Reviewerly/exHarmony huggingface dataset:
A-VLAD: An End-to-End Attention-Based Neural Network for Writer Identification in Historical Documents

This paper presents an end-to-end attention-based neural network for identifying writers in historical documents. The proposed network does not require any preprocessing stages such as binarization or segmentation. It has three main parts: a feature extractor using Convolutional Neural Network (CNN) to extract features from an input image; an attention filter to select key points; and a generalized deep neural VLAD model to form a representative vector by aggregating the extracted key points. The whole network is trained end-to-end by a combination of cross-entropy and triplet losses. In the experiments, we evaluate the performance of our model on the HisFragIR20 dataset that consists of about 120,000 historical fragments from many writers. The experiments demonstrate better mean average precision and accuracy at top-1 in comparison with the state-of-the-art results on the HisFragIR20 dataset. This model is rather new for dealing with various sizes of historical document fragments in the writer identification and image retrieval.
Secure Quantum Computation with Classical Communication

The study of secure multi-party computation (MPC) has thus far been limited to the following two settings: every party is fully classical, or every party has quantum capabilities. This paper studies a notion of MPC that allows some classical and some quantum parties to securely compute a quantum functionality over their joint private inputs.
Should We Trust Influencers on Social Networks? On Instagram Sponsored Post Analysis

With online social networks (OSNs), people are exposed to tons of fake information or misleading posts. Celebrities sometimes intentionally create misleading posts in OSNs to guide people for commercial or marketing purposes. The intentional phrases from such posts can affect the online rating and even lead to a frenzy shopping. That’s part of the reasons that the top social media influencers are targeted by merchants to help promote products. The Federal Trade Commission (FTC) requires that all sponsored posts must be clearly disclosed. However, many influencers do not follow the FTC rules. As a result, people may be misled by the undisclosed sponsorship. In this study, for the first time, we explore the credibility of posts on Instagram and analyze if an influencer complies with the FTC requirements. We build an effective Undisclosed Sponsored Post Detection (USPD) framework based on an ensemble of machine learning classifiers. The USPD framework consists of three main processes: (i) feature extraction, (ii) model construction and (iii) credibility and integrity analysis. Our analysis and experiments demonstrate that the proposed framework can achieve a high accuracy of 83% for undisclosed sponsored post detection. The proposed framework also takes advantages of the text, user and image features in OSN posts to effectively analyze how much an influencer can be trusted.
Secure authentication in IoT and cloud server: An ECC based practice

The word Internet-of-Things (IoT) is continuously growing interest in the research sector compared to other advanced paradigms with its well-known real-life applications. However, for communication and resource sharing, these devices need to connect with highresource centers like cloud-server. Such integration involves security risk issues because any flaws in detection or authentication method will allocate a compromised entity to launch dangerous attacks and insert false data that lead to system failure. More importantly, IoT devices commune on a public channel that was more prone to access by prohibited abusers plus perturb the confidentiality of authentic abusers. In this article, we designed an authentication proposal raised on ECC for IoT devices with Cloud Server. We explore the fuzzy system service for channel non-reciprocal and ECC, one-way hash functions for secure authentication. Finally, we analyze the designed technique with the AVISPA tool and compare the computational communication costs with existing models.
Text-Guided Visual Feature Refinement for Text-Based Person Search

Text-based person search is a task to retrieve the corresponding person in a large-scale image database given a textual description, which has important value in various fields like video surveillance. In the inferring phase, language descriptions, serving as queries, guide to search the corresponding person images. Most existing methods apply cross-modal signals to guide feature refinement. However, they employ visual features from the gallery to refine textual features, which may cause high similarity between unmatched pairs. Besides, the similarity-based cross-modal attention could disturb the choice of interested areas for descriptions. In this paper, we analyze the deficiency of previous methods and carefully design a Text-guided Visual Feature Refinement network (TVFR), which utilizes text as reference to refine visual representations. Firstly, we divide each visual feature into several horizontal stripes for fine-grained refinement. After that, we employ a text-based filter generation module to generate description-customized filters, which are used to indicate the corresponding stripes mentioned in the textual input. Thereafter, we employ a text-guided visual feature refinement module to fuse part-level visual features adaptively for each description. In experiments, we validate our TVFR through extensive experiments on CUHK-PEDES, which is the only available dataset for text-based person search. To the best of our knowledge, the TVFR outperforms other state-of-the-art methods.
Action Command Encoding for Surrogate-Assisted Neural Architecture Search

