Reviewerly/exHarmony huggingface dataset:
Solution for sports image classification using modified MobileNetV3 optimized by modified battle royal optimization algorithm

Sports image classification using image processing and machine vision is a growing area of research that involves the use of algorithms and techniques to identify and analyze objects in sports images and videos. This technology has a wide range of applications, including detecting illegal plays, analyzing team performance, and creating highlight reels. Additionally, it can provide valuable visual feedback during training and competition. In this paper, we propose a novel deep learning and optimization hybrid framework for sports image classification. Specifically, we use a modified version of the Battle Royal optimization algorithm as a feature selector to reduce the dimensionality of the images and achieve higher accuracy with only the essential features. We evaluate the proposed framework using sports images and demonstrate that our WOA-based framework outperforms other methods in terms of both classification accuracy and dimensionality reduction. Our results highlight the effectiveness of the proposed approach and its potential to improve sports image classification.
Expert System for Screening of Borderline Personality Disorder

Individuals who have symptoms of borderline personality disorder (BPD) usually experience intense feelings of hatred, anger, depression, or anxiety. These feelings are often frightening, overwhelming and difficult to control. In the midst of intense emotions, individuals with BPD may act impulsively to reduce emotional pain, calm themselves, or communicate emotional distress. This results in 10% of people with BPD dying by suicide. The system is made to anticipate and minimize these problems. The expert system built in this study can measure a person's level of borderline personality disorder, so that it can increase knowledge in the general public to help someone who is detected as having mental health problems or BPD. The certainty factor method is used to assume the degree of confidence in the value used, and Forward chaining as forward tracking which starts from gathering facts and events that support the assumption/hypothesis, until a conclusion is obtained. Based on tests using 50 test data using accuracy testing, it shows that the CF method has a presentation of 100% accuracy in the test results that have been carried out in this study.
A Standardized Trust Model for Enabling Data Security and Interoperability within Smart Distributed Systems

Smart distributed systems (SDS) are the foundation of smart cities, utilities, supply chains and hospitals. They support digital marketplaces where data is exchanged to generate value in the form of social services, business functionality or money. They are the engine of modern economies. They communicate data and real-time action requests from multiple parties, with diverse owners, enable better insights from AI, and deliver safer, more efficient and sustainable environments. But what is the impact if the data used by these systems is compromised? Without data security and interoperability, these systems would be wasteful and inefficient, and could even become lethal weapons. In this paper, we propose a solution to allow secure collaboration of data within SDSes. We propose establishing an achievable global standard that lays the framework for interoperability, security, and authentication across the totality of the smart distributed system.
Identifying key factors and actions: Initial steps in the Open Science Policy Design and Implementation Process

The coronavirus pandemic has illustrated the lack of a holistic approach in implementing Open Science (OS), leading to an inability to fully utilise its potential to inform prompt, evidence-based policy responses. In this view, this study aims to identify and categorise the factors influencing the adoption of OS and proposes possible actions for decision-makers to develop relevant policies. To achieve this, semi-structured interviews were conducted with 36 experts from Australia, France, the Netherlands, South Korea, the United Kingdom, and the United States as well as eminent international entities. During the interviews, they were asked to answer a range of questions that emerged from a systematic literature review. The responses were coded and analysed using a grounded theory approach. This led to the identification of four thematic clusters, containing a total of 24 factors that can either enable or inhibit OS practices, namely, (a) external; (b) institutional and regulatory; (c) resource-related; and (d) individual and motivational. Drawing upon Ostrom’s Institutional Analysis and Development framework, we also propose a conceptual model that integrates these factors, accompanied with corresponding actions, into a tangible process of OS policy design and implementation.
A Comparative Study On Image Compression in Cloud Computing

Cloud computing is a computing model that offers integrated ICT infrastructure, for example, virtual resources, including processing activities, storage, software, and services, accessible through an Internet network. These resources are distributed and can be accessed from any device and location as needed. The use of cloud computing has been increasing, particularly for image storage, to alleviate the storage burden on physical media. Therefore, this research aims to compare different image compression techniques, namely Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Karhunen-Loève Transform (KLT), within the context of cloud computing.The study involves comparing the effectiveness of these four methods based on the level of compression achieved, processing speed during compression, and the similarity between the compressed and original images. Six sample images are utilized for testing purposes. The results indicate that the DWT method outperforms the SVD, KLT, and DCT methods in terms of file compression size, achieving an average compression rate of 12% compared to the original file. Additionally, the DWT method exhibits an average compression speed of 4.31 seconds and a grayscale similarity score of 0.96 with the original image. However, in terms of color scale similarity, the SVD method achieves a higher score of 13.1 compared to the DWT method.
Beyond humans: Consumer reluctance to adopt zoonotic artificial intelligence

Abstract In addition to humanoid‐robotic designs, an increasing number of artificial intelligence (AI)‐powered services are being represented by animals, referred to as zoonotic design. Yet, little is known about the consequential effects of such zoonotic AI on consumer adoption of these services. Drawing on the concepts of prototypicality, Cognitive Load Theory, and the “Match‐up” Hypothesis, the current research uncovers how the use of zoonotic designs, as opposed to robotic ones, may negatively influence consumers’ adoption of AI over a human provider. The results of seven studies suggest that consumers are less likely to choose an AI over a human provider for performing tasks when the AI has a zoonotic embodiment rather than a robotic embodiment. This negative effect is mediated by the increased cognitive difficulty associated with linking the AI prototype to the task. However, such a negative effect decreases when the characteristics of the animal are congruent with the task and is even reversed when the congruent task is of a hedonic nature. These findings advance the understanding of consumer–AI interactions in the context of zoonotic embodiment and provide valuable managerial insights into when and how firms should use zoonotic design for AI‐powered services.
Social Media, Social Control, and the Politics of Public Shaming

While there is disagreement over the value of public shaming, scholars largely agree that social media introduce pathologies. But while scholars rightly identify the effects of online public shaming (OPS), they misidentify the cause. Rather than solely a problem of scale, OPS’s effects are also shaped by the network structure within which they take place. In this article, I argue that the social conditions necessary for productive public shaming are more likely to obtain in a closed social network structure. Using the cases of Twitter, Wikipedia, and Reddit, I show how the design of social media platforms facilitates different network structures among users, with differing results for OPS. In evaluating OPS by way of network structure, I argue, we can not only better understand why OPS works productively in some cases and not in others, but also derive lessons for how to deploy, discuss, and respond to it more effectively.
A unique hybrid domain hand-crafted feature to classify colorectal tissue histopathological images using multiheaded CNN

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant feature transform and mel-cepstrum domain features. The developed hand-crafted features use spatial as well as spectral information. Furthermore, the developed hand-crafted features are given as input to a newly developed 1D multiheaded convolutional neural network (1D MHCNN) for the classification of colorectal tissue utilizing histopathological images. The performance of the proposed network is compared with other existing methods. Based on the experiments, the proposed network performed with accuracy of 96.80%, specificity of 99.76%, precision of 97.12%, sensitivity of 96.64%, F1 score of 0.9688, and area under the curve of 0.9820. The proposed approach may be utilized to improve clinical diagnosis measurement performance.
Underwater quantum key distribution with continuous-variable via photon additions

Quantum key distribution with continuous-variable (CVQKD) provides an alternative way for the security improvement of underwater optical communication, but facing into the awkward situation of short distance. For this reason, here we exploit photon addition operation at the emitter into enhancing the performance of underwater CVQKD. Based on numerical simulation, it is shown that applying photon-addition non-Gaussian operation between entangled resource and the sender can further enhance both the secure key rate and the transmission distance, compared to apply photon subtraction operation between entangled resource and the receiver. In addition, the influence of seawater quality factors in underwater CVQKD system is also considered, so as to provide technical support for underwater safety communication.
ISODF-ENN:Imbalanced data mixed sampling method based on improved diffusion model and ENN

In the era of big data, the complexity of data is increasing. Problems such as data imbalance and class overlap pose challenges to traditional classifiers. Meanwhile, the importance of imbalanced data has become increasingly prominent, it is necessary to find appropriate methods to enhance classification performance of classifiers on such datasets. In response, this paper proposes a mixed sampling method (ISODF-ENN) based on iterative self-organizing (ISODATA) denoising diffusion algorithm and edited nearest neighbors (ENN) data cleaning algorithm. The algorithm first uses iterative self-organizing clustering algorithm to divide minority class into different sub-clusters, then it uses denoising diffusion algorithm to generate new minority class data for each sub-cluster, and finally it uses ENN algorithm to preprocess majority class data to remove the overlap with the minority class data. Each sub-cluster is oversampled according to sampling ratio, so that the oversampled minority class data also conforms to the distribution of original minority class data. Experimental results on keel datasets demonstrate that the proposed method outperforms other methods in terms of F-value and AUC, effectively addressing the issues of class imbalance and class overlap.
Reinforced Hybrid Graph Transformer for Medical Recommendations

An enormous amount of heterogeneous Textual Medical Knowledge (TMK), which is crucial to healthcare information systems, has been produced by the explosion of healthcare information. Existing efforts to incorporate and use textual medical knowledge primarily concentrate on setting up simple links and pay less attention to creating computers comprehend information accurately and rapidly. Self-diagnostic symptom checkers and clinical decision support systems have seen a significant rise in demand in recent years. Existing systems rely on knowledge bases that are either automatically generated using straightforward paired statistics or manually constructed through a time-consuming procedure. The study explored process to learn textual data, linking disease and symptoms from web-based documents. Medical concepts were scrapped and collected from different web-based sources. The research aims to generate a disease- symptom-diagnosis knowledge graph (DSDKG), with the help of web-based documents. Moreover, the knowledge graph is fed in to Graph neural network with Attention Mechanism (GAT) for learning the nodes and edges relationships. . Lastly Generative Pretrained Transformer 2 (GPT2) all enclosed in a Reinforced learning environment, is used on the trained model to generate text based recommendations.
EI-RNN-based text generation for the static and dynamic isolated sign language videos

SLR, which assists hearing-impaired people to communicate with other persons by sign language, is considered as a promising method. However, as the features of some of the static SL could be the same as the feature in a single frame of dynamic Isolated Sign Language (ISL), the generation of accurate text corresponding to the SL is necessary during the SLR. Therefore, Edge-directed Interpolation-based Recurrent Neural Network (EI-RNN)-centered text generation with varied features of the static and dynamic Isolated SL is proposed in this article. Primarily, ISL videos are converted to frames and pre-processed with key frame extraction and illumination control. After that, the foreground is separated with the Symmetric Normalised Laplacian-centered Otsu Thresholding (SLOT) technique for finding accurate key points in the human pose. The human pose’s key points are extracted with the Media Pipeline Holistic (MPH) pipeline approach and to improve the features of the face and hand sign, the resultant frame is fused with the depth image. After that, to differentiate the static and dynamic actions, the action change in the fused frames is determined with a correlation matrix. After that, to engender the output text for the respective SL, features are extracted individually as of the static and dynamic frames. It is obtained from the analysis that when analogized to the prevailing models, the proposed EI-RNN’s translation accuracy is elevated by 2.05% in INCLUDE 50 Indian SL based Dataset and Top 1 Accuracy 2.44% and Top 10 accuracy, 1.71% improved in WLASL 100 American SL.
Disambiguation of Visual Representations

Vision-language (VL) models usually use an object detector to extract the features for the visual representation. The feature extractors relying on object detectors tend to suffer from ambiguity in the extracted visual embeddings caused by an overlap of (over-sampled) image regions. This in turn causes semantic misalignment of the corresponding visual and language embeddings in VL tasks. In this paper, we propose a simple approach to disambiguate the visual embeddings through segmentation and alignment within a word embedding space. In our experiments, we assess the impact of instance segmentation to improve the visual representations. We visualize bounding-box and segmentation-based feature vectors and show that the segmentation features contribute towards image region feature disambiguation. We also investigate an alignment process based on projection of image region features to a word embedding space and training a projection network using the corresponding object category labels. For the object categories assessed it is found that projecting image region features onto the word embedding space significantly reduces the cosine similarity between each category and all the other categories. This shows that the projection onto the word embedding space is an effective approach in achieving an alignment between the visual and language embeddings, which in turn helps disambiguate the image region features for further downstream VL tasks.
A Multimodal Software Architecture for Serious Exergames and Its Use in Respiratory Rehabilitation

Serious Exergames (SEGs) have been little concerned with flexibility/equivalence, complementarity, and monitoring (functionalities of systems that deal with a wide variety of inputs). These functionalities are necessary for health SEGs due to the variety of treatments and measuring requirements. No known SEG architectures include these three functionalities altogether. In this paper, we present the 123-SGR software architecture for the creation of an SEG that is appropriate to the needs of professionals and patients in the area of rehabilitation. An existing SEG was adapted and therapy-related sensor devices (Pneumotachograph, Manovacuometer, Pressure Belt, and Oximeter) were built to help the patient interact with the SEG. The architecture allows the most varied input combinations, with and without fusion, and these combinations are possible for both conscious and unconscious signals. Health and Technology professionals have assessed the SEG and found that it had the functionalities of flexibility/equivalence, complementarity, and monitoring, and that these are really important and necessary functionalities. The 123-SGR architecture can be used as a blueprint for future SEG development.
Grounding End-to-End Pre-trained architectures for Semantic Role Labeling in multiple languages

Situated natural language interactions between humans and robots are strictly necessary for complex applications: communication here implies the reference to the environment shared between a user and the robot. This paper proposes a transformer-based architecture that supports the integration of spatial information (as logical representation) about a semantic map of the environment and the input utterances. The generated interpretation is a logical form of the command that makes references to the state of the world through a single end-to-end process, stimulated at each interaction by an explicit linguistic description of the environment. In this specific work, the end-to-end capability of the targeted transformer is studied in light of its multilingual applications where the robot can be queried in different natural languages. The obtained experimental results confirm the applicability of transformers to grounded human-robotic interaction, with benefits in terms of both portability of the approach across domains and effectiveness in terms of reachable accuracy. Moreover, language-specific processing chains are shown to be preferable to large-scale multilingual models for their better trade-off between accuracy and complexity. Overall, the proposed architecture outperforms previous approaches and paves the way for sustainable multilingual architectures.
KVSEV

AMD's Secure Encrypted Virtualization (SEV) is a hardware-based Trusted Execution Environment (TEE) designed to secure tenants' data on the cloud, even against insider threats. The latest version of SEV, SEV-Secure Nested Paging (SEV-SNP), offers protection against most well-known attacks such as cold boot and hypervisor-based attacks. However, it remains susceptible to a specific type of attack known as Active DRAM Corruption (ADC), where attackers manipulate memory content using specially crafted memory devices. The in-memory key-value store (KVS) on SEV is a prime target for ADC attacks due to its critical role in cloud infrastructure and the predictability of its data structures. To counter this threat, we propose KVSEV, an in-memory KVS resilient to ADC attacks. KVSEV leverages SNP's Virtual Machine Management (VMM) and attestation mechanism to protect the integrity of key-value pairs, thereby securing the KVS from ADC attacks. Our evaluation shows that KVSEV secures in-memory KVSs on SEV with a performance overhead comparable to other secure in-memory KVS solutions.
The Effects of Stopwords, Stemming, and Lemmatization on Pre-trained Language Models for Text Classification: A Technical Study

Pre-trained models such as BERT and other models, including their variants, have different approaches to the embedding process and the use of vocabulary files. Therefore, the use of preprocessing on the data will affect how the input vector is formed. The lack of studies studying the effect of preprocessing on pre-trained models in natural language processing tasks such as text classification is an opportunity for more research. This study aims to seize this opportunity by conducting experiments on fine-tuning eight different pre-trained models for text classification tasks to investigate the effect of preprocessing techniques on a wider variety of pre-trained models. The combination of these preprocessing techniques was applied to see their impact on classification performance. Each model was also studied for its performance in the defined configuration. As a result, the use of preprocessing techniques such as stopword removal, stemming, and lemmatization does not significantly improve the performance of the classification model. The results also indicate that the use of stemming tends to decrease performance. By obtaining this information, it becomes clearer whether the use of such preprocessing techniques is still required when developing text classification applications in the world of pre-trained models.
A novel geometrically invariant quantum watermarking scheme utilizing Quantum Error Correction

This paper proposes a novel geometrically invariant quantum watermarking scheme using Quantum Error Correction (QEC) and Novel Enhanced Quantum Image Representation (NEQR). The Geometric Transformation-based Image Assembling (GTA) mechanism is proposed to reconstruct the quantum watermark image utilizing QEC principle. The proposed GTA mechanism involves Quantum Image Rotation (QIR) and Quantum Block Rotation (QBR), thus two corresponding quantum watermarking methods, GTA_QIR and GTA_QBR, are proposed and detailed quantum circuits with complexity O(n) are designed. Then the Quantum Majority Finder (QMF) is proposed for embedding the watermark uniformly. In the experiments, we employ dataset USC-SIPI to evaluate the visual quality and robustness of the proposed scheme. Comparisons with the recent quantum watermarking methods is conducted and the results show that our methods have a superior performance. When under attack of 'Salt and Pepper' noise addition, the proposed GTA_QIR and GTA_QBR improve the average Peak Signal-to-Noise Ratio (PSNR) by 22.57% and 23.49% at a density of 0.05, while at a density of 0.1, the average improvement is 11.13% and 12.05% respectively. Moreover, to the best of our knowledge, this is one of the first quantum watermarking scheme which demonstrates resistance to geometric attacks.