Reviewerly/exHarmony huggingface dataset:
Segmental DCT Coefficient Reversal Based Anti-Collusion Audio Fingerprinting Mechanism

Collusion attacks are challenging to tackle in audio fingerprinting. A new direction to resist collusion attacks is to degrade the perceptual quality of the colluded files so that these files cannot be reused. The existing method in this direction has low embedding capacity and limited anti-collusion performance when the number of colluders is odd. In this letter, we present an anti-collusion mechanism that has a higher embedding capacity and can significantly degrade the perceptual quality of the colluded files regardless of the number of colluders. In the proposed mechanism, we first segment the host audio file into frames and perform the discrete cosine transform (DCT) on each frame. Then multiple fingerprint bits are embedded into each frame by reversing the DCT coefficients of the corresponding frequency band. As a result, when a collusion attack occurs, our proposed embedding mechanism can introduce perceptibly annoying differences between frames in the colluded file, which leads to severe perceptual quality degradation. Theoretical analysis and experimental results validate the superiority of the proposed anti-collusion mechanism.
Adversarial Training for Multi Domain Dialog System

Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities.Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems.Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity.Most language understanding systems process user input in a sequential order: domain is first predicted, then intent and slots are filled according to the semantic frames of the predicted domain.This pipeline approach, however, has many disadvantages including downstream error; i.e., if the system fails to predict the domain, then the system also fails to predict intent and slot.The main purpose of this paper is to mitigate the risk of downstream error propagation in traditional pipelined models and improve the predictive performance of domain, intent, and slot-all of which are critical steps for speech understanding and dialog systems-with a deep learning-based single joint model trained with an adversarial approach and long shortterm memory (LSTM) algorithm.The systematic experimental analysis shows significant improvements in predictive performance for domain, intent, and entity with the proposed adversarial joint model, compared to the base joint model.
High-Rate Quantum Private Information Retrieval with Weakly Self-Dual Star Product Codes

In the classical private information retrieval (PIR) setup, a user wants to retrieve a file from a database or a distributed storage system (DSS) without revealing the file identity to the servers holding the data. In the quantum PIR (QPIR) setting, a user privately retrieves a classical file by receiving quantum information from the servers. The QPIR problem has been treated by Song et al. in the case of replicated servers, both with and without collusion. QPIR over [n, k] maximum distance separable (MDS) coded servers was recently considered by Allaix et al., but the collusion was essentially restricted to t = n - <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> servers in the sense that a smaller <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> would not improve the retrieval rate. In this paper, the QPIR setting is extended to allow for retrieval with high rate for any number of colluding servers <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> with 1 ≤ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> ≤ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> - k. Similarly to the previous cases, the rates achieved are better than those known or conjectured in the classical counterparts, as well as those of the previously proposed coded and colluding QPIR schemes. This is enabled by considering the stabilizer formalism and weakly self-dual generalized Reed-Solomon (GRS) star product codes.
Knowledge graph question answering based on TE-BiLTM and knowledge graph embedding

Abstract: Knowledge graph question answering (KGQA) aims to use facts in the knowledge graph to answer natural language questions. Relation extraction, as one of the sub-tasks of the KGQA, is an important and difficult problem in the KGQA. To improve the accuracy of relation extraction in KGQA, in this paper, we propose a new deep neural network model called Transformer Encoder-BiLSTM (TE-BiLSTM). We give the detailed design of our method and our experimental results demonstrate that our approach can not only achieve better results in relation extraction, but can also outperform the state-of-the-art approaches in the KGQA.
Quantum-controlled cluster states

Quantum optical cluster states have been increasingly explored in the light of their importance for measurement-based quantum computing. Here we set forth a method for generating quantum controlled cluster states: pumping an optical parametric oscillator with spatially structured light. We show that state-of-the-art techniques for producing clusters in the spectral and temporal domains are improved by a structured light pump, which manipulates the spatial mode couplings in the parametric interaction. We illustrate the method considering a second-order pump structure and show that a simple mode rotation yields different cluster states, including but not limited to the two-dimensional square and hexagonal lattices. We also introduce a novel technique for generating nonuniform cluster states and propose a simple setup for its implementation.
IHR-NomDB: The Old Degraded Vietnamese Handwritten Script Archive Database

