Reviewerly/exHarmony huggingface dataset:
Convergence of de Finetti’s mixing measure in latent structure models for observed exchangeable sequences

Mixtures of product distributions are a powerful device for learning about heterogeneity within data populations. In this class of latent structure models, de Finetti's mixing measure plays the central role for describing the uncertainty about the latent parameters representing heterogeneity. In this paper, posterior contraction theorems for de Finetti's mixing measure arising from finite mixtures of product distributions will be established; under the setting the number of exchangeable sequences of observed variables increases while sequence length(s) may be either fixed or varied. The role of both the number of sequences and the sequence lengths will be carefully examined. In order to obtain concrete rates of convergence, a first-order identifiability theory for finite mixture models and a family of sharp inverse bounds for mixtures of product distributions will be developed via a harmonic analysis of such latent structure models. This theory is applicable to broad classes of probability kernels composing the mixture model of product distributions for both continuous and discrete domain X. Examples of interest include the case the probability kernel is only weakly identifiable in the sense of (Ann. Statist. 44 (2016) 2726–2755), the case where the kernel is itself a mixture distribution as in hierarchical models, and the case the kernel may not have a density with respect to a dominating measure on an abstract domain X, such as Dirichlet processes.
Chinese Calligraphy Generation Based on Residual Dense Network

Chinese calligraphy, as a writing art of Chinese characters, plays an important role in the inheritance of traditional culture. Compared with simple English fonts, the generation of Chinese calligraphy fonts is more challenging. This paper proposes Chinese calligraphy generation based on residual dense network. The generator network combines residual network structure and dense network structure, and uses local residual learning and global feature fusion to enhance feature reuse and information continuous transmission. The discriminator network adopts an autoencoder structure, and restores the similarity between the distributions by an approximate convergence strategy to achieve fast and stable training. At the same time, the structural similarity loss is introduced to quantify the similarity between the generated font image and the real font image. Experiments show that the proposed method achieves good calligraphy font generation effect, which effectively improves the quality of the generated font images and the rate of training.
Combined techniques based query expansion approach for document retrieval system

Automatic query expansion technique is used generally to resolve term mismatch problems existing in Information Retrieval System (IRS). This paper explores the necessary conditions to obtain the best query using knowledge sources and other IRS parameters. This paper proposes a new combined techniques based query expansion approach to enrich the performance of documents retrieval process. In this work, all the experiments are performed on two English written datasets CACM and CISI. At last, the proposed is tested and compared with recently developed similar types of query expansion techniques on the datasets, as mentioned above. The obtained results show a significant improvement in the performance of query expansion.
Deep Flow Collaborative Network for Online Visual Tracking

The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracking efficiency of these trackers is not very high due to the slow feature extraction for each frame in a video. In this paper, we propose an effective tracking algorithm to alleviate the time-consuming problem. Specifically, we design a deep flow collaborative network, which executes the expensive feature network only on sparse keyframes and transfers the feature maps to other frames via optical flow. Moreover, we raise an effective adaptive keyframe scheduling mechanism to select the most appropriate keyframe. We evaluate the proposed approach on large-scale datasets: OTB2013 and OTB2015. The experiment results show that our algorithm achieves considerable speedup and high precision as well.
Exploiting Low Rank Prior for Depth Map Completion

Occurrence of regions with missing data in depth maps either captured by active sensors or estimated by different passive computer vision algorithms, is unavoidable due to several reasons. The task of depth inpainting from a single degraded depth map is more challenging as compared to using multiple depth observations or RGB-D data. Recently, low rank techniques have become popular and shown supremacy over several state-of-the-art techniques for image deblurring, denoising, upsampling, etc. Since completion of missing regions in a given degraded depth observation is a severely ill-posed problem, low rank property of the inpainted depth map can be posed as the regularization constraint. We perform several experiments to show the superiority of the proposed method over the state-of-the-art depth inpainting techniques.
AI-Driven Smart Production

Nippon Hoso Kyokai (NHK, Japan Broadcasting Corporation), has developed a new artificial intelligence (AI)-driven broadcasting technology called “Smart Production” designed to quickly and accurately gather and analyze diverse types of social information and deliver information to a wide range of viewers. Smart Production uses AI to analyze information obtained from social media and open data as well as the know-how related to program production possessed by broadcast stations. This approach makes it possible to extract events and incidents in society and present the results of the analysis to producers. In particular, image analysis technology for recognizing objects in video and speech recognition technology for generating transcripts of interviews enable metadata to be automatically generated for video footage. Additionally, research and development are progressing on technology for automatically converting broadcast data into content that can be easily understood by viewers with special needs.
Using Augmented Reality, Gaming Technologies, and Transmedial Storytelling to Develop and Co-design Local Cultural Heritage Experiences

