Query: Deep Semantic Frame-Based Deceptive Opinion Spam Analysis

Relevant passages:
Detecting product review spammers using rating behaviors <=SEP=> This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.

Combating Web Spam with TrustRank <=SEP=> Web spam pages use various techniques to achieve higher-than-deserved rankings in a search engine’s results. While human experts can identify spam, it is too expensive to manually evaluate a large number of pages. Instead, we propose techniques to semiautomatically separate reputable, good pages from spam. We first select a small set of seed pages to be evaluated by an expert. Once we manually identify the reputable seed pages, we use the link structure of the web to discover other pages that are likely to be good. In this paper we discuss possible ways to implement the seed selection and the discovery of good pages. We present results of experiments run on the World Wide Web indexed by AltaVista and evaluate the performance of our techniques. Our results show that we can effectively filter out spam from a significant fraction of the web, based on a good seed set of less than 200 sites.

Towards a General Rule for Identifying Deceptive Opinion Spam <=SEP=> Consumers’ purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam— fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews. Our approach tries to capture the general difference of language usage between deceptive and truthful reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their sites.1

What Yelp Fake Review Filter Might Be Doing? <=SEP=> Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp’s algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp’s filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90% accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.

Finding unusual review patterns using unexpected rules <=SEP=> In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.

A fast garment fitting algorithm using skeleton-based error metric <=SEP=> Funding information National Natural Science Foundation of China, Grant/Award Number: 61732015; National Key R&D Program of China, Grant/Award Number: 2017YFB1002600; Key Research and Development Program of Zhejiang Province, Grant/Award Number: 2018C01090; National Natural Science Foundation of China, Grant/Award Number: 61472355 Abstract We present a fast and automatic method to fit a given 3D garment onto a human model with various shapes and poses, without using a reference human model. Our approach uses a novel skeleton-based error metric to find the pose that best fits the input garment. Specifically, we first generate the skeleton of the given human model and its corresponding skinning weights. Then, we iteratively rotate each bone to find its best position to fit the garment. After that, we rig the surface of the human model according to the transformations of the skeleton. Potential penetrations are resolved using collision handling and physically based simulation. Finally, we restore the human model back to the original pose in order to obtain the desired fitting result. Our experiment results show that besides its efficiency and automation, our method is about two orders of magnitudes faster than existing approaches, and it can handle various garments, including jacket, trousers, skirt, a suit of clothing, and even multilayered clothing.

Exploring Context with Deep Structured Models for Semantic Segmentation <=SEP=> We propose an approach for exploiting contextual information in semantic image segmentation, and particularly investigate the use of patch-patch context and patch-background context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets.

Two-Switch Voltage Equalizer Using an LLC Resonant Inverter and Voltage Multiplier for Partially Shaded Series-Connected Photovoltaic Modules <=SEP=> Various kinds of differential power processing converters and voltage equalizers have been proposed for series-connected photovoltaic (PV) modules to prevent negative influences of partial shading, such as significant reduction in power generation and the occurrence of multiple maximum power points (MPPs), including local and global MPPs, that hinders and confuses MPP tracking algorithms to operate properly. However, since conventional topologies are based on multiple individual dc-dc converters, the required switch count increases proportionally to the number of modules connected in series, increasing the complexity. A two-switch voltage equalizer using an LLC resonant inverter and voltage multiplier is proposed in this paper. The circuitry can be dramatically simplified compared with conventional topologies due to the two-switch configuration. Detailed operation analyses for the LLC resonant inverter and voltage multiplier are separately performed. Experimental equalization tests emulating partially shaded conditions were performed for four PV modules connected in series. With the proposed voltage equalizer, local MPPs successfully disappeared, and extractable maximum power increased compared with those without equalization, demonstrating the effectiveness and performance of the proposed voltage equalizer.

