Query: Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world

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OpinionFinder: A System for Subjectivity Analysis <=SEP=> Vancouver, October 2005. OpinionFinder: A system for subjectivity analysis Theresa Wilson‡, Paul Hoffmann‡, Swapna Somasundaran†, Jason Kessler†, Janyce Wiebe†‡, Yejin Choi§, Claire Cardie§, Ellen Riloff∗, Siddharth Patwardhan∗ ‡Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260 †Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260 §Department of Computer Science, Cornell University, Ithaca, NY 14853 ∗School of Computing, University of Utah, Salt Lake City, UT 84112

ARSA: a sentiment-aware model for predicting sales performance using blogs <=SEP=> Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.

Learning Extraction Patterns for Subjective Expressions <=SEP=> This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.

Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis <=SEP=> This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automatically identify thecontextual polarityfor a large subset of sentiment expressions, achieving results that are significantly better than baseline.

Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing <=SEP=> Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Visual Path Prediction in Complex Scenes with Crowded Moving Objects <=SEP=> This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.

A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera <=SEP=> This paper proposes and validates a real-time on-road vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.

A Blind Baud-Rate ADC-Based CDR <=SEP=> This paper proposes a 10-Gb/s blind baud-rate ADC-based CDR. The blind baud-rate operation is made possible by using a 2UI integrate-and-dump filter, which creates intentional ISI in adjacent bit periods. The blind samples are interpolated to recover center-of-the-eye samples for a speculative Mueller-Muller PD and a 2-tap DFE operation. A test chip, fabricated in 65-nm CMOS, implements a 10-Gb/s CDR with a measured high-frequency jitter tolerance of 0.19 UIPP and ±300 ppm of frequency offset.

Three-axis NC milling simulation based on adaptive triangular mesh <=SEP=> 0360-8352/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.cie.2010.06.016 * Corresponding author. Tel.: +86 21 67791413; fax E-mail address: mainpowers@gmail.com (J. Mao). NC milling simulation has become an important step in computer aided manufacturing (CAM). To achieve real-time simulation, the total number of polygons has to be reduced, which results in poor image quality. This paper presents an adaptive triangular mesh algorithm to reduce the number of polygons while image quality remains high. Binary tree is used to represent the milling surface, and the optimization of the mesh is performed dynamically in the process of simulation. In this algorithm, the resolution of triangles is automatically updated according to local surface flatness, thus greatly reducing the number of triangles at planar regions. By doing this, real-time and high quality of visual presentation is insured and the translation, rotation and zooming operations are still applicable. When machining precision is evaluated, or overcut, undercut and interference are inspected, full resolution model stored in memory is automatically loaded to ensure the accuracy and correctness of these inspections. Finally, an example is presented to illustrate the validity of proposed algorithm. 2010 Elsevier Ltd. All rights reserved.

Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity <=SEP=> Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies.

Surface Plasmon Resonance Imaging Sensors : A Review <=SEP=> Surface plasmon resonance (SPR) imaging sensors realize label free, real-time, highly sensitive, quantitative, high throughput biological interaction monitoring and the binding profiles from multi-analytes further provide the binding kinetic parameters between different bio-molecules. In the past two decades, SPR imaging sensors found rapid increasing applications in fundamental biological studies, medical diagnostics, drug discovery, food safety, precision measurement and environmental monitoring. In this paper, we review the recent advances of SPR imaging sensor technology towards high throughput multi-analyte screening. Finally, we describe our multiplex colorimetric SPR imaging biosensor based on polarization orientation for high throughput bio-sensing applications.

Individual differences in non-verbal number acuity correlate with maths achievement <=SEP=> Human mathematical competence emerges from two representational systems. Competence in some domains of mathematics, such as calculus, relies on symbolic representations that are unique to humans who have undergone explicit teaching. More basic numerical intuitions are supported by an evolutionarily ancient approximate number system that is shared by adults, infants and non-human animals—these groups can all represent the approximate number of items in visual or auditory arrays without verbally counting, and use this capacity to guide everyday behaviour such as foraging. Despite the widespread nature of the approximate number system both across species and across development, it is not known whether some individuals have a more precise non-verbal ‘number sense’ than others. Furthermore, the extent to which this system interfaces with the formal, symbolic maths abilities that humans acquire by explicit instruction remains unknown. Here we show that there are large individual differences in the non-verbal approximation abilities of 14-year-old children, and that these individual differences in the present correlate with children’s past scores on standardized maths achievement tests, extending all the way back to kindergarten. Moreover, this correlation remains significant when controlling for individual differences in other cognitive and performance factors. Our results show that individual differences in achievement in school mathematics are related to individual differences in the acuity of an evolutionarily ancient, unlearned approximate number sense. Further research will determine whether early differences in number sense acuity affect later maths learning, whether maths education enhances number sense acuity, and the extent to which tertiary factors can affect both.

