title: Automatic Drum Transcription for Polyphonic Recordings Using Soft Attention Mechanisms and Convolutional Neural Networks

abstract: Automatic drum transcription is the process of generating symbolic notation for percussion instruments within audio recordings. To date, recurrent neural network (RNN) systems have achieved the highest evaluation accuracies for ::: both drum solo and polyphonic recordings, however the accuracies within a polyphonic context still remain relatively low. To improve accuracy for polyphonic recordings, we present two approaches to the ADT problem: First, to capture the dynamism of features in multiple time-step hidden ::: layers, we propose the use of soft attention mechanisms (SA) and an alternative RNN configuration containing additional peripheral connections (PC). Second, to capture these same trends at the input level, we propose the use of a convolutional neural network (CNN), which uses a larger set of time-step features. In addition, we propose the use of a bidirectional recurrent neural network (BRNN) in the peak-picking stage. The proposed systems are evaluated along with two state-of-the-art ADT systems in five ::: evaluation scenarios, including a newly-proposed evaluation methodology designed to assess the generalisability of ADT systems. The results indicate that all of the newly proposed systems achieve higher accuracies than the stateof- the-art RNN systems for polyphonic recordings and that ::: the additional BRNN peak-picking stage offers slight improvement in certain contexts.


title: Positive Effects of a Weighted Blanket on Insomnia

abstract: Insomnia is a common occurrence and can have a negative impact on physiological, psychological and social well-being. There is a need for simple, effective solutions to increase sleep quality. It has been suggested that weighted blankets and vests can provide a beneficial calming effect, especially in clinical disorders. Hence, we aimed to investigate the effects of a chain weighted blanket on insomnia, using objective and subjective measures. Objectively, we found that sleep bout time increased, as well as a decrease in movements of the participants, during weighted blanket use. Subjectively, the participants liked sleeping with the blanket, found it easier to settle down to sleep and had an improved sleep, where they felt more refreshed in the morning. Overall, we found that when the participants used the weighted blanket, they had a calmer night's sleep. A weighted blanket may aid in reducing insomnia through altered tactile inputs, thus may provide an innovative, non-pharmacological approach and complementary tool to improve sleep quality.


title: Big data in smart cities

abstract: In this paper, we discuss the concept of the smart city, summarize its development, analyze the motivation and goals of building smart cities in China, and illustrate the supporting technologies of the smart city. Next, we propose a smart city infrastructure that is based on the concept of the digital city and integrates the Internet of Things and cloud computing technologies, thereby achieving automatic control and intelligence services for people and logistics in physical cities. We analyze the big data of smart cities according to ubiquitous sensor networks. Facing the problems and challenges caused by big data of smart cities, we propose a strategy to handle big data that focuses on cloud computing and data mining. In addition, we present a basic framework for cloud computing platforms and propose the establishment of an operating center for smart cities. In closing, we discuss the bright prospects of smart cities.


title: Fast People Counting Using Head Detection from Skeleton Graph

abstract: In this paper, we present a new method for counting people.This method is based on the head detection after asegmentation of the human body by skeleton graph process.The skeleton silhouette is computed and decomposed into aset of segments corresponding to the head , torso and limbs.This structure captures the minimal information about theskeleton shape. No assumption is made about the viewpoint,this is done after the head pose process. Several resultspresent the efficiency of the labelling process , particularlyits structural properties for the detection of heads within acrowd. A proposed method has been tested with an experimentof counting the number of pedestrians passing in aspecific area.


title: Immersive VR: a non-pharmacological analgesic for chronic pain?

abstract: This paper describes the research work being carried out by the Transforming Pain Research Group -- the only group whose work is entirely focused on the use of immersive VR for chronic pain management. Unlike VR research for acute or short-term pain, which relies on pain "distraction," this research posits a new paradigm for the use of VR. In addition to providing an overview of our work, the present paper also describes one of our current works in detail: the Virtual Meditative Walk.


title: Orientation Robust Text Line Detection in Natural Images

abstract: In this paper, higher-order correlation clustering (HOCC) is used for text line detection in natural images. We treat text line detection as a graph partitioning problem, where each vertex is represented by a Maximally Stable Extremal Region (MSER). First, weak hypothesises are proposed by coarsely grouping MSERs based on their spatial alignment and appearance consistency. Then, higher-order correlation clustering (HOCC) is used to partition the MSERs into text line candidates, using the hypotheses as soft constraints to enforce long range interactions. We further propose a regularization method to solve the Semidefinite Programming problem in the inference. Finally we use a simple texton-based texture classifier to filter out the non-text areas. This framework allows us to naturally handle multiple orientations, languages and fonts. Experiments show that our approach achieves competitive performance compared to the state of the art.


title: An occlusion-aware feature for range images

abstract: This paper presents a novel local feature for 3D range image data called ‘the line image’. It is designed to be highly viewpoint invariant by exploiting the range image to efficiently detect 3D occupancy, producing a representation of the surface, occlusions and empty spaces. We also propose a strategy for defining keypoints with stable orientations which define regions of interest in the scan for feature computation. The feature is applied to the task of object classification on sparse urban data taken with a Velodyne laser scanner, producing good results.


title: Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference

abstract: The anchor words algorithm performs provably efficient topic model inference by finding an approximate convex hull in a high-dimensional word co-occurrence space. However, the existing greedy algorithm often selects poor anchor words, reducing topic quality and interpretability. Rather than finding an approximate convex hull in a high-dimensional space, we propose to find an exact convex hull in a visualizable 2- or 3-dimensional space. Such low-dimensional embeddings both improve topics and clearly show users why the algorithm selects certain words.


title: Do special economic areas matter in attracting FDI? Evidence from Poland, Hungary and Czech Republic

abstract: Among the instruments aimed to attract internationally mobile investors, “special economic areas” can be considered as well-defined zones where usually offering low rates of taxation, and infrastructures and services accessible on preferential basis. Are those instruments effective in attracting FDI? The first empirical evidence based on Poland, Hungary and Czech Republic suggests a positive answer. However, according to a survey on research and development intensity in special economic areas in our sample countries, it is not possible to confirm clearly that scientific and technology parks were capable in attracting high-tech FDIs, even though due to global competition, international agreements and EU membership, fiscal incentives are nowadays not enough while technology and an “innovation-friendly” environment are emerging as competitive advantages.


title: A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domain

abstract: Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of biomedical abstracts, a corpus of clinical notes used in the 2010 i2b2 shared task, and two general-domain corpora (the British National Corpus and Switchboard). We find that, with the exception of the cTAKES system, the toolkits we evaluate perform noticeably worse on clinical text than on general-domain text. We identify and discuss major classes of errors, and suggest directions for future work to improve SBD methods in the clinical domain. We also make the code used for SBD evaluation in this paper available for download at http://github.com/drgriffis/SBD-Evaluation.


title: Self-driving and driver relaxing vehicle

abstract: In the modern era, the vehicles are focused to be automated to give human driver relaxed driving. In the field of automobile various aspects have been considered which makes a vehicle automated. Google, the biggest network has started working on the self-driving cars since 2010 and still developing new changes to give a whole new level to the automated vehicles. In this paper we have focused on two applications of an automated car, one in which two vehicles have same destination and one knows the route, where other don't. The following vehicle will follow the target (i.e. Front) vehicle automatically. The other application is automated driving during the heavy traffic jam, hence relaxing driver from continuously pushing brake, accelerator or clutch. The idea described in this paper has been taken from the Google car, defining the one aspect here under consideration is making the destination dynamic. This can be done by a vehicle automatically following the destination of another vehicle. Since taking intelligent decisions in the traffic is also an issue for the automated vehicle so this aspect has been also under consideration in this paper.


title: Nondeterminism in Game Semantics via Sheaves

abstract: Harmer and McCusker have developed a fully abstract game model for nondeterministic Idealised Algol and, at the same time, revealed difficulties in constructing game models for stateless nondeterministic languages and infinite nondeterminism. We propose a novel approach in which a strategy is not a set, but a tree, of plays, and develop a fully abstract game model for a nondeterministic stateless language. Mathematically such a strategy is formalised as a sheaf over an appropriate site of plays. We conclude with a study on the difficulties pointed out by Harmer and McCusker in terms of the structure of the coverage of the sites.


title: The Undecidability of the Generalized Collatz Problem

abstract: The Collatz problem, widely known as the 3x + 1 problem, asks whether or not a certain simple iterative process halts on all inputs. In this paper, we build on work of J. H. Conway to show that a natural generalization of the Collatz problem is Π20 complete.


