Reviewerly/exHarmony huggingface dataset:
AI Ekphrasis: Multi-Modal Learning with Foundation Models for Fine-Grained Poetry Retrieval

Artificial intelligence research in natural language processing in the context of poetry struggles with the recognition of holistic content such as poetic symbolism, metaphor, and other fine-grained attributes. Given these challenges, multi-modal image–poetry reasoning and retrieval remain largely unexplored. Our recent accessibility study indicates that poetry is an effective medium to convey visual artwork attributes for improved artwork appreciation of people with visual impairments. We, therefore, introduce a deep learning approach for the automatic retrieval of poetry suitable to the input images. The recent state-of-the-art CLIP provides a way for multi-modal visual and text features matched using cosine similarity. However, it lacks shared cross-modality attention features to model fine-grained relationships. The proposed approach in this work takes advantage of strong pre-training of the CLIP model and overcomes its limitations by introducing shared attention parameters to better model the fine-grained relationship between both modalities. We test and compare our proposed approach using the expertly annotated MiltiM-Poem dataset, which is considered the largest public image–poetry pair dataset for English poetry. The proposed approach aims to solve the problems of image-based attribute recognition and automatic retrieval for fine-grained poetic verses. The test results reflect that the shared attention parameters alleviate fine-grained attribute recognition, and the proposed approach is a significant step towards automatic multi-modal retrieval for improved artwork appreciation of people with visual impairments.
Research on the measurement of the large-components flatness based on gravitational straightedge method of adaptive two-end growth

Rail flatness is an essential indicator of large rail components’ quality, and is of great significance in inspecting the product quality of rail mills. The high-speed railways can only be built by rails with excellent flatness, while the standard ones are built by those with moderate defects in this respect. However, the rails must be straightened or discarded once the flatness are out of specific tolerances. In order to measure flatness, a gravitational straightedge method of adaptive two-end growth is proposed. This paper extracted the original flatness curve with a laser profilometer and calculated the maximum difference using the processing method for such curve. According to comparison of experimental data, the precision in repeatability was ±0.01 mm in such measurement conducted based on this algorithm, showing a difference of less than 0.02 mm compared with that achieved manually with a mechanical straightedge. Therefore, the algorithm was proved to be highly robust and accurate.
Secret Message CDMA Multiplexing via QR Codes

In this paper, we propose an innovative secret message multiplexing scheme for QR code based on the technology of Code Division Multiple Access (CDMA). Multiple secret messages distributed for several participants are encoded into a CDMA codeword through CDMA multiplexing and then embedded in the padding region of the cover QR code. In this way, everyone can read the public message of marked QR code by any standard QR code reader, but only the authorized user can decode the dedicated hidden secret message using a correct secret key. This feature helps the proposed scheme to possess strong data security. Meanwhile, the robust error correction capacity of QR code is still maintained. The proposed secret message multiplexing scheme is efficient and feasible for private message delivery applications.
A compact MLCP-based projection recurrent neural network model to solve shortest path problem

We develop a projection recurrent neural network (RNN) to obtain the solution of the shortest path problem (SPP). Our focus on the paper is to give a compact single-layer structure RNN model to solve the SPP. To present the RNN model, we utilise a mixed linear complementarity problem (MLCP). Moreover, the developed RNN is proved to be globally stable. Finally, some numerical simulations are stated to show the performance of the presented approach. We compare the results with some other methods.
Segmentation of breast tumors using cutting-edge semantic segmentation models

Early detection of breast cancer is the most important area of mammography research at the moment. It is critical to use computer-aided diagnosis to screen for and prevent breast cancer. In this study, the effectiveness of cutting-edge deep segmentation models for mammography in the detection of breast tumors was investigated. A medical images dataset was compiled and annotated at Lady Reading Hospital, one of the largest teaching hospitals in Pakistan in collaboration with the local health specialists, radiologists, and technologists. A comparison was made between the performance of the segmentation techniques used, and the model that performed the best in detecting tumors and normal breast regions was selected. The evaluation metrics, such as the mean IoU, pixel accuracy, and an in-depth experimental evaluation were used as performance parameters. This investigation determined how well semantic segmentation techniques were performed based on two datasets (cityscapes and mammograms) in this study. The global Dilation 10 semantic segmentation model outperformed the other three semantic segmentation models with a pixel accuracy of 92.98 percent in comparison tests. This paper demonstrates the efficacy of pixel-wise image segmentation techniques and their superiority to other techniques by outperforming other current state-of-the-art automatic image segmentation models.
How does knowledge spur the productivity of private Egyptian firms? An instrumental variable approach

