine databases were searched on the basis of four main descriptors which provided identification of 812 articles. A close review of the abstracts of these articles led to selection of 123 articles which were found relevant. Each of these articles was further reviewed and classified based on the main channel of CRM implementation. The articles were further classified in sub‐categories under each main channel theme.Findings – The selected articles were categorized under four main themes based on the channel – CRM, multichannel CRM, eCRM, mCRM. The articles in the CRM category were mainly focused on the strategic alignment along with research on increasing customer loyalty and use of data mining in CRM. Under the mult...


title: Spherical super wideband (SWB) monopole antenna with micromachined tapered feeding line

abstract: A spherical super wideband (SWB) monopole antenna is designed, fabricated and characterized. The low frequency cutoff is mainly determined by the diameter of the conductive sphere monopole antenna at its quarter wavelength, where the sphere serves as a main radiator. The upper cutoff is greatly enlarged by using the tapered feeding line between the coaxial and the sphere, which is fabricated using thick photopatternable polyurethane (D50, MacDerimid Inc.) and 3D multidirectional UV lithography. The implemented SWB antenna has a 10dB-bandwidth between 2.4 and 23.2 GHz (a ratio bandwidth of 9.7:1), and an omni-directional radiation pattern with a maximum gain of approximately 2.9 dBi at 10 GHz.


title: Human Sensing Using Visible Light Communication

abstract: We present LiSense, the first-of-its-kind system that enables both data communication and fine-grained, real-time human skeleton reconstruction using Visible Light Communication (VLC). LiSense uses shadows created by the human body from blocked light and reconstructs 3D human skeleton postures in real time. We overcome two key challenges to realize shadow-based human sensing. First, multiple lights on the ceiling lead to diminished and complex shadow patterns on the floor. We design light beacons enabled by VLC to separate light rays from different light sources and recover the shadow pattern cast by each individual light. Second, we design an efficient inference algorithm to reconstruct user postures using 2D shadow information with a limited resolution collected by photodiodes embedded in the floor. We build a 3 m x 3 m LiSense testbed using off-the-shelf LEDs and photodiodes. Experiments show that LiSense reconstructs the 3D user skeleton at 60 Hz in real time with 10 degrees mean angular error for five body joints.


title: Localization in underwater sensor networks: survey and challenges

abstract: In underwater sensor networks (UWSNs), determining the location of every sensor is important and the process of estimating the location of each node in a sensor network is known as localization. While various localization algorithms have been proposed for terrestrial sensor networks, there are relatively few localization schemes for UWSNs. The characteristics of underwater sensor networks are fundamentally different from that of terrestrial networks. Underwater acoustic channels are characterized by harsh physical layer environments with stringent bandwidth limitations. The variable speed of sound and the long propagation delays under water pose a unique set of challenges for localization in UWSN. This paper explores the different localization algorithms that are relevant to underwater sensor networks, and the challenges in meeting the requirements posed by emerging applications for such networks, e.g. offshore engineering.


title: Capacity and Examples of Template-Protecting Biometric Authentication Systems

abstract: In this paper, we formulate precisely the requirements for privacy protecting biometric authentication systems. The secrecy capacity Cs is investigated for the discrete and the continuous case. We present, furthermore, a general algorithm that meets the requirements and achieves Cs as well as Cid (the identification capacity). Finally, we present some practical constructions of the general algorithm and analyze their properties.


title: Location Assisted Coding (LAC): Embracing Interference in Free Space Optical Communications

abstract: As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.


title: Mimetic Isomorphism and Technology Evaluation: Does Imitation Transcend Judgment?

abstract: Although contemporary technology adoption theories incorporate societal norms or peer references, it is unclear to what extent these factors influence choices. In this research, we apply institutional theory and the concept of mimetic isomorphism as peer influences to the technology evaluation process to determine the degree to which managers conform when selecting between competing information technologies. More specifically, we test if peer influence is sufficient to overcome a product evaluation where the choice is believed to be inferior. An experiment is conducted using the World Wide Web and a national sample of 348 senior information technology and business decision makers. Significant effects are found where inferior technologies are selected if respondents are informed that competitors have selected them. Further research is warranted to investigate the presence and extent of these effects but overall implications are that product evaluations may be more ornamental than substantive.


title: How sustainable agriculture can address the environmental and human health harms of industrial agriculture.

