Reviewerly/exHarmony huggingface dataset:
Post-processing optimization for continuous-variable quantum key distribution

The performance of a continuous-variable quantum key distribution (CVQKD) protocol depends on the efficiency of the post-processing of measurement results. The post-processing methods extract statistical information from the raw data, establish the mutual knowledge between the parties, and produce a final key that provides absolute security. The post-processing phase is a bottleneck in CVQKD with crucial importance to the efficiency and protocol attributes. Post-processing uses the raw data of the parties generated by the quantum-level transmission and a classical authenticated channel to generate a secret key between the parties. The current reconciliation procedures require high-complexity coding with moderate resulting efficiency. Here we define an optimization method for post-processing in continuous-variable quantum key distribution. The reconciliation method achieves additive Gaussian noise on the random secret for arbitrarily low dimensional blocks. The model consumes all information from the raw data blocks to provide maximal efficiency and security via standard operations. The results can be realized by generic Gaussian coding schemes, allowing an easily implementation for experimental CVQKD protocols.
Computer Aided English Translation System Based on Artificial Intelligence Technology

In order to overcome the limitations of computer- aided English translation system, this paper proposes a novel computer-aided English translation system based on artificial intelligence technology. The system fully combines the advantages of artificial intelligence technology, can simulate human intelligence, according to the needs of users to carry out a series of work. Based on this, the system can also achieve innovation on the basis of the existing computer-aided translation system, and improve the quality of translation from terms management and quality management. The results show that the system can strengthen the combination of artificial intelligence technology and computer-aided translation system, and give full play to the advantages of artificial intelligence technology and computer-aided translation technology.
Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports.

The task of text and sentence classification is associated with the need for large amounts of labelled training data. The acquisition of high volumes of labelled datasets can be expensive or unfeasible, especially for highly-specialised domains for which documents are hard to obtain. Research on the application of supervised classification based on small amounts of training data is limited. In this paper, we address the combination of state-of-the-art deep learning and classification methods and provide an insight into what combination of methods fit the needs of small, domain-specific, and terminologically-rich corpora. We focus on a real-world scenario related to a collection of safeguarding reports comprising learning experiences and reflections on tackling serious incidents involving children and vulnerable adults. The relatively small volume of available reports and their use of highly domain-specific terminology makes the application of automated approaches difficult. We focus on the problem of automatically identifying the main themes in a safeguarding report using supervised classification approaches. Our results show the potential of deep learning models to simulate subject-expert behaviour even for complex tasks with limited labelled data.
Normalising Lustre Preserves Security

The synchronous reactive data flow language Lustre is an expressive language, equipped with a suite of tools for modelling, simulating and model-checking a wide variety of safety-critical systems. A critical intermediate step in the formally certified compilation of Lustre involves translation to a well-behaved sub-language called “Normalised Lustre” (NLustre). Recently, we proposed a simple Denning-style lattice-based secure information flow type system for NLustre, and proved its soundness by establishing that security-typed programs are non-interfering with respect to the co-inductive stream semantics. In this paper, we propose a similar security type system for unrestricted Lustre, and show that Bourke et al.’s semantics-preserving normalisation transformations from Lustre to NLustre are security-preserving as well. A novelty is the use of refinement security types for node calls. The main result is the preservation of security types by the normalisation transformations. The soundness of our security typing rules is shown by establishing that well-security-typed programs are non-interfering, via a reduction to type-preservation (here), semantics-preservation (Bourke et al.) and our previous result of non-interference for NLustre.
Computer Vision and Image Processing the Challenges and Opportunities for new technologies approach: A paper review

Abstract Digital image processing has numerous applications in many sectors of the world. It expands from initial information registration into methods and thoughts combining pattern recognition, computer vision, and machine learning. The wide utilization has pulled in many researchers to integrate with a range of related specializations. This work gives a study of the latest development and theoretical ideas clarifying an improvement of computer vision particularly with pattern recognition and image processing, utilizing various regions of their field implementation. Digital image processing causes researchers to analyze images to get important data and comprehend information. It utilized a technique of multi-range implementation and huge information analysis. This work aims to focus on the latest studies related to image processing, pattern recognition, and computer vision. In this paper, Computer vision standard has been categorized into groups. For example, pattern recognition, image processing, and AI. Additionally, we give a short clarification of the forward-thinking data about the methods and their realization. This survey is restricted to computer vision, and more research can include detecting the conduct and properties of the object including people actions.
Word-Copying on a Website as a Word Complexity Indicator and the Relation to Web Users’ Preferred Languages

