Reviewerly/exHarmony huggingface dataset:
Prosodic Segmentation of Belarusian Texts in NooJ

The article describes the syntactic grammar for automatic text segmentation into syntagms in Belarusian by means of NooJ. It is based on the principle of defining sequences of linguistic elements associated with certain semantic relationships and aimed at searching structural and semantic components of utterances and delimiting them into accentual units. Its implementation is essential for improving the synthetic speech generated by Belarusian text-to-speech systems using prepared syntactic grammars in NooJ.
Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching

Finding key terms in scanned historical manuscripts is invaluable for accessing our written cultural heritage. While keyword spotting (KWS) approaches based on machine learning achieve the best spotting results in the current state of the art, they are limited by the fact that annotated learning samples are needed to infer the writing style of a particular manuscript collection. In this paper, we propose an annotation-free KWS method that does not require any labeled handwriting sample but learns from a printed font instead. First, we train a deep convolutional character detection system on synthetic pages using printed characters. Afterwards, the structure of the detected characters is modeled by means of graphs and is compared with search terms using graph matching. We evaluate our method for spotting logographic Chu Nom characters on the newly introduced Kieu database, which is a historical Vietnamese manuscripts containing 719 scanned pages of the famous Tale of Kieu. Our results show that search terms can be found with promising precision both when providing handwritten samples (query by example) as well as printed characters (query by string).
Graph Similarity Using Tree Edit Distance

Graph similarity is the process of finding similarity between two graphs. Graph edit distance is one of the key techniques to find the similarity between two graphs. The main disadvantage of graph edit distance is that it is computationally expensive and in order to do exhaustive search, it has to perform exponential computation. Whereas for tree, which is a special kind of graph, we have relatively efficient method known as tree edit distance, which finds the minimum number of modifications required to convert one tree to another. In this paper we use tree edit distance to find the similarity between two graphs. To find the similarity between two graph we convert them to spanning trees, find the tree edit distance between these two spanning tree, and then augment the tree edit distance with additional edit operations to perform the graph matching.
Human Description in the Wild: Description of the Scene with Ensembles of AI Models

Describing an image scene in Natural Language is a very complex procedure for a machine. Many researchers have used Natural Language Processing approaches. In this paper Machine Learning and Computer Vision models will be illustrated with the purpose of describing a picture in the wild. Action Recognition models, Face Recognition with gender and age and Clothing Recognition will be performed in combination with the purpose of generating a textual sentence belonging to natural language describing the scene in the picture. The proposed technique can target multiple domains, specifically useful for preventing cyberbullying situations. In addition, an attempt will be made to exceed for each model the current SoA.
INT-RUP Security of SAEB and TinyJAMBU

The INT-RUP security of an authenticated encryption (AE) scheme is a well studied problem which deals with the integrity security of an AE scheme in the setting of releasing unverified plaintext model. Popular INT-RUP secure constructions either require a large state (e.g. GCM-RUP, LOCUS, Oribatida) or employ a two-pass mode (e.g. MONDAE) that does not allow on-the-fly data processing. This motivates us to turn our attention to feedback type AE constructions that allow small state implementation as well as on-the-fly computation capability. In CT-RSA 2016, Chakraborti et al. have demonstrated a generic INT-RUP attack on rate-1 block cipher based feedback type AE schemes. Their results inspire us to study about feedback type AE constructions at a reduced rate. In this paper, we consider two such recent designs, SAEB and TinyJAMBU and we analyze their integrity security in the setting of releasing unverified plaintext model. We found an INT-RUP attack on SAEB with roughly $$2^{32}$$ decryption queries. However, the concrete analysis shows that if we reduce its rate to 32 bits, SAEB achieves the desired INT-RUP security bound without any additional overhead. Moreover, we have also analyzed TinyJAMBU, one of the finalists of the NIST LwC, and found it to be INT-RUP secure. To the best of our knowledge, this is the first work reporting the INT-RUP security analysis of the block cipher based single state, single pass, on-the-fly, inverse-free authenticated ciphers.
Minimizing Even-Mansour Ciphers for Sequential Indifferentiability (Without Key Schedules)