With the development of neural architecture search, the performance of deep neural networks has been considerably enhanced with less human expertise. While the existing work mainly focuses on the development of optimizers, the design of encoding scheme is still in its infancy. This article thus proposes a novel encoding scheme for neural architecture search, termed action command encoding (ACEncoding). Inspired by the gene expression process, ACEncoding defines several action commands to indicate the addition and clone of layers, connections, and local modules, where an architecture grows from empty according to multiple action commands. ACEncoding provides a compact and rich search space that can be explored by various optimizers efficiently. Furthermore, a surrogate-assisted performance evaluator is tailored for ACEncoding, termed sequence-to-rank (Seq2Rank). By integrating the Seq2Seq model with RankNet, Seq2Rank embeds the variable-length encoding of ACEncoding into a continuous space, and then predicts the rankings of architectures based on the continuous representation. In the experiments, ACEncoding brings improvement to neural architecture search with existing encoding schemes and Seq2Rank shows better accuracy than existing performance evaluators. The neural architectures obtained by ACEncoding and Seq2Rank have competitive test errors and complexities on image classification tasks, and also show high transferability between different data sets.
Detector–tracker integration framework and attention mechanism for multi–object tracking

Online multi-object tracking is a process of extending multi-object trajectories with only past information. In this process, tracking drift, missing detection, and occlusion among objects are common problems. To address the problem of tracking drift, in this paper, a detector–tracker integration framework is proposed and the framework includes the linear regression model (LRM) that can detect and track objects simultaneously. The two kinds of results are combined using LRM to suppress tracking drift. Moreover, to overcoming missing detection and the occlusion, a structural similarity calculation based on the attention mechanism is proposed. The attention mechanism is utilized to learn the discriminative features of each object, and a structural similarity calculation are used to improve the ability to extend the objects’ trajectories. Based on these, we design a multi-object tracking strategy, enabling the trajectory’s initialization, extension, and termination. Finally, experiments and analysis are executed on MOT16 and MOT17 benchmarks datasets, and the proposed method obtains multi-object tracking accuracy of 60%.
Database Principles and Challenges in Text Analysis

A common conceptual view of text analysis is that of a two-step process, where we first extract relations from text documents and then apply a relational query over the result. Hence, text analysis shares technical challenges with, and can draw ideas from, relational databases. A framework that formally instantiates this connection is that of the document spanners. In this article, we review recent advances in various research efforts that adapt fundamental database concepts to text analysis through the lens of document spanners. Among others, we discuss aspects of query evaluation, aggregate queries, provenance, and distributed query planning.
Pattern-Based Acquisition of Scientific Entities from Scholarly Article Titles

We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles. Two observations motivated the approach: (i) noting salient aspects of an article's contribution in its title; and (ii) pattern regularities capturing the salient terms that could be expressed in a set of rules. Only those lexico-syntactic patterns were selected that were easily recognizable, occurred frequently, and positionally indicated a scientific entity type. The rules were developed on a collection of 50,237 CL titles covering all articles in the ACL Anthology. In total, 19,799 research problems, 18,111 solutions, 20,033 resources, 1,059 languages, 6,878 tools, and 21,687 methods were extracted at an average precision of 75%.
Development of a Testbed for Fully Homomorphic Encryption Solutions

Homomorphic encryption is a computing trend that promises to query a computer-based system without giving personal data. By means of these methods, a software is fed with encrypted data and the result of the computation is the encryption of the result obtained as if clear data were input to the same software. As this paradigm is attracting public and private funds, the number of the different approaches (supported by different software libraries) is growing over time. As theoretical principles underlying these libraries are different, functional and non-functional behaviours are different, too. The present work proposes to design and develop a testbed for these kinds of algorithms, making possible for a final user to build his/her own final functions and to test them against the variation of some parameters. A concrete prototype of the presented architecture is reported, based on the Pyfhel Python library and the underlying SEAL encryption library.
What Would Margaret Atwood Do? Designing for Ustopia in HCI