Domain Independent Sentence Generation from RDF Representations for the Semantic Web


title: The Surface Water and Ocean Topography Mission: Observing Terrestrial Surface Water and Oceanic Submesoscale Eddies

abstract: The elevation of the ocean surface has been measured for over two decades from spaceborne altimeters. However, existing altimeter measurements are not adequate to characterize the dynamic variations of most inland water bodies, nor of ocean eddies at scales of less than about 100 km, notwithstanding that such eddies play a key role in ocean circulation and climate change. For terrestrial hydrology, in situ and spaceborne measurements of water surface elevation form the basis for estimates of water storage change in lakes, reservoirs, and wetlands, and of river discharge. However, storage in most inland water bodies, e.g., millions of Arctic lakes, is not readily measured using existing technologies. A solution to the needs of both surface water hydrology and physical oceanography communities is the measurement of water elevations along rivers, lakes, streams, and wetlands and over the ocean surface using swath altimetry. The proposed surface water and ocean topography (SWOT) mission will make such measurements. The core technology for SWOT is the Ka-band radar interferometer (KaRIN), which would achieve spatial resolution on the order of tens of meters and centimetric vertical precision when averaged over targets of interest. Average revisit times will depend upon latitude, with two to four revisits at low to mid latitudes and up to ten revisits at high latitudes per ~20-day orbit repeat period.


title: Collective topic modeling for heterogeneous networks

abstract: In this paper, we propose a joint probabilistic topic model for simultaneously modeling the contents of multi-typed objects of a heterogeneous information network. The intuition behind our model is that different objects of the heterogeneous network share a common set of latent topics so as to adjust the multinomial distributions over topics for different objects collectively. Experimental results demonstrate the effectiveness of our approach for the tasks of topic modeling and object clustering.


title: Relationship between sedimentation sign and morphological grade in symptomatic lumbar spinal stenosis

abstract: PURPOSE ::: We aimed to study the relationship between two morphological parameters recently described on MRI images in relation to lumbar spinal stenosis (LSS): the first is the sedimentation sign (SedS) and the second is the morphological grading of lumbar stenosis. ::: ::: ::: MATERIALS AND METHODS ::: MRIs from a total of 137 patients were studied. From those, 110 were issued from a prospective database of symptomatic LSS patients, of whom 73 were treated surgically and 37 conservatively based on symptom severity. A third group consisting of 27 subjects complaining of low back pain (LBP) served as control. Severity of stenosis was judged at disc level using the four A to D grade morphological classification. The presence of a SedS was judged at pedicle level, above or below the site of maximal stenosis. ::: ::: ::: RESULTS ::: A positive SedS was observed in 58, 69 and 76% of patients demonstrating B, C and D morphology, respectively, but in none with grade A morphology. The SedS was positive in 67 and 35% of the surgically and conservatively treated patients, respectively, and in 8% of the LBP group. C and D morphological grades were present in 97 and 35% of patients in the surgically and conservatively treated group, respectively, and in 18% of the LBP group. Presence of a positive SedS carried an increased risk of being submitted to surgery in the symptomatic LSS group (OR 3.5). This risk was even higher in the LSS patients demonstrating grade C or D morphology (OR 65). ::: ::: ::: DISCUSSION AND CONCLUSION ::: One-third of surgically treated LSS patients do not present a SedS. This sign appears to be a lesser predictor of treatment modality in our setting of symptomatic LSS patients compared to the severity of stenosis judged by the morphological grade.


title: Systematic integration of biomedical knowledge prioritizes drugs for repurposing

abstract: The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.


title: A Quantitative Analysis of the Impact of Arbitrary Blockchain Content on Bitcoin

abstract: Blockchains primarily enable credible accounting of digital events, e.g., money transfers in cryptocurrencies. However, beyond this original purpose, blockchains also irrevocably record arbitrary data, ranging from short messages to pictures. This does not come without risk for users as each participant has to locally replicate the complete blockchain, particularly including potentially harmful content. We provide the first systematic analysis of the benefits and threats of arbitrary blockchain content. Our analysis shows that certain content, e.g., illegal pornography, can render the mere possession of a blockchain illegal. Based on these insights, we conduct a thorough quantitative and qualitative analysis of unintended content on Bitcoin’s blockchain. Although most data originates from benign extensions to Bitcoin’s protocol, our analysis reveals more than 1600 files on the blockchain, over 99% of which are texts or images. Among these files there is clearly objectionable content such as links to child pornography, which is distributed to all Bitcoin participants. With our analysis, we thus highlight the importance for future blockchain designs to address the possibility of unintended data insertion and protect blockchain users accordingly.


title: Software defined radio for stepped-frequency, ground-penetrating radar

abstract: Software defined radio (SDR) is a rapidly developing technology that implements signal processing components partially or completely in software. In this paper, SDR's potential as a platform for ground-penetrating radar (GPR) is explored. The stepped-frequency radar method is implemented using off-the-shelf SDR hardware and open-source software. SDR is typically designed for communications applications, so special consideration is necessary for remote sensing. Precisely timed commands achieve RF phase coherence as well as digital sample synchronization. A multi-tone digital signal takes advantage of instantaneous bandwidth, and a sequential tuning routine expands effective bandwidth to cover the 500–5000 MHz band. Initial design challenges are weighed against potential advantages of design flexibility and hardware versatility.


An Intriguing Influence of Visual Data in Learning a Representation


title: Faster SGD training by minibatch persistency

abstract: It is well known that, for most datasets, the use of large-size minibatches for Stochastic Gradient Descent (SGD) typically leads to slow convergence and poor generalization. On the other hand, large minibatches are of great practical interest as they allow for a better exploitation of modern GPUs. Previous literature on the subject concentrated on how to adjust the main SGD parameters (in particular, the learning rate) when using large minibatches. In this work we introduce an additional feature, that we call minibatch persistency, that consists in reusing the same minibatch for K consecutive SGD iterations. The computational conjecture here is that a large minibatch contains a significant sample of the training set, so one can afford to slightly overfitting it without worsening generalization too much. The approach is intended to speedup SGD convergence, and also has the advantage of reducing the overhead related to data loading on the internal GPU memory. We present computational results on CIFAR-10 with an AlexNet architecture, showing that even small persistency values (K=2 or 5) already lead to a significantly faster convergence and to a comparable (or even better) generalization than the standard"disposable minibatch"approach (K=1), in particular when large minibatches are used. The lesson learned is that minibatch persistency can be a simple yet effective way to deal with large minibatches.


title: An Approach for Factory-Wide Control Utilizing Virtual Metrology

abstract: In the semiconductor manufacturing industry, market demands and technology trends drive manufacturers towards increases in wafer size and decreases in device size. Application of factory-wide advanced process control (APC) techniques down to the wafer-to-wafer (W2W) level control capability is becoming the only choice to cope with the demanding situation. However, one of the main limitations in undertaking W2W control is the nonavailability of timely metrology data at the wafer level. Recently virtual metrology (VM) techniques have been proposed to provide timely wafer level metrology data; they have the potential to be used in realizing W2W control. In this paper, the VM approach to W2W control on factory level is described. VM for an individual process is realized by utilizing the preprocess metrology and the process data from the underlying tools that is generally collected in real time for fault detection purposes. The VM implementation for factory-wide run-to-run control brings unique opportunities and issues to the forefront such as dealing with expected lower quality of VM data, coordination between VM modules of cascading processes for better prediction quality, flexibility of the factory-wide controller to accommodate lower quality VM data, dynamic adjustments to the target values of individual processes by the factory-wide controller when using VM data, and dealing with metrology delays at the factory level. Near and long-term solutions are presented to address these issues in order to allow VM to be used today and become an integral part of the factory-wide APC solution for W2W control.


