Reviewerly/exHarmony huggingface dataset:
Progressive deep feature learning for manga character recognition via unlabeled training data

The recognition of manga (Japanese comics) characters is an essential step in industrial applications, such as manga character retrieval, content analysis and copyright protection. However, conventional methods for manga character recognition are mainly based on handcrafted features which are not robust enough for manga of various style. The emergence of deep learning based methods provides representational features, which has a huge demand for labeled data. In this paper, we propose a framework to exploit unlabeled manga data to facilitate the discriminative capability of deep feature representations for manga character recognition (i.e., unsupervised learning on manga images), which does not rely on any manual annotation. Specifically, we first train an initial feature model using an anime character dataset. Then, we adopt a Progressive Main Characters Mining (PMCM) strategy which iterates between two steps: 1) produce selected data with estimated labels from unlabeled data, 2) update the feature model by the selected data. These two steps are mutually promoted in essence. Experimental results on Manga109 dataset, to which we introduce new head annotations, demonstrate the effectiveness of the proposed framework and the usefulness in manga character verification and retrieval.
DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification

Copresence verification based on context can improve usability and strengthen security of many authentication and access control systems. By sensing and comparing their surroundings, two or more devices can tell whether they are copresent and use this information to make access control decisions. To the best of our knowledge, all context-based copresence verification mechanisms to date are susceptible to context-manipulation attacks. In such attacks, a distributed adversary replicates the same context at the (different) locations of the victim devices, and induces them to believe that they are copresent. In this paper we propose DoubleEcho, a context-based copresence verification technique that leverages acoustic Room Impulse Response (RIR) to mitigate context-manipulation attacks. In DoubleEcho, one device emits a wide-band audible chirp and all participating devices record reflections of the chirp from the surrounding environment. Since RIR is, by its very nature, dependent on the physical surroundings, it constitutes a unique location signature that is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR data with various mobile devices and in a range of different locations. We show that DoubleEcho mitigates context-manipulation attacks whereas all other approaches to date are entirely vulnerable to such attacks. DoubleEcho detects copresence (or lack thereof) in roughly 2 seconds and works on commodity devices.
Layered Software Architecture for the Development of Third-Generation Video Surveillance Systems

Mobile distributed systems of third-generation video surveillance (MDSV) have become a useful tool to provide multiple security services to people. For this type of systems, three key aspects must be carried out: 1) protection, which consists of preventing undesirable events; 2) detection, which refers to determining the exact moment in which the event occurred; and 3) reply, in this regard, actions such as activating alarms and generating warnings are executed. Previous works have proposed software architectures to development video surveillance systems on mobile distributed systems (MDS). However, these architectures focus mainly on providing services/aspects of protection and detection; without considering in its design the requirements that arise from the characteristics of the MDS, such as limited processing and storage capacities of devices, frequent disconnections, among others. In this paper, we introduce a layered software architecture to build MDSV. The proposed architecture considers and satisfies the requirements that arise from the critical aspects of protection, detection, and reply, including the characteristics of the MDS. Based on our architecture, an MDSV prototype was implemented. The tests carried out on the prototype show that the proposed architecture correctly provides users with various essential services in terms of protection, detection, and reply. From our point of view, the most important advantages of our proposed software architecture are the following: define the basic technical guidelines that an MDSV must have and accomplish; streamline overall development, providing a solid framework for developers; and contribute to satisfying the requirements that arise from quality attributes that the MDSV must possess.
Doubly Semi-Supervised Multimodal Adversarial Learning for Classification, Generation and Retrieval

Learning over incomplete multi-modality data is a challenging problem with strong practical applications. Most existing multi-modal data imputation approaches have two limitations: (1) they are unable to accurately control the semantics of imputed modalities; and (2) without a shared low-dimensional latent space, they do not scale well with multiple modalities. To overcome the limitations, we propose a novel doubly semi-supervised multi-modal learning framework (DSML) with a modality-shared latent space and modality-specific generators, encoders and classifiers. We design novel softmax-based discriminators to train all modules adversarially. As a unified framework, DSML can be applied in multi-modal semi-supervised classification, missing modality imputation and fast cross-modality retrieval tasks simultaneously. Experiments on multiple datasets demonstrate its advantages.
Eye movements reveal learning and information-seeking in attentional template acquisition