Iterated Even-Mansour (IEM) schemes consist of a small number of fixed permutations separated by round key additions. They enjoy provable security, assuming the permutations are public and random. In particular, regarding chosen-key security in the sense of sequential indifferentiability (seq-indifferentiability), Cogliati and Seurin (EUROCRYPT 2015) showed that without key schedule functions, the 4-round Even-Mansour with Independent Permutations and no key schedule $$\text {EMIP} _4(k,u) = k \oplus \textbf{p} _4\big ( k \oplus \textbf{p} _3\big ( k \oplus \textbf{p} _2( k\oplus \textbf{p} _1(k \oplus u) ) \big ) \big )$$ is sequentially indifferentiable. Minimizing IEM variants for classical strong (tweakable) pseudorandom security has stimulated an attractive line of research. In this paper, we seek for minimizing the $$\text {EMIP} _4$$ construction while retaining seq-indifferentiability. We first consider $$\text {EMSP}$$ , a natural variant of $$\text {EMIP}$$ using a single round permutation. Unfortunately, we exhibit a slide attack against $$\text {EMSP}$$ with any number of rounds. In light of this, we show that the 4-round $$\text {EM2P}_4^{\textbf{p} _1,\textbf{p} _2} (k,u)=k\oplus \textbf{p} _1\big (k \oplus \textbf{p} _2\big (k\oplus \textbf{p} _2(k\oplus \textbf{p} _1(k\oplus u))\big )\big )$$ using 2 independent random permutations $$\textbf{p} _1,\textbf{p} _2$$ is seq-indifferentiable. This provides the minimal seq-indifferentiable IEM without key schedule.
Quantum Attacks on PRFs Based on Public Random Permutations

Plenty of permutation-based pseudorandom functions (PRFs) were proposed. In order to analyze their quantum security uniformly, we proposed three general frameworks F1, F2, and F3 for n-to-n-bit PRFs with one, two parallel, and two serial public permutation calls respectively, where every permutation is preceded and followed by any bitwise linear mappings. We analyze them in the Q2 model where attackers have quantum-query access to PRFs and permutations. Our results show F1 is not secure with $$\mathcal {O}(n)$$ quantum queries while its PRFs achieve n/2-bit security in the classical setting, and F2, F3 are not secure with $$\mathcal {O}(2^{n/2}n)$$ quantum queries while their PRFs, such as SoEM, PDMMAC, and pEDM, achieve 2n/3-bit security in the classical setting. Besides, we attack three general instantiations XopEM, EDMEM, and EDMDEM of F2, F3 with at most $$\mathcal {O}(2^{n/2}n)$$ quantum queries, which derive from replacing the two PRPs in Xop, EDM, and EDMD with two independent EM constructions. We also attack pre-existing concrete PRF instantiations of F2, F3: DS-SoEM, PDMMAC, pEDM, and SoKAC21, with at most $$\mathcal {O}(2^{n/2}n)$$ quantum queries.
Encrypted 5G Smallcell Backhaul Traffic Classification Using Deep Learning

5G small-cell base stations (“Smallcell”) have been widely deployed to enhance network capacity in densely populated city centers. Generally speaking, IPSec is used to secure the backhaul links between the Smallcell network and the core network. However, encryption sometimes serves as a tunnel to hide malware. Encrypted traffic classification of 5G Smallcell backhaul links is rarely discussed in the literature. To our knowledge, we are the first to classify encrypted 5G Smallcell backhaul links using 1D-CNN. We are able to classify 5G Voice, SMS and Internet data with 99.99% accuracy rate and above, and the model is validated using real network data. The work can be used for classifying real encrypted network traffic in general.
Towards a Language for Defining Reusable Programming Language Components

Developing programming languages is a difficult task that requires a lot of time, effort, and expertise. Reusable programming language components make this task easier, by allowing language designers to grab off-the-shelf components for common language features. Modern functional programming languages, however, lack support for reuse of definitions, and thus language components defined using algebraic data types and pattern matching functions cannot be reused without modifying or copying existing code. To improve the situation, we introduce CS, a functional meta-language for developing reusable programming language components, that features built-in support for extensible data types and functions, as well as effects and handlers. In CS, we can define language components using algebraic data types and pattern matching functions, such that we can compose language components into larger languages and define new interpretations for existing components without modifying existing definitions.
Blockchain Technologies for Internet of Medical Things (BIoMT) Based Healthcare Systems: A New Paradigm for COVID-19 Pandemic

Blockchain and Internet of Medical Things (IOMT) technologies are becoming increasingly vital in the healthcare industry, with considerable growth predicted in the near future. Numerous blockchain-related applications have been developed and planned in the health sector. The decentralization, reliability, and transparency of data are all elements that place this technology as a revolution for industry, healthcare professionals and covid-19 patients. Connected devices generate and transmit data to improve patient outcomes, make workplaces and workflows more efficient, reduce medical errors, and enable buildings to meet the needs of those who occupy them better. Connected devices are sometimes excellent targets for hackers because of their capacity to access medical information in real time across various devices; thus, it is critical to protect the privacy of covid-19 patients. Thus, the contribution of the blockchain is more specifically related to health data. This technology addresses the issues of trust, security, and auditability of these data, thus complementing AI and IoT offerings based on similar data. In the realm of healthcare, the Internet of Things (IoT) has recently moved closer to the topic of blockchain, and new use cases combining these two technologies have emerged. In terms of the blockchain of IoMT (BIOMT), it would be feasible to take a significant step toward connected device autonomy while maintaining improved security: (1) data would not pass through a cloud but instead be sent directly to the service platform; (2) hacking entry points would be drastically reduced; (3) medical data would be dematerialized, saving time; and (4) the connected object could conduct medical transactions with complete security and transparency.
Face Image Generation of Anime Characters Using an Advanced First Order Motion Model with Facial Landmarks

