title: Suitability of Soccer Training Drills for Endurance Training

abstract: Recent evidence suggests that certain soccer ::: drills produce exercise intensities suitable for physical conditioning. ::: However, it remains debatable whether soccer drills ::: can provide a sufficiently unified exercise intensity among different ::: players and on repetition of a drill, because movement ::: patterns cannot be externally controlled during soccer drills. ::: Good reliability and low variability of exercise intensity would ::: enable all players to receive an appropriate training stimulus. ::: The purpose of this study was to investigate intersubject variability ::: and intrasubject reliability in exercise intensity during ::: soccer drills. It was hypothesized that soccer drills that involve ::: the highest exercise intensities would demonstrate the lowest ::: intersubject variability and the highest intrasubject reliability. ::: Heart rates of 23 professional soccer players were recorded during ::: a range of soccer training drills. The drills consisted of 2 vs. ::: 2 to 8 vs. 8 normal scoring games and 2 further possession ::: games. Heart rate responses were examined for variability, reliability, ::: and suitability for soccer endurance training. Coeffi- ::: cients of variation across players were less than 3% for all drills. ::: Paired t-tests showed no significant differences in heart rate on ::: repetition of the drills and 95% ratio limits of agreement were ::: 1.8-3.8%. There were no significant correlations between exercise ::: intensity and the statistical measures of variability and reliability. ::: Several drills produced exercise intensities suitable for ::: soccer endurance training with mean heart rate responses ranging ::: from 87-91% HRmax. Soccer drills such as those used in the ::: present study appear to be an adequate substitute for physical ::: training without the ball and thus provide simultaneous skill ::: and fitness training. The increase in training time spent developing ::: technical ability and/or a reduction in total training time ::: required may be useful for soccer teams.


title: A general criterion and an algorithmic framework for learning in multi-agent systems

abstract: We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one's rewards in the presence of other agents who might also be learning (using the same or other learning algorithms). This new criterion takes in as a parameter the class of opponents. We then provide a modular approach for achieving effective agent-centric learning; the approach consists of a number of basic algorithmic building blocks, which can be instantiated and composed differently depending on the environment setting (for example, 2- versus n-player games) as well as the target class of opponents. We then provide several specific instances of the approach: an algorithm for stationary opponents, and two algorithms for adaptive opponents with bounded memory, one algorithm for the n-player case and another optimized for the 2-player case. We prove our algorithms correct with respect to the formal criterion, and furthermore show the algorithms to be experimentally effective via comprehensive computer testing.


AuthLoop: End-to-End Cryptographic Authentication for Telephony over Voice Channels


title: Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention

abstract: This paper focuses on two key problems for audio-visual emotion recognition in the video. One is the audio and visual streams temporal alignment for feature level fusion. The other one is locating and re-weighting the perception attentions in the whole audio-visual stream for better recognition. The Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is employed as the main classification architecture. Firstly, soft attention mechanism aligns the audio and visual streams. Secondly, seven emotion embedding vectors, which are corresponding to each classification emotion type, are added to locate the perception attentions. The locating and re-weighting process is also based on the soft attention mechanism. The experiment results on EmotiW2015 dataset and the qualitative analysis show the efficiency of the proposed two techniques.


Automated Concept Extraction From Plain Text


title: AMS Without 4-Wise Independence on Product Domains

abstract: In their seminal work, Alon, Matias, and Szegedy introduced several sketching techniques, including showing that 4-wise independence is sufficient to obtain good approximations of the second frequency moment. In this work, we show that their sketching technique can be extended to product domains $[n]^k$ by using the product of 4-wise independent functions on $[n]$. Our work extends that of Indyk and McGregor, who showed the result for $k = 2$. Their primary motivation was the problem of identifying correlations in data streams. In their model, a stream of pairs $(i,j) \in [n]^2$ arrive, giving a joint distribution $(X,Y)$, and they find approximation algorithms for how close the joint distribution is to the product of the marginal distributions under various metrics, which naturally corresponds to how close $X$ and $Y$ are to being independent. By using our technique, we obtain a new result for the problem of approximating the $\ell_2$ distance between the joint distribution and the product of the marginal distributions for $k$-ary vectors, instead of just pairs, in a single pass. Our analysis gives a randomized algorithm that is a $(1 \pm \epsilon)$ approximation (with probability $1-\delta$) that requires space logarithmic in $n$ and $m$ and proportional to $3^k$.


title: Dynamic Neural Networks for Text Classification

abstract: This research proposes an approach for text classification that uses a simple neural network called Dynamic Text Classifier Neural Network (DTCNN). The neural network uses as input vectors of words with variable dimension without information loss called Dynamic Token Vectors (DTV). The proposed neural network is designed for the classification of large and short text into categories. The learning process combines competitive and Hebbian learning. Due to the combination of these learning rules the neural network is able to work in a supervised or semi-supervised mode. In addition, it provides transparency in the classification. The network used in this paper is quite simple, and that is what makes enough for its task. The results of evaluation the proposed method shows an improvement in the text classification problem using the DTCNN compared to baseline approaches.


title: Designing for Exploratory Search on Touch Devices

abstract: Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.


title: Cross-dataset Person Re-Identification Using Similarity Preserved Generative Adversarial Networks

abstract: Person re-identification (Re-ID) aims to match the image frames which contain the same person in the surveillance videos. Most of the Re-ID algorithms conduct supervised training in some small labeled datasets, so directly deploying these trained models to the real-world large camera networks may lead to a poor performance due to underfitting. The significant difference between the source training dataset and the target testing dataset makes it challenging to incrementally optimize the model. To address this challenge, we propose a novel solution by transforming the unlabeled images in the target domain to fit the original classifier by using our proposed similarity preserved generative adversarial networks model, SimPGAN. Specifically, SimPGAN adopts the generative adversarial networks with the cycle consistency constraint to transform the unlabeled images in the target domain to the style of the source domain. Meanwhile, SimPGAN uses the similarity consistency loss, which is measured by a siamese deep convolutional neural network, to preserve the similarity of the transformed images of the same person. Comprehensive experiments based on multiple real surveillance datasets are conducted, and the results show that our algorithm is better than the state-of-the-art cross-dataset unsupervised person Re-ID algorithms.


title: A Language-Independent Feature Schema for Inflectional Morphology

abstract: This paper presents a universal morphological feature schema that represents the finest distinctions in meaning that are expressed by overt, affixal inflectional morphology across languages. This schema is used to universalize data extracted from Wiktionary via a robust multidimensional table parsing algorithm and feature mapping algorithms, yielding 883,965 instantiated paradigms in 352 languages. These data are shown to be effective for training morphological analyzers, yielding significant accuracy gains when applied to Durrett and DeNero’s (2013) paradigm learning framework.


title: A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina

abstract: In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. Hence, detection of exudates is an important diagnostic task, in which computer assistance may play a major role. Exudates are found using their high grey level variation, and their contours are determined by means of morphological reconstruction techniques. The detection of the optic disc is indispensable for this approach. We detect the optic disc by means of morphological filtering techniques and the watershed transformation. The algorithm has been tested on a small image data base and compared with the performance of a human grader. As a result, we obtain a mean sensitivity of 92.8% and a mean predictive value of 92.4%. Robustness with respect to changes of the parameters of the algorithm has been evaluated.


title: CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks

abstract: With the widespread applications of deep convolutional neural networks (DCNNs), it becomes increasingly important for DCNNs not only to make accurate predictions but also to explain how they make their decisions. In this work, we propose a CHannel-wise disentangled InterPretation (CHIP) model to give the visual interpretation to the predictions of DCNNs. The proposed model distills the class-discriminative importance of channels in networks by utilizing the sparse regularization. Here, we first introduce the network perturbation technique to learn the model. The proposed model is capable to not only distill the global perspective knowledge from networks but also present the class-discriminative visual interpretation for specific predictions of networks. It is noteworthy that the proposed model is able to interpret different layers of networks without re-training. By combining the distilled interpretation knowledge in different layers, we further propose the Refined CHIP visual interpretation that is both high-resolution and class-discriminative. Experimental results on the standard dataset demonstrate that the proposed model provides promising visual interpretation for the predictions of networks in image classification task compared with existing visual interpretation methods. Besides, the proposed method outperforms related approaches in the application of ILSVRC 2015 weakly-supervised localization task.