abstract: The industrial agriculture system consumes fossil fuel, water, and topsoil at unsustainable rates. It contributes to numerous forms of environmental degradation, including air and water pollution, soil depletion, diminishing biodiversity, and fish die-offs. Meat production contributes disproportionately to these problems, in part because feeding grain to livestock to produce meat—instead of feeding it directly to humans—involves a large energy loss, making animal agriculture more resource intensive than other forms of food production. The proliferation of factory-style animal agriculture creates environmental and public health concerns, including pollution from the high concentration of animal wastes and the extensive use of antibiotics, which may compromise their effectiveness in medical use. At the consumption end, animal fat is implicated in many of the chronic degenerative diseases that afflict industrial and newly industrializing societies, particularly cardiovascular disease and some cancers. In terms of human health, both affluent and poor countries could benefit from policies that more equitably distribute high-protein foods. The pesticides used heavily in industrial agriculture are associated with elevated cancer risks for workers and consumers and are coming under greater scrutiny for their links to endocrine disruption and reproductive dysfunction. In this article we outline the environmental and human health problems associated with current food production practices and discuss how these systems could be made more sustainable. Key words: diet, environment, health, industrial agriculture, sustainability, sustainable agriculture. Environ Health Perspect 110:445‐456 (2002). [Online 20 March 2002] http://ehpnet1.niehs.nih.gov/docs/2002/110p445-456horrigan/abstract.html


title: Citation Summarization Through Keyphrase Extraction

abstract: This paper presents an approach to summarize single scientific papers, by extracting its contributions from the set of citation sentences written in other papers. Our methodology is based on extracting significant keyphrases from the set of citation sentences and using these keyphrases to build the summary. Comparisons show how this methodology excels at the task of single paper summarization, and how it out-performs other multi-document summarization methods.


title: Activity Recognition in the Home Using Simple and Ubiquitous Sensors

abstract: In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used.


title: Non-invasive methods of penile lengthening: fact or fiction?

abstract: What's known on the subject? and What does the study add? ::: ::: Penile lengthening methods remain a controversial issue. Surgical procedures of “lengthening phalloplasty” are characterized by poorly defined indications and an unacceptably high rate of complications, as recently outlined by a literature review, while non-surgical techniques are largely popularized by the media but often lack scientific evidence. In the literature we found only ten articles/abstracts of studies pertaining to the topic of our review. ::: ::: With our review, we aimed to explore whether non-surgical methods of penile lengthening may have some scientific background. We focused specifically on penile extenders, which among conservative methods are those whose efficacy is supported by some scientific evidence. It seems that penile traction devices should be proposed as the first-line treatment option for patients seeking a penile lengthening procedure. ::: ::: Penile size is a matter of great interest among men who are affected by ‘short penis syndrome’ or just believe themselves to have a small penis, even though the dimensions of the organ fall within the normal range. Surgical procedures of ‘lengthening phalloplasty’ lack standardized indications and carry a high risk of complications. Several non-invasive methods of penile lengthening have been described, such as vacuum devices, penile traction devices and penoscrotal rings; even ‘physical exercises’ have been popularized through the media. Most of these techniques, however, are not supported by any scientific evidence. We briefly analyse the efficacy and scientific background of such non-surgical methods of penile lengthening. It seems that penile extenders represent the only evidence-based technique of penile elongation. Results achieved do not seem to be inferior to surgery, making these traction devices an ideal first-line treatment option for patients seeking a penile lengthening procedure.


title: Online shopping behavior model: A literature review and proposed model

abstract: In this study, we conducted extensive reviews of online shopping literatures and proposed a hierarchy model of online shopping behavior. We collected 47 studies and classified them by variables used. Some critical points were found that research framework, methodology, and lack of cross-cultural comparison, etc So we developed a cross-cultural model of online shopping including shopping value, attitudes to online retailer's attributes and online purchasing based on the integrated V-A-B model.


title: Testing for factorial invariance of the Modified Leadership Scale for Sports: using a Japanese version

abstract: The objective of this study was to provide empirical evidence to support psychometric properties of a modified four-dimensional model of the Leadership Scale for Sports (LSS). The study tested invariance of all parameters (i.e., factor loadings, error variances, and factor variances–covariances) in the four-dimensional measurement model between two groups of student-athletes. For testing multi-group invariance of the proposed scale, 335 middle school and 320 high school student-athletes in Japan participated in this study. The modified version of the LSS consists of 35 items representing training instruction, democratic behaviour, positive feedback, and social support. A chi-square difference test was employed for model comparisons. The results supported configural, metric, scalar and factor variance–covariance invariance in the modified LSS across the two student-athlete groups.