Recently, deep learning for image generation with a guide for the generation has been progressing. Many methods have been proposed to generate the animation of facial expression change from a single face image by transferring some facial expression information to the face image. In particular, the method of using facial landmarks as facial expression information can generate a variety of facial expressions. However, most methods do not focus on anime characters but humans. Moreover, we attempted to apply several existing methods to anime characters by training the methods on an anime character face dataset; however, they generated images with noise, even in regions where there was no change. The first order motion model (FOMM) is an image generation method that takes two images as input and transfers one facial expression or pose to the other. By explicitly calculating the difference between the two images based on optical flow, FOMM can generate images with low noise in the unchanged regions. In the following, we focus on the aspect of the face image generation in FOMM. When we think about the employment of facial landmarks as targets, the performance of FOMM is not enough because FOMM cannot use a facial landmark as a facial expression target because the appearances of a face image and a facial landmark are quite different. Therefore, we propose an advanced FOMM method to use facial landmarks as a facial expression target. In the proposed method, we change the input data and data flow to use facial landmarks. Additionally, to generate face images with expressions that follow the target landmarks more closely, we introduce the landmark estimation loss, which is computed by comparing the landmark detected from the generated image with the target landmark. Our experiments on an anime character face image dataset demonstrated that our method is effective for landmark-guided face image generation for anime characters. Furthermore, our method outperformed other methods quantitatively and generated face images with less noise.
Development of Hybrid Personalized E-commerce Using Collaborative Filtering and Content-Based Filtering for South Cartel Clothing Company

E-commerce plays an essential role in selling products or services online because it can reach more customers than traditional retail. If the customer data is appropriately mishandled, it disrupts the business’ data organization and poor customer relationship management. The study focuses on creating an e-commerce website that efficiently handles the data and integrates a personalized hybrid recommender system. Content-based and collaborative filtering methods were used in the recommender system to improve customer relationship management, streamline procedures, organize inventory and sales, and increase profits. Sales forecasting using ARIMA was also added to use the customer data for efficient business decisions. ISO 9126 was the software quality model used to evaluate the developed system using the software quality characteristics functionality, usability, maintainability, and efficiency. The system got an overall mean score of 4.57, which is excellent, which means the system can perform smooth transactions from ordering up to the checkout and organized products, sales, and inventory. The integration of the recommender systems was able to give recommendations based on the customer's preferences, which enhances the user experience that may lead to an increase in sales of the business since the suggestions are tailored recommendations to the users.
Using a Q-Methodology in Demystifying Typologies for Cybersecurity Practitioners: A Case Study

The increasing number of cybersecurity breaches has forced organizations to apply specific strategies for ensuring business continuity and minimizing business risks. The complex socio-organizational dynamics in organizations influence the adoption of cybersecurity measures. This has created difficulties to align the implementation of cybersecurity measures with cybersecurity compliance. This study constructs a typology of cybersecurity practitioners in public organizations based on their perception of cybersecurity measures using a Q-methodology. Based on the analysis of the collected data from 14 participants in different organizations, the study reveals three distinctive typologies of cybersecurity practitioners including strategy, management and operation, and technology. Such proposed typologies can be used for developing cybersecurity mitigation strategies and understanding the prioritization of cybersecurity measures for their implementation.
Cancer Cell Detection and Classification from Digital Whole Slide Image

The World Health Organisation has identified cancer as one of the foremost causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatment option for the patient. Pathological data have huge tumour information that can be used to diagnose cancer. Digitizing pathological data into images and its analysis using Deep learning applications will be a significant contribution to clinical testing. Due to advancements in technology, artificial intelligence (AI) and digital pathology can now be combined allowing for image-based diagnosis. This study uses residual networks (ResNet-50) and convolutional neural network (CNN), which is pre-trained on ImageNet dataset to train and categories lung histopathology images into non-cancerous, lung adenocarcinoma, and lung squamous cell carcinoma delivering an accuracy of 98.9%. Experimentation results show that the ResNet-50 model delivers finer classification results when compared to state-of-the-art methods.
Self-supervised Learning Approach to Local Trajectory Planning for Mobile Robots Using Optimization of Trajectories