title: Online digital filters for biological signals: some fast designs for a small computer

abstract: After reviewing the design of a class of lowpass recursive digital filters having integer multiplier and linear phase characteristics, the possibilities for extending the class to include high pass, bandpass, and bandstop (‘notch’) filters are described. Experience with a PDP 11 computer has shown that these filters may be programmed simply using machine code, and that online operation at sampling rates up to about 8 kHz is possible. The practical application of such filters is illustrated by using a notch desgin to remove mains-frequency interference from an e.c.g. waveform.


title: Does it matter which exercise? A randomized control trial of exercise for low back pain.

abstract: STUDY DESIGN ::: Multicentered randomized controlled trial. ::: ::: ::: OBJECTIVES ::: To determine if previously validated low back pain (LBP) subgroups respond differently to contrasting exercise prescriptions. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: The role of "patient-specific" exercises in managing LBP is controversial. ::: ::: ::: METHODS ::: A total of 312 acute, subacute, and chronic patients, including LBP-only and sciatica, underwent a standardized mechanical assessment classifying them by their pain response, specifically eliciting either a "directional preference" (DP) (i.e., an immediate, lasting improvement in pain from performing either repeated lumbar flexion, extension, or sideglide/rotation tests), or no DP. Only DP subjects were randomized to: 1) directional exercises "matching" their preferred direction (DP), 2) exercises directionally "opposite" their DP, or 3) "nondirectional" exercises. Outcome measures included pain intensity, location, disability, medication use, degree of recovery, depression, and work interference. ::: ::: ::: RESULTS ::: A DP was elicited in 74% (230) of subjects. One third of both the opposite and non-directionally treated subjects withdrew within 2 weeks because of no improvement or worsening (no matched subject withdrew). Significantly greater improvements occurred in matched subjects compared with both other treatment groups in every outcome (P values <0.001), including a threefold decrease in medication use. ::: ::: ::: CONCLUSIONS ::: Consistent with prior evidence, a standardized mechanical assessment identified a large subgroup of LBP patients with a DP. Regardless of subjects' direction of preference, the response to contrasting exercise prescriptions was significantly different: exercises matching subjects' DP significantly and rapidly decreased pain and medication use and improved in all other outcomes. If repeatable, such subgroup validation has important implications for LBP management.


Affinity Studies Between Drugs and Clays as Adsorbent Material


title: Evaluation of the mental health continuum–short form (MHC–SF) in setswana-speaking South Africans

abstract: A continuous assessment and a categorical diagnosis of the presence of mental health, described as flourishing, and the absence of mental health, characterized as languishing, is applied to a random sample of 1050 Setswana-speaking adults in the Northwest province of South Africa. Factor analysis revealed that the mental health continuum–short form (MHC–SF) replicated the three-factor structure of emotional, psychological and social well-being found in US samples. The internal reliability of the overall MHC–SF Scale was 0.74. The total score on the MHC–SF correlated 0.52 with a measure of positive affect, between 0.35 and 0.40 with measures of generalized self-efficacy and satisfaction with life, and between 0.30 and 0.35 with measures of coping strategies, sense of coherence, and community collective self-efficacy. The total score on the MHC–SF correlated −0.22 with the total score on the General Health Questionnaire. Criteria for the categorical diagnosis were applied, and findings revealed that 20% were flourishing, 67.8% were moderately mentally healthy, and 12.2% were languishing. Confirmatory factor analysis supported the hypothesized two-continua model of mental health and mental illness found in the USA. Copyright © 2008 John Wiley & Sons, Ltd.


Risk Factors Versus Alzheimer's Disease or Symptoms Associated with Alzheimer's Disease.


title: Urogenital Complications among Girls with Genital Mutilation: A Hospital-Based Study in Khartoum

abstract: To explore paediatric complications of female genital mutilation (FGM), 255 consecutive girls aged 4–9 years presenting to an emergency ward in Sudan were included in this clinical study. Full examination, including inspection of genitalia, was performed. Dipsticks for nitrite and leucocytes were used to diagnose suspected urinary tract infection (UTI). Girls with a form of FGMnarrowing vulva had significantly more UTI than others, and among girls below the age of seven there was a significant association between FGM and UTI. Only 8% of girls diagnosed as having UTI reported urogenital symptoms. In spite of the fact that 73% of the girls subjected to FGM were reported to have been bedridden for one week or more after the operation, only 10% stated immediate complications. We conclude that FGMcontributes significantly to morbidity among girls, a large share of which does not come to medical attention. African Journal of Reproductive Health Vol. 9(2) 2005: 118-124


Information design for cultural documentation Abstract.


title: A survey study of the association between mobile phone use and daytime sleepiness in California high school students

abstract: BackgroundMobile phone use is near ubiquitous in teenagers. Paralleling the rise in mobile phone use is an equally rapid decline in the amount of time teenagers are spending asleep at night. Prior research indicates that there might be a relationship between daytime sleepiness and nocturnal mobile phone use in teenagers in a variety of countries. As such, the aim of this study was to see if there was an association between mobile phone use, especially at night, and sleepiness in a group of U.S. teenagers.MethodsA questionnaire containing an Epworth Sleepiness Scale (ESS) modified for use in teens and questions about qualitative and quantitative use of the mobile phone was completed by students attending Mountain View High School in Mountain View, California (n = 211).ResultsMultivariate regression analysis indicated that ESS score was significantly associated with being female, feeling a need to be accessible by mobile phone all of the time, and a past attempt to reduce mobile phone use. The number of daily texts or phone calls was not directly associated with ESS. Those individuals who felt they needed to be accessible and those who had attempted to reduce mobile phone use were also ones who stayed up later to use the mobile phone and were awakened more often at night by the mobile phone.ConclusionsThe relationship between daytime sleepiness and mobile phone use was not directly related to the volume of texting but may be related to the temporal pattern of mobile phone use.


Extending a Research-Paper Recommendation System with Bibliometric Measures


Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping


title: A continuous-time instrumentation amplifier employing a novel auto-zeroing structure

abstract: This paper introduces a low-noise, low-power amplifier for high-impedance sensors. An innovative circuit using an auto-zeroed architecture combined with frequency modulation to reject offset and low-frequency noise is proposed and analysed. Special care was given to avoid broadband noise aliasing and chopping in the signal path, and to minimize both the resulting equivalent input offset voltage and equivalent input biasing current. The theoretical noise analysis of the proposed topology covers most of the noise sources of the circuit. Simulations show that the input-referred noise level of the circuit is 13.4nV/√Hz for a power consumption of 85μA with a power supply from 1.8V to 3.6V.


title: Modeling User Exposure in Recommendation

abstract: Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not consume, are taken into consideration. But this assumption does not accord with the common sense understanding that users have a limited scope and awareness of items. For example, a user might not have heard of a certain paper, or might live too far away from a restaurant to experience it. In the language of causal analysis (Imbens & Rubin, 2015), the assignment mechanism (i.e., the items that a user is exposed to) is a latent variable that may change for various user/item combinations. In this paper, we propose a new probabilistic approach that directly incorporates user exposure to items into collaborative filtering. The exposure is modeled as a latent variable and the model infers its value from data. In doing so, we recover one of the most successful state-of-the-art approaches as a special case of our model (Hu et al. 2008), and provide a plug-in method for conditioning exposure on various forms of exposure covariates (e.g., topics in text, venue locations). We show that our scalable inference algorithm outperforms existing benchmarks in four different domains both with and without exposure covariates.


title: The university business incubator: A strategy for developing new research/technology-based firms

abstract: Abstract This article describes the university business incubator (UBI) as a strategy being employed for promoting the development of new research/technology-based firms (RTBFs). The business incubator is widely believed as providing a nurturing environment for new business start-ups, however, the role played by university relationships in providing a nurturing environment for the survival and growth of small RTBFs has escaped systematic review in the U.S. due to a lack of historical data. To fill this gap, this paper presents empirical data on two established UBI programs: the Enterprise Development Inc. at Case Western Reserve University, and the Ben Craig Center at University of North Carolina at Charlotte. These cases provide insights for policy makers and aspiring entrepreneurs into the various facility design, management policy, and value-added aspects of this modem tool employed by some entrepreneurial universities as a strategy for supporting the development of new RTBFs.