Visual attention serves to select relevant visual information. However, observers often first need to find out what is relevant. Little is known about this information-seeking process and how it affects attention. We employed a cued visual search task in combination with eye tracking to investigate which oculomotor measures reflect the acquisition of information for a subsequent task. A cue indicated as to which target to look for in a following search display. Cue-target combinations were repeated several times, enabling learning of the target. We found that reductions in cue fixation times and saccade size provided stable indices of learning. Despite the learning, participants continued to attend to repeated cues. Several factors contribute to people attending to information they already know: First, the information value provided by the cue continues to drive attention. Second, even in the absence of information value, attention continues to be directed to cue features that previously signalled relevant information. Third, the decision to attend to a known cue depends on cognitive effort. We propose that this combination of information value, previous relevance, and effort is best captured within an information-seeking framework, and that oculomotor parameters provide a useful proxy for uncovering these factors and their interactions.
An Element Sensitive Saliency Model with Position Prior Learning for Web Pages

Understanding human visual attention is important for multimedia applications. Many studies have attempted to build saliency prediction models on natural images. However, limited efforts have been devoted to saliency prediction for Web pages, which are characterized by diverse content elements and spatial layouts. In this paper, we propose a novel end-to-end deep generative saliency model for Web pages. To capture position biases introduced by page layouts, a Position Prior Learning (PPL) sub-network is proposed, which models the position biases with a variational auto-encoder. To model different elements of a Web page, a Multi Discriminative Region Detection (MDRD) branch and a Text Region Detection (TRD) branch are introduced, which extract discriminative localizations and prominent text regions, respectively. We validate the proposed model with a public Web-page dataset 'FIWI', and show that the proposed model outperforms the state-of-art models for Web-page saliency prediction.
Towards Forward Secure Internet Traffic

Forward Secrecy (FS) is a security property in key-exchange algorithms which guarantees that a compromise in the secrecy of a long-term private-key does not compromise the secrecy of past session keys. With a growing awareness of long-term mass surveillance programs by governments and others, FS has become widely regarded as a highly desirable property. This is particularly true in the TLS protocol, which is used to secure Internet communication. In this paper, we investigate FS in pre-TLS 1.3 protocols, which do not mandate FS, but are still widely used today. We conduct an empirical analysis of over 10 million TLS servers from three different datasets using a novel heuristic approach. Using a modern TLS client handshake algorithms, our results show 5.37% of top domains, 7.51% of random domains, and 26.16% of random IPs do not select FS key-exchange algorithms. Surprisingly, 39.20% of the top domains, 24.40% of the random domains, and 14.46% of the random IPs that do not select FS, do support FS. In light of this analysis, we discuss possible paths toward forward secure Internet traffic. As an improvement of the current state, we propose a new client-side mechanism that we call “Best Effort Forward Secrecy” (BEFS), and an extension of it that we call “Best Effort Forward Secrecy and Authenticated Encryption” (BESAFE), which aims to guide (force) misconfigured servers to FS using a best effort approach. Finally, within our analysis, we introduce a novel adversarial model that we call “discriminatory” adversary, which is applicable to the TLS protocol.
Lexical Text Simplification Using WordNet

Internet is distributed environment and hence, huge amount of information is available on it. People use internet to access the information on the web. While referring to any information people face difficulty to understand the complex sentences and words used related to technology and science. Technical and scientific words are mostly found in research papers, medical reports, newspapers and other reading material. Text simplification is a technique used to automatically transform complicated text into simpler form. In the proposed system an efficient text simplification technique has been developed using word net model available in the Natural Language toolkit (NLTK). The dataset used for experimentation is collected through a random survey from web sources. Here, the proposed system is divided into 3 phases. In the first phase data collection and pre-processing has been performed. In second phase complex words are identified and in the 3rd phase replacement of complex words with their simple synonyms is being done. The performance of the system has been analyzed by user review to accuracy of 87%.
Revocable Sliced CipherText Policy Attribute Based Encryption Scheme in Cloud Computing

Cloud Computing is the most promising paradigm in recent times. It offers a cost-efficient service to individual and industries. However, outsourcing sensitive data to entrusted Cloud servers presents a brake to Cloud migration. Consequently, improving the security of data access is the most critical task. As an efficient cryptographic technique, Ciphertext Policy Attribute Based Encryption(CP-ABE) develops and implements fine-grained, flexible and scalable access control model. However, existing CP-ABE based approaches suffer from some limitations namely revocation, data owner overhead and computational cost. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated RS-CPABE. We applied splitting algorithm. We execute symmetric encryption with Advanced Encryption Standard (AES)in large data size and asymmetric encryption with CP-ABE in constant key length. We re-encrypt in case of revocation one single slice. To prove the proposed model, we expose security and performance evaluation.
Fine-Grained Visual Dribbling Style Analysis for Soccer Videos With Augmented Dribble Energy Image