title: Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

abstract: MOTIVATION: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. RESULTS: The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching : >90% in some cases. SUMMARY: The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. AVAILABILITY AND IMPLEMENTATION: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 The Decombinator package is available at github.com/innate2adaptive/Decombinator The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html CONTACT: b.chain@ucl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.


title: Cyberbullying: Another main type of bullying?

abstract: Cyberbullying has recently emerged as a new form of bullying and harassment. 360 adolescents (12–20 years), were surveyed to examine the nature and extent of cyberbullying in Swedish schools. Four categories of cyberbullying (by text message, email, phone call and picture/video clip) were examined in relation to age and gender, perceived impact, telling others, and perception of adults becoming aware of such bullying. There was a significant incidence of cyberbullying in lower secondary schools, less in sixth-form colleges. Gender differences were few. The impact of cyberbullying was perceived as highly negative for picture/video clip bullying. Cybervictims most often chose to either tell their friends or no one at all about the cyberbullying, so adults may not be aware of cyberbullying, and (apart from picture/video clip bullying) this is how it was perceived by pupils. Findings are discussed in relation to similarities and differences between cyberbullying and the more traditional forms of bullying.


title: Prediction of Parkinson's disease using speech signal with Extreme Learning Machine

abstract: Speech impairments analysis has been used as an efficient tool for early detection of Parkinson's disease (PD). In this paper, we have proposed an efficient approach using Extreme Learning Machine to predict Parkinson's disease accurately utilising speech samples. The performance of the method has been assessed with a reliable dataset from UCI repository. The proposed method distinguishes Parkinson diseased subjects and healthy subjects with an accuracy of 90.76% and 0.81 MCC for the training dataset. When tested with an independent dataset comprising of Parkinson diseased patients, the proposed method gives 81.55% accuracy. The performance of our method is compared with existing techniques such as Neural Network and Support Vector Machine. The results obtained depict that the proffered method is reliable for identifying the Parkinson's disease.


title: A Spectral Algorithm for Learning Hidden Markov Models

abstract: Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computationally hard (under cryptographic assumptions), and practitioners typically resort to search heuristics which suffer from the usual local optima issues. We prove that under a natural separation condition (bounds on the smallest singular value of the HMM parameters), there is an efficient and provably correct algorithm for learning HMMs. The sample complexity of the algorithm does not explicitly depend on the number of distinct (discrete) observations---it implicitly depends on this quantity through spectral properties of the underlying HMM. This makes the algorithm particularly applicable to settings with a large number of observations, such as those in natural language processing where the space of observation is sometimes the words in a language. The algorithm is also simple, employing only a singular value decomposition and matrix multiplications.


title: An Inchworm-inspired Rigid-reinforced Soft Robot with Combined Functions of Locomotion and Manipulation

abstract: The imitation of the functions of soft animals has always been the goal of soft robotic design. However, most of the current soft terrestrial robots just imitate the basic gaits and are difficult to perform in three dimensional space. In this work, we present an inchworm-inspired soft robot that integrates the locomotion and manipulation functions together. The soft robot is able to move in the vertical plane from one pole to another or work as a soft manipulator. The two functions can switch between each other naturally. Besides, the soft robot is unfettered by gravity, which greatly expands the applications of the soft robot. The deformation and load capacities of the soft components were tested, and to enhance the stiffness of the whole soft robot, the rigid-frame reinforcement was used. The performance of the soft robot shows great potential of actual applications, such as spatial maintenance and agricultural picking. Most importantly, the combination of the locomotion and manipulation functions provides a new idea for designing more applicable soft robots.


Remote Attacks on Automated Vehicles Sensors : Experiments on Camera and LiDAR


title: An Error-Oriented Approach to Word Embedding Pre-Training

abstract: We propose a novel word embedding pre-training approach that exploits writing errors in learners' scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The comparison is achieved by using the aforementioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we investigate augmenting our model with error corrections and monitor the impact on performance. Our results show that our error-oriented approach outperforms other comparable ones which is further demonstrated when training on more data. Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.


title: Specific Comic Character Detection Using Local Feature Matching

abstract: Comic books are a kind of storytelling graphic publications mainly expressed by abstract line drawings. As a clue of story lines, comic characters play an important role in the story, and their detection is an essential part of comic book analysis. For this purpose, the task includes (1) locating characters in comics pages and (2) identifying them, which is called specific character detection. Corresponding to different scenes of comic books, one specific character can be represented by various expressions coupled with rotations, occlusions, and other perspective drawing effects, which challenge the detection. In this paper, we focus on stable features regarding the possible transformations and proposed a framework to detect them. Specifically, some discriminative features are selected as detectors for characterizing characters, on the basis of a training dataset. Based on the detectors, the drawings of the same characters in different scenes can be detected. The methodology has been experimented and validated on 6 titles of comics. Despite the terrific changes for different scenes, the proposed method achieved detection of 70% comic characters.


title: Discrete Latent Factor Model for Cross-Modal Hashing

abstract: Due to its storage and retrieval efficiency, cross-modal hashing~(CMH) has been widely used for cross-modal similarity search in multimedia applications. According to the training strategy, existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than relaxation-based continuous methods, but the training of discrete methods is time-consuming. In this paper, we propose a novel CMH method, called discrete latent factor model based cross-modal hashing~(DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments on real datasets show that DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of relaxation-based continuous methods which are much faster than existing discrete methods.


Adult literacy education in the United States


title: The Beauty and Joy of Computing: Computer Science for Everyone

abstract: “The Beauty and Joy of Computing” is a computer science course for undergraduate non-majors that combines a deep programming experience with lectures, readings, and discussions about nonprogramming topics such as the social context of computing and the future and limitations of computing. The course is designed to appeal to a wide range of students, including women and underrepresented minorities. The programming half of the course uses BYOB, an extension to Scratch adding first class procedures, lists, and objects. The course has been chosen as one of the pilots for a coming (2016) high school Advanced Placement exam. Our current work includes further curriculum development, an NSF-funded teacher preparation program, and the implementation of SNAP!, a new browser-based version of BYOB.


title: Can maternity services open up to the indigenous traditions of midwifery

abstract: Mainstreaming the ayurveda, yoga, unani, siddha, homeopathy systems should imply deep changes in maternity care. Big changes there are, but in the wrong direction. Aiming to reduce maternal and neonatal mortality, the National Rural Health Mission's Janani Suraksha Yojana scheme presses expectant mothers to go for "institutional delivery" and avoid home births. The nrhm document also speaks of revitalising local health traditions. This means that the dais who have been part of the local health traditions should be incorporated into the government structure at the lowest end as has always been the case. This paper raises various questions related to maternity care governance for the benefit of local communities and looks at the possibilities of strengthening the services with serious inputs from dais. It acknowledges the dimensions of class, caste, gender, power and ideology that would be implicated in the acceptance of the "dai tradition" within the healthcare services system.


title: PowerMate – A Safe and Intuitive Robot Assistant for Handling and Assembly Tasks

abstract: In this contribution a robot assistant for handling and assembly tasks is presented. The main emphasis of PowerMate was to create a robot assistant that works together with a human worker that is conform to safety category 3 according to DIN ISO 954 and to use components suitable for industrial use, which leads to certain constrains on the possible control mechanisms. An overview of existing concepts of man-machine-cooperation is given. Different architectures for these kinds of robot assistants are discussed. A user-customizable motion control for PowerMate is presented. A description of the force-movement-calculations, the hardware and software architecture and the safety system of the robot assistant is given.


title: Exosomes derived from embryonic stem cells inhibit doxorubicin and inflammation-induced pyroptosis in muscle cells.