A Low-Cost Real-Time Embedded Stereo Vision System for Accurate Disparity Estimation Based on Guided Image Filtering <=SEP=> Stereo matching, a key element towards extracting depth information from stereo images, is widely used in several embedded consumer electronic and multimedia systems. Such systems demand high processing performance and accurate depth perception, while their deployment in embedded and mobile environments implies that cost, energy and memory overheads need to be minimized. Hardware acceleration has been demonstrated in efficient embedded stereo vision systems. To this end, this paper presents the design and implementation of a hardware-based stereo matching system able to provide high accuracy and concurrently high performance for embedded vision devices, which are associated with limited hardware and power budget. We first implemented a compact and efficient design of the guided image filter, an edge-preserving filter, which reduces the hardware complexity of the implemented stereo algorithm, while at the same time maintains high-quality results. The guided filter design is used in two parts of the stereo matching pipeline, showing that it can simplify the hardware complexity of the Adaptive Support Weight aggregation step, and efficiently enable a powerful disparity refinement unit, which improves matching accuracy, even though cost aggregation is based on simple, fixed support strategies. We implemented several variants of our design on a Kintex-7 FPGA board, which was able to process HD video (1,280 × 720) in real-time (60 fps), using ~57.5k and ~71k of the FPGA's logic (CLB) and register resources, respectively. Additionally, the proposed stereo matching design delivers leading accuracy when compared to state-of-the-art hardware implementations based on the Middlebury evaluation metrics (at least 1.5 percent less bad matching pixels).

Generalization Properties and Implicit Regularization for Multiple Passes SGM <=SEP=> We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical findings.

Trajectory Outlier Detection: A Partition-and-Detect Framework <=SEP=> Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.

Decision support for diffuse pollution management <=SEP=> The effort to manage diffuse pollution at the catchment scale is an ongoing challenge that needs to take into account trade-offs between environmental and economic objectives. Best Management Practices (BMPs) are gaining ground as a means to address the problem, but their application (and impact) is highly dependant on the characteristics of the crops and of the land in which they are to be applied. In this paper, we demonstrate a new methodology and associated decision support tool that suggests the optimal location for placing BMPs to minimise diffuse surface water pollution at the catchment scale, by determining the trade-off among economic and multiple environmental objectives. The decision support tool consists of a non-point source (NPS) pollution estimator, the SWAT (Soil and Water Assessment Tool) model, a genetic algorithm (GA), which serves as the optimisation engine for the selection and placement of BMPs across the agricultural land of the catchment, and of an empirical economic function for the estimation of the mean annual cost of BMP implementation. In the proposed decision support tool, SWAT was run a number of times equal to the number of tested BMPs, to predict nitrates nitrogen (N-NO3) and total phosphorus (TP) losses from all the agricultural Hydrologic Response Units (HRUs) and possible BMPs implemented on them. The results were then saved in a database which was subsequently used for the optimisation process. Fifty different BMPs, including sole or combined changes in livestock, crop, soil and nutrient application management in alfalfa, corn and pastureland fields, were evaluated in the reported application of the tool in a catchment in Greece, by solving a three-objective optimisation process (cost, TP and N-NO3). The relevant two-dimensional trade-off curves of cost-TP, cost-N-NO3 and N-NO3eTP are presented and discussed. The strictest environmental target, expressed as a 45% reduction of TP at the catchment outlet, which also resulted in a 25% reduction of the annual N-NO3 yield was met at an affordable annual cost of 25 V/person by establishing an optimal combination of BMPs. The methodology could be used to assist in a more cost-effective implementation of environmental

100-Mb/s NRZ Visible Light Communications Using a Postequalized White LED <=SEP=> This letter describes a high-speed visible light communications link that uses a white-light light-emitting diode (LED). Such devices have bandwidths of few megahertz, severely limiting the data rates of any communication system. Here, we demonstrate that by detecting only the blue component of the LED, and using a simple first-order analogue equalizer, a data rate of 100 Mb/s can be achieved using on-off keying nonreturn-to-zero modulation.

3D People Tracking with Gaussian Process Dynamical Models <=SEP=> We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of human motion data, with a density function that gives higher probability to poses and motions close to the training data. With Bayesian model averaging a GPDM can be learned from relatively small amounts of data, and it generalizes gracefully to motions outside the training set. Here we modify the GPDM to permit learning from motions with significant stylistic variation. The resulting priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and significant occlusions.

Systematic literature studies: Database searches vs. backward snowballing <=SEP=> Systematic studies of the literature can be done in different ways. In particular, different guidelines propose different first steps in their recommendations, e.g. start with search strings in different databases or start with the reference lists of a starting set of papers.
 In software engineering, the main recommended first step is using search strings in a number of databases, while in information systems, snowballing has been recommended as the first step. This paper compares the two different search approaches for conducting literature review studies.
 The comparison is conducted by searching for articles addressing "Agile practices in global software engineering". The focus of the paper is on evaluating the two different search approaches.
 Despite the differences in the included papers, the conclusions and the patterns found in both studies are quite similar. The strengths and weaknesses of each first step are discussed separately and in comparison with each other.
 It is concluded that none of the first steps is outperforming the other, and the choice of guideline to follow, and hence the first step, may be context-specific, i.e. depending on the area of study.