As technologies are integrated in museum and cultural heritage contexts, digital heritage design increasingly depends on innovative, embodied and experimental storytelling features focused on users. These developments create opportunities to incorporate gaming technologies that may include immersive, affective mixed reality (MR) systems with narrative innovation at the core. To support such engagements, researchers in the Division of Game Development at the University of Skövde have developed a number of projects, educational programs, interdisciplinary research practices and collaborations. In our chapter we will foreground the KLUB project, a sub-project in the KASTiS project (in English, the “Cultural Heritage and Game Technologies in Skaraborg” project), a funded regional development initiative in western Sweden focused on engaging citizens in local cultural heritage at a number of municipalities. KLUB uses transmedial storytelling techniques and gaming elements within an Augmented Reality enhanced children’s book series and related media (board games, locative experiences) that have been co-designed with a number of stakeholders to tell and play the local micro-histories of the Skaraborg region in Sweden. We contextualize our research in humanistic interventions and practices for co-designing transmedial game/stories and outline some of our related intra-disciplinary activities and impacts in research.
Epoch Estimation from a Speech Signal Using Gammatone Wavelets in a Scattering Network

In speech production, epochs are glottal closure instants where significant energy is released from the lungs. Extracting an epoch accurately is important in speech synthesis, analysis, and pitch oriented studies. The time-varying characteristics of the source and the system, and channel attenuation of low-frequency components by telephone channels make estimation of epoch from a speech signal a challenging task. In this paper, we propose a new technique that employs a Gammatone wavelet filterbank and compute a scattering sequence whose local maxima define the candidate epochs in the speech signal. Results are presented for both normal and telephone channel speech by considering the differential electroglottograph from CMU-Arctic database as the ground-truth. The proposed method gives significant improvements with respect to multiple performance metrics when compared with state-of-the-art techniques for epoch estimation.
Deep Learning Approach to Detect Plagiarism in Sinhala Text

In simple terms, plagiarism is taking someone else's work or ideas and passing them without giving credit to the original author. Due to the availability of a vast amount of documents on the Internet, plagiarism has become a severe problem in this digital world. Hence the need for an automatic plagiarism detection tool is inevitable. There is a right amount of existing research on automated plagiarism detection, and there are several language-dependent and language-independent plagiarism detection tools available. The lack of adequate research for Sinhala plagiarism detection and the inefficiency of plagiarism detection tools for Sinhala text motivated this research which focuses on developing a Deep learning approach for plagiarism detection in Sinhala documents. In natural language processing (NLP), Word embedding has become a popular language modelling and feature learning technique. A word embedding model is capable of representing a word or a phrase as a vector of real numbers known as a word vector. A word vector represents the semantic and syntactic similarity of a word or phrase with other words in a corpus. Further, a sentence can be represented as a vector of values using the word vectors for words in that sentence. This research work proposes a method for Sinhala plagiarism detection by representing sentences as a vector of values and comparing the sentences based on these vector of values. For plagiarised sentences, the similarity of the vector of values would be high. A word embedding model is built using a Deep learning neural network and a Sinhala text corpus as part of this study. The word2vec algorithm and the publicly available UCSC_Sinhala_News corpus are used for this purpose. In the proposed model, a simple aggregation method is used to represent a sentence as a vector of values from the word vectors for words in that sentence. The cosine similarity and soft-cosine similarity metrics are used to quantify the similarity of sentences represented as a vector of values. The sentence pairs with the similarity scores higher than a threshold value are considered as plagiarised. The proposed model was implemented and tested on a newly created data set and found to be capable of detecting plagiarism with an accuracy of 97%. The model is found to be detecting direct and sophisticated copying such as replacing words with synonyms or changing the order of words in a sentence.
A Multi-task Approach to Open Domain Suggestion Mining Using Language Model for Text Over-Sampling

Consumer reviews online may contain suggestions useful for improving commercial products and services. Mining suggestions is challenging due to the absence of large labeled and balanced datasets. Furthermore, most prior studies attempting to mine suggestions, have focused on a single domain such as Hotel or Travel only. In this work, we introduce a novel over-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our over-sampling technique, coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.
Steganographic Framework for Hiding a Color Image into Digital Images