title: Substrate-Integrated Two-Port Dual-Frequency Antenna

abstract: A two-port dual-frequency substrate-integrated antenna with a large frequency difference is presented. It consists of a differentially fed slot antenna and a substrate-integrated dielectric resonator antenna for low- and high-frequency radiation, respectively. The former is loaded by a hollow patch, whereas the latter is fabricated inside the hollow region of the patch by using air holes and metalized vias. Beneath the antenna substrate is a second substrate on which slot-coupled sources are printed to feed the two antennas. For demonstration, a two-port dual-frequency antenna working at 5.2-GHz WLAN band and 24-GHz ISM band was designed, fabricated, and measured. The S-parameters, radiation patterns, and antenna gains of the two antenna parts are reported. Reasonable agreement between the measured and simulated results is observed. Very good isolation of over 35 dB between the two antenna parts is observed.


title: Kindergarten Readiness: The Impact of Sensory Integration on Preschool Children's Readiness for the Transition to Kindergarten

abstract: The goal of this quantitative, descriptive pilot study was to identify the frequency of sensory processing disorders (SPD) in preschool students who are preparing for the transition into kindergarten. The research also explored the relationship between preschool teachers' perspectives of challenging classroom behavior and parents' reports of sensory processing in the home. Researchers distributed the Sensory Processing Measure for Preschoolers (SPM-P) assessment to parents and a behavioral questionnaire to teachers in three northern California preschools. Thirty-two SPM-P assessments were distributed to parents and 15 (47%) were accurately completed and returned. Of this sample, 2 (13%) student scores on the SPM-P reflected SPD. The SPM-P assessments not returned by parents were categorized as nonSPD students. Based on this assumption, 6.2% of the children within the greater population of 32 demonstrated sensory processing disorder. Results of the behavioral questionnaire found that teachers identified 3 or more maladaptive classroom behaviors for students who met the criteria for SPD. The research suggests that there may be a relationship between maladaptive classroom behavior and a child’s ability to process sensory information. Thus, further education on sensory processing and environmental adaptations that encourage positive participation in school may benefit teachers, parents, and children.


title: Veracity of information in twitter data: A case study

abstract: Twitter is a powerful real-time micro-blogging service and a platform where users communicate with each other instantaneously. Thus, tweets form an integral part of big data ecosystem. While this platform serves as an efficient information diffusion medium, it can also be used to spread misinformation intentionally or unintentionally, which can damage the reputation of an individual or a corporation. Misinformation could also be harmful to society in general. As veracity in big data gains more attention, it is also important to develop methods to estimate veracity of tweets. There are no definitive measures to determine the veracity of tweets from tweets themselves. Other information that are required to verify tweets may not be readily available. Hence, there is a need for such mechanisms to determine the level of accuracy of tweets from available data. In this paper we propose three quantitative measures we name as topic diffusion, geographic dispersion, and spam index as indicators of veracity of tweets. These measures are derived from tweets themselves independent of any corroborating data. The proposed measures are tested using tweets about oil companies as validators. To validate the proposed measures, information extracted from tweets are compared with information collected from official data sources. Our experiments show that the proposed measures were able to estimate the level of veracity among tweets in most topics we tested. We also found the measures useful to compare the veracity of different topics as points in a 3-dimensional space. Another application of veracity indices to positions of political candidates is also described.


title: Testing for Information Asymmetries in Real Estate Markets

abstract: We study equilibrium outcomes in markets with asymmetric information about asset values among both buyers and sellers. In residential real estate markets hard-to-observe neighborhood characteristics are a key source of information heterogeneity: sellers are usually better informed about neighborhood values than buyers, but there are some sellers and some buyers that are better informed than their peers. We propose a new theoretical framework for analyzing such markets with many heterogeneous assets and differentially informed agents. Consistent with the predictions from this framework, we find that changes in the seller composition towards (i) more informed sellers and (ii) sellers with a larger supply elasticity predict subsequent house-price declines and demographic changes in that neighborhood. This effect is larger for houses whose value depends more on neighborhood characteristics, and smaller for houses bought by more informed buyers. Our findings suggest that home owners have superior information about important neighborhood characteristics, and exploit this information to time local market movements.


Debela Tesfaye & Ermias Abebe Designing a Rule Based Stemmer for Afaan Oromo Text


title: An overview of the configuration and manipulation of soft robotics for on-orbit servicing

abstract: Soft robots refer to robots that are softer and more flexible when compared with conventional rigid-bodied robots. Soft robots are adapted to unstructured environments due to their flexibility, deformability and energy-absorbing properties. Thus, they have tremendous application prospects in on-orbit servicing (OOS). This study discusses the configuration and manipulation of soft robotics. Usually, learning from living beings is used to develop the configurations of most soft robots. In this study, typical soft robots are introduced based on what they mimic. The discussion of manipulation is divided into two parts, namely actuation and control. The study also involves describing and comparing several types of actuations. Studies on the control of soft robots are also reviewed. In this study, potential application of soft robotics for on-orbit servicing is analyzed. A hybrid configuration and manipulation of space soft robots for future research are proposed based on the current development of soft robotics, and some challenges are discussed.


title: The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems

abstract: In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose. It contains data for male and female actors in English and a male actor in French. The database covers 5 emotion classes so it could be suitable to build synthesis and voice transformation systems with the potential to control the emotional dimension in a continuous way. We show the data's efficiency by building a simple MLP system converting neutral to angry speech style and evaluate it via a CMOS perception test. Even though the system is a very simple one, the test show the efficiency of the data which is promising for future work.


title: Statistical parametric speech synthesis using deep neural networks

abstract: Conventional approaches to statistical parametric speech synthesis typically use decision tree-clustered context-dependent hidden Markov models (HMMs) to represent probability densities of speech parameters given texts. Speech parameters are generated from the probability densities to maximize their output probabilities, then a speech waveform is reconstructed from the generated parameters. This approach is reasonably effective but has a couple of limitations, e.g. decision trees are inefficient to model complex context dependencies. This paper examines an alternative scheme that is based on a deep neural network (DNN). The relationship between input texts and their acoustic realizations is modeled by a DNN. The use of the DNN can address some limitations of the conventional approach. Experimental results show that the DNN-based systems outperformed the HMM-based systems with similar numbers of parameters.


title: Remembering through lifelogging: A survey of human memory augmentation

abstract: Human memory is unquestionably a vital cognitive ability but one that can often be unreliable. External memory aids such as diaries, photos, alarms and calendars are often employed to assist in remembering important events in our past and future. The recent trend for lifelogging, continuously documenting ones life through wearable sensors and cameras, presents a clear opportunity to augment human memory beyond simple reminders and actually improve its capacity to remember. This article surveys work from the fields of computer science and psychology to understand the potential for such augmentation, the technologies necessary for realising this opportunity and to investigate what the possible benefits and ethical pitfalls of using such technology might be.


title: Context Encoding for Semantic Segmentation

abstract: Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpass the winning entry of COCO-Place Challenge in 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10 times more layers. The source code for the complete system are publicly available.


Dexamethasone and sodium carboxymethyl cellulose prevent postoperative intraperitoneal adhesions in rats