Recent advances in interpretations of soccer are predominantly made through analyzing high-level contents of soccer videos. This work targets on these highlight actions and movements in soccer games and it focuses on dribbling skills performed by the top players. Our work leverages understanding of complex dribbling video clips by representing a video sequence with a single Dribble Energy Image(DEI) that is informative for dribbling styles recognition. To overcome the shortage of labelled data, this paper introduces a dataset of soccer video clips from Youtube, employs Mask-RCNN to segment out dribbling players and OpenPose to obtain joints information of dribbling players. Besides, to solve issues caused by camera motions in highlight soccer videos, our work proposes to register a video sequence to generate a single image representation DEI and dribbling styles classification. Our approach can achieve an accuracy of 87.65% on dribbling styles classification and it is observed that data augmentation using joints-reasoned GAN can improve the classification performance.
The visual human face super-resolution reconstruction algorithm based on improved deep residual network

Deep learning is a hot method in face super-resolution reconstruction in recent years, but it needs to further improve the details of reconstructed images and speed up network training. This paper improves the deep residual network from two aspects of residual unit and network structure and proposes a face super-resolution reconstruction algorithm based on the improved model. We improve the network structure of the residual unit by connecting with a densely connected convolutional layer and removing the BN layer, thereby enhancing the information flow between the inner convolutional layers and eliminate the damage to the spatial information of the image by batch normalization processing. At the same time, we combine the output characteristics of each residual unit on the basis of the global residual structure, so the face feature information is more fully utilized and the model detail recovery ability is also improved. Experiments on FDDB and AFLW face datasets show that the proposed method has better performance in feature description and detail information reconstruction, and higher PSNR and SSIM than other methods.
Gated Hierarchical Attention for Image Captioning

Attention modules connecting encoder and decoders have been widely applied in the field of object recognition, image captioning, visual question answering and neural machine translation, and significantly improves the performance. In this paper, we propose a bottom-up gated hierarchical attention (GHA) mechanism for image captioning. Our proposed model employs a CNN as the decoder which is able to learn different concepts at different layers, and apparently, different concepts correspond to different areas of an image. Therefore, we develop the GHA in which low-level concepts are merged into high-level concepts and simultaneously low-level attended features pass to the top to make predictions. Our GHA significantly improves the performance of the model that only applies one level attention, e.g., the CIDEr score increases from 0.923 to 0.999, which is comparable to the state-of-the-art models that employ attributes boosting and reinforcement learning (RL). We also conduct extensive experiments to analyze the CNN decoder and our proposed GHA, and we find that deeper decoders cannot obtain better performance, and when the convolutional decoder becomes deeper the model is likely to collapse during training.
Reproducing kernel orthogonal polynomials on the multinomial distribution

Diaconis and Griffiths (2014) study the multivariate Krawtchouk polynomials orthogonal on the multinomial distribution. In this paper we derive the reproducing kernel orthogonal polynomials Qn(x,y;N,p) on the multinomial distribution which are sums of products of orthonormal polynomials in x and y of fixed total degree n=0,1,…,N. The Poisson kernel ∑n=0NρnQn(x,y;N,p) arises naturally from a probabilistic argument. An application to a multinomial goodness of fit test is developed, where the chi-squared test statistic is decomposed into orthogonal components which test the order of fit. A new duplication formula for the reproducing kernel polynomials in terms of the 1-dimensional Krawtchouk polynomials is derived. The duplication formula allows a Lancaster characterization of all reversible Markov chains with a multinomial stationary distribution whose eigenvectors are multivariate Krawtchouk polynomials and where eigenvalues are repeated within the same total degree. The χ2 cutoff time, and total variation cutoff time is investigated in such chains. Emphasis throughout the paper is on a probabilistic understanding of the polynomials and their applications, particularly to Markov chains.
Study on the promotion effect of informationization on entrepreneurship: an empirical evidence from China

With the in-depth development of informatization, the role of new ventures and small and micro enterprises in economic growth has become increasingly prominent. However, rare literature systematically studies the impact of informatization on entrepreneurship. Based on the data from the 2011 China Household Finance Survey and the Report of China Informationization Development Assessment (2013), this paper uses the probit model, the multiple-choice probit model, and the Heckman two-stage model to examine the influence of informationization on entrepreneurial choices and entrepreneurial performance. The research finds the following conclusions. Firstly, informationization not only significantly improves the probability of entrepreneurship, but also facilitates the realization of income and employment effects from entrepreneurship. Secondly, information infrastructure, government informationization, and resident informationization, as informationization dimensions, can significantly increase the probability of entrepreneurship, although enterprise informationization plays an opposite role. Finally, the influence of informationization on promoting entrepreneurship varies with entrepreneurial type and entrepreneurial industry.
FISH: Face intensity-shape histogram representation for automatic face splicing detection

