Reviewerly/exHarmony huggingface dataset:
Multi-scale Neural Style Transfer Based on Deep Semantic Matching

Existing Neural Style Transfer (NST) algorithms do not migrate styles well to a reasonable location where the output image can render the correct spatial structure of the object being painted. We propose a deep semantic matching-based multi-scale (DSM-MS) neural style transfer method, which can achieve the reasonable transfer of styles guided by the prior spatial segmentation and illumination information of input images. First, according to real drawing process, before an artist decides how to paint a stroke, he/she needs to observe and then understand subjects, segmenting space into different regions, objects and structures and analyzing the illumination conditions on each object. To simulate the two visual cognition processes, we define a deep semantic space (DSS) and propose a method for calculating DSSs using manual image segmentation, automatic illumination estimation and convolutional neural network (CNN). Second, we define a loss function, named deep semantic loss, which uses DSS to guide reasonable style transfer. Third, we propose a multi-scale optimization strategy for improving the efficiency of our method. Finally, we achieve an interdisciplinary application of our method for the first time–painterly rendering 3D scenes by neural style transfer. The experimental results show that our method can synthesize images in better original structures, with more reasonable placement of each styles and visual aesthetic feeling.
Interactive Summarization of Large Document Collections

We present a new system for custom summarizations of large text corpora at interactive speed. The task of producing textual summaries is an important step to understand large collections of topic-related documents and has many real-world applications in journalism, medicine, and many more. Key to our system is that the summarization model is refined by user feedback and called multiple times to improve the quality of the summaries iteratively. To that end, the human is brought into the loop to gather feedback in every iteration about which aspects of the intermediate summaries satisfy their individual information needs. Our system consists of a sampling component and a learned model to produce a textual summary. As we show in our evaluation, our system can provide a similar quality level as existing summarization models that are working on the full corpus and hence cannot provide interactive speeds.
Semi-supervised Data Mining Tool Design with Self-tuning Optimization Techniques

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Presented in this article are semi-supervised artificial neural network- (ANN) and support vector machine- (SVM) based classifiers designed by the self-configuring genetic algorithm (SelfCGA) and the fuzzy controlled meta-heuristic approach Co-operation of Biology Related Algorithms (COBRA). Both data mining tools are based on dividing instances from different classes using both labelled and unlabelled examples. A new collective bionic algorithm, namely fuzzy controlled cooperation of biology-related algorithms, which solves constrained optimization problems, COBRA-cf, has been developed for the design of semi-supervised SVMs. Firstly, the experimental results obtained by the two types of fuzzy controlled COBRA are presented and compared and their usefulness is demonstrated. Then the performance and behaviour of the proposed semi-supervised SVMs and semi-supervised ANNs were studied under common experimental settings and their workability was established. Then their efficiency was estimated on a speech-based emotion recognition problem. Thus, the workability of the proposed meta-heuristic optimization algorithms was confirmed.
Research on Dynamic and Static Fusion Polymorphic Gesture Recognition Algorithm for Interactive Teaching Interface

In order to solve the problem of teachers’ excessive energy dissipation due to interaction with teaching equipment in traditional classrooms, an interactive and intelligent teaching interface is proposed to enable teachers to use the gestures to give students a geometry lesson. The traditional algorithm of gesture recognition mainly consists of feature extraction and classifier, which requires human-designed features. The recognition is mainly based on static gesture or dynamic gesture singular state recognition algorithm. The recognition accuracy is not robust enough and different people Identification results do not have the universality and ease of operation. In order to solve this problem, we propose a multi-state gesture recognition algorithm based on the deep learning network, which combines the large database of hand gestures and the deep learning algorithms. The innovation of this algorithm is as follows: Aiming at the static gesture images, a sequence reduction algorithm is proposed. According to the sequence of dynamic gestures, the first and last frame fixed and intermediate frame traversal combination algorithm are proposed to get the dynamic and static fusion gesture training datasets, and then the dynamic and static fusion datasets are input to the deep learning network GoogLeNet for training. After repeated training, we found the optimal rule of deep learning network training. According to the optimization law, we got GoogLeNet_model which can recognize 23 kinds of dynamic and static fusion gestures, the recognition rate is 97.09%. We use this model in interactive teaching interface, and achieved good application effect.
Value-Oriented Ranking of Online Reviews Based on Reviewer-Influenced Graph