abstract: Doxorubicin (Dox) is an effective anticancer drug. Unfortunately, it causes cardiac and muscle toxicity due to increased oxidative stress and inflammation; however, it remains unknown whether Dox induces "pyroptosis" - an inflammation-mediated cell death. We investigated whether Dox induces pyroptosis in mouse soleus muscle (Sol 8) cells in vitro and to show the protective effect of embryonic stem cell exosomes (ES-exos) on pyroptosis. Dox and inflammation-induced in vitro model was generated. Pyroptosis was confirmed using immunohistochemistry (with putative markers caspase-1, IL-1β, and pro-inflammatory cytokine IL-18) and Western blotting of caspase-1 and IL-1β. The results show significant increase in the expression of caspase-1, IL-1β, and IL-18 following treatment with Dox, which was inhibited by ES-exos but not mouse embryonic fibroblast exosomes. Moreover, GW4869 compound inhibited functional activity of ES-exos, suggesting these vesicles are key players in the inhibition of pyroptosis. These results suggest that Dox induces inflammatory pyroptosis in Sol 8 cells, which is attenuated by ES-exos in vitro.


title: Demystifying automotive safety and security for semiconductor developer

abstract: Advances in both semiconductor and automotive industry are today enabling the next generation of vehicles with significant electronics content than ever before. Consumers can now avail vehicle offerings in the form of Electric and Hybrid Electric Vehicles (EV/HEV) that have improved fuel efficiency, provide enhanced driver-passenger comfort and experience through Advance Driver Assistance Systems (ADAS) and car infotainment systems, and more. Increasing electronics, software content, and connectivity drive two consumer concerns — “functional safety” and “security” — to the forefront. In this tutorial, we dissect these concerns from an end application perspective and translate the system level requirements and standards into semiconductor development requirements. We indicate both current and emerging practices, and touch upon areas requiring new or optimal design and electronic design automation (EDA) solutions. While functional safety is the primary focus for deep-dive in this tutorial, we also examine key facets of automotive security which is now emerging as a critical area for further understanding and standardization.


title: Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

abstract: Collective entity disambiguation, or collective entity linking aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extent to which these entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, this paper shows that the semantic relationships between mentioned entities within a document are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity, and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the problem of entity disambiguation. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design Pair-Linking, a novel iterative solution for the MINTREE optimization problem. The idea of Pair-Linking is simple: instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments on eight benchmark datasets, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms.


title: 3D Convolutional Neural Networks Fusion Model for Lung Nodule Detection onClinical CT Scans

abstract: Automatically accurate pulmonary nodule detection plays an important role in lung cancer diagnosis and early treatment. We propose a three-dimensional (3D) Convolutional Neural Networks (ConvNets) fusion model for lung nodule detection on clinical CT scans. Two 3D ConvNets models are trained separately without any pre-training weights: One trained on the LUng Nodule Analysis 2016 dataset (LUNA) and additional augmented data to learn the nodules’ representative features in volumetric space, which may cause overfitting problems meanwhile, so we train another network on original data and fuse the results of the two best-performing models to reduce this risk. Both use reshaped objective function to solve the class imbalance problem and differentiate hard samples from easy samples. More importantly, 335 patients’ CT scans from the hospital are further used to evaluate and help optimize the performance of our approach in the real situation, and we develop a system based on this method. Experimental results show a sensitivity of 95.1% at 8 false positives per scan in Free Receiver Operating Characteristics (FROC) curve analysis, and our system has a pleasing generalization ability in clinical data.


Physiological adaptation in the radiation of New Zealand triplefin fishes (Family Tripterygiidae)


title: Egoscanning: quickly scanning first-person videos with egocentric elastic timelines

abstract: This work presents EgoScanning, a video fast-forwarding interface that helps users find important events from lengthy first-person videos continuously recorded with wearable cameras. This interface features an elastic timeline that adaptively changes playback speeds and emphasizes egocentric cues specific to first-person videos, such as hand manipulations, moving, and conversations with people, on the basis of computer-vision techniques. The interface also allows users to input which of such cues are relevant to events of their interest. Through our user study, we confirm that users can find events of interest quickly from first-person videos thanks to benefits of using the EgoScanning interface.


title: Digital-Content Authoring Large-Scale Physics-Based Terrain Editing

abstract: Physics-based approaches could simplify terrain modeling by increasing its realism. However, most simulations provide only a low level of user control because they fail on large-scale phenomena or focus only on the modeling of limited effects. A new physics-based system for digital terrain editing is suitable for digital-content authors such as game designers, artists, and 3D modelers. It doesn't assume in-depth knowledge about physics-based simulations. Users can load large terrains from external sources, generate them procedurally, or create them manually, and they can edit them at interactive frame rates on a GPU. To allow large-scale editing, the system divides terrain into tiles of different resolutions according to the terrain's complexity, and it stores each tile as a mip-map texture. In addition, the physics-based simulation uses different levels of detail, depending on the terrain-change dynamics. Compared to nonadaptive computation, this approach can achieve 50 percent speedup and use 25 percent less memory. The Web extra is a video that shows how physics-based approaches to modeling can process terrain sizes larger than what was previously possible.


title: ORIGINAL CONTRIBUTION Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

abstract: A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator ( N ) in ART 1 by the MIN operator ( /~ ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binarv case. Category proliferation is prevented by, normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the M1N operator (/\) and the MAX operator (V) of fuzzy set theory play complementary roles. Complement coding uses on-cells and of cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of categorv "boxes." Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.


title: A Procedure For Quantitatively Comparing The Syntactic Coverage Of English Grammars

abstract: The problem of quantitatively comparing the performance of different broad-coverage grammars of English has to date resisted solution. Prima facie, known English grammars appear to disagree strongly with each other as to the elements of even the simplest sentences. For instance, the grammars of Steve Abney (Bellcore), Ezra Black (IBM), Dan Flickinger (Hewlett Packard), Claudia Gdaniec (Logos), Ralph Grishman and Tomek Strzalkowski (NYU), Phil Harrison (Boeing), Don Hindle (AT&T), Bob Ingria (BBN), and Mitch Marcus (U. of Pennsylvania) recognize in common only the following constituents, when each grammarian provides the single parse which he/she would ideally want his/her grammar to specify for three sample Brown Corpus sentences:The famed Yankee Clipper, now retired, has been assisting (as (a batting coach)).One of those capital-gains ventures, in fact, has saddled him (with Gore Court).He said this constituted a (very serious) misuse (of the (Criminal court) processes).


Background Digital Calibration Techniques for Pipelined ADC's


title: Flare: Native Compilation for Heterogeneous Workloads in Apache Spark

abstract: The need for modern data analytics to combine relational, procedural, and map-reduce-style functional processing is widely recognized. State-of-the-art systems like Spark have added SQL front-ends and relational query optimization, which promise an increase in expressiveness and performance. But how good are these extensions at extracting high performance from modern hardware platforms? ::: While Spark has made impressive progress, we show that for relational workloads, there is still a significant gap compared with best-of-breed query engines. And when stepping outside of the relational world, query optimization techniques are ineffective if large parts of a computation have to be treated as user-defined functions (UDFs). ::: We present Flare: a new back-end for Spark that brings performance closer to the best SQL engines, without giving up the added expressiveness of Spark. We demonstrate order of magnitude speedups both for relational workloads such as TPC-H, as well as for a range of machine learning kernels that combine relational and iterative functional processing. ::: Flare achieves these results through (1) compilation to native code, (2) replacing parts of the Spark runtime system, and (3) extending the scope of optimization and code generation to large classes of UDFs.


title: Building a Twitter opinion lexicon from automatically-annotated tweets

abstract: We propose a supervised model for expanding an opinion lexicon for Twitter.We combine automatically annotated tweets with existing hand-made opinion lexicons.We use POS tags and associations between words and sentiment as word-level features.Expanded words are mapped to a positive, negative, and neutral distribution.We outperform the performance obtained by using PMI semantic orientation alone. Opinion lexicons, which are lists of terms labeled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media.In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet.To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modeled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labeled by a seed lexicon formed by combining multiple hand-annotated lexicons.Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve significant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments.