abstract: Using Causal Induction in Humans to Learn and Infer Causality from Video Amy Fire (amy.fire@ucla.edu) Song-Chun Zhu (sczhu@stat.ucla.edu) Center for Vision, Cognition, Learning, and Art University of California, Los Angeles Los Angeles, CA 90095 USA Abstract For both human and machine learners, it is a challenge to make high-level sense of observations by identifying causes, effects, and their connections. Once these connections are learned, the knowledge can be used to infer causes and effects where visual data might be partially hidden or ambiguous. In this paper, we present a Bayesian grammar model for human-perceived causal relationships that is learnable from video. Two exper- iments investigate high-level causal induction from low-level visual cues. In the first experiment, we show that a computer can apply known heuristics used for causal induction by hu- mans to learn perceptual causal relationships. In the second experiment, we show that our learned model can represent hu- mans’ performance in reasoning about hidden effects in video, even when the computer initially misdetects those effects. Keywords: Perceptual causality; causal induction; statistical models. Introduction A man approaches a closed door. He reaches out to grasp the handle and then stands there. Is it locked? Does he not have the key? He knocks and waits, but the door remains closed. Is there no one on the other side to open it? Watching these events unfold, humans can readily answer these questions based on their causal knowledge. One way humans can learn causal relationships is through daily ob- servation by internally measuring co-occurrence of events (Griffiths & Tenenbaum, 2005). Research suggests that humans use a few heuristics to determine whether a co- occurrence is causal, including: • whether the temporal lag between cause and effect is short, and the cause precedes the effect (Carey, 2009) and • whether agent actions are responsible for causes (Saxe, Tenenbaum, & Carey, 2005). However, learning from daily observation is limited: many actions and effects are hidden. Our prior knowledge about causal relationships between actions and effects allows us to fill in information about the events in the scene. Some current models represent knowledge with Bayesian networks, e.g., (Griffiths & Tenenbaum, 2005). These mod- els, however, are disjoint from the low-level visual data that people observe. Instead, models are built using high-level an- notations. In reality, agents build knowledge by observing low-level visual data, and models need to be able to deal with uncertainty in observation. Although Bayesian networks are commonly used to repre- sent causality (Pearl, 2009), grammar models have the ex- pressive power to represent a greater breadth of possibili- ties than a single instance of a Bayesian network (Griffiths & Tenenbaum, 2007). Grammar models allow for multiple configurations and high-level structures, making them more suitable for applications grounded on visual cues; Bayesian networks lack the representative power needed for this. Grammar models are represented graphically in the And- Or Graph (AOG). In the AOG, Or-nodes represent the mul- tiple alternatives, and And-nodes represent hierarchical de- compositions. The AOG naturally lends itself to represent causation where multiple alternative causes can lead to an ef- fect, and each cause is composed of conditions necessary for the effect. In this paper, we introduce a grammar model for repre- senting causal relationships between actions and object-status changes, the Causal And-Or Graph (C-AOG). We describe methods for learning the model by using co-occurrence to identify potential causal relationships between events and ap- plying the heuristics listed above to those potential relation- ships. In two experiments, we investigate how the model matches human perceptions of causality. Experiment 1 uses input typical of computer vision detection systems to investi- gate learning the C-AOG and human perceptions of causality. Experiment 2 demonstrates that the C-AOG models human judgments on imputing hidden variables from video. A Grammar Model for Causality In this section, we introduce the Causal And-Or Graph for causal reasoning, which ties agent actions to fluents. Fluents and Actions Specifically defining those object statuses that vary over time, the term fluents comes from the commonsense-reasoning lit- erature (Mueller, 2006). Relevant here are two kinds of flu- ents that intentional agents can change: object fluents (e.g., a light can be on or off) and fluents of the mind (e.g., an agent can be thirsty or not thirsty). The values of these fluents change as a result of agent ac- tions and also trigger rational agents to take action. A lack of change-inducing action (also known as the inertial action) causes the fluent to maintain its value; for example, a door that is closed will remain closed until some action changes it. In this work, fluents are modeled discriminatively. Actions (A i ) are modeled using the Temporal And-Or Graph (T-AOG), a grammar model for actions (Pei, Jia, & Zhu, 2011). In the T-AOG, And-nodes group the necessary ways for an action to be performed that allow detection of the action (e.g., object/agent spatial relations, agent poses, scene contexts, and temporal relationships), and Or-nodes provide


title: [Precocious puberty].

abstract: Precocious puberty is an early sexual maturation before the age of 8 in case of girls and 9 in boys. There are two types: isosexual precocious puberty--characteristic are appropriate for the child's genetic and gonadal sex; and heterosexual precocious puberty--sexual characteristic are inappropriate for the genetic sex (feminizing syndrome in boys or virilizing syndrome in girls). Precocious puberty is an important problem in childhood gynecology pediatrics, endocrinology and psychology.


title: A 0.6 mW/Gb/s, 6.4–7.2 Gb/s Serial Link Receiver Using Local Injection-Locked Ring Oscillators in 90 nm CMOS

abstract: This paper describes a quad-lane, 6.4-7.2 Gb/s serial link receiver prototype using a forwarded clock architecture. A novel phase deskew scheme using injection-locked ring oscillators (ILRO) is proposed that achieves greater than one UI of phase shift for multiple clock phases, eliminating phase rotation and interpolation required in conventional architectures. Each receiver, optimized for power efficiency, consists of a low-power linear equalizer, four offset-cancelled quantizers for 1:4 demultiplexing, and an injection-locked ring oscillator coupled to a low-voltage swing, global clock distribution. Measurement results show a 6.4-7.2 Gb/s data rate with BER < 10-12 across 14 cm of PCB, and also an 8.0 Gb/s data rate through 4 cm of PCB. Designed in a 1.2 V, 90 nm CMOS process, the ILRO achieves a wide tuning range from 1.6-2.6 GHz. The total area of each receiver is 0.0174 mm2, resulting in a measured power efficiency of 0.6 mW/Gb/s.


title: Lattice-based homomorphic encryption of vector spaces

abstract: In this paper we introduce a new probabilistic lattice-based bounded homomorphic encryption scheme. For this scheme the sum of two encrypted messages is the encryption of the sum of two messages and the scheme is able to preserve a vector spave structure of the message. The size of the public key is rather large ap 3 Mb but the encryption and the decryption operations are very fast (of the same speed order than NTRU). The homomorphic operation, i.e. the addition of ciphertexts is dramatically fast compared to homomorphic schemes based on group theory like Paillier or El Gamal.


title: The price of innovation: new estimates of drug development costs

abstract: The research and development costs of 68 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per new drug is US$ 403 million (2000 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 11% yields a total pre-approval cost estimate of US$ 802 million (2000 dollars). When compared to the results of an earlier study with a similar methodology, total capitalized costs were shown to have increased at an annual rate of 7.4% above general price inflation. © 2003 Elsevier Science B.V. All rights reserved.


title: A 1.4 million element, full frame CCD image sensor with vertical overflow drain for anti-blooming and low color crosstalk

abstract: Blooming and color crosstalk must be greatly suppressed in solid-state image sensors for nearly all imaging applications. A vertical overflow drain has been developed for a 1.4 megapixel image sensor for blooming suppression and low color crosstalk. The overflow drain is formed using a uniform flat p-well. This paper describes the modeling, fabrication, and experimental data associated with implementing vertical overflow in this device.


title: Modified dual band gysel power divider with isolation bandwidth improvement

abstract: This paper presents a new design of a dual band power divider with high power handling capability and isolation bandwidth improvement, which contains six branch line sections, two grounded resistors, one resistor between output ports, an extension line at the input, and a single stub line at the end. Exact closed-form equations are derived for the proposed structure using even-mode and odd-mode analysis. In this design, flexibility in choosing two grounded resistors enables us to determine the frequency ratio range, bandwidth of the circuit, and the level of power handling capability. In addition, line admittance analysis shows that the proposed structure can support the acceptable frequency ratio range. For verification, the proposed structure is simulated, fabricated and measured using microstrip lines. Simulation and measurement results show wide isolation bandwidth, while maintaining high power handling capability over the Wilkinson power divider.


title: Kinematic and dynamic modelling of UR5 manipulator

abstract: UR robotic arms are from a series of lightweight, fast, easy to program, flexible, and safe robotic arms with 6 degrees of freedom. The fairly open control structure and low level programming access with high control bandwidth have made them of interest for many researchers. This paper presents a complete set of mathematical kinematic and dynamic, Matlab, and Simmechanics models for the UR5 robot. The accuracy of the developed mathematical models are demonstrated through kinematic and dynamic analysis. The Simmechanics model is developed based on these models to provide high quality visualisation of this robot for simulation of it in Matlab environment. The models are developed for public access and readily usable in Matlab environment. A position control system has been developed to demonstrate the use of the models and for cross validation purpose.


title: NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

abstract: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.


title: Techniques and tools for analyzing intrusion alerts

abstract: Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive attacks, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. This paper presents a sequence of techniques to address this issue. The first technique constructs attack scenarios by correlating alerts on the basis of prerequisites and consequences of attacks. Intuitively, the prerequisite of an attack is the necessary condition for the attack to be successful, while the consequence of an attack is the possible outcome of the attack. Based on the prerequisites and consequences of different types of attacks, the proposed method correlates alerts by (partially) matching the consequences of some prior alerts with the prerequisites of some later ones. Moreover, to handle large collections of alerts, this paper presents a set of interactive analysis utilities aimed at facilitating the investigation of large sets of intrusion alerts. This paper also presents the development of a toolkit named TIAA, which provides system support for interactive intrusion analysis. This paper finally reports the experiments conducted to validate the proposed techniques with the 2000 DARPA intrusion detection scenario-specific datasets, and the data collected at the DEFCON 8 Capture the Flag event.


title: A survey of randomized algorithms for training neural networks

abstract: As a powerful tool for data regression and classification, neural networks have received considerable attention from researchers in fields such as machine learning, statistics, computer vision and so on. There exists a large body of research work on network training, among which most of them tune the parameters iteratively. Such methods often suffer from local minima and slow convergence. It has been shown that randomization based training methods can significantly boost the performance or efficiency of neural networks. Among these methods, most approaches use randomization either to change the data distributions, and/or to fix a part of the parameters or network configurations.źThis article presents a comprehensive survey of the earliest work and recent advances as well as some suggestions for future research.