Purpose This study aims to dissect firm knowledge into two main types: internal firm knowledge (knowledge workers) and external firm knowledge (relational knowledge with other firms). This study aims to investigate how each type affects the productivity of the firms. This study also examines how this effect differs among Egyptian firms in the agriculture, manufacturing and service sectors. Design/methodology/approach The authors use firm-level data in Egypt on the sectoral level. The properties of instrumental variables regression using two-stage least-squares estimation are adopted to overcome endogeneity and omitted variable bias in the empirical estimations. Findings The study’s findings reveal that the effects of internal and external knowledge on the firm productivity are sector-specific; knowledge-workers and relational knowledge are two times more effective for agriculture than manufacturing and service firms; external knowledge plays a vital role in increasing productivity relative to internal knowledge for the manufacturing sector; finally, internal and external knowledge has the same effect on the service firms. Originality/value This research adds to the body knowledge-based theory of the firm by examining the effects of internal and external knowledge on the firms’ productivity. In particular, the paper differentiates this effect across three sectors: agriculture, manufacturing and services. This paper also suggests a novel empirical methodology to address endogeneity and omitted variable bias in this literature of firm knowledge and productivity.
An Analysis on Cryptographic Algorithms for Handling Network Security Threats

In the recent times the need for network security has been increasing rapidly. Network Security is the main issue that arises using transmission of data on the internet. Cryptography is a concept to protect systems against malicious attacks and data transmission over wireless network. The main aim of cryptography is to deliver safety measures to the data when the transmission occurs. Cryptography acts as an important tool for security. It is mainly used for maintaining the confidentiality of data, providing authentication and integrity of the information passed as well as give services like non-repudiation. In order to provide security to the data, we use some algorithms where the data is scrambled into an unreadable text that can only be decoded by a client who possesses the associated key. Nowadays, modern cryptographic methods are introduced to perform these operations in a more efficient manner and solve complex issues faced due to lack of network security. This paper aims to review the various types of security threats in network, the importance of cryptography in handling network security, the process of encryption and decryption with the types of cryptography. The various types of symmetric key algorithms as well as asymmetric key algorithms with their improvements that have been used for network security are also discussed in this paper.
Color Images encryption by using Bit Plane Crypt Algorithm

Image Encryption method is used to provide security and privacy in various applications, by using the process of a model to change initial format of an image (plaintext), into other formats like cipher text, unreadable format or in encrypted form. In this paper, the method of lossless encryption for color images using binary key images has been put forwarded and in this process, key image size and original image size are same. It is either an edge map or a bit plane generated from another image.
Communication-based Optimization Algorithm: A Meta-heuristic Technique for Solving Single-Objective Problems

In this paper, a science-inspired meta-heuristic optimization algorithm is proposed and called communication-based optimization algorithm (COA). The COA originates from the power allocation policy to users in non-orthogonal multiple access (NOMA)-based wireless communication networks. Four main steps of COA such as channel gain of users, power allocation, velocity of users and movement mechanism are modeled for equipping the COA with high exploration and exploitation phases. The COA is evaluated with standard mathematical test problems and compared with PSO, GSA, MFO, SSA, and FDA. The results show that the proposed algorithm is very competitive compared to well-known algorithms and in some cases its performance outperforms other meta-heuristic techniques, significantly.
Online Passive-Aggressive Multilabel Classification Algorithms

Most existing multilabel classification methods are batch learning methods, which may suffer from expensive retraining costs when dealing with new incoming data. In order to overcome the drawbacks of batch learning, we develop a family of online multilabel classification algorithms, which can update the model instantly and efficiently, and make a timely online prediction when new data arrive. Our algorithms all take a closed-form update, which is obtained by solving a constrained optimization problem in each round of online learning. Label correlation is explicitly modeled in our optimization problem. The label thresholding function, an important component of our online classifier, can also be learned online. Our algorithms can be easily generalized to the nonlinear prediction cases using Mercer kernels. The worst case loss bounds for our algorithms are provided. The bounds are relative to the cumulative loss suffered by the best fixed predictive model that can be attained in hindsight. Finally, we corroborate the merits of our algorithms in both linear and nonlinear predictions on nine open multilabel benchmark datasets.
Knock Knock: A Children-oriented Vocabulary Learning Tangible User Interaction System