title: A 45-GHz, 2-bit power DAC with 24.3 dBm output power, >14 Vpp differential swing, and 22% peak PAE in 45-nm SOI CMOS

abstract: A novel high efficiency, large output-power, 2-bit tuned mm-wave DAC realized in 45-nm SOI CMOS is demonstrated. A record breaking 24.3 dBm output power is achieved at 45 GHz with 18.3 dB saturated gain, >14.5 V pp differential output swing, a drain efficiency of 21.3% and 14.6% PAE. The best PAE is 22% at an output power of 23.5 dBm. Operation at 45 GHz with up to 2.5 Gbps BPSK and up to 1.25 Gbps ASK modulation is shown.


title: The role of prefrontal cortex in psychopathy

abstract: Psychopathy is a personality disorder characterized by remorseless and impulsive antisocial behavior. Given the significant societal costs of the recidivistic criminal activity associated with the disorder, there is a pressing need for more effective treatment strategies, and hence, a better understanding of the psychobiological mechanisms underlying the disorder. The prefrontal cortex (PFC) is likely to play an important role in psychopathy. In particular, the ventromedial and anterior cingulate sectors of PFC are theorized to mediate a number of social and affective decision-making functions that appear to be disrupted in psychopathy. This article provides a critical summary of human neuroimaging data implicating prefrontal dysfunction in psychopathy. A growing body of evidence associates psychopathy with structural and functional abnormalities in ventromedial PFC and anterior cingulate cortex. Although this burgeoning field still faces a number of methodological challenges and outstanding questions that will need to be resolved by future studies, the research to date has established a link between psychopathy and PFC.


title: A rainfall prediction model using artificial neural network

abstract: The multilayered artificial neural network with learning by back-propagation algorithm configuration is the most common in use, due to of its ease in training. It is estimated that over 80% of all the neural network projects in development use back-propagation. In back-propagation algorithm, there are two phases in its learning cycle, one to propagate the input patterns through the network and other to adapt the output by changing the weights in the network. The back-propagation-feed forward neural network can be used in many applications such as character recognition, weather and financial prediction, face detection etc. The paper implements one of these applications by building training and testing data sets and finding the number of hidden neurons in these layers for the best performance. In the present research, possibility of predicting average rainfall over Udupi district of Karnataka has been analyzed through artificial neural network models. In formulating artificial neural network based predictive models three layered network has been constructed. The models under study are different in the number of hidden neurons.


title: Exploring technology use behavior — A case study of adopting e-readers into higher education classroom

abstract: The widespread use of e-readers provides a useful tool to students. This has brought us to explore the essential factors that affect student's willing of using new technology in the higher educational classroom. To understand the relationship between reading habits and e-reader functions, we applied grounded theory to analyze data. Results suggest that users' expectations and operation are affected by their previous experiences of using technology and their reading habits. Whether the actual features of the product satisfy the user's expectation or not, there are five different reactions: rejection, complaint, satisfaction, surprise and useless. This study analyzes students' reactions to different features of new technology and provides teachers and manufacturers references for applying new technology into learning environment.


title: Th17 cells contribute to nontypeable Haemophilus influenzae-specific protective immunity induced by nasal vaccination with P6 outer membrane protein and α-galactosylceramide.

abstract: Nasal vaccination is an effective therapeutic means of preventing upper respiratory infection. Recently, nasal vaccination with P6 outer membrane protein of nontypeable Haemophilus influenzae (NTHi) and alpha-galactosylceramide (α-GalCer) was reported to induce NTHi-specific protective immunity. The present study investigated the role of the Th17 cells induced by nasal vaccination. Mice were immunized with P6 and α-GalCer, and their P6-specific immune responses were examined. Cytokine-producing cells were analyzed by flow cytometry, and expression of cytokines in P6-specific CD4+ T cells was determined by reverse transcription-polymerase chain reaction. Bacterial challenges were performed with live NTHi. To examine the role of Th17 cells, bacterial clearance was also evaluated after interleukin (IL)-17 neutralization. P6-specific nasal wash immunoglobulin (Ig) A and serum IgG were increased after immunization with P6 and α-GalCer. Specific IgA-producing cells increased markedly in the nasal passages (NPs) of the immunized mice. In addition to P6-specific Th1 and Th2 cells, IL-17-producing Th17 cells were induced in the NPs and spleen. Bacterial clearance was enhanced by nasal vaccination. Interestingly, impaired NTHi clearance was shown after IL-17 neutralization. These findings suggest that nasal vaccination with P6 and α-GalCer is an effective regimen for the induction of NTHi-specific protective immunity in the upper respiratory tract. In addition to antigen-specific secretory-IgA, specific Th17 cells induced by nasal vaccination contribute to protection against NTHi.


title: A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model

abstract: This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem. The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels, such as the original RGB face image, the detail layer image, and the lighting layer image. With a carefully designed CNN model of deep structure, large input size and small convolutional kernels, we have achieved a high prediction correlation of 0.88. This result convinces us that the problem of facial attractiveness prediction can be solved by deep learning approach, and it also shows the important roles of the facial smoothness, lightness, and color information that were involved in facial beauty perception, which is consistent with the result of recent psychology studies. Furthermore, we analyze the high-level features learnt by CNN through visualization of its hidden layers, and some interesting phenomena were observed. It is found that the contours and appearance of facial features, especially eyes and moth, are the most significant facial attributes for facial attractiveness prediction, which is also consistent with the visual perception intuition of human.


title: Combining Bilingual and Comparable Corpora for Low Resource Machine Translation

abstract: Statistical machine translation (SMT) performance suffers when models are trained on only small amounts of parallel data. The learned models typically have both low accuracy (incorrect translations and feature scores) and low coverage (high out-of-vocabulary rates). In this work, we use an additional data resource, comparable corpora, to improve both. Beginning with a small bitext and corresponding phrase-based SMT model, we improve coverage by using bilingual lexicon induction techniques to learn new translations from comparable corpora. Then, we supplement the model’s feature space with translation scores estimated over comparable corpora in order to improve accuracy. We observe improvements between 0.5 and 1.7 BLEU translating Tamil, Telugu, Bengali, Malayalam, Hindi, and Urdu into English.


title: Effect of pre-ignition heating cycle method, air injection flux, and reservoir viscosity on the THAI heavy oil recovery process

abstract: Abstract Heavy oil and bitumen reserves can potentially bridge the gap in world energy demand during the transition between conventional hydrocarbon fuels and sustainable energy sources, while the latter are developed. However, the development of efficient extraction methods with low emissions is necessary. The in-situ combustion process, toe-to-heel air injection (THAI), is such a method, but some issues remain in improving its performance. In this paper, the performance of the THAI process has been investigated at the laboratory scale using a validated simulation model. We have studied the effect of pre-ignition heating cycle (PIHC) method, reservoir oil viscosity, and air injection flux, on the stability and operability of the THAI process. It was found that the use of steam, instead of electrical heaters, for the PIHC resulted in an increase in oil recovery. However, use of steam also caused a reduction in oxygen utilisation. The use of a horizontal injector (HI) well, in combination with the steaming during the PIHC, causes some of the oxygen to bypass the combustion front during the early stage of air injection. It is concluded that steaming using a staggered line drive, or 2VIHP well arrangement, is recommended to be used at the field scale. It was also found that THAI operates more stably for higher viscosity oil, albeit at higher operating cost, due to increased cumulative air-oil ratio (CAOR), compared to for a low oil viscosity reservoir. In contrast to previous suppositions, it has been found that oil recovery increases non-linearly with an increase in air flux, which needs to be taken into account on scale-up.