To mitigate the uncertainty of online purchases, people rely on reviews written by customers who already bought the product to make their decisions. The key challenge in this situation is how to identify the most helpful reviews among a large number of candidate reviews with different quality. Existing work normally employs diversified text and sentiment analysis algorithms to analyze the helpfulness of reviews. Voting on reviews is another popular valuation way adopted by many websites, which also has difficulties to reflect the real helpfulness of the reviews due to the problem of data sparseness. In this paper, a reviewer-influenced graph model is constructed based on the reviewers’ historical reviews and voting information to measure the influence of reviewers’ quality on the helpfulness of reviews. Experimental results with actual review data from Amazon.com demonstrate the effectiveness of our approach.
Cognitive Deep Learning

Deep learning states the scientific algorithms that are accustomed to come through a particular assignment. Such tidy issues could also be meteorology or brain diagnosing wherever records are obtainable as text. In such a state of affairs, cognitive computing can help medical practitioners to diagnose patterns that they might not observe, and they will extend the flexibility to diagnose the brain with efficiency. Deep learning is additionally able to introduce new APIs.
Reducing Costs in Building a Dataset Using Deep and Active Learning

Abstract Deep learning, a subset of machine learning, allows computers to perform certain tasks, such as image or video recognition, with human level performance. However, deep models need huge amounts of data to learn from, which requires that experts spend their time in the repetitive and non-scalable task of labelling datasets. Active learning suggests that one can minimize the cost of annotation if a model is allowed to smartly choose the best data samples to be labelled. Therefore, we propose a deep and active learning approach that aims to minimize the labelling effort while maximizing the performance of a model for a certain task. We present the task of detecting fish in Remote Operated Vehicles (ROV) videos as a real world problem in which our framework can be successfully applied. To start with, we demonstrate that active learning outperforms random sampling, which is the simplest approach for building a dataset. Besides, we study several active learning settings for the given task, namely different acquisition and aggregation functions. Finally, the proposed methodology is shown to achieve top performance in detecting fish by using only 19% of the available data, thus reducing the cost of building our fish dataset by more than 80%.
Computationally Efficient and Secure HVS Based Composite Fingerprinting Scheme Using SHS and ECDSA

Fingerprinting is an extension to the watermarking principle where each copy of the image is embedded with unique purchaser’s information. Secure fingerprinting aims to prevent efforts to identify the embedded contents. It offers an effective tracing contrivance and protects the legitimacy of digital data. Only an authenticated user can retrieve the implanted contents. In this chapter a secure Human Visual System (HVS) based fingerprinting scheme is proposed for embedding unique composite fingerprints to safeguard buyer’s certification accompanied by image integrity. Combination of authentication and reliability issues is determined using Secure Hash Standard (SHS): Secure Hash Algorithm (SHA-512/256) and Elliptic Curve Digital Signature Algorithm (ECDSA). SHS generates a unique authentication code by integrating image information with buyer’s credentials. Uniqueness property of digest allows for identification of authentication deceit by ensuring maximum collision resistance strength. Composite fingerprint (CF) created using ECDSA is embedded using a maximally separated imperceptible mode, by means of HVS approach. The positions take advantage of color content of digital images to reduce the visible distortion introduced by embedding CF. Signing and verifying digital signatures are two core elements adopted while embedding and retrieving of CF. Test cases are generated for different image dimensions used for embedding varying size fingerprints. Experimental results prove the approach to be beneficial by forming a maximally resilient counter to collusion attacks. An efficient imperceptible semi-blind fingerprinting scheme is achieved without degrading the quality and integrity of the host image.
Colocation, Colocation, Colocation: Optimizing Placement in the Hybrid Cloud

