Reviewerly/exHarmony huggingface dataset:
Learning Minimal Intra-Genre Multimodal Embedding from Trailer Content and Reactor Expressions for Box Office Prediction

Movie watching is one of the most popular leisure activities in our daily life. The box office revenue, especially in the first week, is critical for financial planning in the movie industry. Most existing movie box office prediction relies on meta data, viewer's comments, and trailer content. However, when viewers are immersed in a movie experience, they would naturally manifest expressions invoked by the media content. In this work, we propose a novel movie box office prediction framework by joint modeling meta attributes, trailer content, and viewer's natural expressions gathered from YouTube reactor videos. The proposed network learns a discriminabilityenhanced content and expression embeddings using a minimal intra-genre distance loss function. The proposed architecture achieves 79.07%, 73.79%, and 76.82% for low/high movie box office tier classification (top 30%, top 10%, and top 5%) on a large scale trailer-reactor database. Furthermore, we provide an analysis on the effectiveness of viewer's reaction and our intra-genre projection. Most existing movie box office prediction relies on meta data, viewer's comments, and trailer content. However, when viewers are immersed in a movie experience, they would naturally manifest expressions invoked by the media content. In this work, we propose a novel movie box office prediction framework by joint modeling meta attributes, trailer content, and viewer's natural expressions gathered from YouTube reactor videos. The proposed network learns a discriminability-enhanced content and expression embeddings using a minimal intra-genre distance loss function. The proposed architecture achieves 79.07%, 73.79%, and 76.82% for low/high movie box office tier classification (top 30%, top 10%, and top 5%) on a large scale trailer-reactor database. Furthermore, we provide an analysis on the effectiveness of viewer's reaction and our intra-genre projection.
LibraryThing for Libraries: How Tag Moderation and Size Limitations Affect Tag Clouds

The aim of this study is to analyse differences between tags on LibraryThing’s web page and tag clouds in their “LibraryThing for Libraries” service, and assess if, and how, the LibraryThing tag moderation and limitations to the size of the tag cloud in the library catalogue affect the description of the information resource. An e-mail survey was conducted with personnel at LibraryThing, and the results were compared against tags for twenty different fiction books, collected from two different library catalogues with disparate tag cloud sizes, and LibraryThing’s web page. The data were analysed using a modified version of Golder and Huberman’s tag categories (2006). The results show that while LibraryThing claims to only remove the inherently personal tags, several other types of tags are found to have been discarded as well. Occasionally a certain type of tag is included in one book, and excluded in another. The comparison between the two tag cloud sizes suggests that the larger tag clouds provide a more pronounced picture regarding the contents of the book but at the cost of an increase in the number of tags with synonymous or redundant information.
Handwritten Kannada Numerals Recognition by Using Zone Features and CNN Classifier

The proposed system presents a pre-processing, segmentation, features extraction approach and Deep Convolutional Neural Network (DCNN) classifier for recognition of handwritten Kannada numerals. Pre-processing have different steps like median filter, gray scale to binary, normalization, thinning, skew correction and slant removal. Segmentation process contains different methods like vertical projection profile for word and novel character segmentation. Collections of best discriminable features are very important part in achieving high rate of identification in automatic numeral detection systems. Kannada is the major south Indian character verbal by about 50 million people. This article presents a well-organized and novel technique for recognition of handwritten Kannada numerals using zone and distance matrix. An appropriate feature extractor and a superior classifier play most important task in achieving high detection rate for a recognition scheme. This article determines a variety of feature extraction approaches and classification techniques which are designed to recognize handwritten numerals of Kannada script. The DCNN classifier approach is used to classify the testing samples of each Kannada handwritten numerals. The experimental result gives the acceptable performance rate.
Factorization of composed polynomials and applications

Let Fq be the finite field with q elements, where q is a prime power and let n be a positive integer. In this paper, we explore the factorization of f(xn) over Fq, where f(x) is an irreducible polynomial over Fq. Our main results provide generalizations of recent works on the factorization of binomials xn−1. As an application, we provide an explicit formula for the number of irreducible factors of f(xn) under some generic conditions on f and n.
Create an Automatic Uncertainty Elimination Tools in Software Engineering

In Today's time, due to the advancement in innovations, Information Technology faces bunch of difficulties. These difficulties increment the conceivable outcomes of vulnerabilities in Software designing item advancement process. We are proposing a structure of automated uncertainty elimination technique which analyse, organised and develop patches to solve the issues caused by uncertainty. This is a stage wise action of uncertainty elimination. In our methodology, we examine the uncertainty and its sources and classify them. The Solution patches, based on the practice of learning from past experiences, are produced and applied on the problematic areas. This framework process also has auto feedback system which contains "create solution patches" and "apply patches" states. The process is reevaluated after the uncertainty removal. The paper is categorising as follows: First area contains the presentation. Second segment arranges the past work in uncertainty the executives and uncertainty appraisal. Third segment portrays the model phase of Automatic Uncertainty Removal Elimination (AURE) and its calculation and segment fourth includes the significance of AURE and offers our determination and an outline of future research.
Data Acquisition Procedures for A&amp;DM Systems Dedicated for the Foundry Industry

The article presents the effects of cooperation with Polish and European foundries regarding the design of procedures useful in acquisition and data mining systems (Acquisition & Data Mining, A&DM). The author's procedures for collecting data from foundry processes, including the topography of data sources, have been presented. These procedures have been associated with the possibilities of extended data analysis, which should be implemented in dedicated A&DM type systems. Specialized systems seem to be the most appropriate tools for rapid analyses of complex production processes (multivariate process). These systems allow to assess the stability of selected process parameters, and subsequently identify the cause and effect relationship related to the quality of castings. The choice of the number and type of parameters that can be associated with anomalies of processes depends on the system user, his knowledge and experience. This paper indicates the importance of dedicated A&DM systems built from scratch, developed in cooperation with a specific foundry.
Investigation of Tools, Techniques and Languages for Model Driven Software Product Lines (SPL): A Systematic Literature Review

Software development is a complex and difficult task that requires the investment of sufficient resources and carries major risk of failure.Model Driven Engineering (MDE) focuses on creating software models and automating code generation from the models.Model Driven Software Development (MDSD) offers significantly more effective approaches.These approaches improve the way of building software.Model driven approaches partially increase developer productivity, decrease the cost of software construction, improve software reusability, and make software more maintainable.This paper investigates the methods where Model Driven Software Development is integrated with Software Product Line (SPL).This SLR has been conducted to identify 71 research works published since 2014.We have collected 18 tools, 14 techniques and 17 languages used for MDSD for SPL.We analyze which technique is suitable for SPL.We compare the techniques on the basis of features provided by these tools to understand the better-quality results.
Comparing the Effects of Annotation Type on Machine Learning Detection Performance

The most prominent machine learning (ML) methods in use today are supervised, meaning they require ground-truth labeling of the data on which they are trained. Annotating data is arduous and expensive. Additionally, data sets for image object detection may be annotated by drawing polygons, drawing bounding boxes, or providing single points on targets. Selection of annotation technique is a tradeoff between time to annotate and accuracy of the annotation. When annotating a dataset for machine object recognition algorithms, researchers may not know the most advantageous method of annotation for their experiments. This paper evaluates the performance tradeoffs of three alternative methods of annotating imagery for use in ML. A neural network was trained using the different types of annotations and compares the detection accuracy of and differences between the resultant models. In addition to the accuracy, cost is analyzed for each of the models and respective datasets.
Sinhala Handwritten Postal Address Recognition for Postal Sorting

Sri Lankan post office mail sorting process is done manually, even today. Though employees are well experienced, it takes considerable time and employees need to work overtime in places like Central Mail Exchange (CME). With major issues like unclear handwriting, having trouble to recognize some uncommon or ambiguous names, and carrying these duties twice a day create a negative impact on the efficiency of the postal delivery system. In the prevailing system, forward mails and delivery mails are the two categories of separating mails at the sorting centers. Delivery mails are the posts which can be delivered to its destination directly. Forward mails are the ones which need to be sent to an appropriate post office that can deliver the particular post to its destination. Majority of Sri Lankans use Sinhala language for their day to day activities. The primary objective of the research is to identify the automatic way of forwarding the letter to the next post office from the current post office. Proposed system is focused on the recognition of Sinhala handwriting using Optical Character Recognition (OCR) and image processing technologies. Data collected under different criteria were used for training and testing the solution. Genetic Algorithm (GA) was used to generate more optimized results faster with higher accuracy. Given addresses are written in the default format. This format can be extended to more formats as improvements in future. The algorithm shows accuracy over 92% for addresses which are recognized with 3 misrecognized characters. This algorithm can be used on practice scenario as the AI Recognition has more than 79 % of accuracy.
Content to Node: Self-Translation Network Embedding

This paper concerns the problem of network embedding (NE), which aims to learn low-dimensional representations for network nodes. Such dense representations offer great promises for many network analysis problems. However, existing approaches are still faced with challenges posed by the characteristics of complex real-world networks. First, for networks associated with rich content information, previous methods often learn separated content and structure representations, which requires post-processing of combination. Empirical combination strategies often make the final vectors suboptimal. Second, existing methods preserve the structure information by considering short and fixed neighborhood scope, such as the first- and/or the second-order proximities. However, it is hard to decide the neighborhood scope in complex problems. To this end, we propose a novel sequence to sequence model based NE framework referred to as Self-Translation Network Embedding (STNE). With the sampled node sequences, STNE translates each sequence itself from the content sequence to the node sequence. On the one hand, the bi-directional LSTM encoder fuses the content and structure information seamlessly from the raw input. On the other hand, high-order proximity can be flexibly learned with the memories of LSTM to capture long-range structural information. Experimental results on three real-world datasets demonstrate the superiority of STNE.
Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional Problems

Metaheuristics have been very successful to solve NP-hard optimization problems. However, some problems such as big optimization problems are too expensive to be solved using classical computing. Naturally, the increasing availability of high performance computing (HPC) is an appropriate alternative to solve such complex problems. In addition, the use of HPC can lead to more accurate metaheuristics if their internal mechanisms are enhanced. Particle swarm optimization (PSO) is one of the most know metaheuristics and yet does not have many parallel versions of PSO which take advantage of HPC via algorithmic modifications. Therefore, in this article, the authors propose a cooperative asynchronous parallel PSO algorithm (CAPPSO) with a new velocity calculation that utilizes a cooperative model of sub-swarms. The asynchronous communication among the sub-swarms makes CAPPSO faster than a parallel and more accurate than the master-slave PSO (MS-PSO) when the tested big problems.
Multi-Scale Proposal Regression Network for Temporal Action Proposal Generation

Temporal action detection, as a branch of video analysis, aims to locate the time points when the actions start and end, and classify the actions occurred in videos into correct categories. Generating high-quality proposals is a key step in temporal action detection task. In this paper, we introduce a novel network, named multi-scale proposal regression network (MPRN), for temporal action proposal generation. First, we take encoding visual features as input and predict action scores for time points, in order to group them to generate rough proposals. Then, we regress the proposal's boundaries to obtain more precise proposals via our multi-scale proposal regression network. Compared with SSN and TURN, our multi-scale regression segments are characterized by flexible boundaries. Experiments show that 1) Our method is better than other proposal generation methods on THUMOS-14 dataset and ActivityNet-v1.3 dataset. 2) The effectiveness of our method is due to its own architecture, not the selection of visual feature encoders. 3) Our proposal generation method can generate temporal proposals for unseen action classes, which shows the good generalization ability of our proposal generation method.
End-to-End Dialogue System with Multi Languages for Hospital Receptionist Robot

Task-oriented dialogue systems aim to assist users in accomplishing specific tasks with natural language. Recently, with the success of end-to-end chit-chat system, there have been many attempts to build RNN based task-oriented dialogue systems. Hybrid Code Network (HCN) is a practical and efficient end-to-end model using domain-specific software and Recurrent Neural Network (RNN). This paper presents an end-to-end dialogue system for hospital receptionist robot with multi languages using HCN. For this, we synthetically generated dialogue corpus including several tasks. Original HCN was only applied Long Short-Term Memory (LSTM) to train dialogues. We replenish HCN by applying other RNN structures in this architecture such as stacked, reversed input sequence and bidirectional structures. Our proposed RNN model achieved higher performance on the hospital receptionist domain in both English and Korean languages.
Retrospective convolution and static sample synthesis for instantaneous change detection

Change detection has been a challenging visual task due to the dynamic nature of real-world scenes. Good performance of existing methods depends largely on prior background images or a long-term observation. These methods, however, suffer severe degradation when they are applied to detection of instantaneously occurred changes with only a few preceding frames provided. In this paper, we exploit spatio-temporal convolutional networks to address this challenge, and propose a novel retrospective convolution, which features efficient change information extraction between the current frame and frames from historical observation. To address the problem of foreground-specific overfitting in learning-based methods, we further propose a data augmentation method, named static sample synthesis, to guide the network to focus on learning change-cued information rather than specific spatial features of foreground. Trained end-to-end with complex scenarios, our framework proves to be accurate in detecting instantaneous changes and robust in combating diverse noises. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods.
Classification of Encryption Algorithms Based on Ciphertext Using Pattern Recognition Techniques

In digital era security in data communication is a challenging task. For secure data communication between the parties, encryption algorithms are being used. Recently many attacks have been reported for breaking ciphertext using various cryptanalytic techniques. Cryptanalysts are trying to break the cipher without knowing the key. Present day scenario an attacker may not know an encryption algorithm being used by communication entities. Identifying the algorithm itself is a challenging task. Once the encryption algorithm has identified, the cryptanalysts can analyzes the weakness of the encryption algorithm and be able to retrieve the plain text without knowledge of key. Here we present various pattern recognition techniques for identifying encryption algorithm which are used between two parties for communication of data using ciphertext. Thus, we consider the block cipher algorithms DES, IDEA, AES and RC operating in Electronic Code Book (ECB) mode. The classification techniques used are Support Vector Machine (SVM), Bagging (Ba), AdaBoostM1, Neural Network, Naïve Bayesian (NB), Instance Based Learning (IBL), Rotation Forest (RoFo) and Decision Trees to identify the right algorithm for the given ciphertext.
Naturalness‐Preserving Image Tone Enhancement Using Generative Adversarial Networks

Abstract This paper proposes a deep learning‐based image tone enhancement approach that can maximally enhance the tone of an image while preserving the naturalness. Our approach does not require carefully generated ground‐truth images by human experts for training. Instead, we train a deep neural network to mimic the behavior of a previous classical filtering method that produces drastic but possibly unnatural‐looking tone enhancement results. To preserve the naturalness, we adopt the generative adversarial network (GAN) framework as a regularizer for the naturalness. To suppress artifacts caused by the generative nature of the GAN framework, we also propose an imbalanced cycle‐consistency loss. Experimental results show that our approach can effectively enhance the tone and contrast of an image while preserving the naturalness compared to previous state‐of‐the‐art approaches.
Multi-level Weight Indexing Scheme for Memory-Reduced Convolutional Neural Network

Targeting the resource-limited intelligent mobile systems, in this paper, we present a multi-level weight indexing method that relaxes the memory requirements for realizing the convolutional neural networks (CNNs). In contrast that the previous works are only focusing on the positions of unpruned weights, the proposed work considers the consecutive pruned positions to generate the group-level validations. Denoting the survived indices only for the valid groups, the proposed multi-level indexing scheme reduces the amount of indexing data. In addition, we introduce the indexing-aware multi-level pruning and indexing methods with variable group sizes, which can further optimize the memory overheads. For the same pruning factor, as a result, the memory size for storing the indexing information is remarkably reduced by up to 81%, leading to the practical CNN architecture for intelligent mobile devices.
A robust face emotion recognition approach through optimized SIFT features and adaptive deep belief neural network

Facial expression is a significant indications for non verbal communication between individuals. The assignment of face emotion recognition is predominantly intricate for two reasons. Initial one is the non-existence of large database of training images and second issue is about classifying the emo tions, which can be complex based on if the input image is static or not. Hence, this paper intends to propose a model, where two contributions are made both in the feature extraction and classification process. Initially, in feature extraction process, SIFT features are extracted as it is more associated with facial emotion features. However, numerous key point during SIFT extraction tends to provide redundant information. Hence, the key points are optimized using Whale Optimization Algorithm (WOA). Subsequently, the features extracted from the selected keypoints are multiplied with a weight factor, which has to be optimized by WOA. The resultant features are given to Deep Belief Network (DBN) for the classification of face emotion, in which the number of hidden neurons is also optimized by WOA along with feature weight. It is because of complexity that occurs with the utilization of both forward and reverse training in standard DBN model. Hence, after feature extraction and classification, the face emotion image is recognized accurately, while comparing the proposed Adaptive DBN (ADBN) using WOA over traditional classifiers and other optimization algorithms.
Testing for mean reversion in Bitcoin returns with Gibbs-sampling-augmented randomization

In the present paper, we attempt to verify whether the Bitcoin log-returns are mean reverted in the presence of heteroskedastic disturbances driven by a mixture distribution. To tackle this problem, we use the autoregression test of mean reversion based on the Gibbs-sampling-augmented randomization methodology. In general, our results indicated that Bitcoin is mean averting for different returns horizons, model specifications and for sub-sample periods, which show the explosive characteristic of the Bitcoin in the period of analysis from 2010 to 2019.
Cross-Site Scripting for Graphic Data: Vulnerabilities and Prevention

In this paper, we present an overview of the problems associated with the cross-site scripting (XSS) in the graphical content of web applications. The brief analysis of vulnerabilities for graphical files and factors responsible for making SVG images vulnerable to XSS attacks are discussed. XML treatment methods and their practical testing are performed. As a result, the set of rules for protecting the graphic content of the websites and prevent XSS vulnerabilities are proposed.