This paper introduces a new handwritten database IHR-NomDB, for an old Vietnamese writing system called ChuNom. Over 260 pages of ChuNom were collected from Vietnamese Nom Preservation Foundation to analyze and annotate the bounding boxes manually to generate more than 5000 patches in which containing the images of handwriting texts, the corresponding digital ChuNom characters and its translation in modern Vietnamese script. Along with this handwriting dataset is a new Synthetic Nom String dataset, which consists of 101, 621 images generated using our collected bank of ChuNom sentences. Totally, 13, 254 characters are presented on the two parts of the database, making this the first and largest publicly available database for researching in this old Vietnamese writing script. For the baseline results, we have performed the testing on the validation set of the handwriting dataset using the Convolution Recurrent Neural Network (CRNN) pretrained on the Synthetic Nom String dataset with CTC Loss and achieved \(42.70\%\) accuracy at sentence level and \(82.28\%\) accuracy at character level. The database is available to download at https://morphoboid.labri.fr/ihr-nom.html.
An Incentive Mechanism for Trading Personal Data in Data Markets

With the proliferation of the digital data economy, digital data is considered as the crude oil in the twenty-first century, and its value is increasing. Keeping pace with this trend, the model of data market trading between data providers and data consumers, is starting to emerge as a process to obtain high-quality personal information in exchange for some compensation. However, the risk of privacy violations caused by personal data analysis hinders data providers' participation in the data market. Differential privacy, a de-facto standard for privacy protection, can solve this problem, but, on the other hand, it deteriorates the data utility. In this paper, we introduce a pricing mechanism that takes into account the trade-off between privacy and accuracy. We propose a method to induce the data provider to accurately report her privacy price and, we optimize it in order to maximize the data consumer's profit within budget constraints. We show formally that the proposed mechanism achieves these properties, and also, validate them experimentally.
A comparison of deep saliency map generators on multispectral data in object detection

Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. However, these methods lack of a good semantic understanding of what happens internally. The question, why a COVID-19 detector has classified a stack of lung-ct images as positive, is sometimes more interesting than the overall specificity and sensitivity. Especially when human domain expert knowledge disagrees with the given output. This way, human domain experts could also be advised to reconsider their choice, regarding the information pointed out by the system. In addition, the deep learning model can be controlled, and a present dataset bias can be found. Currently, most explainable AI methods in the computer vision domain are purely used on image classification, where the images are ordinary images in the visible spectrum. As a result, there is no comparison on how the methods behave with multimodal image data, as well as most methods have not been investigated on how they behave when used for object detection. This work tries to close the gaps by investigating three saliency map generator methods on how their maps differ in the different spectra. This is achieved via an accurate and systematic training. Additionally, we examine how they perform when used for object detection. As a practical problem, we chose object detection in the infrared and visual spectrum for autonomous driving. The dataset used in this work, is the Multispectral Object Detection Dataset,1 where each scene is available in the long-wave (FIR), mid-wave (MIR) and short-wave (NIR) infrared as well as the visual (RGB) spectrum. The results show, that there are differences between the infrared and visual activation maps. Further, an advanced training with both, the infrared and visual data not only improves the network's output, it also leads to more focused spots in the saliency maps.
MANTIS: Multiple Type and Attribute Index Selection using Deep Reinforcement Learning

DBMS performance is dependent on many parameters, such as index selection, cache size, physical layout, and data partitioning. Some combinations of these parameters can lead to optimal performance for a given workload but selecting an optimal or near-optimal combination is challenging, especially for large databases with complex workloads. Among the hundreds of parameters, index selection is arguably the most critical parameter for performance. We propose a self-administered framework, called the Multiple Type and Attribute Index Selector (MANTIS), that automatically selects near-optimal indexes. The framework advances the state-of-the-art index selection by considering both multi-attribute and multiple types of indexes within a bounded storage size constraint, a combination not previously addressed. MANTIS combines supervised and reinforcement learning, a Deep Neural Network recommends the type of index for a given workload while a Deep Q-Learning network recommends the multi-attribute aspect. MANTIS is sensitive to storage cost constraints and incorporates noisy rewards in its reward function for better performance. Our experimental evaluation shows that MANTIS outperforms the current state-of-art methods by an average of 9.53% [email protected]
Automatic Translation and Multilingual Cultural Heritage Retrieval: A Case Study with Transcriptions in Europeana