In last years, steganography has been used for security purposes, the main idea is hide information inside some cover media, in such a way that it should be invisible to Human Visual System (HVS). In this paper, we propose a new steganography framework to hide a 24 bits color image inside digital images, where the Discrete Cosine Transform (DCT) is employed to reduce the amount of data representing secret image, and later, this new information is embedded into the middle-frequency DCT coefficients of the cover image, using the Quantization Index Modulation Dither-Modulation (QIM-DM). In experimental results, we have demonstrated that this technique supports JPEG compression attacks; also, the secret information is imperceptible to the human eye according to the obtained PSNR and SSIM values.
Characterization of microseepage-induced diagenetic changes in the Upper-Red Formation, Qom region, Iran. Part II: A new insight using reflectance spectroscopic analysis

The classic microseepage model supports the notion that hydrocarbon pools leak to the surface over time and trigger an array of diagenetic physicochemical and mineralogical changes in the overlying sediments. Mapping the induced alteration mineralogy for microseepage targeting has been an appealing yet ambiguous practice, mostly because of our incomplete understanding of the diversity of changes occurring in the environment. To close this gap, an integrated spectroscopic approach is adopted here to investigate the variability, quantity, and physicochemistry of diagenetic minerals induced by active microseepage systems over the Alborz oil reservoir in the Qom region, Iran. The area includes an effectively sealed carbonate reservoir overlain by thick Oligocene sediments of the Upper Red Formation. A large suite of samples collected from microseepage-affected zones was spectrally characterized in the 350–2500 nm wavelength region and then statistically contrasted against unaffected samples gathered from a nearby control area. The analysis reveals a novel array of mineralogic indicators for microseepage systems. It indicates that smectites (montmorillonite, nontronite), Al-rich mixed-layer illite/smectite, Fe-rich chlorite and traces of palygorskite constitute the clay alterations. This array is associated with the enrichment of K2O and loss of Na2O and MgO from the system, as indicated by X-ray fluorescence measurements. In the arid climate of the region, the iron-rich red-beds (Fe2O3 > 6%) are only moderately bleached and, thus, the subsequent chemical weathering of the relict irons has triggered goethite (Al-poor), and likely ferrihydrite and maghemite precipitation. Consequently, besides the typical ferric iron bleaching, the modification in the mineralogy of iron oxides/oxyhydroxides (marked by a shift in the absorption minimum at ~900 nm) is suggested as an additional indicator of microseepage systems. Fe-chlorites constitute the dominant ferrous mineral present in the environment. The affected strata are also associated with prevalent calcite and gypsum cements, and occasionally jarosite occurrences. Based on the silica contents of the host-rock, the microseepage-induced alterations are classified into two categories, namely 'felsic' and 'mafic', wherein the latter is typified by the assemblages described here. This study provides new insights into microseepage-induced alterations and highlights the significance of reflectance spectroscopic techniques in facies determination and the alteration footprints mapping of onshore microseepage systems.
A Framework for Parameters Estimation of Image Operator Chain

Currently, many effective techniques have been proposed to estimate the parameters of tampering operations. Most of them consider the situation that an image is tampered by only one operation. However, multiple manipulation operations are always used to tamper an image in our daily life. Moreover, since the tampering traces of previous operations may be weakened or eliminated by later ones, the detecting accuracy of methods used for detecting single operations would be reduced. In this paper, we propose a new method to estimate the parameters of operations in different manipulation chains. Especially, we first investigate the correlation of operations and divide the degree of interactions between them into uncoupled and coupled. Furthermore, resizing and median filtering are adopted to reveal the assessment framework. Meanwhile, we propose well-directed features including energy density of difference-image to estimate operation parameters. The experiment proves the effectiveness of our method.
Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution

One of the major challenges faced by video surveillance is recognition from low-resolution videos or person identification. Image enhancement methods play a significant role in enhancing the resolution of the video. This article introduces a technique for face super resolution based on a deep convolutional neural network (Deep CNN). At first, the video frames are extracted from the input video and the face detection is performed using the Viola-Jones algorithm. The detected face image and the scaling factors are fed into the Fractional-Grey Wolf Optimizer (FGWO)-based kernel weighted regression model and the proposed Deep CNN separately. Finally, the results obtained from both the techniques are integrated using a fuzzy logic system, offering a face image with enhanced resolution. Experimentation is carried out using the UCSD face video dataset, and the effectiveness of the proposed Deep CNN is checked depending on the block size and the upscaling factor values and is evaluated to be the best when compared to other existing techniques with an improved SDME value of 80.888.
ILFS: Intelligent Lost and Found System using Multidimensional Matching Model