title: Beyond the Transfer-and-Merge Wordnet Construction: plWordNet and a Comparison with WordNet

abstract: Wordnets are lexico-semantic resources essential in many NLP tasks. Princeton WordNet is the most widely known, and the most influential, among them. Word- nets for languages other than English tend to adopt unquestioningly WordNet's struc- ture and its net of lexicalised concepts. We discuss a large wordnet constructed inde- pendently of WordNet, upon a model with a small yet significant difference. A map- ping onto WordNet is under way; the large portions already linked open up a unique perspective on the comparison of similar but not fully compatible lexical resources. We also try to characterise numerically a wordnet's aptitude for NLP applications.


title: Compact RFID Tag Antenna With Circular Polarization and Embedded Feed Network for Metallic Objects

abstract: A low-profile radio frequency identification (RFID) tag antenna is proposed with a compact size of 45 ×45 mm ::: 2 ::: and circular polarization (CP) for metallic surfaces. A star-shaped slot is etched onto the antenna for the CP and antenna size reduction. A terminal-grounded L-shaped feedline is embedded in the etched slot to efficiently excite the wideband CP mode, complexly match the tag-antenna impedance, and further reduce the antenna size. The embedded feed structure also helps achieve a relatively higher gain, resulting in a longer reading range. The proposed antenna can cover the impedance bandwidth of 60 MHz (from 880 to 940 MHz) as well as the 3-dB CP bandwidth of 12 MHz (from 905 to 917 MHz). Further results demonstrate that the proposed tag antenna provides a stable reading range when mounted on metal plates of various sizes.


title: Foreign language knowledge can influence native language performance in exclusively native contexts

abstract: In three experiments, we studied the influence of foreign language knowledge on native language performance in an exclusively native language context. Trilinguals with Dutch as their native and dominant language (L1), English as their second language (L2), and French as their third language (L3) performed a word association task (Experiment 1) or a lexical decision task (Experiments 2 and 3) in L1. The L1 stimulus words were cognates with their translations in English, cognates with their translations in French, or were noncognates. In Experiments 1 and 2 with trilinguals who were highly proficient in English and relatively low in proficiency in French, we observed shorter word association and lexical decision times to the L1 words that were cognates with English than to the noncognates. In these relatively low-proficiency French speakers, response times (RTs) for the L1 words that were cognates with French did not differ from those for the noncognates. In Experiment 3, we tested Dutch-English- French trilinguals with a higher level of fluency in French (i.e., equally fluent in English and in French). We now observed faster responses on the L1 words that were cognates with French than on the noncognates. Lexical decision times to the cognates with English were also shorter than those to the noncognates. The results indicate that words presented in the dominant language, to naive participants, activate information in the nontarget, and weaker, language in parallel, implying that the multilinguals’ processing system is profoundly nonselective with respect to language. A minimal level of nontarget language fluency seems to be required, however, before any weaker language effects become noticeable in L1 processing.


title: Effects of temperature and diet on black soldier fly, Hermetia illucens (L.) (Diptera: Stratiomyidae), development.

abstract: The black soldier fly, Hermetia illucens, is recognised for its use in a forensic context as a means for estimating the time of colonisation and potentially postmortem interval of decomposing remains. However, little data exist on this species outside of its use in waste management. This study offers a preliminary assessment of the development, and subsequent validation, of H. illucens. Larvae of H. illucens were reared at three temperatures (24.9°C, 27.6°C and 32.2°C) at 55% RH on beef loin muscle, pork loin muscle and a grain-based diet (control). Each of the temperatures and diets were found to significantly (P<0.05) affect all stages of immature growth except for pupation time. Overall, those reared on the pork diet required on average ≈23.1% and ≈139.7% more degree hours to complete larval development than those reared on the beef and grain-based diets, respectively. Larvae reared at 27.6°C and 32.2°C required on average ≈8.7% more degree hours to complete development and had a final larval weight ≈30% greater than larvae reared at 24.9°C. The validity of the laboratory larval length and weight data sets was assessed via estimating the age of field-reared larvae. Grain-diet data lacked accuracy when used to estimate larval age in comparison to estimates made with beef and pork-diet data, which were able to predict larval age for ≈55.6% and ≈88.9% of sampling points, respectively, when length and weight data were used in conjunction. Field-reared larval sizes exceeded the maximum observed under laboratory conditions in almost half of the samples, which reduced estimate accuracy. Future research should develop additional criteria for identifying development of each specific instar, which may aid in improving the accuracy and precision of larval age estimates for this species.


