Query: Predicting movie success with machine learning techniques: ways to improve accuracy

Relevant passages:
Multi-class AdaBoost ∗ <=SEP=> Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems. We show that the proposed multi-class AdaBoost algorithm is equivalent to a forward stagewise additive modeling algorithm that minimizes a novel exponential loss for multi-class classification. Furthermore, we show that the exponential loss is a member of a class of Fisher-consistent loss functions for multi-class classification. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.

Predicting Movie Sales from Blogger Sentiment <=SEP=> The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job.

Classification and Regression Trees <=SEP=> 

Opinion observer: analyzing and comparing opinions on the Web <=SEP=> The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data <=SEP=> Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

A Live-User Study of Opinionated Explanations for Recommender Systems <=SEP=> This paper describes an approach for generating rich and compelling explanations in recommender systems, based on opinions mined from user-generated reviews. The explanations highlight the features of a recommended item that matter most to the user and also relate them to other recommendation alternatives and the user's past activities to provide a context.

MO-SLAM: Multi object SLAM with run-time object discovery through duplicates <=SEP=> In this paper, we present MO-SLAM, a novel visual SLAM system that is capable of detecting duplicate objects in the scene during run-time without requiring an offline training stage to pre-populate a database of objects. Instead, we propose a novel method to detect landmarks that belong to duplicate objects. Further, we show how landmarks belonging to duplicate objects can be converted to first-order entities which generate additional constraints for optimizing the map. We evaluate the performance of MO-SLAM with extensive experiments on both synthetic and real data, where the experimental results verify the capabilities of MO-SLAM in detecting duplicate objects and using these constraints to improve the accuracy of the map.

Speed planning for solar-powered electric vehicles <=SEP=> Electric vehicles (EVs) are the trend for future transportation. The major obstacle is range anxiety due to poor availability of charging stations and long charging time. Solar-powered EVs, which mostly rely on solar energy, are free of charging limitations. However, the range anxiety problem is more severe due to the availability of sun light. For example, shadings of buildings or trees may cause a solar-powered EV to stop halfway in a trip. In this paper, we show that by optimally planning the speed on different road segments and thus balancing energy harvesting and consumption, we can enable a solar-powered EV to successfully reach the destination using the shortest travel time. The speed planning problem is essentially a constrained non-linear programming problem, which is generally difficult to solve. We have identified an optimality property that allows us to compute an optimal speed assignment for a partition of the path; then, a dynamic programming method is developed to efficiently compute the optimal speed assignment for the whole trip with significantly low computation overhead compared to the state-of-the-art non-linear programming solver. To evaluate the usability of the proposed method, we have also developed a solar-powered EV prototype. Experiments show that the predictions by the proposed technique match well with the data collected from the physical EV. Issues on practical implementation are also discussed.

Increase Physical Fitness and Create Health Awareness through Exergames and Gamification - The Role of Individual Factors, Motivation and Acceptance <=SEP=> Demographic change and the aging population push health and welfare system to its limits. Increased physical fitness and increased awareness for health issues will help elderly to live independently for longer and will thereby reduce the costs in the health care system. Exergames seem to be a promising solution for promoting physical fitness. Still, there is little evidence under what conditions Exergames will be accepted and used by elderly. To investigate promoting and hindering factors we conducted a user study with a prototype of an Exergame. We contrasted young vs. elderly players and investigated the role of gamer types, personality factors and technical expertise on the performance within the game and changes in the attitude towards individual health after the game. Surprisingly, performance within the game is not affected by performance motivation but by gamer type. More importantly, a universal positive effect on perceived pain is detected after the Exergame

A CNN-based segmentation model for segmenting foreground by a probability map <=SEP=> This paper proposes a CNN-based segmentation model to segment foreground from an image and a prior probability map. Our model is constructed based on the FCN model that we simply replace the original RGB-based three channel input layer by a four channel, i.e., RGB and prior probability map. We then train the model by constructing various image, prior probability maps and the groundtruths from the PASCAL VOC dataset, and finally obtain a CNN-based foreground segmentation model that is suitable for general images. Our proposed method is motivated by the observation that the classical graphcut algorithm using GMM for modeling the priors can not capture the semantic segmentation from the prior probability, and thus leads to low segmentation performance. Furthermore, the efficient FCN segmentation model is for specific objects rather than general objects. We therefore improve the graph-cut like foreground segmentation by extending FCN segmentation model. We verify the proposed model by various prior probability maps such as artifical maps, saliency maps, and discriminative maps. The ICoseg dataset that is different from the PASCAL Voc dataset is used for the verification. Experimental results demonstrates the fact that our method obviously outperforms the graphcut algorithms and FCN models.

Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval <=SEP=> This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as “image cosegmentation,” iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1]. In the second phase (i.e., “region colabeling”), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [2], we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29%/88.23% segmentation accuracy and 65.52%/63.89% recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results.

Face recognition based on convolutional neural network and support vector machine <=SEP=> Face recognition is an important embodiment of human-computer interaction, which has been widely used in access control system, monitoring system and identity verification. However, since face images vary with expressions, ages, as well as poses of people and illumination conditions, the face images of the same sample might be different, which makes face recognition difficult. There are two main requirements in face recognition, the high recognition rate and less training time. In this paper, we combine Convolutional Neural Network (CNN) and Support Vector Machine (SVM) to recognize face images. CNN is used as a feature extractor to acquire remarkable features automatically. We first pre-train our CNN by ancillary data to get the updated weights, and then train the CNN by the target dataset to extract more hidden facial features. Finally we use SVM as our classifier instead of CNN to recognize all the classes. With the input of facial features extracted from CNN, SVM will recognize face images more accurately. In our experiments, some face images in the Casia-Webfaces database are used for pre-training, and FERET database is used for training and testing. The results in experiments demonstrate the efficiency with high recognition rate and less training time.

Progress on a cognitive-motivational-relational theory of emotion. <=SEP=> The 2 main tasks of this article are 1st, to examine what a theory of emotion must do and basic issues that it must address. These include definitional issues, whether or not physiological activity should be a defining attribute, categorical versus dimensional strategies, the reconciliation of biological universals with sociocultural sources of variability, and a classification of the emotions. The 2nd main task is to apply an analysis of appraisal patterns and the core relational themes that they produce to a number of commonly identified emotions. Anger, anxiety, sadness, and pride (to include 1 positive emotion) are used as illustrations. The purpose is to show the capability of a cognitive-motivational-relational theory to explain and predict the emotions. The role of coping in emotion is also discussed, and the article ends with a response to criticisms of a phenomenological, folk-theory outlook.

A Survey of Logical Models for OLAP Databases <=SEP=> In this paper, we present different proposals for multidimensional data cubes, which are the basic logical model for OLAP applications. We have grouped the work in the field in two categories: commercial tools (presented along with terminology and standards) and academic efforts. We further divide the academic efforts in two subcategories: the relational model extensions and the cube-oriented approaches. Finally, we attempt a comparative analysis of the various efforts.

Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer <=SEP=> Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club’s performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season’s economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.

Answering Schrödinger's question: A free-energy formulation <=SEP=> The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.

Reconnaissance with ultra wideband UHF synthetic aperture radar <=SEP=> 

Unseen Noise Estimation Using Separable Deep Auto Encoder for Speech Enhancement <=SEP=> Unseen noise estimation is a key yet challenging step to make a speech enhancement algorithm work in adverse environments. At worst, the only prior knowledge we know about the encountered noise is that it is different from the involved speech. Therefore, by subtracting the components which cannot be adequately represented by a well defined speech model, the noises can be estimated and removed. Given the good performance of deep learning in signal representation, a deep auto encoder (DAE) is employed in this work for accurately modeling the clean speech spectrum. In the subsequent stage of speech enhancement, an extra DAE is introduced to represent the residual part obtained by subtracting the estimated clean speech spectrum (by using the pre-trained DAE) from the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown parameters of the noise DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. The enhanced speech signal is thus obtained by transforming the estimated clean speech spectrum back into time domain. The above proposed technique is called separable deep auto encoder (SDAE). Given the under-determined nature of the above optimization problem, the clean speech reconstruction is confined in the convex hull spanned by a pre-trained speech dictionary. New learning algorithms are investigated to respect the non-negativity of the parameters in the SDAE. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the conventional baselines.

An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information <=SEP=> Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.

A variable splitting augmented Lagrangian approach to linear spectral unmixing <=SEP=> This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via split augmented Lagrangian (SISAL). Following Craig's seminal ideas, hyperspectral linear unmixing amounts to finding the minimum volume simplex containing the hyperspectral vectors. This is a nonconvex optimization problem with convex constraints. In the proposed approach, the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The obtained problem is solved by a sequence of augmented Lagrangian optimizations. The resulting algorithm is very fast and able so solve problems far beyond the reach of the current state-of-the art algorithms. The effectiveness of SISAL is illustrated with simulated data.

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation <=SEP=> We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

How to put probabilities on homographies <=SEP=> We present a family of "normal" distributions over a matrix group together with a simple method for estimating its parameters. In particular, the mean of a set of elements can be calculated. The approach is applied to planar projective homographies, showing that using priors defined in this way improves object recognition.

From Skimming to Reading: A Two-stage Examination Model for Web Search <=SEP=> User's examination of search results is a key concept involved in all the click models. However, most studies assumed that eye fixation means examination and no further study has been carried out to better understand user's examination behavior. In this study, we design an experimental search engine to collect both the user's feedback on their examinations and the eye-tracking/click-through data. To our surprise, a large proportion (45.8%) of the results fixated by users are not recognized as being "read". Looking into the tracking data, we found that before the user actually "reads" the result, there is often a "skimming" step in which the user quickly looks at the result without reading it. We thus propose a two-stage examination model which composes of a first "from skimming to reading" stage (Stage 1) and a second "from reading to clicking" stage (Stage 2). We found that the biases (e.g. position bias, domain bias, attractiveness bias) considered in many studies impact in different ways in Stage 1 and Stage 2, which suggests that users make judgments according to different signals in different stages. We also show that the two-stage examination behaviors can be predicted with mouse movement behavior, which can be collected at large scale. Relevance estimation with the two-stage examination model also outperforms that with a single-stage examination model. This study shows that the user's examination of search results is a complex cognitive process that needs to be investigated in greater depth and this may have a significant impact on Web search.

Artificial Intelligence Technique Applied to Intrusion Detection <=SEP=> Communication network is facilitated with different protocol .Each protocol supported to increase the network performance in a secured manner. In communication process, user’s connectivity, violations of policy on access of information are handles through intrusion. Intrusion prevention is the process of performing intrusion detection and attempting to stop detected possible incidents. It focused on identifying possible incidents, logging information about them, attempting to stop them, and reporting them to security administrators. However, organizations use Intrusion detection and prevention system (IDPS) for other purposes, such as identifying problems with security policies, documenting existing threats, and determining individuals from violating security policies. Communication architecture is built up on IP.Internet Control Message Protocol (ICMP ) protocol is tightly integrated with IP. ICMP messages, delivered in IP packets, are used for out-of-band messages related to network operation or mis-operation.In this paper describes about HDLC , ICMP protocol sequences which is used to detect the intrusion on hybrid network and its attributes are recommend the standardized protocol for the intrusion detection process. This standardization protocol compare the previous part of the research of HDLC and ICMP protocols.

Enhanced LSB technique for audio steganography <=SEP=> The idea of this paper is to invent a new strategy in Steganography to get the minimum effect in audio which is used to hide data into it. “Progress always involves risk” Fredrick Wilcox observed that technological progress of computer science and the Internet altered the way we lived, and will continue to cast our life[1] . In this paper we have presented a Steganography method of embedding text data in an audio file. The basic approach behind this paper is to provide a good, well-organized method for hiding the data and sent to the destination in safer manner. In the proposed technique first the audio file is sampled and then appropriate bit is modified. In selected sample one bit is modified at least significant bit .The remaining bits may be used but it may be cause noise. We have attempted to provide an overview, theoretical framework about audio Steganography techniques and a novel approach to hide data in an audio using least significant bit (LSB).

A stable-isotope dilution GC-MS approach for the analysis of DFRC (derivatization followed by reductive cleavage) monomers from low-lignin plant materials. <=SEP=> The derivatization followed by reductive cleavage (DFRC) method is a well-established tool to characterize the lignin composition of plant materials. However, the application of the original procedure, especially the chromatographic determination of the DFRC monomers, is problematic for low-lignin foods. To overcome these problems a modified sample cleanup and a stable-isotope dilution approach were developed and validated. To quantitate the diacetylated DFRC monomers, their corresponding hexadeuterated analogs were synthesized and used as internal standards. By using the selected-ion monitoring mode, matrix-associated interferences can be minimized resulting in higher selectivity and sensitivity. The modified method was applied to four low-lignin samples. Lignin from carrot fibers was classified as guaiacyl-rich whereas the lignins from radish, pear, and asparagus fibers where classified as balanced lignins (guaiacyl/syringyl ratio=1-2).

Action Design Research and Visualization Design <=SEP=> In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly account for the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into addressing this issue. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design work, while aiming to ensure reliability of the process and design, and emphasizing opportunities for conducting research. We explore the utility of ADR in increasing the reliability of applied visualization design research by: describing ADR in the language and constructs developed within the visualization community; comparing ADR to existing visualization methodologies; and analyzing a recent design study retrospectively through the lens of ADR's framework and principles.

Econometric Modelling of Markov-Switching Vector Autoregressions using MSVAR for Ox <=SEP=> 

An X-band to Ka-band SPDT switch using 200 nm SiGe HBTs <=SEP=> This paper presents the design and measured performance of an X-band to Ka-band SiGe HBT SPDT switch. The proposed SPDT switch was fabricated using a 200 nm, 150 GHz peak fT silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) BiCMOS technology. The SPDT switch design uses diode-connected SiGe HBTs in a series-shunt configuration to improve the switch bandwidth and isolation. Between 8 and 40 GHz, this SPDT switch achieves an insertion loss of less than 4.3 dB, an isolation of more than 20.3 dB, and a return loss of more than 9 dB.

Measuring dimensions of intergenerational contact: factor analysis of the Queen's University Scale. <=SEP=> OBJECTIVES
Intergenerational contact has been linked to a range of health outcomes, including greater engagement and lower depression. Measures of contact are limited. Informed by Allport's contact theory, the Queen's University Scale consists of items rating contact with elders. We administered the survey to a young adult sample (N = 606) to identify factors that may optimize intervention programming and enhance young persons' health as they age.


METHODS
We conducted exploratory factor analysis (EFA) in the structural equation modeling framework and then confirmatory factor analysis with items pertaining to the general elder population.


RESULTS
EFAs did not yield an adequate factor structure. We tested two alternative confirmatory models based on findings from the EFA. Neither a second-order model nor a first-order model allowing double loadings and correlated errors proved adequate.


CONCLUSION
Difficulty finding an adequate factor solution reflects challenges to measuring intergenerational contact with this scale. Items reflect relevant topics but subscale models are limited in interpretability. Knox and colleagues' analyses led them to recommend a brief, global scale, but we did not find empirical support for such a measure. Next steps include development and testing of a reliable, valid scale measuring dimensions of contact as perceived by both youth and elders.