Adult hippocampal neurogenesis and its role in Alzheimer's disease <=SEP=> The hippocampus, a brain area critical for learning and memory, is especially vulnerable to damage at early stages of Alzheimer's disease (AD). Emerging evidence has indicated that altered neurogenesis in the adult hippocampus represents an early critical event in the course of AD. Although causal links have not been established, a variety of key molecules involved in AD pathogenesis have been shown to impact new neuron generation, either positively or negatively. From a functional point of view, hippocampal neurogenesis plays an important role in structural plasticity and network maintenance. Therefore, dysfunctional neurogenesis resulting from early subtle disease manifestations may in turn exacerbate neuronal vulnerability to AD and contribute to memory impairment, whereas enhanced neurogenesis may be a compensatory response and represent an endogenous brain repair mechanism. Here we review recent findings on alterations of neurogenesis associated with pathogenesis of AD, and we discuss the potential of neurogenesis-based diagnostics and therapeutic strategies for AD.

Joint SFM and detection cues for monocular 3D localization in road scenes <=SEP=> We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.

Dynamic Traffic Diversion in SDN: testbed vs Mininet <=SEP=> In this paper, we first propose a simple Dynamic Traffic Diversion (DTD) algorithm for Software Defined Networks (SDN). After implementing the algorithm inside the controller, we then compare the results obtained under two different test environments: 1) a testbed using real Cisco equipment and 2) a network emulation using Mininet. From the results, we get two key messages. First, we can clearly see that dynamically diverting important traffic on a backup path will prevent packet loss and reduce jitter. Finally, the two test environments provide relatively similar results. The small differences could be explained by the early field trial image that was used on the Cisco equipment and by the many setting parameters that are available in both environments.

Neural Joint Model for Transition-based Chinese Syntactic Analysis <=SEP=> We present neural network-based joint models for Chinese word segmentation, POS tagging and dependency parsing. Our models are the first neural approaches for fully joint Chinese analysis that is known to prevent the error propagation problem of pipeline models. Although word embeddings play a key role in dependency parsing, they cannot be applied directly to the joint task in the previous work. To address this problem, we propose embeddings of character strings, in addition to words. Experiments show that our models outperform existing systems in Chinese word segmentation and POS tagging, and perform preferable accuracies in dependency parsing. We also explore bi-LSTM models with fewer features.

A Novel CMOS Envelope Detector Structure <=SEP=> A novel high performance envelope detector is offered in this work. Proposed circuit holds the signal peaks in two periods and combines them to obtain the envelope of the signal. This way, ripple is fixed by the peak holder and tracking can be improved without the traditional compensation between keeping and tracking required in these circuits. A comparison is offered between a conventional circuit, a previous work and the proposed envelope detector. It is shown the superior performance of the latter obtaining small ripple (<1%), fast settling (0.4mus) and high linearity.

Exploring the Role of Prior Beliefs for Argument Persuasion <=SEP=> Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one’s stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader-level factors: political and religious ideology. We find that prior beliefs affected by these reader-level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.

77 GHz radar scattering properties of pedestrians <=SEP=> Using radars for detecting and tracking pedestrians has important safety and security applications. Most existing human detection radar systems operate in UHF, X-band, and 24GHz bands. The newly allocated 76-77 GHz frequency band for automobile collision avoidance radar systems has once again raised great interest in pedestrian detection and tracking at these frequencies due to its longer detection range and better location accuracy. The electromagnetic scattering properties must be thoroughly understood and analyzed so a catalog of human scattering can be utilized for intensive automotive radar testing. Measurements of real human subjects at these frequencies is not a trivial task. This paper presents validation between the measured radar cross section (RCS) patterns of various human subjects and a full-wave EM simulation. The RCS of a human is shown to depend on a number of factors, such as posture, body shape, clothing type, etc.

An Evaluation Framework for Plagiarism Detection <=SEP=> We present an evaluation framework for plagiarism detection.1 The framework provides performance measures that address the specifics of plagiarism detection, and the PAN-PC-10 corpus, which contains 64 558 artificial and 4 000 simulated plagiarism cases, the latter generated via Amazon’s Mechanical Turk. We discuss the construction principles behind the measures and the corpus, and we compare the quality of our corpus to existing corpora. Our analysis gives empirical evidence that the construction of tailored training corpora for plagiarism detection can be automated, and hence be done on a large scale.

It's always April fools' day!: On the difficulty of social network misinformation classification via propagation features <=SEP=> Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.

Scaling Human-Object Interaction Recognition Through Zero-Shot Learning <=SEP=> Recognizing human object interactions (HOI) is an important part of distinguishing the rich variety of human action in the visual world. While recent progress has been made in improving HOI recognition in the fully supervised setting, the space of possible human-object interactions is large and it is impractical to obtain labeled training data for all interactions of interest. In this work, we tackle the challenge of scaling HOI recognition to the long tail of categories through a zero-shot learning approach. We introduce a factorized model for HOI detection that disentangles reasoning on verbs and objects, and at test-time can therefore produce detections for novel verb-object pairs. We present experiments on the recently introduced large-scale HICODET dataset, and show that our model is able to both perform comparably to state-of-the-art in fully-supervised HOI detection, while simultaneously achieving effective zeroshot detection of new HOI categories.