title: The KIT Prosthetic Hand: Design and Control

abstract: The development and control of prosthetic hands is an active research area and recently progress in mechatronics, sensor integration and innovative control has been made. However, integration of different components into a prosthetic hand remains challenging due to space constraints, the requirements regarding holistic integration and the need for a user interface. In this paper, we present the KIT prosthetic hand, a novel five-finger 3D printed hand prosthesis, with its underactuated mechanism, sensors and embedded control system. The hand mechanics is based on the underactuated TUAT/Karlsruhe mechanism with two motors actuating 10 degrees of freedom. The mechanism has been realized in 3D printing technologies to facilitate a personalization of the prosthetic hand in terms of size and kinematic parameters. The prosthesis has been designed as a 50th percentile male hand. It integrates an advanced embedded system as well as an RGB camera in the base of the palm and a colour display in the back of the hand. Experiments indicate a finger tip force of 7.48 N to 11.82 N, a hook grasp force of 120 N and a hand closing time of ∼ 1.3 s.


title: MEMS Vertical Probe Cards With Ultra Densely Arrayed Metal Probes for Wafer-Level IC Testing

abstract: We have developed a MEMS probe-card technology for wafer-level testing ICs with 1-D line-arrayed or 2-D area-arrayed dense pads layouts. With a novel metal MEMS fabrication technique, an area-arrayed tip matrix is realized with an ultradense tip pitch of 90 mum times196 mum for testing 2-D pad layout, and a 50-mum minimum pitch is also achieved in line-arrayed probe cards for testing line-on-center or line-on-perimeter wafers. By using the anisotropic etching properties of single-crystalline silicon, novel oblique concave cavities are formed as electroplating moulds for the area-arrayed microprobes. With the micromachined cavity moulds, the probes are firstly electroplated in a silicon wafer and further flip-chip packaged onto a low-temperature cofired ceramic board for signal feeding to an automatic testing equipment. The microprobes can be efficiently released using a silicon-loss technique with a lateral underneath etching. The measured material properties of the electroplated nickel and the Sn-Ag solder bump are promising for IC testing applications. Mechanical tests have verified that the microprobes can withstand a 65-mN probing force, while the tip displacement is 25 mum, and can reliably work for more than 100 000 touchdowns. The electric test shows that the probe array can provide a low contact resistance of below 1 Omega, while the current leakage is only 150 pA at 3.3 V for adjacent probes.


title: An Empirical Evaluation of Deep Learning on Highway Driving

abstract: Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.


title: Social interaction based video recommendation: Recommending YouTube videos to facebook users

abstract: Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTubepopularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.


title: Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

abstract: This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two coupled projections that project the source domain and target domain data into low-dimensional subspaces where the geometrical shift and distribution shift are reduced simultaneously. The objective function can be solved efficiently in a closed form. Extensive experiments have verified that the proposed method significantly outperforms several state-of-the-art domain adaptation methods on a synthetic dataset and three different real world cross-domain visual recognition tasks.


title: A study on 11 MHz ~ 1537 MHz DCO using tri-state inverter for DAB application

abstract: Digitally Controlled Oscillator (DCO) for Digital Audio Broadcasting (DAB) is proposed using 0.18 μm CMOS process parameters with 1.8 V supply voltage. In this paper, the proposed DCO consist of tri-state inverter array and calibration block. Tri-state inverter array is 5-stage ring oscillator and each stage has 160 tri-state inverters. Calibration block is composed of 64 NMOS pass transistors and 64 Metal-Insulator-Metal (MIM) capacitors with different values. The proposed DCO has the frequency tuning range from 10.5088 MHz to 1537.32 MHz by controlling 800 tri-state inverters. BAND-III (174.928 MHz ~ 239.200 MHz) and L-BAND (1452.960 MHz ~ 1492.624 MHz) are synthesized for DAB application. In order to synthesize channel frequency for DAB system, tri-state inverter array that adjusts frequency coarsely and calibration block that adjusts frequency finely have to work together and both of them are controlled by 6-bit digital word. So the proposed DCO could tune frequencies of 64 channels for DAB accurately. It has simulated phase noise of −114.93 dBc/Hz at 1MHz offset and power consumption of 3.28 mW at 174.928 MHz. And it has simulated phase noise of −106.35 dBc/Hz at 1MHz offset and power consumption of 25.77 mW at 1490.624 MHz.


title: A recursive camera resectioning technique for off-line video-based augmented reality

abstract: In this paper, we propose a new recursive framework for camera resectioning and apply it to off-line video-based augmented reality. Our method is based on an unscented particle filter and an independent Metropolis-Hastings chain, which deal with nonlinear dynamic systems without local linearization, and lead to more accurate results than other nonlinear filters. The proposed method has some desirable properties for camera resectioning: Since it does not rely on erroneous linear solutions, initialization problems do not occur, in contrast to the previous resectioning methods. Jittering error can be reduced by considering consistency and coherency between adjacent frames in our recursive framework. Our method is fairly accurate comparable to nonlinear optimization methods, which in general have higher levels of computation and complexity. As a result, the proposed algorithm outperforms the standard camera resectioning algorithm. We verify the effectiveness of our method through several experiments using synthetic and real image sequences comparing the estimation performance with other linear and nonlinear methods.


Speech Synthesis for Mixed-Language Navigation Instructions.


title: Treatment for an endosseous implant migrated into the maxillary sinus not causing maxillary sinusitis: case report.

abstract: Placement of endosseous implants in the maxilla has been proven to be a reliable treatment modality. If there is lack of supporting bone, the placed implant may not have enough primary stability and may migrate into the maxillary sinus. Displaced implants must be removed. If there are no signs of maxillary sinusitis, augmentation of the resulting alveolar defect can be performed during the same procedure.


title: The Scaled-Sized Model: A Revision of Amdahl’s Law

abstract: A popular argument, generally attributed to Amdahl [1], is that vector and parallel architectures should not be carried to extremes because the scalar or serial portion of the code will eventually dominate. Since pipeline stages and extra processors obviously add hardware cost, a corollary to this argument is that the most cost-effective computer is one based on uniprocessor, scalar principles. For architectures that are both parallel and vector, the argument is compounded, making it appear that near-optimal performance on such architectures is a near-impossibility. A new argument is presented that is based on the assumption that program execution time, not problem size, is constant for various amounts of vectorization and parallelism. This has a dramatic effect on Amdahl’s argument, revealing that one can be much more optimistic about achieving high speedups on massively parallel and highly vectorized machines. The revised argument is supported by recent results of over 1000 times speedup on 1024 processors on several practical scientific applications [2].


Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas


title: NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

abstract: Most of the current anti money laundering (AML) systems, using handcrafted ::: rules, are heavily reliant on existing structured databases, which are not capable ::: of effectively and efficiently identifying ::: hidden and complex ML activities, especially those with dynamic and timevarying characteristics, resulting in a high ::: percentage of false positives. Therefore, ::: analysts1 ::: are engaged for further investigation which significantly increases human capital cost and processing time. To ::: alleviate these issues, this paper presents ::: a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language ::: processing (NLP) technologies in a distributed and scalable manner to augment ::: AML monitoring and investigation. The ::: proposed distributed framework performs ::: news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and ::: tweets) to provide additional evidence to ::: human investigators for final decisionmaking. Each NLP module is evaluated ::: on a task-specific data set, and the overall experiments are performed on synthetic ::: and real-world datasets. Feedback from ::: AML practitioners suggests that our system can reduce approximately 30% time ::: and cost compared to their previous manual approaches of AML investigation.


title: A spiking neuromorphic design with resistive crossbar

abstract: Neuromorphic systems recently gained increasing attention for their high computation efficiency. Many designs have been proposed and realized with traditional CMOS technology or emerging devices. In this work, we proposed a spiking neuromorphic design built on resistive crossbar structures and implemented with IBM 130nm technology. Our design adopts a rate coding scheme where pre- and post-neuron signals are represented by digitalized pulses. The weighting function of pre-neuron signals is executed on the resistive crossbar in analog format. The computing result is transferred into digitalized output spikes via an integrate-and-fire circuit (IFC) as the post-neuron. We calibrated the computation accuracy of the entire system through circuit simulations. The results demonstrated a good match to our analytic modeling. Furthermore, we implemented both feedforward and Hopfield networks by utilizing the proposed neuromorphic design. The system performance and robustness were studied through massive Monte-Carlo simulations based on the application of digital image recognition. Comparing to the previous crossbar-based computing engine that represents data with voltage amplitude, our design can achieve >50% energy savings, while the average probability of failed recognition increase only 1.46% and 5.99% in the feedforward and Hopfield implementations, respectively.