Early childhood English learning is attracting growing attention. Among the different aspects of English learning: listening, speaking, reading and writing, vocabulary learning is fundamental for the following aspects. However, traditional vocabulary learning methods, such as dictionary learning or literacy-rich environment learning, are tedious and lack interaction between children and the environment. In this work, we propose Knock Knock, a children-oriented vocabulary learning tangible user interaction system. Children can learn English vocabulary with a natural and simple knock gesture using our proof-of-concept prototype. We also developed a mobile application that could augment children's learning experiences and encourage them to make more friends.
A Blockchain-Based Decentralized Microservices: Minimal Architecture for Accounting

Blockchain has become an inevitable prospect on all domains, specifically facilitating and securing transactions between several companies. This article will present a minimalist architecture based on the Blockchain to solve the traceability and authenticity of accounting data, internal and external operations between several entities, thus facilitating the government&#x2019;s audit and taxation procedure&#x2019;s financial bodies. The novelty of this method is the capacity to dispatch blockchain into assets group. This architecture is composed of Remote procedure call Microservices wrapping a blockchain which interacts with enterprise&#x2019;s information systems.
Genesis of the Tang Zagh iron deposit by using mineralogical and geochemical data, Hormozgan province

Genesis of the Tang Zagh iron deposit by using mineralogical and geochemical data, Hormozgan province
Blockchain Empowered Federated Learning for Distributed Network Security Behaviour Knowledge Base in 6G

The malicious flow originating from massive access devices in 6G network will increase sharply. In order to effectively reduce malicious flow, we hope to establish a new framework for coordination of security monitoring and malicious behaviour control in 6G network. Federated learning provides data and privacy protection for the distributed network security behaviour knowledge base. However, since the equipment of its participants needs to upload the original data to the central server for model training, this may lead to data leakage in the knowledge base. Therefore, in this article, we first use the knowledge graph to describe network security behaviours, then build a universal network security malicious behaviour knowledge base, and discuss its application scenarios. Then, we propose a blockchain empowered federated learning (BeFL) for distributed network security malicious behaviour knowledge base architecture to ensure the security of knowledge transmission. Finally, we deployed the designed distributed knowledge base in the prototype system and compared it with the other two baseline methods to verify the performance. Relevant results show that our method outperforms other methods in terms of user identification, flow detection, and attack source tracing.
Shifting Perspectives on AI Evaluation: The Increasing Role of Ethics in Cooperation

Evaluating AI is a challenging task, as it requires an operative definition of intelligence and the metrics to quantify it, including amongst other factors economic drivers, depending on specific domains. From the viewpoint of AI basic research, the ability to play a game against a human has historically been adopted as a criterion of evaluation, as competition can be characterized by an algorithmic approach. Starting from the end of the 1990s, the deployment of sophisticated hardware identified a significant improvement in the ability of a machine to play and win popular games. In spite of the spectacular victory of IBM’s Deep Blue over Garry Kasparov, many objections still remain. This is due to the fact that it is not clear how this result can be applied to solve real-world problems or simulate human abilities, e.g., common sense, and also exhibit a form of generalized AI. An evaluation based uniquely on the capacity of playing games, even when enriched by the capability of learning complex rules without any human supervision, is bound to be unsatisfactory. As the internet has dramatically changed the cultural habits and social interaction of users, who continuously exchange information with intelligent agents, it is quite natural to consider cooperation as the next step in AI software evaluation. Although this concept has already been explored in the scientific literature in the fields of economics and mathematics, its consideration in AI is relatively recent and generally covers the study of cooperation between agents. This paper focuses on more complex problems involving heterogeneity (specifically, the cooperation between humans and software agents, or even robots), which are investigated by taking into account ethical issues occurring during attempts to achieve a common goal shared by both parties, with a possible result of either conflict or stalemate. The contribution of this research consists in identifying those factors (trust, autonomy, and cooperative learning) on which to base ethical guidelines in agent software programming, making cooperation a more suitable benchmark for AI applications.
Decision-Makers’ Understanding of Cyber-Security’s Systemic and Dynamic Complexity: Insights from a Board Game for Bank Managers

Cyber-security incidents show how difficult it is to make optimal strategic decisions in such a complex environment. Given that it is hard for researchers to observe organisations’ decision-making processes driving cyber-security strategy, we developed a board game that mimics this real-life environment and shows the challenges of decision-making. We observed cyber-security experts participating in the game. The results showed that decision-makers who performed poorly tended to employ heuristics, leading to fallacious decision approaches (overreaction strategies in place of proactive ones), and were not always aware of their poor performances. We advocate the need for decision support tools that capture this complex dynamic nature.
Detection of multiple emotions in texts using a new deep convolutional neural network