The first step toward accessibility improvement in the context of Human-Computer Interaction (HCI) is the identification of potential barriers. A recent study shows that words that are frequently copied to the clipboard by web users are relatively complex. A plausible reason is that users copy challenging words to search for a translation or more information. Accordingly, tracking word-copying operations of web users may be useful in identifying complex words. This study focuses on the users that apply word-copying. It shows significant differences in the frequency of word-copying operations among different populations of users. On the examined website, whose content is in English, users whose preferred language is not English copied single words to the clipboard significantly more frequently than users whose preferred language is English. Further analysis of the data also shows that word-copying was more frequent among users whose preferred languages have low proximity to English, such as Asian languages, compared to Western European languages. These results support the observation that word-copying indicates complexity, as it is reasonable to expect that native speakers of foreign languages (and especially of languages that are considerably different from the website’s language) are more likely to need help with complex words. Word complexity is subjective and audience-dependent. This study contributes to the understanding of which users tend to use word-copying, and accordingly, in which context word-copying data can be used as a word complexity indicator. It also introduces a new practical approach for detecting language barriers in order to improve language accessibility on global websites.
Image-Based Relation Classification Approach for Table Structure Recognition

In recent years, the use of tabular data has become a major area of research and development. However, the number of tables structured in a machine-readable format is still limited. A major challenge that is encountered when using tabular data is converting the table information in a free-format document into a structured format. Unlike markup languages such as HTML, XML, and JSON, free-format documents such as PDF, Word, Excel, and images generally have no tags or separators. Therefore, the table structure should be recognized from the positional information of the table elements. A major approach of table structure recognition is to classify the relationship between each pair of bounding boxes of the table elements. Recent works have achieved significant improvements by applying graph convolutional networks (GCNs) to the graph structure of the bounding boxes. However, fully recognizing a complex table structure is still a major challenge, owing to the presence of spanning cells. In this study, we propose a novel, simple image-based approach to this relation classification task. Our model efficiently exploits information such as the geometry of the table elements and ruled lines through an image cropping strategy based on the pairs of bounding boxes. We evaluate our approach on two real-world table datasets by comparing four baselines including two state-of-the-art GCN approaches. We observe that our approach significantly outperforms the baseline in the exact matching ratio for tables by up to 6.7%.
MTL-FoUn: A Multi-Task Learning Approach to Form Understanding

Form layout understanding is a task of extracting and structuring information from scanned documents, and consists of primarily three tasks: (i) word grouping, (ii) entity labeling and (iii) entity linking. While the three tasks are dependent on each other, current approaches have solved each of these problems independently. In this work, we propose a multi-task learning approach to jointly learn all the three tasks simultaneously. Since the three tasks are related, the idea is to learn a shared embedding that can perform better on all three tasks. Further, the publicly available form understanding datasets are too small, and not ideal to train complex deep learning models. Multi-task learning is an effective method to provide some degree of regularization to the model for such small sized datasets. The proposed model, MTL-FoUn, outperforms existing approaches of learning the individual form understanding tasks on the publicly available data.
Non-disjoint Combined Unification and Closure by Equational Paramodulation

Closure properties such as forward closure and closure via paramodulation have proven to be very useful in equational logic, especially for the formal analysis of security protocols. In this paper, we consider the non-disjoint unification problem in conjunction with these closure properties. Given a base theory E, we consider classes of theory extensions of E admitting a unification algorithm built in a hierarchical way. In this context, a hierarchical unification procedure is obtained by extending an E-unification algorithm with some additional inference rules to take into account the rest of the theory. We look at hierarchical unification procedures by investigating an appropriate notion of E-constructed theory, defined in terms of E-paramodulation. We show that any E-constructed theory with a finite closure by E-paramodulation admits a terminating hierarchical unification procedure. We present modularity results for the unification problem modulo the union of E-constructed theories sharing only symbols in E. Finally, we also give sufficient conditions for obtaining terminating (combined) hierarchical unification procedures in the case of regular and collapse-free E-constructed theories.
A Semantic Search Engine for Historical Handwritten Document Images