Multilinguality is of particular interest for digital libraries in Cultural Heritage (CH), where the language of the data may not match users' languages. However, multilingual access is rarely implemented beyond the use of multilingual interfaces. We have run an experiment using the Europeana CH digital library as a use case. We evaluate the effectiveness of a multilingual information retrieval strategy using machine translations to English as pivot language. We conducted an indirect evaluation that should be considered preliminary. Yet, together with a manual analysis of the query translations, it already shows (or confirms) some of the benefits and challenges of deploying such systems in CH.
Personal Knowledge Base Construction from Multimodal Data

With the passage of time, people often have misty memories of their past experiences. Information recall support for people by collecting personal lifelogs is emerging. Recently, people tend to record their daily life via filming Video Weblog (VLog), which contains visual and audio data. These large scale multimodal data can be used to support information recall service that enables users to query their past experiences. The challenging issue is the semantic gap between the visual concept and the textual query. In this paper, we aim to extract personal life events from vlogs shared on YouTube and construct a personal knowledge base (PKB) for individuals. A multitask learning model is proposed to extract the components of personal life events, such as subjects, predicates and objects. The evaluation is performed on a video collection from three YouTubers who are English native speakers. Experimental results show our model achieves promising performance.
Secure and Efficient Task Matching with Multi-keyword in Multi-requester and Multi-worker Crowdsourcing

Crowdsourcing enables users (task requesters) to outsource complex tasks to an unspecified crowd of workers. To guarantee the quality of crowdsourcing service, it is necessary to select the most appropriate task workers to complete the tasks. To this end, the crowdsourcing platform (broker) must conduct the mutual matching between tasks and workers based on the task requirements and worker preferences. However, both task requirements and worker preferences may contain sensitive information (e.g., time, location of the task, etc.), which should not be revealed to the broker and other adversaries. In this paper, we propose a secure and efficient task matching scheme to enable the broker to conduct the mutual matching between tasks and workers, according to task requirements and worker preferences with multiple keywords, while preserving the privacy of keywords contained in task requirements and worker preferences. Specifically, we design a new multi-reader and multi-writer searchable encryption primitive that can support the batch matching of multiple keywords. The security proof shows that our proposed task matching scheme is provably secure in the random oracle model under the Bilinear Diffie-Hellman (BDH) assumption. The performance evaluation shows that our multi-keyword batch matching can significantly reduce the computation cost compared to existing methods.
Logic Programming with Post-Quantum Cryptographic Primitives for Smart Contract on Quantum-Secured Blockchain

This paper investigates the usage of logic and logic programming in the design of smart contracts. Our starting point is the logic-based programming language for smart contracts used in a recently proposed framework of quantum-secured blockchain, called Logicontract (LC). We then extend the logic used in LC by answer set programming (ASP), a modern approach to declarative logic programming. Using ASP enables us to write various interesting smart contracts, such as conditional payment, commitment, multi-party lottery and legal service. A striking feature of our ASP implementation proposal is that it involves post-quantum cryptographic primitives, such as the lattice-based public key encryption and signature. The adoption of the post-quantum cryptographic signature overcomes a specific limitation of LC in which the unconditionally secure signature, despite its strength, offers limited protection for users of the same node.
DeMatch: Towards Understanding the Panel of Chart Documents

Chart document understanding is a challenging task in document analysis because of the complex format and specific semantics it contains. In this paper, we first define the chart panel analysis problem and propose a complete framework that can be performed on various types of chart. Generally, a chart document contains multiple types of elements, such as title, axis, legend, plot elements and comments. To solve the problem of chart panel analysis, we developed our system focusing on two aspects: axis analysis and legend analysis. For axis analysis, we first design a fully convolutional network to detect tick marks and then use the clustering method to distinguish the X-axis and Y-axis. A rectangle-growing matching rule is proposed to associate the predicted tick mark with its corresponding text. For legend analysis, we use a cascaded head detector to determine the accurate location of the legend marks and mark-text pairs; and then we design the highest IoU matching rule to determine the legend label text and its corresponding legend mark. Experimental results on synthetic and real data sets demonstrate the effectiveness of the proposed method. Specifically, we obtained the best result on the test set of the ICDAR2019 competition on harvesting raw tables from infographics, and state-of-the-art performance on UB PMC2020 data set of the ICPR2020 competition. Code will be at https://github.com/iiiHunter/CHART-DeMatch.
Sequence Learning Model for Syllables Recognition Arranged in Two Dimensions