Chapter 7 Towards Automatically-Tuned Deep Neural Networks <=SEP=> Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any human intervention. The first version, Auto-Net 1.0, builds upon ideas from the competition-winning system Auto-sklearn by using the Bayesian Optimization method SMAC and uses Theano as the underlying deep learning (DL) framework. The more recent Auto-Net 2.0 builds upon a recent combination of Bayesian Optimization and HyperBand, called BOHB, and uses PyTorch as DL framework. To the best of our knowledge, Auto-Net 1.0 was the first automatically-tuned neural network to win competition datasets against human experts (as part of the first AutoML challenge). Further empirical results show that ensembling Auto-Net 1.0 with Auto-sklearn can perform better than either approach alone, and that Auto-Net 2.0 can perform better yet.

A Model Based on Aras-G and AHP Methods for Multiple Criteria Prioritizing of Heritage Value <=SEP=> The paper discusses the meaning and nature of urban cultural heritage, and the available methods for its valuation in the perspective of sustainable city development. From this perspective, decision-making problems of renovation often involve a complex decision-making process in which multiple requirements and conditions have to be taken into consideration simultaneously. In project development it is hardly possible to get exhaustive and accurate information. As a result, the situations occur, the consequences of which can be very damaging to the project. Sometimes the loss is related to symbolic values that the public perceive as disregarded by the project, despite the overall improved conditions. This paper presents the multiple criteria assessment of alternatives of the cultural heritage renovation projects in Vilnius city. The model consists of the following elements: determining attributes set a®ecting built and human environment renovation; information collection and analysis, decision modeling and solution selection. The main purpose of the model is to improve the condition of the built and human environment through e±cient decision making in renovation supported by multiple attribute evaluation. Delphi, AHP and ARAS-G methods, considering di®erent environment factors as well as stakeholders' needs, are applied to solve problem.