Utopia and dystopia can be seen to exist within each other. What is a desirable future for one might put someone else in an uncomfortable or conflicted position. Speculative design serves as a tool to let people experience situations through different perspectives of criticality. This paper argues to use speculative methodologies to design for Ustopia. We consider different perspectives from Margaret Atwood’s novels “The Handmaid’s Tale” and “The Testaments”, and use these to define different critical perspectives that speculative design and speculative designers could take to pave the way to Ustopia. We present pastiche scenarios to illustrate how the perspectives could be applied to a transition that impacts all of us: the introduction of the COVID-19 vaccines. We use these pastiche scenarios and related work from the field of speculative design as examples to illustrate how the different perspectives might be integrated into one speculative experience. We propose that Ustopia can be used to harness and encompass such complexity.
Repeated Attribute Optimization for Big Data Encryption

Big data denotes the variety, velocity, and massive volume of data. Existing databases are unsuitable to store big data owing to its high volume. Cloud computing is an optimal solution to process and store big data. However, the significant issue lies in handling access control and privacy, wherein the data should be encrypted and unauthorized user access must be restricted through efficient access control. Attribute-based encryption (ABE) permits users to encrypt and decrypt data. However, for the policy to work in practical scenarios, the attributes must be repeated. In the case of specific policies, it is not possible to avoid attribute repetition even after the application of Boolean optimization approaches to obtain a Boolean formula. For these policies, there exists a variety of evaluated secret shares for the repeated attributes. Therefore, the calculation of cipher text for these irreducible policies seems to be lengthy and computationally intensive. To address this problem, an improved meta-heuristic-based repeated attributes optimization on cipher-text policy-ABE (CP-ABE) is developed in this study. Here, the improved meta-heuristic concept is developed in the encryption phase, which returns the optimized single share value of each repeated attribute after considering all the attribute shares. The optimization process not only minimizes the encryption cost but also the communication cost. Herein, the improved sun flower optimization (SFO), called the newly updated SFO (NU-SFO) is used to perform the repeated attribute optimization in CP-ABE. Finally, the performance evaluation confirms the reliability and robustness of the developed scheme through comparisons with traditional constructions.
A Study of Batik Style Transfer using Neural Network

In this study, two remarkable applications of convolutional neural network, i.e. texture synthesis and style transfer are applied to batik texture. Individual layers from four pre-trained networks such as VGG-19, Inception V3, ResNet-50, and DenseNet-121 are compared and analyzed. Different batik motifs with some specific criteria are also compared to see the capability of original style transfer algorithm to regenerate concrete texture of Batik. Lastly, we propose a way to reconstruct batik images with some patterns following an object’s shape contained in content image.
Learning Bayesian networks from incomplete data with the node-average likelihood

Bayesian network (BN) structure learning from complete data has been extensively studied in the literature. However, fewer theoretical results are available for incomplete data, and most are related to the Expectation-Maximisation (EM) algorithm. Balov [1] proposed an alternative approach called Node-Average Likelihood (NAL) that is competitive with EM but computationally more efficient; and he proved its consistency and model identifiability for discrete BNs. In this paper, we give general sufficient conditions for the consistency of NAL; and we prove consistency and identifiability for conditional Gaussian BNs, which include discrete and Gaussian BNs as special cases. Furthermore, we confirm our results and the results in Balov [1] with an independent simulation study. Hence we show that NAL has a much wider applicability than originally implied in Balov [1], and that it is competitive with EM for conditional Gaussian BNs as well.
Towards an Ontology-Driven Information System for Archaeological Pottery Studies: The Greyware Experience

The archaeological analysis of medieval and modern pottery has benefited from the consolidation of archaeometry in the domain of Medieval Archaeology in the past few decades. As part of an ongoing research project devoted to the characterization of pottery production, distribution processes and technological transfer, we deal with a considerable amount of data that are very diverse in origin and nature and must be exploited within an integrated information system in order to provide information for historical knowledge. The Greyware system has been designed to fulfil this goal and provides the main categories for pottery analysis within a shareable and reusable scenario. Its development and application prove that a little semantics goes a long way and that the creation of domain ontologies for archaeological research is an iterative process under development, as long as several projects sharing data, resources and time can develop a collaborative framework to maximize the assets of individual expertise and collaborative work. In this paper, we discuss the requirements of the system, the challenge of developing strategies for normalized data management and their potential for exploiting historical vestiges from an integrated perspective.
Linear codes with few weights from weakly regular plateaued functions