Tampered images spread nowadays over any visual media influencing our judgement in many aspects of our life. This is particularly critical for face splicing manipulations, where recognizable identities are put out of context. To contrast these activities on a large scale, automatic detectors are required. In this paper, we present a novel method for automatic face splicing detection, based on computer vision, that exploits inconsistencies in the lighting environment estimated from different faces in the scene. Differently from previous approaches, we do not rely on an ideal mathematical model of the lighting environment. Instead, our solution, built upon the concept of histogram-based features, is able to statistically represent the current interaction of faces with light, untied from the actual and unknown reflectance model. Results show the effectiveness of our solution, that outperforms existing approaches on real-world images, being more robust to face shape inaccuracies.
Efficient Algorithms to Test Digital Convexity

A set $S \subset \mathbb{Z}^d$ is digital convex if $conv(S) \cap \mathbb{Z}^d = S$, where $conv(S)$ denotes the convex hull of $S$. In this paper, we consider the algorithmic problem of testing whether a given set $S$ of $n$ lattice points is digital convex. Although convex hull computation requires $\Omega(n \log n)$ time even for dimension $d = 2$, we provide an algorithm for testing the digital convexity of $S\subset \mathbb{Z}^2$ in $O(n + h \log r)$ time, where $h$ is the number of edges of the convex hull and $r$ is the diameter of $S$. This main result is obtained by proving that if $S$ is digital convex, then the well-known quickhull algorithm computes the convex hull of $S$ in linear time. In fixed dimension $d$, we present the first polynomial algorithm to test digital convexity, as well as a simpler and more practical algorithm whose running time may not be polynomial in $n$ for certain inputs.
Partial Compilation of ASP Programs

Answer Set Programming (ASP) is a well-known declarative formalism in logic programming. Efficient implementations made it possible to apply ASP in many scenarios, ranging from deductive databases applications to the solution of hard combinatorial problems. State-of-the-art ASP systems are based on the traditional ground\&solve approach and are general-purpose implementations, i.e., they are essentially built once for any kind of input program. In this paper, we propose an extended architecture for ASP systems, in which parts of the input program are compiled into an ad-hoc evaluation algorithm (i.e., we obtain a specific binary for a given program), and might not be subject to the grounding step. To this end, we identify a condition that allows the compilation of a sub-program, and present the related partial compilation technique. Importantly, we have implemented the new approach on top of a well-known ASP solver and conducted an experimental analysis on publicly-available benchmarks. Results show that our compilation-based approach improves on the state of the art in various scenarios, including cases in which the input program is stratified or the grounding blow-up makes the evaluation unpractical with traditional ASP systems.
Network-Programmable Operational Flow Profiling

A detailed understanding of networks and network flows is key for many important operational problems, such as optimizing network performance, identifying causes of degradations or service-level fluctuations, and detecting security threats. However, gaining such insights remains a highly challenging task. This article presents a new approach for obtaining detailed behavioral and performance-related insights at the level of individual packets and flows, based on the concept of network-programmable operational flow profiling. Our approach leverages BPP, a novel networking framework which allows operational flow profiles to be programmed from the network edge without requiring heavy analytics and data collection frameworks.
Proposing a Framework for Evaluating the Blockchain Platform

the blockchain is an emerging technology with a core of a distributed ledger of connected blocks consisting of immutable transactions with decentralized management. The importance of blockchain technology and its impact in different domains highlights the need for understanding and evaluation of this technology. Understanding and evaluating technology from different aspects requires identifying the characteristics and specific criteria of that technology. In this regard, in this paper, after discussing the subject literature, the concept of the blockchain platform is described and a number of selected blockchain platforms are examined. Besides, the high-level benchmarking of the platform is evaluated from two aspects of the expected features of the platforms and their architectural components. Finally, due to the importance and significance of the criteria and their frequency, a set of effective criteria is chosen and accordingly, selected platforms of the blockchain are analyzed.
An elementary derivation of the Chinese restaurant process from Sethuraman’s stick-breaking process

The usual proof that Sethuraman’s stick-breaking process gives rise to the Chinese restaurant process is indirect, relying on abstract properties of the Dirichlet process. This note provides a direct elementary proof, without using any measure theory.