In Industry 4.0, various control methods have been developed for autonomous navigation of robots. Some investigations are based on the use of SLAMs or routing systems for route tracking, but there are some limitations when it comes to obstacle avoidance and real-time parameter changes. Current work shows an algorithm based on the use of DQN and reinforcement learning. The model maximizes rewards and extracts information about the robot’s position and obstacles within the simulated environment as the robot performs its actions. A series of experiments have been conducted to build the algorithm, and the results show that the robot learns through exploration and uses the knowledge gained in the previous scenarios. Using a simulated environment, the DQN network computes complex functions due to randomness, resulting in better learning performance than other control methods.
Using Deep Learning for Implementing Paraphrasing in a Social Robot

Social robotics is intended for interacting and communicating with people. This communication needs to be natural and fluid, similar to a human conversation. For instance, people do not always use exactly the same words when transmitting the same message in different situations. One way to imitate this is through paraphrase generation, allowing the robot to use different sentences while keeping the same meaning. In this paper we propose the application of deep learning models to generate these paraphrases in a social robot speaking in Spanish. This application has been integrated into the Mini robot both in English and Spanish, and can be run locally or from an external server. We integrated and evaluated 3 different models. The T5 and Pegasus Transformers have been considered as a base, and we have chosen three different models finetuned for paraphrase generation, one based on Pegasus, and the other two on T5 (Finetuned T5 and Parrot). Compared to the Parrot and Pegasus models, that provide similar results, the Finetuned T5 model can generate paraphrases that do not vary as much from the original sentence, but keep more of the meaning. However, the main advantage of the Finetuned T5 model is its average execution time, around one second faster than the other considered models.
Intelligent Decision Support Based on Mental User Models: Research Design

This paper describes the research design in the field of user modeling for intelligent decision support. The relevance of the research, the novelty and the significance of the research results for the development of a new type of interfaces based on cognitive visualization of spatial data are described. This paper is the first step in a research project aimed at developing new approaches for creating cognitive user interfaces. The paper describes the overall research design, the research problem, the specific tasks within the problem, the scientific novelty of the research, and the expected results. In addition, we give a brief overview of the current state of research on the problem, describe the main directions of research in world science, as well as suggest some methods and approaches to implement the main stages of the project. The duration of the research is two years, the number of performers is three people. Thus, we present a brief overview in the field of intelligent decision support based on mental user models, describe our research strategy and discuss the problems of creating and using mental user models for decision-making purposes.
Simulation of Multiple Autonomous Mobile Robots Using a Gazebo Simulator

Simulation is an indivisible part of the development and testing of robotic systems. This is also true for the simulation of interactions among multiple mobile robots. This paper presents a design of a multi-mobile robot simulation that is built on ROS and a gazebo simulator. Focus is given to an interaction between the ROS node which encapsulates access to hardware and the gazebo. In this design communication with hardware is redirected to the gazebo server with the usage of its transport protocol. All robots are executed inside docker containers and communicate with the gazebo server which also runs inside a docker container. Further in the paper is a description of the gazebo model plugin which acts as a virtual PLC and provides a behavior of hardware. Since all robot models are spawned in one virtual environment they can interact with each other. This is demonstrated in the conclusion where is an example of a factory environment that holds three mobile robots which could see each other.
Artificial Intelligence Trends and Applications in Service Systems

Artificial intelligence (AI) has been increasingly adopted in service production systems [...]
A Deep Reinforcement Learning Framework with Formal Verification

Artificial Intelligence (AI) and data are reshaping organizations and businesses. Human Resources (HR) management and talent development make no exception, as they tend to involve more automation and growing quantities of data. Because this brings implications on workforce, career transparency, and equal opportunities, overseeing what fuels AI and analytical models, their quality standards, integrity, and correctness becomes an imperative for those aspiring to such systems. Based on an ontology transformation to B-machines, this article presents an approach to constructing a valid and error-free career agent with Deep Reinforcement Learning (DRL). In short, the agent's policy is built on a framework we called Multi State-Actor (MuStAc) using a decentralized training approach. Its purpose is to predict both relevant and valid career steps to employees, based on their profiles and company pathways (observations). Observations can comprise various data elements such as the current occupation, past experiences, performance, skills, qualifications, and so on. The policy takes in all these observations and outputs the next recommended career step, in an environment set as the combination of an HR ontology and an Event-B model, which generates action spaces with respect to formal properties. The Event-B model and formal properties are derived using OWL to B transformation.