title: Knowledge management and business intelligence: the importance of integration

abstract: Purpose – The purpose of the paper is to provide a thorough analysis of the difference between business intelligence (BI) and knowledge management (KM) and to establish a framework for relating one field to the other.Design/methodology/approach – A review of the literature from approximately 1986 through 2004 served as the basis for analysis and comparison of BI and KM. The theoretical scope of the paper is to distinguish between BI and KM to clarify the role of each in a business environment.Findings – BI focuses on explicit knowledge, but KM encompasses both tacit and explicit knowledge. Both concepts promote learning, decision making, and understanding. Yet, KM can influence the very nature of BI itself. Hence, this paper explains the nature of the integration between BI and KM and makes it clear that BI should be viewed as a subset of KM.Originality/value – This paper establishes a clear distinction between two important fields of study, BI and KM, establishing an expanded role for BI. That is, the ro...


title: Automatic detection and recognition of road sign for driver assistance system

abstract: Automatic road-signs recognition is becoming a part of Driver Assisting Systems which role is to increase safety and driving comfort. This paper presents an efficient approach for detecting and recognizing road sign in traffic scene images acquired from a moving vehicle. The developed road sign recognition system is divided into two stages: detection stage to localize signs from a whole image, and classification stage that classifies the detected sign into one of the reference signs. The detection module segments the input image in the YCBCR colour space, and then detects road signs using a shape filtering method. The classification module determines the type of detected road signs using a Multi-layer Perceptron neural networks. An extensive experimentation has shown that the proposed approach is robust enough to detect and recognize road signs under varying translation, rotation and lighting conditions.


title: A hybrid fingerprint matcher

abstract: We describe a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. A set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A square tessellation of the filtered images is then used to construct an eight-dimensional feature map, called the. ridge feature map. The ridge feature map along with the minutiae set of a fingerprint image is used for matching purposes. The genuine accept rate of the hybrid matcher is observed to be /spl sim/10% higher than that of a minutiae-based matcher at low false accept rates. Fingerprint verification using the hybrid matcher on a Pentium III (800 MHz) processor takes /spl sim/1.4 seconds.


title: Cloud Storage as the Infrastructure of Cloud Computing

abstract: As an emerging technology and business paradigm, Cloud Computing has taken commercial computing by storm. Cloud computing platforms provide easy access to a company’s high-performance computing and storage infrastructure through web services. With cloud computing, the aim is to hide the complexity of IT infrastructure management from its users. At the same time, cloud computing platforms provide massive scalability, 99.999% reliability, high performance, and specifiable configurability. These capabilities are provided at relatively low costs compared to dedicated infrastructures. This article gives a quick introduction to cloud storage. It covers the key technologies in Cloud Computing and Cloud Storage, several different types of clouds services, and describes the advantages and challenges of Cloud Storage after the introduction of the Cloud Storage reference model.


title: Investigation on the layout strategy of ggNMOS ESD protection devices for uniform conduction behavior and optimal width scaling

abstract: Gate-grounded NMOS (ggNMOS) transistors have widely served as electro-static discharge (ESD) protection devices for integrated circuits. The layout strategy of ggNMOS greatly influences its ESD protection characteristics. Layout strategies forvariation of the number of substrate-pickup stripes are investigated in this paper. Direct current and transmission-line pulsing test results are presented to verify that adjustable holding voltages are accessed by variation of the number of substrate-pickup stripes. The design with two evenly distributed substrate-pickup stripes among different fingers is found to exhibit the highest second break current and optimal width-scaling characteristics.


title: Defining a Standard for Particle Swarm Optimization

abstract: Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developments that can be expected to improve performance on standard measures. This standard algorithm is intended for use both as a baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community


title: Learning Noun Phrase Query Segmentation

abstract: Query segmentation is the process of taking a user’s search-engine query and dividing the tokens into individual phrases or semantic units. Identication of these query segments can potentially improve both document-retrieval precision, by rst returning pages which contain the exact query segments, and document-retrieval recall, by allowing query expansion or substitution via the segmented units. We train and evaluate a machine-learned query segmentation system that achieves 86% segmentationdecision accuracy on a gold standard set of segmented noun phrase queries, well above recently published approaches. Key enablers of this high performance are features derived from previous natural language processing work in noun compound bracketing. For example, token association features beyond simple N-gram counts provide powerful indicators of segmentation.


title: Performance Evaluation Criteria for Autonomous Cleaning Robots

abstract: This paper is a report on the initial trial for its kind in the development of the performance index of the autonomous mobile cleaning robot. The unique characteristic features of the cleaning robot have been identified as autonomous mobility, dust collection, and operation noise. Along with the identification of the performance indices the standardized performance-evaluation methods including the corresponding performance evaluation platform for each indices have been developed as well. The validity of the proposed performance evaluation methods has been demonstrated by applying the proposed evaluation methods on two commercial cleaning robots available in market. The proposed performance evaluation methods can be applied to general-purpose autonomous service robots which will be introduced in the consumer market in near future.


title: Flexible Electronics and Displays: High-Resolution, Roll-to-Roll, Projection Lithography and Photoablation Processing Technologies for High-Throughput Production

abstract: Flexible electronics are increasingly being used in a number of applications which benefit from their low profile, light weight, and favorable dielectric properties. However, despite these advantages, the range of practical, high-volume, applications for flexible electronics will remain limited in the future unless a number of challenges related to lithographic patterning on flexible substrates are successfully addressed. The most critical of these pertain to system parameters that affect the cost and performance of flexible circuits, including the resolution, panel size, process throughput, substrate distortion, material handling, and yield. We present a new class of roll-to-roll lithography systems, developed in recent years, that were designed to address the challenges in each of these critical areas. These systems provides high-resolution projection imaging over very large exposure areas, on flexible substrate materials. Additionally, they achieve high-precision alignment by means of image scaling to compensate for substrate distortion due to processing; and they also performs high-throughput photoablation, patterning millions of pixels simultaneously, by means of projection imaging. This technology is attractive for current and emerging applications, such as flexible circuit boards and flexible chip carriers, as well as for potential future applications such as such as flexible displays and macroelectronic systems.


title: Integration of Design and Assembly Using Augmented Reality

abstract: This paper presents a methodology and a system for augmented reality aided assembly work. We concentrate in particular on the requirements on information processing and data flow for implementing augmented assembly systems in real life production environments. A pilot case with an augmented assembly task at the Finnish tractor company Valtra is described.


title: Probabilistic long-term prediction for autonomous vehicles

abstract: Long-term prediction of traffic participants is crucial to enable autonomous driving on public roads. The quality of the prediction directly affects the frequency of trajectory planning. With a poor estimation of the future development, more computational effort has to be put in re-planning, and a safe vehicle state at the end of the planning horizon is not guaranteed. A holistic probabilistic prediction, considering inputs, results and parameters as random variables, highly reduces the problem. A time frame of several seconds requires a probabilistic description of the scene evolution, where uncertainty or accuracy is represented by the trajectory distribution. Following this strategy, a novel evaluation method is needed, coping with the fact, that the future evolution of a scene is also uncertain. We present a method to evaluate the probabilistic prediction of real traffic scenes with varying start conditions. The proposed prediction is based on a particle filter, estimating behavior describing parameters of a microscopic traffic model. Experiments on real traffic data with random leading vehicles show the applicability in terms of convergence, enabling long-term prediction using forward propagation.