The handwritten text recognition from images is a challenging task due to the unique features of human handwriting styles, numerous overlaps and interrupting characters. It is especially difficult for languages where syllables are written by alphabetic letters arranged in two dimensions. Sequence learning architectures have a lot of potential to be applied for solving this type of tasks because they can access global and local contextual information. In this paper, we propose a multi-dimensional sequence learning model for handwriting recognition with residual connections between and inside Separable Blocks and self-attention along horizontal and vertical image directions instead of recurrence. The performance of recurrent and attention-based Directional Blocks on synthetic multi-line MNIST-based datasets is explored. We generated such data to force the model to learn local and global context during recognition. It is shown that a pre-trained model on MNIST-based datasets along with a syllable decomposition can successfully tackle Hangul handwriting recognition. To the best of our knowledge, our approach surpasses state-of-the-art results by achieving accuracy of 97.90% on PE92 and 97.76% on SERI95 for the syllable recognition tasks .
Document Image Quality Assessment via Explicit Blur and Text Size Estimation

We introduce a novel two-stage system for document image quality assessment (DIQA). The first-stage model of our system was trained on synthetic data to explicitly extract blur and text size features. The second-stage model was trained to assess the quality of optical character recognition (OCR) based on the extracted features. The proposed system was tested on two publicly available datasets: SmartDoc-QA and SOC. The discrepancies in the results between our system and current state-of-the art methods are within statistical error. At the same time, our results are balanced for both datasets in terms of Pearson and Spearman Correlation Coefficients. In the proposed approach, features are extracted from image patches taken at different scales, thus making the system more stable and tolerant of variations in text size. Additionally, our approach results in a flexible and scalable solution that allows a trade-off between accuracy and speed. The source code is publicly available on github: https://github.com/RodinDmitry/QA-Two-Step.
A Neural Language Understanding for Dialogue State Tracking

Dialogue State Tracking (DST) is an important part of the task-oriented dialogue system, which is used to predict the current state of the dialogue given all the preceding conversations. In the stage of encoding historical dialogue into context representation, recurrent neural networks (RNNs) have been proven to be highly effective and achieves significant improvements in many tasks. However, hard to model extremely long dependencies and gradient vanishing are two practical and yet concerned studied problems of recurrent neural networks. In this work, based on the recently proposed TRADE model, we have made corresponding improvements in the encoding part, explore a new context representation learning on sequence data, combined convolution and self-attention with recurrent neural networks, striving to extract both local and global features at the same time to learn a better representation on dialogue context. Empirical results demonstrate that our proposed model achieves 50.04% joint goal accuracy for the five domains of MultiWOZ2.0.
Differential Privacy for Binary Functions via Randomized Graph Colorings

We present a framework for designing differentially private (DP) mechanisms for binary functions via a graph representation of datasets. Datasets are nodes in the graph and any two neighboring datasets are connected by an edge. The true binary function we want to approximate assigns a value (or true color) to a dataset. Randomized DP mechanisms are then equivalent to randomized colorings of the graph. A key notion we use is that of the boundary of the graph. Any two neighboring datasets assigned a different true color belong to the boundary. Under this framework, we show that fixing the mechanism behavior at the boundary induces a unique optimal mechanism. Moreover, if the mechanism is to have a homogeneous behavior at the boundary, we present a closed expression for the optimal mechanism, which is obtained by means of a pullback operation on the optimal mechanism of a line graph. For balanced mechanisms, not favoring one binary value over another, the optimal (ε, 6)-DP mechanism takes a particularly simple form, depending only on the minimum distance to the boundary, on ε, and on 6. A full version of this paper can be found in [1].
On Efficiently Equi-Joining Graphs

Despite the growing popularity of techniques related to graph summarization, a general operator for joining graphs on both the vertices and the edges is still missing. Current languages such as Cypher and SPARQL express binary joins through the non-scalable and inefficient composition of multiple traversal and graph creation operations. In this paper, we propose an efficient equi-join algorithm that is able to perform vertex and path joins over a secondary memory indexed graph, also the resulting graph is serialised in secondary memory. The results show that the implementation of the proposed model outperforms solutions based on graphs, such as Neo4J and Virtuoso, and the relational model, such as PostgreSQL. Moreover, we propose two ways how edges can be combined, namely the conjunctive and disjunctive semantics, Preliminary experiments on the graph conjunctive join are also carried out with incremental updates, thus suggesting that our solution outperforms materialized views over PostgreSQL.