DEFORMATIONAL STRUCTURES ASSOCIATED WITH GRAVITATIONAL BLOCK GLIDING : EXAMPLES FROM SEDIMENTARY OLISTOLITHS IN THE KALVAG MELANGE, WESTERN NORWAY


title: Palm oil industry in South East Asia and the effluent treatment technology—A review

abstract: Abstract The progress of Palm Oil Mill Effluent (POME) treatment in meeting with the regulation and standard stipulated by the Environmental Authority always been a major issue in Palm oil industries. To occupy the palm oil world market demand, palm oil industry needs to produce more than the market demand to supply necessity. Currently, South East Asia country such as Malaysia, Indonesia and Thailand rank the top country with the largest production of palm oil in the world. However, the increasing demand for the palm oil has resulted in even massive waste especially palm oil mill effluent (POME). Direct discharge of POME will adversely affect the environment. In 2011, 53 million tonnes metric of palm oil produced and 89% of this production comes from Malaysia and Indonesia. Thailand, however, used the palm oil solely for domestic usage. Since POME has been declared among the major source of pollution, a great deal of research and development including application devoted to enhance the current treatment method for POME to consistently meet the proposed stringent regulatory requirement by environmental authority. Conventional treatment such as ponding system is the most commonplace method to treat POME through the application of ponding system which is include aerobic and anaerobic treatment. Recently, the alternative methods such as coagulation, flocculation, adsorption, advanced oxidation process (AOP) and membrane technology to treat POME has shown a promising result compared to the conventional method.


title: GraphScript: implementing complex graph algorithms in SAP HANA

abstract: Real-world graph applications are typically domain-specific and model complex business processes in the property graph data model. To implement a domain-specific graph algorithm in the context of such a graph application, simply providing a set of built-in graph algorithms is usually not sufficient nor does it allow algorithm customization to the user's needs. To cope with these issues, graph database vendors provide---in addition to their declarative graph query languages---procedural interfaces to write user-defined graph algorithms. In this paper, we introduce G raph S cript , a domain-specific graph query language tailored to serve advanced graph analysis tasks and the specification of complex graph algorithms. We describe the major language design of Graph Script , discuss graph-specific optimizations, and describe the integration into an enterprise data platform.


title: Ant colony system for a dynamic vehicle routing problem

abstract: An aboundant literature on vehicle routing problems is available. However, most of the work deals with static problems, where all data are known in advance, i.e. before the optimization has started.


title: Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels

abstract: Training deep networks for semantic segmentation requires annotation of large amounts of data, which can be time-consuming and expensive. Unfortunately, these trained networks still generalize poorly when tested in domains not consistent with the training data. In this paper, we show that by carefully presenting a mixture of labeled source domain and proxy-labeled target domain data to a network, we can achieve state-of-the-art unsupervised domain adaptation results. With our design, the network progressively learns features specific to the target domain using annotation from only the source domain. We generate proxy labels for the target domain using the network's own predictions. Our architecture then allows selective mining of easy samples from this set of proxy labels, and hard samples from the annotated source domain. We conduct a series of experiments with the GTA5, Cityscapes and BDD100k datasets on synthetic-to-real domain adaptation and geographic domain adaptation, showing the advantages of our method over baselines and existing approaches.


title: Activation Functions: Comparison of trends in Practice and Research for Deep Learning

abstract: Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.


title: Design and implementation of bandwidth-aware memory placement and migration policies for heterogeneous memory systems

abstract: Heterogeneous memory systems that comprise memory nodes based on widely-different device technologies (e.g., DRAM and nonvolatile memory (NVM)) are emerging in various computing domains ranging from high-performance to embedded computing. Despite the extensive prior work on architectural and system software support for heterogeneous memory systems, relatively little work has been done to investigate the OS-level memory placement and migration policies that consider the bandwidth differences of heterogeneous memory nodes. To bridge this gap, this work investigates the design and implementation of memory placement and migration policies for bandwidth-intensive applications on heterogeneous memory systems. Specifically, we propose three bandwidth-aware memory placement policies (i.e., bandwidth-aware interleave, random, and local policies) and a bandwidth-aware memory migration policy and implement the proposed policies in the Linux kernel. Through our quantitative evaluation based on real system software and hardware stacks, we demonstrate that the bandwidth-aware memory placement and migration policies achieve significantly higher performance than the conventional bandwidth-oblivious policies across a wide range of the DRAM-to-NVM bandwidth ratios when executing bandwidth-intensive workloads.


title: Multi-Domain Service Orchestration Over Networks and Clouds: A Unified Approach

abstract: End-to-end service delivery often includes transparently inserted Network Functions (NFs) in the path. Flexible service chaining will require dynamic instantiation of both NFs and traffic forwarding overlays. Virtualization techniques in compute and networking, like cloud and Software Defined Networking (SDN), promise such flexibility for service providers. However, patching together existing cloud and network control mechanisms necessarily puts one over the above, e.g., OpenDaylight under an OpenStack controller. We designed and implemented a joint cloud and network resource virtualization and programming API. In this demonstration, we show that our abstraction is capable for flexible service chaining control over any technology domains.


title: ScrumS: a model for safe agile development

abstract: The utilization of the Scrum method for software development offers major benefits to its users, such as the process acceleration and resources to deal with the instability of technological environments. Fast customer feedback and support for volatile requirement results in a higher product value, however it hinders the team in dealing appropriately with a critical aspect of every system, which is the information security. Whereas attacks have become more sophisticated that even simpler systems can be potential targets, so it is essential to treat software security within the agile method itself, in order to make it part of the process. Aiming to improve system's quality, reliability, and security, this work proposes an accessory to the Scrum agile method named ScrumS, which adds specific security techniques for a risk analysis project.


title: Modelling of network traffic usage using self-organizing maps techniques

abstract: Currently, the Internet is one important part of the campus infrastructure that supports the teaching and learning activities. The important part of the internet facility is the provided bandwidth as Bandwidth management to teaching and learning is indispensable. In this study, an analysis and cluster of the university internet traffic is required as bandwidth management decision support. Therefore, Self-Organizing Maps (SOM) as a clustering algorithm bandwidth usage was implemented. The results showed that the SOM method can perform clustering. Furthermore, the clustering result could be a recommendation management bandwidth for network administrator.


title: A Dynamic Online Traffic Classification Methodology Based on Data Stream Mining

abstract: Recently, traffic classification (TC) becomes more and more important for network management and measurement tasks. The new-coming machine learning based classification methods can achieve high classification accuracy and fast identification ability; however, all these related TC methods up to now always have the assumption of the stability of classification model constituted from network traffic. It is not true since seldom real-world traffic is static. In this paper, we make a first step towards classifying dynamic online traffic in a data stream perspective to handle the dynamic real-time network traffic. In this paper, we validate the dynamic feature of real-world traffic for the first time, using concept drift from two different levels: overall traffic level and application level. The conclusion convinces us that the user behavior reflected in traffic can vary dramatically due to different conditions and different periods. We then propose a novel integrated dynamic online traffic classification framework; called DSTC (Data Stream based Traffic Classification). This DSTC differs from previous work since it aims to deal with dynamic traffic with online identification ability. It is a more realistic framework in which training phase can go simultaneously with classification phase and more accurate training model can be constructed with the feedback from classification result. Experiment results have shown that DSTC can have a high stable classification accuracy of above 95% for network traffic with different periods and user conditions, while accuracy for the traditional classification methodology can vary from 81% to 97% when dealing with different traffic.


title: Brain mechanisms linking language and action

abstract: For a long time the cortical systems for language and actions were believed to be independent modules. However, as these systems are reciprocally connected with each other, information about language and actions might interact in distributed neuronal assemblies. A critical case is that of action words that are semantically related to different parts of the body (for example, 'lick', 'pick' and 'kick'): does the comprehension of these words specifically, rapidly and automatically activate the motor system in a somatotopic manner, and does their comprehension rely on activity in the action system?


title: Expanding the spectrum of frontal fibrosing alopecia: a unifying concept.