title: Policy change, power and the development of Great Britain's Renewable Heat Incentive

abstract: The role of socio-political power is central to the development of policy, but systematic analyses of power associated with the development of energy policy are rare. Power is also an important yet somewhat under-researched aspect of socio-technical transitions research. The Renewable Heat Incentive (RHI) policy aims to increase deployment of low-carbon heat in Great Britain and begin a transformation to a low carbon GB heat system. This article analyses the socio-political power associated with the development of the RHI policy based on Lukes' ‘dimensions of power’ approach using a methodology based on triangulation. We identify a number of policy change episodes during the development of the RHI and describe the influence of key actors on the policy. Despite the common assumption of the power of incumbents, we show that those actors with niche technological expertise, close relationships with Government actors and actors within the administration have been the most powerful drivers of policy development and change. Niche actors sped up the introduction of the RHI scheme and have also had some success in increasing relative support for biomethane injection. The power of a civil servant to slow the introduction of the domestic element of the RHI has also been identified.


title: Mobile e-Health: The Unwired Evolution

abstract: The movement of telemedicine from desktop platforms to wireless and mobile configurations may have a significant impact on future health care. This paper reviews some of the latest technologies in wireless communication and their application in health care. The new technologies can make the remote medical monitoring, consulting, and health care more flexible and convenient. But, there are challenges for successful wireless telemedicine, which are addressed in this paper.


title: Improving online gesture recognition with template matching methods in accelerometer data

abstract: Template matching methods using Dynamic Time Warping (DTW) have been used recently for online gesture recognition from body-worn motion sensors. However, DTW has been shown sensitive under the strong presence of noise in time series. In sensor readings, labeling temporal boundaries of daily gestures precisely is rarely achievable as they are often intertwined. Moreover, the variation in daily gesture execution always exists. Therefore, here we propose two template matching methods utilizing the Longest Common Subsequence (LCSS) to improve robustness against such noise for online gesture recognition. Segmented LCSS utilizes a sliding window to define the unknown boundaries of gestures in the continuous coming sensor readings and detects efficiently a possibly shorter gesture within it. WarpingLCSS is our novel variant of LCSS to determine occurrences of gestures without segmenting data and performs one order of magnitude faster than the Segmented LCSS. The WarpingLCSS requires low-resource settings to process new arriving samples, thus it is suitable for real-time gesture recognition implemented directly on the small wearable devices. We compare our methods with the existing template matching methods based on Dynamic Time Warping (DTW) on two real-world gesture datasets from arm-worn accelerometer data. The results demonstrate that the LCSS approaches outperform the existing template matching approaches (about 12% in accuracy) in the dataset that suffers from boundary noise and execution variation.


title: Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task

abstract: Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. ::: ::: Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/


title: Biometric identification via eye movement scanpaths in reading

abstract: This paper presents an objective evaluation of various eye movement-based biometric features and their ability to accurately and precisely distinguish unique individuals. Eye movements are uniquely counterfeit resistant due to the complex neurological interactions and the extraocular muscle properties involved in their generation. Considered biometric candidates cover a number of basic eye movements and their aggregated scanpath characteristics, including: fixation count, average fixation duration, average saccade amplitudes, average saccade velocities, average saccade peak velocities, the velocity waveform, scanpath length, scanpath area, regions of interest, scanpath inflections, the amplitude-duration relationship, the main sequence relationship, and the pairwise distance between fixations. As well, an information fusion method for combining these metrics into a single identification algorithm is presented. With limited testing this method was able to identify subjects with an equal error rate of 27%. These results indicate that scanpath-based biometric identification holds promise as a behavioral biometric technique.


title: From categories to subcategories: Large-scale image classification with partial class label refinement

abstract: The number of digital images is growing extremely rapidly, and so is the need for their classification. But, as more images of pre-defined categories become available, they also become more diverse and cover finer semantic differences. Ultimately, the categories themselves need to be divided into subcategories to account for that semantic refinement. Image classification in general has improved significantly over the last few years, but it still requires a massive amount of manually annotated data. Subdividing categories into subcategories multiples the number of labels, aggravating the annotation problem. Hence, we can expect the annotations to be refined only for a subset of the already labeled data, and exploit coarser labeled data to improve classification. In this work, we investigate how coarse category labels can be used to improve the classification of subcategories. To this end, we adopt the framework of Random Forests and propose a regularized objective function that takes into account relations between categories and subcategories. Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image classification experiments.


title: Internet of things for sleep quality monitoring system: A survey

abstract: Sleep quality is an important factor for human physical and mental health, day-time performance, and safety. Sufficient sleep quality can reduce risk of chronic disease and mental depression. Sleep helps brain to work properly that can improve productivity and prevent accident because of falling asleep. In order to analyze the sleep quality, reliable continuous monitoring system is required. The emergence of internet-of-things technology has provided a promising opportunity to build a reliable sleep quality monitoring system by leveraging the rapid improvement of sensor and mobile technology. This paper presents the literature study about internet of things for sleep quality monitoring systems. The study is started from the review of sleep quality problem, the importance of sleep quality monitoring, the enabling internet of things technology, and the open issues in this field. Finally, our future research plan for sleep apnea monitoring is presented.


title: Young people's digital lives: The impact of interpersonal relationships and digital media use on adolescents' sense of identity

abstract: The current study investigates the joint effects of interpersonal relationships and digital media use on adolescents' sense of identity. Questionnaires were administered to a sample of 2079 students (57% female) between the ages of 11 and 19years (M=15.4years) attending one of seven secondary schools in Bermuda. Using structural equation modeling, the author found that mothers and friends play an important role in adolescents' lives, with both relationships contributing in positive ways to respondents' self-concept clarity. Further, the results showed that mother relationship quality affected adolescents' self-concept clarity both directly and indirectly, through the positive impact it had on friendship quality. Friends also played a mediating role in relation to aspects of adolescents' digital media use. Specifically, the negative association detected between online identity expression/exploration and self-concept clarity was mediated partially by low friendship quality. Going online to communicate with one's friends appeared to play a more positive role in adolescents' sense of identity. The results showed that online peer communication affected self-concept clarity indirectly through its positive impact on friendship quality.


title: Fronthaul evolution: From CPRI to Ethernet

abstract: It is proposed that using Ethernet in the fronthaul, between base station baseband unit (BBU) pools and remote radio heads (RRHs), can bring a number of advantages, from use of lower-cost equipment, shared use of infrastructure with fixed access networks, to obtaining statistical multiplexing and optimised performance through probe-based monitoring and software-defined networking. However, a number of challenges exist: ultra-high-bit-rate requirements from the transport of increased bandwidth radio streams for multiple antennas in future mobile networks, and low latency and jitter to meet delay requirements and the demands of joint processing. A new fronthaul functional division is proposed which can alleviate the most demanding bit-rate requirements by transport of baseband signals instead of sampled radio waveforms, and enable statistical multiplexing gains. Delay and synchronisation issues remain to be solved.


ensing technologies for precision specialty crop production


title: Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering

abstract: Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.


title: Receptive Field Block Net for Accurate and Fast Object Detection

abstract: Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at this https URL.


title: Automating GUI testing for Android applications

abstract: Users increasingly rely on mobile applications for computational needs. Google Android is a popular mobile platform, hence the reliability of Android applications is becoming increasingly important. Many Android correctness issues, however, fall outside the scope of traditional verification techniques, as they are due to the novelty of the platform and its GUI-oriented application construction paradigm. In this paper we present an approach for automating the testing process for Android applications, with a focus on GUI bugs. We first conduct a bug mining study to understand the nature and frequency of bugs affecting Android applications; our study finds that GUI bugs are quite numerous. Next, we present techniques for detecting GUI bugs by automatic generation of test cases, feeding the application random events, instrumenting the VM, producing log/trace files and analyzing them post-run. We show how these techniques helped to re-discover existing bugs and find new bugs, and how they could be used to prevent certain bug categories. We believe our study and techniques have the potential to help developers increase the quality of Android applications.


RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks