Reviewerly/exHarmony huggingface dataset:
Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing (NLP) and computer vision (CV).It can be driven by applications like image retrieval or indexing, virtual assistants, image understanding, and support of visually impaired people (VIP).Though the VIP uses other senses, touch and hearing, for recognizing objects and events, the quality of life of those persons is lower than the standard level.Automatic Image captioning generates captions that will be read loudly to the VIP, thereby realizing matters happening around them.This article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System (RDOAI-ICS) for Visually Impaired People.The presented RDOAI-ICS technique aids in generating image captions for VIPs.The presented RDOAI-ICS technique utilizes a neural architectural search network (NASNet) model to produce image representations.Besides, the RDOAI-ICS technique uses the radial basis function neural network (RBFNN) method to generate a textual description.To enhance the performance of the RDOAI-ICS method, the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm (BOA) for the RBFNN model, showing the novelty of the work.The experimental evaluation of the RDOAI-ICS method can be tested using a benchmark dataset.The outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.
Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs.People share their thoughts and feelings about various topics, including products, news, blogs, etc.In user reviews and tweets, sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive, neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT).Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words.Later, classification was performed using BER.The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

As the scale of the power system continues to expand, the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore, more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method, this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks, first, the video stream is framed adaptively according to the pixel changes in the video stream.Then, the optimized MobileNet is used to extract the feature map of the video stream, which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally, training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study, and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation, and has good real-time performance.The average accuracy of the algorithm can reach 87.8%, and the frame rate is 61 frames/s, which is of great significance for improving the reliability and accuracy of security control methods.
Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features.It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%-12% higher than the conventional technique.
Direction-guided two-stream convolutional neural networks for skeleton-based action recognition

In skeleton-based action recognition, treating skeleton data as pseudoimages using convolutional neural networks (CNNs) has proven to be effective. However, among existing CNN-based approaches, most focus on modeling information at the joint-level ignoring the size and direction information of the skeleton edges, which play an important role in action recognition, and these approaches may not be optimal. In addition, combining the directionality of human motion to portray action motion variation information is rarely considered in existing approaches, although it is more natural and reasonable for action sequence modeling. In this work, we propose a novel direction-guided two-stream convolutional neural network for skeleton-based action recognition. In the first stream, our model focuses on our defined edge-level information (including edge and edge_motion information) with directionality in the skeleton data to explore the spatiotemporal features of the action. In the second stream, since the motion is directional, we define different skeleton edge directions and extract different motion information (including translation and rotation information) in different directions to better exploit the motion features of the action. In addition, we propose a description of human motion inscribed by a combination of translation and rotation, and explore how they are integrated. We conducted extensive experiments on two challenging datasets, the NTU-RGB+D 60 and NTU-RGB+D 120 datasets, to verify the superiority of our proposed method over state-of-the-art methods. The experimental results demonstrate that the proposed direction-guided edge-level information and motion information complement each other for better action recognition.
Unified benchmark for zero-shot Turkish text classification

Effective learning schemes such as fine-tuning, zero-shot, and few-shot learning, have been widely used to obtain considerable performance with only a handful of annotated training data. In this paper, we presented a unified benchmark to facilitate the problem of zero-shot text classification in Turkish. For this purpose, we evaluated three methods, namely, Natural Language Inference, Next Sentence Prediction and our proposed model that is based on Masked Language Modeling and pre-trained word embeddings on nine Turkish datasets for three main categories: topic, sentiment, and emotion. We used pre-trained Turkish monolingual and multilingual transformer models which can be listed as BERT, ConvBERT, DistilBERT and mBERT. The results showed that ConvBERT with the NLI method yields the best results with 79% and outperforms previously used multilingual XLM-RoBERTa model by 19.6%. The study contributes to the literature using different and unattempted transformer models for Turkish and showing improvement of zero-shot text classification performance for monolingual models over multilingual models.
Does Participative Leadership Promote Employee Innovative Work Behavior in IT Organizations

The responsibilities of leaders have grown as a result of information technology organizations’ increasing reliance on information and development to carry out their work. It is important for leaders to guide and support organizations as they respond to the challenges of continually generating new innovation and information frameworks. The participative initiative, therefore, is characterized as an authority that draws on part data and part insight to reduce progressive obstructions by including individual hierarchical individuals in the decision-making process. The participative initiative encourages a positive style of leadership in which the leader provides workers with the chance to partake in dynamic and critical thinking through consolation, backing, and impact. The purpose of this study is to investigate the influence of participative leadership on employee innovative work behavior through the mediating role of employee knowledge-sharing attitude and absorptive capacity, with project risk management as moderator. The non-probability convenience sampling technique was used and data collection occurred over three months from January 2022 to March 2022. As data collection took place in one time period, the design is cross-sectional in nature. The data were collected from 273 workers (i.e. engineering manager, project manager, project leader, team leader, software engineer, advisor, and expert) working in different public and private sector IT companies of Portugal. Due to the COVID-19 pandemic, both hard and soft copies of surveys were distributed via email. The analysis was performed using Smart PLS version 3.2.8. The results revealed that participative leadership had a direct effect on employee innovative work behavior that was positive and significant. Also, both the knowledge-sharing attitude and absorptive capacity of employees positively and significantly mediate the relationship between participative leadership and employee innovative work behavior. Project risk management, however, only significantly moderates the relationship between absorptive capacity and employee innovative work behavior. Lastly, some theoretical, practical implications, study limitations and future directions were discussed.
SodsBC: A Post-Quantum by Design Asynchronous Blockchain Framework

We present a new framework for asynchronous permissioned blockchain with high performance and post-quantum security. The framework contains two quantum-secure asynchronous Byzantine fault tolerance (aBFT) protocols, SodsBC and SodsBC++. We leverage concurrent preprocessing to accelerate the preparation of three cryptographic objects for the repeated consensus procedure, including common random coins as the needed randomness, secret shares of symmetric encryption keys for censorship resilience, and nested hash values for external validation predicates. The key idea behind our design is that the concurrent preprocessing mechanism can be well-supported by the consensus process of blockchains. The consumed objects in a block have been generated and globally agreed upon in a previous block. All our preprocessed objects utilize proven or commonly believed to be post-quantum cryptographic tools to resist an adversary equipped with quantum computation capabilities. We evaluate our protocols and their competitors in AWS in a typical setting where, the number of participants is 100 and each block part has 20,000 transactions. The results show that SodsBC and SodsBC++ reduce the latency of two state-of-the-art but quantum-sensitive competitors Honeybadger and Dumbo by 53% and 6%, respectively.
Inter-Intra Modal Representation Augmentation With DCT-Transformer Adversarial Network for Image-Text Matching

Image-text matching has become a challenging task in the multimedia analysis field. Many advanced methods have been used to explore local and global cross-modal correspondence in matching. However, most methods ignore the importance of eliminating potential irrelevant features in the original features of each modality and cross-modal common feature. Moreover, the features extracted from regions in images and words in sentences contain cluttered background noise and different occlusion noise, which negatively affects alignment. Different from these methods, we propose a novel DCT-Transformer Adversarial Network (DTAN) for image-text matching in this paper. This work can obtain an effective metric based on two aspects: i) DCT-Transformer uses DCT (Discrete Cosine Transform) method based on a transformer mechanism to extract multi-domain common representations and eliminate irrelevant features from different modalities (inter-modal). Among them, DCT divides multi-modal content into chunks of different frequencies and quantifies them. ii) The adversarial network introduces an adversary idea by combining the original features of various single modalities and the multi-domain common representation, alleviating the background noise within each modality (intra-modal). The proposed adversarial feature augmentation method can easily obtain the common representation that is only useful for alignment. Extensive experiments are completed on the benchmark datasets Flickr30K and MS-COCO, demonstrating the superiority of the DTAN model over the state-of-the-art methods.
Comparative Study On Various Architectures Of Yolo Models Used In Object Recognition

In the last few decades, the deep learning paradigm has been widely used in the Machine Learning Community, thereby accounting for some of the most outstanding results on several complex cognitive results, performing on par or even better than human-level performance. One of these many complex tasks is Object Detection. This paper aims to do a comparative study on the YOLO model used in Object Detection, which would help the visually impaired understand their surroundings. With a wide use case in multiple industries and sectors, it has been a hot topic amongst the community for the past decade. Object detection is a method of finding instances of objects from an image of a certain class. Object Detection has been witnessing a brisk revolutionary change in recent times, resulting in many advanced and complex algorithms like YOLO, SSD, Fast R-CNN, Faster R-CNN, HOG, and many more. This research paper explains the architecture of the YOLO algorithm which is widely used in object detection and classifying objects. We have used the COCO dataset to train our model. Our aim of this research study is to trying to identify the best implementation of the YOLO Model.
Adaptation of block chain technology in SCM for steering managerial strategies: investigative study under Indian context

Purpose Block chain technology (BCT) has apparent capability of handling information in digital format, which has dragged attention of the practitioners for its utility in industrial and manufacturing practices. Conversely, the managerial adoption of BCT is relatively limited, which motivated the authors to identify crucial dimensions that can persuade the acceptance of BCT from an executive perspective. Thus, the present study is aimed to conduct to understand crucial barriers under BCT for managerial implementation in supply chain management (SCM) of small and medium enterprises (SMEs). Design/methodology/approach The present study investigated evident barriers to understand implementation of BCT. A questionnaire based survey is performed to collect primary data from service and manufacturing based companies in India. Survey responses are received online and the data is analyzed in a scorecard. The scorecard embedded the scribed entries of Likert scale to determine the relative score. Findings In present study, sixteen barriers from three categories named as technological, organizational and environmental are evaluated, where, five sub-barriers from technological domain, seven sub-barriers from organizational domain and four sub-barriers from environmental domain are evaluated. The findings of the study determined that the three factors, i.e. “complexity in setup/use”, “Security and privacy concern” and “Technological awareness” mostly affect the adaptation of BCT in SCM. Conversely, “Market dynamics”, “Scalability” and “Cost” do not influence the intention to adopt the technology. Originality/value Only few studies have endeavored to ascertain the BCT adoption in SCM of SMEs in developing country like India. Thus, the study is filling a momentous gap of mapping BCT dimensions in the scholastic literature. The findings are expected to enable SMEs to understand important factors to be considered for adopting BCT in their curriculum. Furthermore, the study may benefit the BCT developers and suppliers to endure customized solutions based on the findings.
Quantum circuit debugging and sensitivity analysis via local inversions

As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
Distributed optical fiber vibration sensing implemented with delayed feedback reservoir computing

A distributed optical fiber vibration sensing system implemented with delayed feedback reservoir computing (RC) is proposed. The reservoir is the sensor. By making the system output close to the target output, the RC system is trained in advance. The sensing process is the RC testing process. To our knowledge, this is the first time that a delayed feedback RC system is used as a distributed optical fiber sensing system, and the sensing process and network testing process become one process. A delayed feedback RC system based on a semiconductor laser is built to implement the proposed sensing system. The signal recognition and location accuracies can reach 100% and 97.54% for two types of signals and six positions on the 1.12 km-long sensing fiber. The experimental results verify the feasibility of the proposed distributed optical fiber sensing system. It provides a new idea for distributed optical fiber sensing, and also opens up a novel application of optical RC.
An Analysis of Adversary-Centric Security Testing within Information and Operational Technology Environments

Assurance techniques such as adversary-centric security testing are an essential part of the risk assessment process for improving risk mitigation and response capabilities against cyber attacks. While the use of these techniques, including vulnerability assessments, penetration tests, and red team engagements, is well established within Information Technology (IT) environments, there are challenges to conducting these within Operational Technology (OT) environments, often due to the critical nature of the OT system. In this article, we provide an analysis of the technical differences between IT and OT from an asset management perspective. This analysis provides a base for identifying how these differences affect the phases of adversary-centric security tests within industrial environments. We then evaluate these findings by using adversary-centric security testing techniques on an industrial control system testbed. Results from this work demonstrate that while legacy OT is highly susceptible to disruption during adversary-centric security testing, modern OT that uses better hardware and more optimised software is significantly more resilient to tools and techniques used for security testing. Clear requirements can, therefore, be identified for ensuring appropriate adversary-centric security testing within OT environments by quantifying the risks that the tools and techniques used during such engagements present to the operational process.
An access control scheme for distributed Internet of Things based on adaptive trust evaluation and blockchain

The Internet of Things (IoT) has the characteristics of limited resources and wide range of points. Aiming at the problems of policy centralization and single point of failure in traditional access control schemes, a distributed access control method based on adaptive trust evaluation and smart contract is proposed to provide fine-grained, flexible and scalable authorization for IoT devices with limited resources. Firstly, a modular access control architecture with integrated blockchain is proposed to achieve hierarchical management of IoT devices. Secondly, an IoT trust evaluation model called AITTE based on adaptive fusion weights is designed to effectively improve the identification of illegal access requests from malicious nodes. Finally, an attribute-based access control model using smart contract called AACSC which is built, which consists of attribute set contract (ASC), registration contract (RC), state judgment contract (SJC), authority permission management contract (AMC), and access control contract (ACC). As experimental results show, the scheme can effectively solve the problem of access security in resource-constrained IoT environments. Moreover, it also ensures the reliability and efficiency of the access control implementation process.