abstract: BACKGROUND ::: In frontal fibrosing alopecia (FFA), scalp alopecia dominates the clinical picture. However, eyebrow loss and hair loss in other body sites may also occur; this has been documented clinically, but rarely histopathologically. We describe the clinicopathological findings of 13 cases of FFA, with histopathologic data from the scalp, eyebrow, and body hair. ::: ::: ::: METHODS ::: Thirteen patients with a diagnosis of FFA, seen between 2006 and 2008, were included. Scalp biopsies were performed in all patients for histology and direct immunofluorescence (DIF). Biopsy specimens for histology were taken from the eyebrow in 6 patients and from the upper limb in 5 patients. ::: ::: ::: RESULTS ::: All 13 patients were female, 11 of whom were postmenopausal. The median age at onset of alopecia was 57 years. Clinical examination revealed a band of frontal hairline recession in all patients. Eyebrow loss was present clinically in all patients, with loss of body hair in 10 of 13. Histopathologic examination of the scalp, eyebrow, and upper limb skin biopsy specimens showed similar features, including a marked reduction in the number of hair follicles and a perifollicular lymphoid cell infiltrate with perifollicular fibrosis. Direct immunofluorescence was negative in all cases. ::: ::: ::: LIMITATIONS ::: Not all patients consented to biopsies of the eyebrows or upper limbs. ::: ::: ::: CONCLUSION ::: Eyebrow and peripheral body hair loss is not uncommon in FFA-a finding that is likely underreported. We have demonstrated that alopecia of the upper limbs in FFA is indeed common and, histopathologically, shows features of lichen planopilaris and scarring, similar to findings in the scalp and eyebrows. Consequently, the process of lichen planopilaris with scarring alopecia is generalized rather than localized only to the frontal scalp and eyebrows.


title: Emotion processing and the amygdala: from a 'low road' to 'many roads' of evaluating biological significance

abstract: A subcortical pathway through the superior colliculus and pulvinar to the amygdala is commonly assumed to mediate the non-conscious processing of affective visual stimuli. We review anatomical and physiological data that argue against the notion that such a pathway plays a prominent part in processing affective visual stimuli in humans. Instead, we propose that the primary role of the amygdala in visual processing, like that of the pulvinar, is to coordinate the function of cortical networks during evaluation of the biological significance of affective visual stimuli. Under this revised framework, the cortex has a more important role in emotion processing than is traditionally assumed.


title: The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes

abstract: Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.


title: Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing

abstract: Synthetic Aperture Radar (SAR) is active and coherent microwave high resolution imaging system, which has the capability to image in all weather and day-night conditions. SAR transmits chirp signals and the received echoes are sampled into In-phase (I) and Quadrature (Q) components, generally referred to as raw SAR data. The various modes of SAR coupled with the high resolution and wide swath requirements result in a huge amount of data, which will easily exceed the on-board storage and downlink bandwidth of a satellite. This paper addresses the compression of the raw SAR data by sampling the signal below Nyquist rate using ideas from Compressed Sensing (CS). Due to the low computational resources available onboard satellite, the idea is to use a simple encoder, with a 2D FFT and a random sampler. Decoding is then based on convex optimization or uses greedy algorithms such as Orthogonal Matching Pursuit (OMP).


title: Mobile phone addiction levels and negative emotions among Chinese young adults: The mediating role of interpersonal problems

abstract: This study evaluated the mediating role of interpersonal problems in the link between mobile phone addiction levels and negative emotions among mobile phone addicts and possible-mobile phone addicts respectively. The purpose of this study was to address two gaps in research. First, previous research has revealed that a positive association existed between mobile phone addiction levels and negative emotions, especially among those with interpersonal problems. The present study extended research by exploring the mechanisms underlying these associations. Second, Mobile phone addicts as a particular group has received much attention, however, research concerning the possible mobile phone addicts as another particular group is limited. A total of 1089 young adults in China were surveyed by using cluster sampling. Results showed that compared with nonaddicts, mobile phone addicts or possible mobile phone addicts spent more money and time on mobile phone use and were more vulnerable to have negative emotions. The effects of the possible mobile phone addiction on negative emotions were significant but weaker than those of the mobile phone addiction. Further, interpersonal problem was a significant mediator between mobile phone addiction levels and negative emotions. Findings and their implications are discussed. Possible mobile phone addiction is prevalent among young adults.Mobile phone addiction level is positively related to negative emotions (NE).Interpersonal problems mediate the effects of mobile phone addiction level on NE.


title: Web usage mining based on probabilistic latent semantic analysis

abstract: The primary goal of Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of user sessions and discovering association rules or frequent navigational paths, do not generally provide the ability to automatically characterize or quantify the unobservable factors that lead to common navigational patterns. It is, therefore, necessary to develop techniques that can automatically discover hidden semantic relationships among users as well as between users and Web objects. Probabilistic Latent Semantic Analysis (PLSA) is particularly useful in this context, since it can uncover latent semantic associations among users and pages based on the co-occurrence patterns of these pages in user sessions. In this paper, we develop a unified framework for the discovery and analysis of Web navigational patterns based on PLSA. We show the flexibility of this framework in characterizing various relationships among users and Web objects. Since these relationships are measured in terms of probabilities, we are able to use probabilistic inference to perform a variety of analysis tasks such as user segmentation, page classification, as well as predictive tasks such as collaborative recommendations. We demonstrate the effectiveness of our approach through experiments performed on real-world data sets.


title: Big spatial data processing with Apache Spark

abstract: Big data technologies have shown great promise for managing geospatial data in recent years. In order to deal with the growing spatial data, a high performance spatial data processing system layered on big data technologies is needed. In this paper, we present an approach to process big spatial data with Apache Spark, a fast and generic engine for large-scale data processing. We developed a software development kit named SparkSpatialSDK, which takes spatial characteristics of geospatial data into consideration and provides a Spark-enabled spatial data structure and API to allow users easily perform spatial analyses with big spatial data. The spatial data structure couples geometric data structure (point, line, and polygon) with Resilient Distributed Datasets (RDD). An interface, called SpatialRDD, is provided to access big spatial data stored in distributed database systems like HBase and load the data in Spark processing engine. We illustrates the applications of the API using some example processing functions such as the spatial range and spatial k-nearest neighbor queries. The results demonstrate the applicability of using SparkSpatialSDK for big geospatial data processing.


title: Approximately Optimal Teaching of Approximately Optimal Learners

abstract: We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks [1] , e.g., teaching students the meanings of words by showing images that exemplify their meanings a la Rosetta Stone [2] and Duo Lingo [3] . The approach is grounded in control theory and capitalizes on recent work by [4] , [5] that frames the “teaching” problem as that of finding approximately optimal teaching policies for approximately optimal learners (AOTAOL). Our work expands on [4] , [5] in several ways: (1) We develop a novel student model in which the teacher's actions can partially eliminate hypotheses about the curriculum. (2) With our student model, inference can be conducted analytically rather than numerically, thus allowing computationally efficient planning to optimize learning. (3) We develop a reinforcement learning-based hierarchical control technique that allows the teaching policy to search through deeper learning trajectories. We demonstrate our approach in a novel ITS for foreign language learning similar to Rosetta Stone and show that the automatically generated AOTAOL teaching policy performs favorably compared to two hand-crafted teaching policies.


title: Implicit pricing across residential rental submarkets

abstract: This paper examines implicit price differences of rental housing characteristics across various property types to measure whether determinants of rents are valued in the aggregate or separately. The results show that hedonic price functions are not identical across property types, which suggests that ordinary least squares is not the appropriate estimation technique when modeling the implicit prices for an aggregate rental market. Generalized least squares estimation of a random coefficient model removes the restriction of fixed parameters imposed by OLS and allows estimation of implicit prices for rental markets containing multiple property types.


title: EthIKS: Using Ethereum to Audit a CONIKS Key Transparency Log

abstract: CONIKS is a proposed key transparency system which enables a centralized service provider to maintain an auditable yet privacy-preserving directory of users’ public keys. In the original CONIKS design, users must monitor that their data is correctly included in every published snapshot of the directory, necessitating either slow updates or trust in an unspecified third-party to audit that the data structure has stayed consistent. We demonstrate that the data structures for CONIKS are very similar to those used in Ethereum, a consensus computation platform with a Turing-complete programming environment. We can take advantage of this to embed the core CONIKS data structures into an Ethereum contract with only minor modifications. Users may then trust the Ethereum network to audit the data structure for consistency and non-equivocation. Users who do not trust (or are unaware of) Ethereum can self-audit the CONIKS data structure as before. We have implemented a prototype contract for our hybrid EthIKS scheme, demonstrating that it adds only modest bandwidth overhead to CONIKS proofs and costs hundredths of pennies per key update in fees at today’s rates.


title: Capsule-oriented programming

abstract: "Explicit concurrency should be abolished from all higher-level programming languages (i.e. everything except - perhaps- plain machine code.)." Dijkstra [1] (paraphrased). A promising class of concurrency abstractions replaces explicit concurrency mechanisms with a single linguistic mechanism that combines state and control and uses asynchronous messages for communications, e.g. active objects or actors, but that doesn't remove the hurdle of understanding non-local control transfer. What if the programming model enabled programmers to simply do what they do best, that is, to describe a system in terms of its modular structure and write sequential code to implement the operations of those modules and handles details of concurrency? In a recently sponsored NSF project we are developing such a model that we call capsule-oriented programming and its realization in the Panini project. This model favors modularity over explicit concurrency, encourages concurrency correctness by construction, and exploits modular structure of programs to expose implicit concurrency.


title: New Single-Input, Multiple-Output Converter Topologies: Combining Single-Switch Nonisolated dc-dc Converters for Single-Input, Multiple-Output Applications

abstract: This article presents a methodology that allows the development of new converter topologies for single-input, multiple-output (SIMO) from different basic configurations of single-input, single-output dc-dc converters. These typologies have in common the use of only one power-switching device, and they are all nonisolated converters. Sixteen different topologies are highlighted, and their main features are explained. The 16 typologies include nine twooutput-type, five three-output-type, one four-output-type, and one six-output-type dc-dc converter configurations. In addition, an experimental prototype of a three-output-type configuration with six different output voltages based on a single-ended primary inductance (SEPIC)-Cuk-boost combination converter was developed, and the proposed design methodology for a basic converter combination was experimentally verified.


title: OTPM: Failure handling in data-intensive analytical processing

abstract: Parallel processing is the key to speedup performance and to achieve high throughput in processing large scale data analytical workloads. However, failures of nodes involved in the analytical query can interrupt the whole process, resulting in the complete restart of the query if the system does not have query fault-tolerance. Complete restart might be too costly for processing query on very large databases and might not be able to meet the time constraints in decision support systems. In this paper, we present an approach to resume query processing after failure by keeping track of the point at which data has been processed by an operator, called operator tracking. We also consider saving intermediate results using partial materialization. We look at several fundamental parallel database techniques which are widely used today and analyze the performance cost of query processing and recovery using those techniques with our OTPM fault-tolerance approach. We perform simulation-based experiments which show that our approach incurs only a small resume overhead compared to complete pipelining and complete materialization of intermediate results. Also, the combination of our approach with vertically partitioned database in a shared-nothing environment yields the best performance among different settings for parallel processing of data intensive analytical workloads.


title: Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks

abstract: Activity recognition from wearable sensor data has been researched for many years. Previous works usually extracted features manually, which were hand-designed by the researchers, and then were fed into the classifiers as the inputs. Due to the blindness of manually extracted features, it was hard to choose suitable features for the specific classification task. Besides, this heuristic method for feature extraction could not generalize across different application domains, because different application domains needed to extract different features for classification. There was also work that used auto-encoders to learn features automatically and then fed the features into the K-nearest neighbor classifier. However, these features were learned in an unsupervised manner without using the information of the labels, thus might not be related to the specific classification task. In this paper, we recommend deep neural networks (DNNs) for activity recognition, which can automatically learn suitable features. DNNs overcome the blindness of hand-designed features and make use of the precious label information to improve activity recognition performance. We did experiments on three publicly available datasets for activity recognition and compared deep neural networks with traditional methods, including those that extracted features manually and auto-encoders followed by a K-nearest neighbor classifier. The results showed that deep neural networks could generalize across different application domains and got higher accuracy than traditional methods.


title: Variable-Grasping-Mode Underactuated Soft Gripper With Environmental Contact-Based Operation

abstract: A novel robotic gripper with soft surfaces and underactuated joints was proposed. The soft surface was fabricated from a deformable rubber bag filled with incompressible fluid and a microgripper inside the fluid. A ratchet was installed at the underactuated joint so that the joint's rotation caused by contact with an environment, such as a supporting surface, can be preserved, and the actions of scooping and enveloping an object are realized. With one actuator, the gripper realized three modes, i.e., parallel gripper, pinching, and enveloping. The range of graspable objects was wide and included soft, rigid, deformable, fragile, small (boundary length less than 30 mm), large (more than 80 mm long), thin (less than 0.5 mm), and heavy (more than 3 kg) objects.


title: Immediate and Long-term Effects of Form-Focused Instruction in the Acquisition of English Relative Clauses: Evidence from Turkish EFL Learners

abstract: This article examines immediate and long-term effects of explicit formal instruction in the learning of relative clauses in English by Turkish native speakers. Forty-five 9-graders (aged 15-17) participated in the study. Data were collected by means of three different sentence combination tests and two different grammaticality judgment tests. Subjects were pretested prior to instruction on relative clauses, post-tested two days after, follow-up tested 6 weeks later and finally they were given a long-term test 6 months after instruction. Although the subjects’ performance seemed to improve considerably on their posttests, these results appeared to be only short term. Our findings support the view that though the immediate effects of form-focused instruction are positive, they do not appear to be long-lasting.


A Secure Method to Hide Confidential Data Using Cryptography and Steganography


title: Forward Models for Physiological Motor Control.

abstract: Based on theoretical and computational studies it has been suggested that the central nervous system (CNS) internally simulates the behaviour of the motor system in planning, control and learning. Such an internal "forward" model is a representation of the motor system that uses the current state of the motor system and motor command to predict the next state. We will outline the uses of such internal models for solving several fundamental computational problems in motor control and then review the evidence for their existence and use by the CNS. Finally we speculate how the location of an internal model within the CNS may be identified. Copyright 1996 Elsevier Science Ltd.


title: Attention-based Pyramid Aggregation Network for Visual Place Recognition

abstract: Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.


title: Semi natural language algorithm to programming language interpreter

abstract: The conversion of an algorithm to code is still at an early stage. Effective conversion of algorithms mentioned in natural English language to code will enable programmers to focus on logic building and free them of syntactical worries, further it will also aid the visually impaired programmers. Although beneficial, implementation of such a converter encounters numerous challenges like limitations imposed due to semantics of the English language, case frames, etc. In this paper we have introduced an interpreter that is capable of converting algorithms in English to C code whose flexibility of interpretation has been enhanced by using synonyms and by the introduction of a personalised training model whose concept has been outlined below. We have defined the conceptual model along with a user scenario which demonstrates the functioning of our model.


title: Temple syndrome: A patient with maternal hetero-UPD14, mixed iso- and hetero-disomy detected by SNP microarray typing of patient-father duos

abstract: Temple syndrome (TS, MIM 616222) is an imprinting disorder involving genes within the imprinted region of chromosome 14q32. TS is a genetically complex disorder, which is associated with maternal uniparental disomy of chromosome 14 (UPD14), paternal deletions on chromosome 14, or loss of methylation at the intergenic differentially methylated region (IG-DMR). Here, we describe the case of a patient with maternal hetero-UPD14, mixed iso-/hetero-disomy mechanism identified by a single nucleotide polymorphism (SNP) array analysis of patient-father duos study. The phenotype of our case is similarities to Prader-Willi syndrome (PWS) during infancy and to Russell-Silver syndrome (RSS) during childhood. This SNP array appears to be an effective initial screening tool for patients with nonspecific clinical features suggestive of chromosomal disorders.


title: Social Incentive Optimization in Online Social Networks

abstract: Most online social networks provide a mechanism for users to broadcast messages to their personalized network through actions like shares, likes and tweets. Receiving positive feedback from the network such as likes, comments and retweets in response to such actions can provide a strong incentive for users to broadcast more often in the future. We call such feedback by the network, that influences a user to perform certain desirable future actions, social incentives. For example, after a user shares an article to her social network, receiving positive feedback such as a ''like'' from a friend can potentially encourage her to continue sharing more regularly. Typically, for every user's visit to an online social network site, good messages need to be ranked and selected by a recommender system from a large set of candidate messages (broadcasted by the user's network). In this paper, we propose a novel recommendation problem: How should we recommend messages to users to incentivize neighbors in their personal network to perform desirable actions in the future with high likelihood, without significantly hurting overall engagement for the entire system? For instance, messages could be content shared by neighbors. The goal in this case would be to encourage more content shares in the future. We call this problem social incentive optimization and study an instance of it for LinkedIn's news feed. We observe that a user who receives positive social feedback from neighbors has a higher likelihood of broadcasting more frequently. Using this observation, we develop a novel recommendation framework that incentivize users to broadcast more often, without significantly hurting overall feed engagement. We demonstrate the effectiveness of our approach through causal analysis on retrospective data and online A/B experiments.