Phytochemical screening and Extraction: A Review <=SEP=> Prashant Tiwari*, Bimlesh Kumar, Mandeep Kaur, Gurpreet Kaur, Harleen Kaur Plants are a source of large amount of drugs comprising to different groups such as antispasmodics, emetics, anti-cancer, antimicrobials etc. A large number of the plants are claimed to possess the antibiotic properties in the traditional system and are also used extensively by the tribal people worldwide. It is now believed that nature has given the cure of every disease in one way or another. Plants have been known to relieve various diseases in Ayurveda. Therefore, the researchers today are emphasizing on evaluation and characterization of various plants and plant constituents against a number of diseases based on their traditional claims of the plants given in Ayurveda. Extraction of the bioactive plant constituents has always been a challenging task for the researchers. In this present review, an attempt has been made to give an overview of certain extractants and extraction processes with their advantages and disadvantages. Department of Pharmaceutical Sciences, Lovely School of Pharmaceutical Sciences, Phagwara, Punjab Date of Submission: 12-01-2011 Date of Acceptance: 22-02-2011 Conflict of interest: Nil Source of support: None

Relationships between handwriting performance and organizational abilities among children with and without dysgraphia: a preliminary study. <=SEP=> Organizational ability constitutes one executive function (EF) component essential for common everyday performance. The study aim was to explore the relationship between handwriting performance and organizational ability in school-aged children. Participants were 58 males, aged 7-8 years, 30 with dysgraphia and 28 with proficient handwriting. Group allocation was based on children's scores in the Handwriting Proficiency Screening Questionnaire (HPSQ). They performed the Hebrew Handwriting Evaluation (HHE), and their parents completed the Questionnaire for Assessing Students' Organizational Abilities-for Parents (QASOA-P). Significant differences were found between the groups for handwriting performance (HHE) and organizational abilities (QASOA-P). Significant correlations were found in the dysgraphic group between handwriting spatial arrangement and the QASOA-P mean score. Linear regression indicated that the QASOA-P mean score explained 42% of variance of handwriting proficiency (HPSQ). Based on one discriminant function, 81% of all participants were correctly classified into groups. Study results strongly recommend assessing organizational difficulties in children referred for therapy due to handwriting deficiency.

FPGA Implementations of the AES Masked Against Power Analysis Attacks <=SEP=> Power analysis attacks are a serious treat for implementations of modern cryptographic algorithms. Masking is a particularly appealing countermeasure against such attacks since it increases the security to a well quantifiable level and can be implemented without modifying the underlying technology. Its main drawback is the performance overhead it implies. For example, due to prohibitive memory costs, the straightforward application of masking to the AES algorithm, with precomputed tables, is hardly practical. In this paper, we exploit both the increased size of state-of-the-art reconfigurable hardware devices and previous optimization techniques to minimize the memory occupation of software S-boxes, in order to provide an efficient FPGA implementation of the AES algorithm, masked against side-channel attacks. We describe two high throughput architectures, based on 32-bit and 128-bit datapaths that are suitable for Xilinx Virtex-5 devices. In this way, we demonstrate the possibility to efficiently combine technological advances with algorithmic optimizations in this context.

The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines <=SEP=> The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing, and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multimicrophone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR.

Detection Method of DNS-based Botnet Communication Using Obtained NS Record History <=SEP=> To combat with botnet, early detection of the botnet communication and fast identification of the bot-infected PCs is very important for network administrators. However, in DNS protocol, which appears to have been used for botnet communication recently, it is difficult to differentiate the ordinary domain name resolution and suspicious communication. Our key idea is that the most of domain name resolutions first obtain the corresponding NS (Name Server) record from authoritative name servers in the Internet, whereas suspicious communication may omit the procedures to hide their malicious activities. Based on this observation, we propose a detection method of DNS basis botnet communication using obtained NS record history. Our proposed method checks whether the destined name server (IP address) of a DNS query is included in the obtained NS record history to detect the botnet communications.

Algorithmic bias amplifies opinion polarization: A bounded confidence model <=SEP=> The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards polarization, which emerges also in conditions where the original model would predict convergence, and b) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Polarization is augmented by a fragmented initial population. 1 ar X iv :1 80 3. 02 11 1v 1 [ ph ys ic s. so cph ] 6 M ar 2 01 8

Automatic License Plate Recognition: a Review <=SEP=> In recent years, many researches on Intelligent Transportation Systems (ITS) have been reported. Automatic License Plate Recognition (ALPR) is one form of ITS technology that not only recognizes and counts vehicles, but distinguishes each as unique by recognizing and recording the license plate’s characters. This paper discusses the main techniques of ALPR. Several open problems are proposed at the end of the paper for future research.

Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain <=SEP=>