Illuminating the Effects of Dynamic Lighting on Student Learning <=SEP=> Light is universally understood as essential to the human condition. Yet light quality varies substantially both in nature and in controlled environments leading to questions of which artificial light characteristics facilitate maximum learning. Recent research has examined lighting variables of color temperature, and illuminance for impacting sleep, mood, focus, motivation, concentration and work and school performance. This has resulted in artificial light systems intended to support human beings in their actualization through dynamic lighting technology allowing for different lighting conditions per task. Eighty-four third graders were exposed to either focus (6000K-100fc average maintained) or normal lighting. Focus lighting led to a higher percentage increase in oral reading fluency performance (36%) than did control lighting (17%). No lighting effects were found for motivation or concentration, possibly attributable to the younger age level of respondents as compared to European studies. These findings illuminate the need for further research on artificial light and learning. The Effects of Lighting on Humans in General The human evolution is shaped by light. In the course of evolution, human beings have adapted and developed an internal clock that under natural light conditions is synchronized to the earth’s 24-hour light-dark rotational cycle (Czeisler et al., 1999). Research reveals the mechanism for how light is essential for human functioning (Boyce, Hunter, & Howlett, 2003). Light is a strong enabler for visual performance (Grangaard, 1995), regulates a large variety of bodily processes such as sleep and alertness (Dijk et al., 1997; Wright et al., 2006; Takasu et al., 2006; Viola et al., 2008), is essential for cognition and mood (Veitch & McColl, 2001; Goven et al., 2011; Taras, 2005), enables production of important hormones such as melatonin and cortisol (Berson, 2002; dijk et al., 1997; Leproult et al., 2001), and is essential for a healthy rest-activity pattern (Wurtman, 1975). Lights of different wavelengths also affect blood pressure, pulse, respiration rates, brain activity, and biorhythms. The role of lighting in our daily lives is essential in order to operate ideally in every environment. Thus, lighting directly influences every dimension of human existence. Tanner reiterated: “Light is the most important environmental input, after food and water, in controlling bodily functions (as cited in Wurtman, 1975). Since the industrial revolution, people spend more and more time indoors while artificial lighting has shown the power to at least partially compensate for the processes that stabilize the body, mind, and emotions (Knez, 1995; Tanner, 2008, van Someren et al.,2005; Takasu et al., 2006; Mishima et al., 2001; Viola et al., 2008). In the following we elaborate a bit more on the proven effects artificial light has on human functioning. Circadian rhythm Sleep is one of the most basic physical requirements for human functioning. Amount and quality of lighting invariably affects the degree and quality of sleep in humans and regulates our biological clocks. In 2002, Berson et al. (2002) identified a new non-image forming photo-pigment residing within a cell type in the retina of the eye. It is referred to as melanopsin and regulates the biological effects of light. When ocular light (light perceived by the eyes) reaches these cells, a complex chemical reaction occurs, producing electrical impulses that are sent via separate nerve pathways to our biological clock, the suprachiasmatic nuclei (SCN). The SCN in turn regulates the circadian (daily) and circannual (seasonal) rhythms of a large variety of bodily processes, such as sleep, and some important hormones, such as melatonin and cortisol, essential for a healthy rest-activity pattern. The system that generates the circadian rhythmicity of biological processes is denoted as the circadian system. The melanopsin pigment is most sensitive to blue light with a peak sensitivity at 480 nm (Brainard et al., 2001; Thapan et al., 2001; Hankins et al., 2008). Sleep consolidation is optimal when sleep timing coincides with the period of melatonin secretion (Dijk et al., 1997). People that sleep during their melatonin peak (as in normal, i.e., well-synchronized, people), are reported to have a longer total sleep time and less wakefulness after sleep onset as compared with people that schedule their wakefulness during the melatonin peak (non-synchronized people) (Wright et al., 2006). Moreover, the same study indicates that cognitive performance (i.e., learning) was better in a synchronized group of people, whereas learning was impaired in a non-synchronized group of people. This indicates that proper alignment between sleep-wakefulness and biological (internal circadian) time is crucial, not only for sleep quality, but also for enhancement of cognitive performance. Vision The most obvious effect of light on humans is to enable vision and performance of visual tasks through the eyes. The eye contains “photoreceptor cells” called rods and cones. These photoreceptor cells regulate the visual effects. When light reaches these cells, a complex chemical reaction occurs. The chemical that is formed creates electrical impulses in the nerve that connects the photoreceptor cells to the visual cortex at the back of the brain. In the visual cortex the electrical impulses are interpreted as “vision”. Rod cells “operate in low-level light situations and do not permit color vision.” Cone cells on the other hand, operate best in “normal daytime lighting conditions,” and are “responsible for sharpness, detail, and color vision.” Studies show that the nature of the task – as well as the amount, spectrum, and distribution of the light – determines the level of performance that is achieved. Mood and Cognition Lighting plays an important role in evoking emotions. Lighting can be used to make an architectural space more aesthetically pleasing or it can create an atmosphere in that space; both affect people’s emotions. In addition, the user’s well-being can be directly influenced by light. Brightness, color, direction, contrast and time are parameters used to create lighting conditions that address this. Nevertheless, concerning the relationship between lighting and mood/cognition, research has not shown consistent results. In a study by Knez (1995), two experiments were performed in order to analyze the effects of color temperature and illuminance levels on mood and cognitive performance tasks including long-term recall, free recall, and performance appraisal between males and females. After each experiment, a test to measure each subject’s mood was administered. The results showed that females performed better in warm white lighting environments, whereas males performed better on cognitive tasks in cool white lighting. Both males and females perceived and responded differently in evaluating the illuminance levels and color index of the lighting and therefore each gender’s mood was affected differently. Positive mood measures showed no increase in mood in both genders; however, the cooler lighting had a more negative effect on females’ moods. Thus, females’ performance on cognitive tasks decreased under cooler lighting. Because physiological changes occur when humans are exposed to light, mood and cognition can be affected indirectly and variably. According to Veitch and McColl (2001), lighting’s cognitive and mood-related effects on people have noteworthy implications: 1) better performance on cognitive related tasks in the workplace or academic environment and 2) overall improved quality of life and well-being. Both visual perception strength and adequate sleep could have a considerable impact on cognitive abilities such as concentration and memory. Mood may also determine the sharpness of these cognitive abilities. Mood can be influenced by the quality and amount of lighting (Partonen et al., 2002; Veitch & McColl, 2001; inter alia Beauchemin & Hays, 1996; Benedetti et al., 2001; Goven, 2011). For instance, light therapy has proven a successful treatment for those with Seasonal Affective Disorder (SAD) and other non-seasonal mood related disorders such as depression and eating disorders (Spiegel et al., 2004; Van Cauter et al., 2000; McColl & Veitch, 2001). Effects for Lighting and Learning Because lighting profoundly impacts numerous levels of human functioning such as vision, circadian rhythms, mood, and cognition, its implicit effects on learning and classroom achievement cannot be dismissed. Several studies have addressed how the quality and color of lighting can either impair or enhance students’ visual skills and thus, academic performance. Visual impairments alone can induce behavioral problems in students as well as level of concentration and motivation in the classroom. Cheatum and Hammond (2000) estimated that around 20% of children that enter the school encounter visual problems (e.g., problems with focusing, eye tracking, training, lazy eye, and trabismus). Among elementary school children, 41% have experienced trouble with tracking, 6% have refractive errors and 4% have strabismus (Koslowe, 1995, as cited in Cheatum & Hammond, 2000). The same study suggests that “the inability of visual tracking is also thought to be the cause of behavioral problems and being illiterate.” Winterbottom (2009) suggested that certain features of lighting can cause discomfort and impair visual and cognitive performance. These features include “imperceptible 100 Hz flicker from fluorescent lighting and glare induced by 1) daylight and fluorescent lighting, 2) interactive whiteboards (IWBs) and dry-erase whiteboards (DWBs)” (2009). The purpose of his study was to determine the degree and magnitude to which students are subjected to the above stated lighting inefficiencies in the classroom. The 100 Hz flicker from fluorescent lighting was displayed in 80% of the 90 UK classrooms u