title: Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

abstract: This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of cross-modality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.


title: Functional connectivity of the PFC via partial correlation

abstract: Abstract Functional near-infrared spectroscopy (fNIRS) has been applied to study of brain oxygenation and metabolism. In this study, we aimed to investigate the partial correlation (PC) in fNIRS signals on functional connectivity in the prefrontal cortex (PFC) during a modified version of the color-word matching Stroop task. A continuous wave 16 channels near-infrared spectroscopy device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in HbO2 and Hb concentrations from 12 healthy volunteers. Partial correlation (PC) values were computed for each stimulus condition. The results of ANOVA test ( p


title: Pathogenetic mechanisms in nephrotoxic acute renal failure.

abstract: Abstract Despite marked variations in the chemical nature of nephrotoxic agents, most probably trigger lethal tubular injury via activation of a limited number of pathogenetic pathways. Elucidation of these pathways has been an area of intense investigation, with the hope that the knowledge obtained might lead to new interventions for preventing acute renal failure. The roles of altered cellular energetics, oxidant stress, cellular calcium overload, and deranged phospholipid homeostasis have undergone intense scrutiny. Recently, the participation of cytoskeletal rearrangements and apoptosis have been increasingly assessed. The purpose of this article is to provide a brief overview of this broad and evolving area of investigation.


title: Basis Adaptation for Sparse Nonlinear Reinforcement Learning

abstract: This paper presents a new approach to representation discovery in reinforcement learning (RL) using basis adaptation. We introduce a general framework for basis adaptation as nonlinear separable least-squares value function approximation based on finding Frechet gradients of an error function using variable projection functionals. We then present a scalable proximal gradient-based approach for basis adaptation using the recently proposed mirror-descent framework for RL. Unlike traditional temporal-difference (TD) methods for RL, mirror descent based RL methods undertake proximal gradient updates of weights in a dual space, which is linked together with the primal space using a Legendre transform involving the gradient of a strongly convex function. Mirror descent RL can be viewed as a proximal TD algorithm using Bregman divergence as the distance generating function. We present a new class of regularized proximal-gradient based TD methods, which combine feature selection through sparse L1 regularization and basis adaptation. Experimental results are provided to illustrate and validate the approach.


title: Self-help interventions for smoking cessation

abstract: Many smokers give up smoking on their own, but materials giving advice and information may help them and increase the number who quit successfully.


title: An Arduino-based device for visually impaired people to play videogames

abstract: Blind players have many difficulties to access video games since most of them rely on impressive graphics and immersive visual experiences. To overcome this limitation, we propose a device designed for visually impaired people to interact with virtual scenes of video games. The device has been designed considering usability, economic cost, and adaptability as main features. To ensure usability, we considered the white cane paradigm since this is the most used device by the blind community. Our device supports left to right movements and collision detection as well as actions to manipulate scene objects such as drag and drop. To enhance realism, it also integrates a library with sounds of different materials to reproduce object collision. To reduce the economic cost, we used Arduino as the basis of our development. Finally, to ensure adaptability, we created an application programming interface that supports the connection with different games engines and different scenarios. To test the acceptance of the device 12 blind participants were considered (6 males and 6 females). In addition, we created three mini-games in Unity3D that require navigation and walking as principal actions. After playing, participants filled a questionnaire related to usability and suitability to interact with games, among others. They scored well in all features without distinction among player gender and being blind from birth. The relationship between device responsiveness and user interaction has been considered satisfactory. Despite our small test sample, our main goal has been accomplished, the proposed device prototype seems to be useful to visually impaired people.


title: Lack of combinatorial productivity in language processing with simple recurrent networks

abstract: An astronomical set of sentences can be produced in natural language by combining relatively simple sentence structures with a human-size lexicon. These sentences are within the range of human language performance. Here, we investigate the ability of simple recurrent networks (SRNs) to handle such combinatorial productivity. We successfully trained SRNs to process sentences formed by combining sentence structures with different groups of words. Then, we tested the networks with test sentences in which words from different training sentences were combined. The networks failed to process these sentences, even though the sentence structures remained the same and all words appeared on the same syntactic positions as in the training sentences. In these combination cases, the networks produced work-word associations, similar to the condition in which words are presented in the context of a random word sequence. The results show that SRNs have serious difficulties in handling the combinatorial productivity that underlies human language performance. We discuss implications of this result for a potential neural architecture of human language processing.