Today’s enterprise customer has to decide how to distribute her services among multiple clouds - between on-premise private clouds and public clouds - so as to optimize different objectives, e.g., minimizing bottleneck resource usage, maintenance downtime, bandwidth usage or privacy leakage. These use cases motivate a general formulation, the uncapacitated (A defining feature of clouds is their elasticity or ability to scale with load) multidimensional load assignment problem - VITA(F) (Vectors-In-Total Assignment): the input consists of n, d-dimensional load vectors $$\bar{V} = \{\bar{V}_i | 1\le i \le n\}$$ , m cloud buckets $$B = \{B_j | 1\le j \le m\}$$ with associated weights $$w_j$$ and assignment constraints represented by a bipartite graph $$G=(\bar{V} \cup B, E \subseteq \bar{V} \times B)$$ restricting load $$\bar{V}_i$$ to be assigned only to buckets $$B_j$$ with which it shares an edge (In a slight abuse of notation, we let $$B_j$$ also denote the subset of vectors assigned to bucket $$B_j$$ ). F can be any operator mapping a vector to a scalar, e.g., $$\max $$ , $$\min $$ , etc. The objective is to partition the vectors among the buckets, respecting assignment constraints, so as to achieve $$\begin{aligned} \min [ \sum _j w_j*F (\sum _{\bar{V}_i \in B_j} \bar{V}_i)] \end{aligned}$$ We characterize the complexity of VITA $$(\min )$$ , VITA $$(\max )$$ , VITA $$(\max - \min )$$ and VITA $$(2^{nd}\max )$$ by providing hardness results and approximation algorithms, LP-Approx involving clever rounding of carefully crafted linear programs. Employing real-world traces from Nutanix, a leading hybrid cloud provider, we perform a comprehensive comparative evaluation versus three natural heuristics - Conservative, Greedy and Local-Search. Our main finding is that on real-world workloads too, LP-Approx outperforms the heuristics, in terms of quality, in all but one case.
Spectrum Trading with Secondary Users’ Privacy Preservation

As described in Sect. 1.2 , spectrum trading benefits both SUs and PUs, while it poses great challenges to maximize PUs’ revenue, since SUs’ demands are uncertain and individual SU’s traffic portfolio contains private information. In this chapter, we propose a data-driven spectrum trading scheme which maximizes PUs’ revenue and preserves SUs’ demand differential privacy. Briefly, we introduce a novel network architecture consisting of the PSP, the SSP and the STED. Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs’ demands. To estimate SUs’ demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs’ demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs’ demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.
Chapter 9 Government Surveillance, National Security, and the American Rights: Using Sentiment Analysis to Extract Citizen Opinions

After the ex-National Security Agency contractor Edward Snowden1 disclosures of the National Security Agency surveillance of Americans’ online and phone communications, the Pew Research Center2 administrated a panel survey to collect data concerning Americans’ opinions about privacy and security. This survey has mixed types of qualitative questions (closed and open-ended). In this context, to our knowledge, until today, no research has been applied on the open-ended part of these data. In this chapter, first the authors present their findings from applying sentiment analysis and topic extraction methods; second, the authors demonstrate their analysis to sentiments polarities; and finally, the authors interpret the semantic relationships between topics and their associated negativity, positivity, and neutral sentiments.
Deep part‐related feature learning for human‐level biometrics

Many human-level biometrics systems rely on visual appearance to solve re-identification problems. Most of them focus on recognizing local visual regions individually, without exploiting the relations between these separated visual regions during learning. In this study, the authors target on learning discriminative part-related features and present novel network architecture, part relation network (PRN). It processes a set of part objects simultaneously in an end-to-end way to model relations of their appearance features, which strengthens the discriminative ability of local representation. Specially, the authors design PRN as a two-branch deep network, each branch of which performs uniform partition with different numbers of stripes to obtain multi-scale and cross-over part features for identification. Experimental results demonstrate that this system achieves state-of-the-art performance on three challenging data sets including Market-1501, CUHK03, and DukeMTMC-reID and demonstrate the effectiveness of modelling part relations for biometrics.
Social and humanistic aspects for the training of technical professionals

Development of soft skills of students is an urgent educational task of modern classical universities. The aim of the article is to demonstrate the experience of approbation of the educational technology for developing soft skills of students in a modern university. The article presents a new approach to the problem of developing soft skills of students at the National Research Tomsk State University (TSU). The authors analyze the essence of the educational technology 'Accelerator' as well as results gained during approbation of this technology based on the Park of Social and Humanistic Technologies of TSU (the structural subdivision of the University). Prospects for introduction of the described technology in classical universities are related to demand for the enhanced quality of soft skills of students in conditions of transition to a new technological paradigm dedicated to social and humanistic technologies. Approbation of the described technology in the modern university allows authors to draw conclusions about its effectiveness and to present results of the experience to a wider audience.
Propositional quantifiers in labelled natural deduction for normal modal logic