Concept Decompositions for Large Sparse Text Data Using Clustering <=SEP=> Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors–a few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical k-means algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain “fractal-like” and “self-similar” behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectors; these decompositions are obtained by taking the least-squares approximation onto the linear subspace spanned by all the concept vectors. We empirically establish that the approximation errors of the concept decompositions are close to the best possible, namely, to truncated singular value decompositions. As our third contribution, we show that the concept vectors are localized in the word space, are sparse, and tend towards orthonormality. In contrast, the singular vectors are global in the word space and are dense. Nonetheless, we observe the surprising fact that the linear subspaces spanned by the concept vectors and the leading singular vectors are quite close in the sense of small principal angles between them. In conclusion, the concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized “basis” for text data sets.

Computer graphics and geometric ornamental design <=SEP=> Computer Graphics and Geometric Ornamental Design

Worthiness of Sentences in Scientific Reports <=SEP=> Does this sentence need citation? In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. The task is to detect whether a sentence in a scientific article needs to be cited or not. It can be incorporated into citation recommendation systems to help automate the citation process by marking sentences where needed. It may also be useful for publishers to regularize the citation process. We construct a dataset using the ACLAnthology Reference Corpus; consisting of over 1.1M “not_cite” and 85K “cite” sentences. We study the performance of a set of state-of-the-art sentence classifiers for the citation worthiness task and show the practical challenges. We also explore sectionwise difficulty of the task and analyze the performance of our best model on a published article. ACM Reference Format: Hamed Bonab, Hamed Zamani, Erik Learned-Miller, and James Allan. 2018. Citation Worthiness of Sentences in Scientific Reports. In SIGIR ’18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8–12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3209978.3210162

Witnessing spouse abuse and experiencing physical abuse: A “double whammy”? <=SEP=> Child witnesses to parental violence, both abused (N =40) and nonabused (N =44), who were temporarily residing in a battered women's shelter were compared with children from a similar economic background (N =66) on measures of anxiety, depression, and behavior problems. Mothers of the three groups of children (comparison, witness, abused/witness) across the age range from 4 to 12 years completed a behavior problem inventory; the youngsters responded to paper-and-pencil self-report measures. Results indicated that the abused/witness children were manifesting significantly more distress on the behavior problem measure than the comparison youngsters, with the witness children showing a moderate amount and the comparison children the least. However, these patterns were mediated by the age of the child. Implications of these differential findings are discussed.

Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children. <=SEP=> BACKGROUND
We examined the relationship between overweight in preschool children and three environmental factors--the proximity of the children's residences to playgrounds and to fast food restaurants and the safety of the children's neighborhoods. We hypothesized that children who lived farther from playgrounds, closer to fast food restaurants, and in unsafe neighborhoods were more likely to be overweight.


METHODS
This was a cross-sectional study of 7,020 low-income children, 36 through 59 months of age living in Cincinnati, OH. Overweight was defined as a measured body mass index > or =95th percentile. The distance between each child's residence and the nearest public playground and fast food restaurant was determined with geographic information systems. Neighborhood safety was defined by the number of police-reported crimes per 1,000 residents per year in each of 46 city neighborhoods.


RESULTS
Overall, 9.2% of the children were overweight, 76% black, and 23% white. The mean (+/- SD) distances from a child's home to the nearest playground and fast food restaurant were 0.31 (+/- 0.22) and 0.70 (+/- 0.38) miles, respectively. There was no association between child overweight and proximity to playgrounds, proximity to fast food restaurants, or level of neighborhood crime. The association between child overweight and playground proximity did not differ by neighborhood crime level.


CONCLUSIONS
Within a population of urban low-income preschoolers, overweight was not associated with proximity to playgrounds and fast food restaurants or with the level of neighborhood crime.

Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario <=SEP=> The success of opinion mining for automatically processing vast amounts of opinionated content available on the Internet has been demonstrated as a less expensive and lower latency solution for gathering public opinion. In this paper, we investigate whether it is possible to predict variations in vote intention based on sentiment time series extracted from news comments, using three Brazilian elections as case study. The contributions of this case study are: a) the comparison of two approaches for opinion mining in user-generated content in Brazilian Portuguese, b) the proposition of two types of features to represent sentiment behavior towards political candidates that can be used for prediction, c) an approach to predict polls vote intention variations that is adequate for scenarios of sparse data. We developed experiments to assess the influence on the forecasting accuracy of the proposed features, and their respective preparation. Our results display an accuracy of 70% in predicting positive and negative variations. These are important contributions towards a more general framework that is able to blend opinions from several different sources to find representativeness of the target population, and make more reliable predictions.

Innocent by association: early recognition of legitimate users <=SEP=> This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.

A Double-Tail Latch-Type Voltage Sense Amplifier with 18ps Setup+Hold Time <=SEP=> A latch-type voltage sense amplifier in 90nm CMOS is designed with a separated input and cross-coupled stage. This separation enables fast operation over a wide common-mode and supply voltage range. With a 1-sigma offset of 8mV, the circuit consumes 92fJ/decision with a 1.2V supply. It has an input equivalent noise of 1.5mV and requires 18ps setup-plus-hold time

MobiMine: Monitoring the Stock Market from a PDA <=SEP=> This paper describes an experimental mobile data mining system that allows intelligent monitoring of time-critical financial data from a hand-held PDA. It presents the overall system architecture and the philosophy behind the design. It explores one particular aspect of the system---automated construction of personalized focus area that calls for user's attention. This module works using data mining techniques. The paper describes the data mining component of the system that employs a novel Fourier analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks. The paper also discusses a quadratic programming-based personalization module that runs on the PDAs and the multi-media based user-interfaces. It reports experimental results using an ad hoc peer-to-peer IEEE 802.11 wireless network.

A Study on Evaluation of E-Government Service Quality <=SEP=> Service quality is the highest requirement by users, especially for the service in electronic government. During the past decades, it has become a major area of academic investigation. Considering this issue, there are a lot of researches that evaluated the dimensions and e-service contexts. This study also identified the dimensions of service quality, but focuses on a new concept and provides a new methodology in developing measurement scales of e-service quality such as information quality, service quality and organization quality. Finally, this study will suggest a key factor to evaluate e-government service quality better. Keywords—E-government, e-service, e-service quality, dimensionality.

Holistic Multi-modal Memory Network for Movie Question Answering <=SEP=> Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial interactions among data sources in one attention hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop. In addition, it takes answer choices into consideration during the context retrieval stage. Therefore, the proposed framework effectively integrates multi-modal context, question, and answer information, which leads to more informative context retrieved for question answering. Our HMMN framework achieves state-of-the-art accuracy on MovieQA dataset. Extensive ablation studies show the importance of holistic reasoning and contributions of different attention strategies.

Graphical Models for Game Theory <=SEP=> We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in (one-stage) games represented by trees or sparse graphs.