title: Mental Illness and Creativity: A Neurological View of the "Tortured Artist"

abstract: As advances in science bring us closer than ever to understanding ourselves, we are forced to reexamine the canon ancient folk wisdom regarding human nature. Our culture is full of assumptions and stereotypes about how the mind works, perhaps none so enduring as the legend of the “tortured artist”. The contradiction of the genius who creates great artwork despite (or because of) mental illness has been part of Western legend for thousands of years. The image is durable, but sci


title: Adaptive Mobile Cloud Computing to Enable Rich Mobile Multimedia Applications

abstract: With worldwide shipments of smartphones (487.7 million) exceeding PCs (414.6 million including tablets) in 2011, and in the US alone, more users predicted to access the Internet from mobile devices than from PCs by 2015, clearly there is a desire to be able to use mobile devices and networks like we use PCs and wireline networks today. However, in spite of advances in the capabilities of mobile devices, a gap will continue to exist, and may even widen, with the requirements of rich multimedia applications. Mobile cloud computing can help bridge this gap, providing mobile applications the capabilities of cloud servers and storage together with the benefits of mobile devices and mobile connectivity, possibly enabling a new generation of truly ubiquitous multimedia applications on mobile devices: Cloud Mobile Media (CMM) applications.


title: A Novel PAPR Reduction Scheme for OFDM System Based on Deep Learning

abstract: High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.


title: A semi-definite programming approach for solving optimal reactive power reserve dispatch

abstract: It is well known that proper reactive power reserve (RPR) is essential to avoid voltage instability and abnormal voltage. Most literatures limits the maximum supply of generator reactive power output by the maximum loading point. Thus the optimization of RPR usually involves two operating points, i.e. current operating point and maximum loading point. However, extreme differences exist between the statuses of generators at those two operating points and cannot be given in advance. That means current techniques cannot solve optimal RPR model directly. In this paper, a semi-definite programming (SDP) approach for solving optimal RPR model is proposed. Proposed method transforms the solution into iterations and each iteration is an optimal power flow problem solved by SDP. For demonstrating the validity of proposed method, IEEE 118 system in three load scenarios is set as the test case. The results prove the effectiveness and robustness of the proposed method.


title: Are We Ready to Drive Software-Defined Networks? A Comprehensive Survey on Management Tools and Techniques

abstract: In the context of the emergent Software-Defined Networking (SDN) paradigm, the attention is mostly directed to the evolution of control protocols and networking functionalities. However, network professionals also need the right tools to reach the same level—and beyond—of monitoring and control they have in traditional networks. Current SDN tools are developed on an ad hoc basis, for specific SDN frameworks, while production environments demand standard platforms and easy integration. This survey aims to foster the definition of the next generation SDN management framework by providing the readers a thorough overview of existing SDN tools and main research directions.


title: Path planning using state lattice for autonomous vehicle

abstract: Path, trajectory and motion planning are three fundamental classification which come under planning of autonomous vehicle. Path planning deals with searching a plausible path, mulling over of geometry, kinematics and constraints of the vehicle without considering its association with time. Here a path planning technique using state lattice strategy is adopted. A path set which incorporates all the geometry, kinematics and constraints of the vehicle, known as motion primitives is pre-figured and rotation matrix is used in this approach which describes the novelty. The primitives are pre-calculated so that while online searching of the path it won't take much investment in time and furthermore the constraints of the vehicle are included in the primitive design. The set of arranged motion primitives is alluded as state lattice. The motion primitive which is stored in structural array is later used to compute a feasible path in a map. Distinctive situations are considered for which the way has been found.


title: Jamming of UAV remote control systems using software defined radio

abstract: Unmanned aerial vehicles (UAVs) have become widely available and their potential unlawful usage introduces new security risks. It has therefore become highly desirable to restrict the unauthorized usage of UAVs in certain areas such as airports, nuclear power plants, etc. Most commercially available UAVs rely on spread spectrum techniques, such as direct sequencing and frequency hopping, in the remote control systems to reduce the impact of interference from neighboring communication systems (including e.g. other remotely controlled UAVs), to increase resistance to jamming and to prevent detection. In this paper, an efficient protocol-aware UAV remote control jamming system is proposed and implemented using an open-source software defined radio (SDR) platform. Experimental results show that for FASST and ACCST remote control systems, the proposed jammer achieves successful jamming at relatively low jam-to-signal ratios (JSRs) as compared to a sweep jammer, therefore requiring less transmitted power to achieve similar results. Furthermore, the proposed jammer impacts other communication systems significantly less than the sweep jammer.


title: Exploring STT-MRAM Based In-Memory Computing Paradigm with Application of Image Edge Extraction

abstract: In this paper, we propose a novel Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM) array design that could simultaneously work as non-volatile memory and implement a reconfigure in-memory logic operation without add-on logic circuits to the memory chip. The computed output could be simply read out like a typical MRAM bit-cell through the modified peripheral circuit. Such intrinsic in-memory computation can be used to process data locally and transfers the "cooked" data to the primary processing unit (i.e. CPU or GPU) for complex computation with high precision requirement. It greatly reduces power-hungry and long distance data communication, and further leads to extreme parallelism within memory. In this work, we further propose an in-memory edge extraction algorithm as a case study to demonstrate the efficiency of in-memory preprocessing methodology. The simulation results show that our edge extraction method reduces data communication as much as 8x for grayscale image, thus greatly reducing system energy consumption. Meanwhile, the F-measure result shows only ∼10% degradation compared to conventional edge detection operators, such as Prewitt, Sobel and Roberts.


Distributed File Systems Implementation on an Edge Router using GlusterFS for Cloud Applications


title: Probabilistic models of information retrieval based on measuring the divergence from randomness

abstract: We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose--Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document--query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model.


title: Event Representations with Tensor-based Compositions

abstract: Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events for such understanding, and has recently attracted renewed attention. However, obtaining effective representations for modeling script-like event sequences is challenging. It requires representations that can capture event-level and scenario-level semantics. We propose a new tensor-based composition method for creating event representations. The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. With the continuous representations, we also devise a simple schema generation method which produces better schemas compared to a prior discrete representation based method. Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations.


title: Urbanisation and infectious diseases in a globalised world

abstract: Summary The world is becoming urban. The UN predicts that the world's urban population will almost double from 3·3 billion in 2007 to 6·3 billion in 2050. Most of this increase will be in developing countries. Exponential urban growth is having a profound effect on global health. Because of international travel and migration, cities are becoming important hubs for the transmission of infectious diseases, as shown by recent pandemics. Physicians in urban environments in developing and developed countries need to be aware of the changes in infectious diseases associated with urbanisation. Furthermore, health should be a major consideration in town planning to ensure urbanisation works to reduce the burden of infectious diseases in the future.


title: Centroid-based summarization of multiple documents

abstract: We present a multi-document summarizer, MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We describe two new techniques, a centroid-based summarizer, and an evaluation scheme based on sentence utility and subsumption. We have applied this evaluation to both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.


title: A Trustless Privacy-Preserving Reputation System

abstract: Reputation systems are crucial for distributed applications in which users have to be made accountable for their actions, such as e-commerce websites. However, existing systems often disclose the identity of the raters, which might deter honest users from submitting reviews out of fear of retaliation from the ratees. While many privacy-preserving reputation systems have been proposed, we observe that none of them is simultaneously truly decentralized, trustless, and suitable for real world usage in, for example, e-commerce applications. In this paper, we present a blockchain based decentralized privacy-preserving reputation system. We demonstrate that our system provides correctness and security while eliminating the need for users to trust any third parties or even fellow users.


title: Exploiting novelty, coverage and balance for topic-focused multi-document summarization

abstract: Novelty, coverage and balance are important requirements in topic-focused summarization, which to a large extent determine the quality of a summary. In this paper, we propose a novel method that incorporates these requirements into a sentence ranking probability model. It differs from the existing methods in that the novelty, coverage and balance requirements are all modeled w.r.t. a given topic, so that summaries are highly relevant to the topic and at the same time comply with topic-aware novelty, coverage and balance. Experimental results on the DUC 2005, 2006 and 2007 benchmark data sets demonstrate the effectiveness of our method.