Linear codes with few nonzero weights have wide applications in secret sharing, authentication codes, association schemes and strongly regular graphs. Recently, Wu et al. (2020) obtained some few-weighted linear codes by employing bent functions. In this paper, inspired by Wu et al. and some pioneers' ideas, we use a kind of functions, namely, general weakly regular plateaued functions, to define the defining sets of linear codes. Then, by utilizing some cyclotomic techniques, we construct some linear codes with few weights and obtain their weight distributions. Notably, some of the obtained codes are almost optimal with respect to the Griesmer bound. Finally, we observe that our newly constructed codes are minimal for almost all cases.
On the Implicit Feedback Based Data Modeling Approaches for Recommendation Systems

Online stores can give their customers access to millions of products, unlike retail stores. In online stores, with a large number of products to review, approaches that can recommend the right product to the customer are needed based on the customer's interests. In the literature, there are different methods that make product recommendations to customers, based on previous choices of customers. In general, we find that these studies are common when customers clearly provide ratings for their products. However, we observe a lack of data modeling approaches that indirectly capture customer ratings based on customer click data. For this purpose, within the framework of this research, we propose two different methods for indirect modeling of user ratings. We have developed a prototype application to show the feasibility of the proposed methods. The obtained data were tested with two state-of-the-art methods, alternating least squares (ALS) and collaborative filtering (CF). Clickstream data, obtained from an e-commerce book site, was used in the study. The results show that the proposed data modeling approaches can be used.
Convolutional Neural Networks for Indonesian Aspect-Based Sentiment Analysis Tourism Review

In recent years, electronic word of mouth (e-WOM) has been widely used by people around the world. Tripadvisor is an e-WOM travel website that provides information about reviews and opinions on travel-related content. To help users gather information faster, aspect-based sentiment analysis is necessary. Aspect-based sentiment analysis helps users to capture and extract important features from the reviews. Therefore, this study aims to build an aspect-based sentiment analysis model of Indonesian tourism review by extracting aspect-category and their corresponding polarities from user reviews. To gain the best model, we performed several experiments by using Convolutional Neural Networks (CNN). Moreover, we compared our CNN model with CNN-LSTM and CNN-GRU to identify the sentiment and aspects from the reviews. We also performed negation handling in our feature extraction process to improve our CNN models. Based on our experiments, CNN combined with both POS tag and negation handling outperformed the other models with the accuracy of sentiment analysis of 0.9522 and aspect category of 0.9551.
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching

Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. However, prior IRL algorithms use on-policy transitions, which require intensive sampling from the current policy for stable and optimal performance. This limits IRL applications in the real world, where environment interactions can become highly expensive. To tackle this problem, we present Off-Policy Inverse Reinforcement Learning (OPIRL), which (1) adopts off-policy data distribution instead of on-policy and enables significant reduction of the number of interactions with the environment, (2) learns a reward function that is transferable with high generalization capabilities on changing dynamics, and (3) leverages mode-covering behavior for faster convergence. We demonstrate that our method is considerably more sample efficient and generalizes to novel environments through the experiments. Our method achieves better or comparable results on policy performance baselines with significantly fewer interactions. Furthermore, we empirically show that the recovered reward function generalizes to different tasks where prior arts are prone to fail.
Graph-based reasoning attention pooling with curriculum design for content-based image retrieval

Global single-pass methods have shown superior efficiency over local aggregation methods on content-based image retrieval. However, they tend to fail under challenging environments since the structural relations among regions are not exploited. To address this issue, we propose a novel Graph-based Reasoning Attention Pooling with Curriculum Design (GRAP-CD) to improve the network capability through training modification and trainable pooling. GRAP-CD can not only explore relations among salient regions but also gradually train the network to achieve better local minima. The graph-based reasoning layers regard the feature map from the last convolution layer as a graph and construct the structural relations. Then the graph-based attention layer enhances the key information guided by the relations. Besides, a front-end curriculum design is introduced to split the training dataset from simple to complex and train the model step by step, which further helps the GRAP firstly learn the basic feature information from simple samples and then learn to dig the more representative features with hard positive samples. Experimental results on popular benchmarks ROxford and RParis datasets achieve improvement over state-of-the-art global single-pass methods and competitive results with local aggregation methods.