Abstract This article concerns the treatment of propositional quantification in a framework of labelled natural deduction for modal logic developed by Basin, Matthews and Viganò. We provide a detailed analysis of a basic calculus that can be used for a proof-theoretic rendering of minimal normal multimodal systems with quantification over stable domains of propositions. Furthermore, we consider variations of the basic calculus obtained via relational theories and domain theories allowing for quantification over possibly unstable domains of propositions. The main result of the article is that fragments of the labelled calculi not exploiting reductio ad absurdum enjoy the Church–Rosser property and the strong normalization property; such result is obtained by combining Girard’s method of reducibility candidates and labelled languages of lambda calculus codifying the structure of modal proofs.
The Third Kind of Bayes’ Theorem Links Membership Functions to Likelihood Functions and Sampling Distributions

For given age population prior distribution P(x) and the posterior distribution P(x|adult), how do we obtain the denotation of a label y = “adult”? With the denotation, e.g., the membership function of class {Adult}, we can make new probability prediction, e.g., likelihood function, for changed P(x). However, existing methods including Likelihood Method and Bayesian Inference cannot resolve this problem. For this purpose, the author proposes and proves the third kind of Bayes’ Theorem, which includes two asymmetrical Bayes’ formulas. The membership function so obtained is equivalent to that from random set statistics proposed by Peizhuang Wang. When samples are very big so that there are continuous sampling distributions P(x, y), we can directly derive a group of membership functions by a new Bayes formula. If samples are not big enough, we can use the semantic information formula, a generalized Kullback-Leibler formula, to optimize the membership functions by sampling distributions. The semantic information criterion is compatible with maximum likelihood criterion and Regularized Least Squares (RLS) criterion. In comparison with the likelihood function and the Bayesian posterior, the membership function so obtained as the predictive model can be used with new source P(x) to produce new likelihood function for better generalization performance. New Bayes’ formulas and the semantic information method can be applied to machine learning. The paper simply introduces their applications to (1) multi-label classifications; (2) maximum mutual information classifications for unseen instances; and (3) mixture models. It is shown that new methods are very simple and reliable. It seems that the membership function so obtained can bridge the gap between logic and probability. With this membership function, we can develop a new mathematical tool: Logical Bayesian Inference.
A Survey on Forecasting Methods by using Various Types of Extreme Learning Machine

A Single Layer Feed Forward Neural webs which is a narrative learning of step by step instruction for (SLFNs) i.e., called as Extreme Learning Machine which helps in training limits of hidden nodes and also involves weights to input and the biases, that is to be set dynamically and need not require to adjust it, at the period of time the resultant weights can scientifically resolved by the operation of simple generalized inverse and exclusively one limit is applied i.e. the number of nodes which is hidden needs to explain. In contrast with other learning step by step instruction of single layer which is hidden i.e., design for feed forward neural webs, ELM gives terribly faster learning rate along with effective generalization performance. Initially it gives a short idea of ELM, explaining the basic concept and its step by step instruction. Further it focuses on the various types of ELM, mainly on incremental, two-stage, error-minimized, pruning, voting-based, ordinal, online sequential, evolutionary and fully complex ELM.
A Local Perspective: Working an Agricultural Information Service into a Rural Community

Accounting for the ways in which ICT initiatives are worked into local communities is part of rendering "the local perspectives" within ICT4D. This paper is part of that. Based on in-depth fieldwork, we investigate the integration of an agricultural information service into a rural community in Bangladesh. We find that it takes work beyond the initial design and cursory introduction of the service to make the service work in a low-income rural community: it takes the strength of the farmers self-help groups to circulate the messages by word-of-mouth, it takes posters placed at key junctions according to a socio-geographic understanding of the village manifest in a map, and it (ideally) involves the support of the elite of the community via the broadcast of messages at places of high symbolic value. It takes all this in addition to a well-made information service delivering relevant and timely messages on for example agricultural matters. Hence, reducing the issue to one of technical delivery mechanism does not tell the full story.
Challenges and Solutions in Motion Surveillance - A Survey

In the past years, Video Surveillance is being an attractive area of research. Its application includes variety of areas such as security, animal movements in city side, vehicle tracking, combat vehicle, satellite, robotics, automation etc,.This method of surveillance consists of mainly three steps namely object detection, tracking and object recognition. While doing so we encounter so many challenges. Some of them are background intensity changes, handling complex background, occlusions, background clutter, camouflage .This paper survey about various issues and the techniques to overcome such problems. Some of the problems highlighted are detecting objects with complex background, abnormal event detection, partially occluded or fully occluded objects, Problem of Object deformation and effective tracking and intensity changes.