Abstract A very large number of historical manuscript collections are available in image formats and require extensive manual processing in order to search through them. So, we propose and build a search engine for automatically storing, indexing and efficiently searching the manuscript images. Firstly, a handwritten text recognition technique is used to convert the images into textual representations. In the next steps, we apply the named entity recognition and historical knowledge graph to build a semantic search model, which can understand the user’s intent in the query and the contextual meaning of concepts in documents, to return correctly the transcriptions and their corresponding images for users.
A Multi-sensor Intelligent Surface Garbage Cleaning Robot

A multi-sensor intelligent water surface garbage cleaning robot is designed to solve the problem of water waste pollution in China,which takes STM32F407 as the core processor, combines multi-sensor information fusion algorithm and PID control algorithm, and applies GPS,vision sensor, ultrasonic sensor, attitude sensor and wind direction sensor. The robot can sense the environment information according to multiple sensors and automatically float garbage near the water surface. At the same time, it can judge the running state of the robot. Test results show that the robot can accurately identify garbage and can run stably and flexibly, which reflects the intelligence of the robot.
EPNS: Efficient Privacy Preserving Intelligent Traffic Navigation from Multiparty Delegated Computation in Cloud-Assisted VANETs

Real-time navigation is a fundamental service with the emerging techniques of intelligent transportation and crowdsensing. Unfortunately, the breach of location privacy significantly impedes its wide adoption. Most existing state-of-the-art exploit either pseudonyms or public key fully homomorphic encryption (FHE) to protect location privacy, which requires an online certificate authority (CA) or loads intolerably heavy computational/communication overhead on resource-constrained vehicular users. In this paper, a new cryptographic primitive named efficient multiparty delegated computation (MPDC) is firstly proposed, where any one-way trapdoor permutation is required to perform constant times (i.e. twice) on each resource-constrained data provider, to encrypt a batch of messages and enable types of secure evaluations over ciphertexts encrypted under multiple keys of multiple parties. Based on MPDC, we devise a lightweight privacy-preserving real-time intelligent traffic navigation scheme (EPNS) in cloud-assisted VANETs. The proposed approach predicts an optimal driving route of shortest time without disclosing either vehicular users' private location or the navigation result to the collusion between the semi-trusted cloud/cryptography service provider (CSP) and unauthorized users, by securely evaluating auto-regression moving average (ARMA) model with spatiotemporal correlations of high accuracy and efficiency. Finally, formal security proofs and the extensive evaluations demonstrate the utility and the practicability of our approaches.
The fluted fragment with transitive relations

The fluted fragment is a fragment of first-order logic (without equality) in which, roughly speaking, the order of quantification of variables coincides with the order in which those variables appear as arguments of predicates. It is known that this fragment has the finite model property. We consider extensions of the fluted fragment with various numbers of transitive relations, as well as the equality predicate. In the presence of one transitive relation (together with equality), the finite model property is lost; nevertheless, we show that the satisfiability and finite satisfiability problems for this extension remain decidable. We also show that the corresponding problems in the presence of two transitive relations (with equality) or three transitive relations (without equality) are undecidable, even for the two-variable sub-fragment.
Text-line-up: Don’t Worry About the Caret

In a freestyle handwritten text-line, sometimes words are inserted using a caret symbol (\(^\wedge \)) for corrections/annotations. Such insertions create fluctuations in the reading sequence of words. In this paper, we aim to line-up the words of a text-line, so that it can assist the OCR engine. Previous text-line segmentation techniques in the literature have scarcely addressed this issue. Here, the task undertaken is formulated as a path planning problem, and a novel multi-agent hierarchical reinforcement learning-based architecture solution is proposed. As a matter of fact, no linguistic knowledge is used here. Experimentation of the proposed solution architecture has been conducted on English and Bengali offline handwriting, which yielded some interesting results.
Algebras of UTxO blockchains

Abstract We condense the theory of UTxO blockchains down to a simple and compact set of four type equations (Idealised EUTxO), and to an algebraic characterisation (abstract chunk systems), and exhibit an adjoint pair of functors between them. This gives a novel account of the essential mathematical structures underlying blockchain technology, such as Bitcoin.
Heterogeneous Information Network-Based Patient Similarity Search