Identifying emotions from the texts can be used in almost every aspect of our daily lives, such as improving computer-human interactions, monitoring people&#x2019;s mental health, or modifying/improving business strategies based on customers&#x2019; emotions. Deep learning techniques have performed well compared to other machine learning methods in all learning problems. All existing machine learning methods for recognizing emotions are taught on datasets that includes single-emotional and multi-emotional samples. Our observations of working with these techniques show that these systems tend to learn more single-emotion samples than multi-emotion samples in a dataset. We also looked at a large number of texts and found that the number of texts from which only one emotion can be deduced is very small compared to texts from which more than one emotion can be deduced. Therefore, in general, the accuracy of existing methods is low. To deal with these two limitations, in this paper, we first created a dataset using all available data so that all texts have at least two emotions. To improve accuracy, because the convolutional neural network (CNN) has performed so well in image processing, we have then proposed a new CNN architecture for extracting emotions from texts. To find semantic, syntactic, and word similarity, we have used fast text and GloVe to embed text data into numeric representation. We have also improved the accuracy of the proposed architecture by using the attention property. The results demonstrated that the proposed model is more accurate compared to the existing methods.
A New Mask for a New Normal: Investigating an AR Supported Future under COVID-19

Wearing masks and social distancing have become the norm during the COVID-19 pandemic. However, these are increasingly seen as a source of frustration in face-to-face communications. While efforts have been made to overcome these impediments to communication, they typically focus on recovering lost communication quality. Herein, we envision a future where everyone augments their vision using face masks with Augmented Reality capabilities, such that people can conduct safe and expressive face-to-face communication in public. To speculate on this vision, we developed an AR mask prototype which can overlay dynamic virtual “masks” on other users. The virtual mask is dynamic in that it accelerates towards any observer who approaches the wearer. Using this system, we conducted an explorative study to further our speculations on the impact of ubiquitous AR technologies.
An English Pronunciation Quality Evaluation Model Based on Multi-dimensional Features

Abstract As a universal language, English has been paid more and more attention, among which oral English learning is very important. In this paper, the two key technologies of pronunciation error detection and quality evaluation are studied, both of them are effectively integrated, aiming to build a model for L2 learners’ English pronunciation quality evaluation. This paper mainly studies two different methods of pronunciation error detection. Based on the speech recognition framework, the standard score is compared with the threshold to judge the correctness of phoneme pronunciation, and the phoneme-dependent threshold is set to improve the maximum Precision to 0.44. By judging the correct pronunciation and confusing phoneme, the accuracy of pronunciation error detection is improved to 81.26%. This paper proposes the fusion algorithm from multi-dimensions of speech fluency and intonation respectively, and a newly designed feature called word duration ratio, which significantly improve the correlation of pronunciation quality evaluation to 0.746.
CNN-LSTM based Social Media Post Caption Generator

In artificial intelligence automatically captioning an image is a challenging problem that brings computer vision and natural language processing together, but the problem becomes more challenging when artificial intelligence has to caption an Instagram post. This is challenging because the caption of an Instagram post is not a simple description of the image but it contains some abstract features like jokes, sarcasm, references, etc. There has been a lot of work on image captioning but there is hard work on captioning social media posts. So, I propose a deep learning model that uses an amalgamation of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). The first major task was to get a dataset but there is no such popular dataset available. The option to scrap Instagram was explored but turns out scraping Instagram is prohibited, luckily, I was able to get a dataset available on Kaggle containing 35,000 records but due to computation limitations, I used 5000 images and captions for training. Eventually, the trained model was not very accurate due to fewer numbers of epochs i.e 20, small training dataset and majorly because of the low-quality dataset. Even though the model did not show better results but the methodology to generate captions for Instagram posts was established.
Design and Application of Chinese English Machine Translation Model Based on Improved Bidirectional Neural Network Fusion Attention Mechanism

In order to improve the effect of Chinese-English machine translation, this paper combines attention mechanism and neural network algorithm and applies it to Chinese-English machine learning translation. Moreover, this paper uses Gaussian distribution instead of chi-square distribution to analyze the approximate error introduced by the Chinese and English speech energy detection method. In addition, this paper studies the overall and specific approximation errors by establishing the normalized mean square error function and the absolute error function, respectively. Finally, this paper proposes a new model for machine translation based on logarithmic position representation and self-attention mechanism. Through the experimental research, it can be seen that the Chinese-English machine translation model integrating attention mechanism and bidirectional neural network proposed in this paper has a good practical translation effect.