Traditional lost-and-found system is based on content management, with low query efficiency and poor privacy. In our demonstration, we present an Intelligent Lost and Found System using Multidimensional Matching Model (ILFS). The front end of this system is WeChat applet, and the background platform takes the multidimensional matching algorithm as the core. The output of this system is matching result of Lost and Found. The innovation of ILFS is a multi-dimensional matching model that protects user privacy and prevents malicious information from being publicized.
Research on Design and Key Technology of New Generation Multi-MCUs Smart Meter

In response to the growing contradiction between the growing demand for grid business and the backwardness of smart meter functions, the International Organization for Legal Metrology (OIML) has redefined the International Recommendation IR46 for electric power meter. In this context, multi-MCUs smart meter design and key technology are researched. Firstly, from the business scenario, business application and business model, the application requirements of smart meter in the new situation are systematically analyzed. On this basis, the function design and hardware design of multi-MCUs smart meter are carried out. Aiming at the key technology of hardware design, the hardware architecture adopts loosely coupled modular structure, including four modules: metering MCU, management MCU, identification MCU and negative control MCU; based on the measurement SOC, the data sampling promotion technology such as high-frequency sampling and self-checking are analyzed; the multi-mode heterogeneous communication technology is analyzed for the application requirements of ubiquitous Federation of things (UFO). In view of the key technologies of software development, the lightweight embedded operating system is analyzed in detail, and two advanced application technologies of non-intrusive load identification technology and ordered charging technology are introduced, and a unified information interaction model is constructed. Finally, the development of smart meter prototype is developed, and the performance and function of multi-MCUs smart meter are tested based on the experimental environment.
Machine Normalization

User-generated text in social media communication (SMC) is mainly characterized by non-standard form. It may contain code switching (CS) text, a widespread phenomenon in SMC, in addition to noisy elements used, especially in written conversations (use of abbreviations, symbols, emoticons) or misspelled words. All of these factors constitute a wall in front of text mining applications. Common text mining tools are dedicated to standard use of standard languages but cannot deal with other forms, especially written text in social media. To overcome these problems, in this work we present our solution for the normalization of non-standard use of standard and non-standard languages (dialects) in SMC text with the use of existent resources and tools. The main processing in our solution consists of CS normalization from multiple to one language by the use of a machine translation--like approach. This processing relies on a linguistic approach of CS, which aims at identifying automatically the translation source and target languages (without human intervention). The remaining processing operations concern the normalization of SMC special expressions and spelling correction of out-of-vocabulary words. To preserve the coded-switched sentence meaning across translation, we adopt a knowledge-based approach for word sense translation disambiguation reinforced with a multi-lingual vertical context. All of these processes are embedded in what we refer to as the machine normalization system. Our solution can be used as a front-end of text mining processing, enabling the analysis of SMC noisy text. The conducted experiments show that our system performs better than considered baselines.
Detecting adversarial manipulation using inductive Venn-ABERS predictors

Inductive Venn-ABERS predictors (IVAPs) are a type of probabilistic predictors with the theoretical guarantee that their predictions are perfectly calibrated. In this paper, we propose to exploit this calibration property for the detection of adversarial examples in binary classification tasks. By rejecting predictions if the uncertainty of the IVAP is too high, we obtain an algorithm that is both accurate on the original test set and resistant to adversarial examples. This robustness is observed on adversarials for the underlying model as well as adversarials that were generated by taking the IVAP into account. The method appears to offer competitive robustness compared to the state-of-the-art in adversarial defense yet it is computationally much more tractable.
Implementation of Search Engine with Lucene in the Document Management System

Manually managing the documents is a very challenging job, an automated or organized system can support to manage and organize the documents in an effective manner. Software like Document Management Systems (DMS) is a crucial approach that manages well to certify the quicker and effective overall working process in the organization. Searching documents takes a lot of efforts and time, recent researches have shown that office worker has spent their nearly 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of an average day for trying to search existing documents or information within documents. In this research, a search engine with Lucene in Document Management System (DMS) has been proposed, which can index and search document in any format. This paper presents a feature of advanced searching not only the document but also full-text search within a document using the Apache Lucene. The DMS in our proposed system contains the web-based admin panel, client application, and mobile application. The results proved that the proposed searching system is a fast and improved text retrieval that helps users to search effectively and retrieved information rapidly.