Patient similarity search is a fundamental and important task in artificial intelligence-assisted medicine service, which is beneficial to medical diagnosis, such as making accurate predictions for similar diseases and recommending personalized treatment plans. Existing patient similarity search methods retrieve medical events associated with patients from Electronic Health Record (EHR) data and map them to vectors. The similarity between patients is expressed by calculating the similarity or dissimilarity between the corresponding vectors of medical events, thereby completing the patient similarity measurement. However, the obtained vectors tend to be high dimensional and sparse, which makes it hard to calculate patient similarity accurately. In addition, most of existing methods cannot capture the time information in the EHR, which is not conducive to analyzing the influence of time factors on patient similarity search. To solve these problems, we propose a patient similarity search method based on a heterogeneous information network. On the one hand, the proposed method uses a heterogeneous information network to connect patients, diseases, and drugs, which solves the problem of vector representation of mixed information related to patients, diseases, and drugs. Meanwhile, our method measures the similarity between patients by calculating the similarity between nodes in the heterogeneous information network. In this way, the challenges caused by high-dimensional and sparse vectors can be addressed. On the other hand, the proposed method solves the problem of inaccurate patient similarity search caused by the lack of use of time information in the patient similarity measurement process by encoding time information into an annotated heterogeneous information network. Experiments show that our method is better than the compared baseline methods.
Table Structure Recognition Using CoDec Encoder-Decoder

Automated document analysis and parsing has been the focus of research since a long time. An important component of document parsing revolves around understanding tabular regions with respect to their structure identification, followed by precise information extraction. While substantial effort has gone into table detection and information extraction from documents, table structure recognition remains to be a long-standing task demanding dedicated attention. The identification of the table structure enables extraction of structured information from tabular regions which can then be utilized for further applications. To this effect, this research proposes a novel table structure recognition pipeline consisting of row identification and column identification modules. The column identification module utilizes a novel Column Detector Encoder-Decoder model (termed as CoDec Encoder Decoder) which is trained via a novel loss function for predicting the column mask for a given input image. Experiments have been performed to analyze the different components of the proposed pipeline, thus supporting their inclusion for enhanced performance. The proposed pipeline has been evaluated on the challenging ICDAR 2013 table structure recognition dataset, where it demonstrates state-of-the-art performance.
Post-quantum Key-Blinding for Authentication in Anonymity Networks

Anonymity networks, such as the Tor network, are highly decentralized and make heavy use of ephemeral identities. Both of these characteristics run in direct opposition to a traditional public key infrastructure, so entity authentication in an anonymity network can be a challenge. One system that Tor relies on is key-blinded signatures, which allow public keys to be transformed so that authentication is still possible, but the identity public key is masked. This is used in Tor during onion service descriptor lookup, in which a .onion address is resolved to a rendezvous point through which a client and an onion service can communicate. The mechanism currently used is based on elliptic curve signatures, so a post-quantum replacement will be needed.
Efficient and Trustworthy Authentication in 5G Networks Based on Blockchain

The global coronavirus pandemic is reshaping our daily lives and has forced many people to work and socialize remotely. This trend also increases the demand for highly available and trustworthy Internet access. Compared with 4G networks, 5G mobile networks are designed to accommodate the increasing number of mobile devices with higher transfer speed, lower latency and improved security. The 5G standard in USA uses millimeter waves, which is much shorter than 4G LTE signals. The shorter wavelength means 5G can transfer data much faster than 4G, but it also results in a much shorter travel range. When a user is moving among 5G cells, repeated authentications will increase delay time which contradicts 5G objective. The blockchain technology has the advantage of better transparency, enhanced security and high efficiency. In this paper, we propose an efficient authentication approach for 5G network using blockchain technology to reduce repeated authentications and keep the cost low and practical. We will compare it with some existing protocols and analyze its security against several possible attacks.
Facial expression recognition with fused handcraft features based on pixel difference local directional number pattern

Facial expression recognition (FER) has been an active research area in recent years, which plays a vital role in national security and human-computer interaction. Due to the lacking of sufficient expression features and facial images, it is challenging to automatically recognize facial expression with high accuracy. In this paper, we propose a fusion handcraft feature method to improve FER from images. Firstly, a new texture feature extraction method PD-LDN (Pixel Difference Local Directional Number pattern) is proposed, which can extract more local information, reduce noise disturbance and feature dimension. Secondly, the handcrafted features including PD-LDN texture features, geometric features, and BOVW (Bag of Visual Words) semantic features are connected in parallel to an improved autoencoder network for fusion. Finally, the fused features are input into the softmax classifier for recognizing facial expression. We conduct extensive experiments on JAFFE and CK+datasets. Our proposed method shows superior performance than the state-of-the-art approaches on recognizing facial expressions.