Reviewerly/exHarmony huggingface dataset:
Revolutionizing Business Decision-Making: An Analysis of AI-Based Services

Artificial intelligence (AI) is increasingly being used to make important decisions due to the exponential growth of data in both society and business. However, current literature does not adequately address how to categorize and organize AI-based services according to decision-making patterns. To fill this gap, we present and assess seven styles for AI-based decision-making processes, focusing on the company context and building on the concept of justification in knowledge. Using correlations, we analyze real-world examples of businesses providing AI-based services. Our research provides a foundation for future studies and aids experts in developing AI-based services, while also contributing to situation theory and descriptive understanding of AI. Customers' reliance on AI-based services for personal and important decisions makes our research particularly relevant.
Marginals Matrix Under a Generalized Mallows Model Based on the Power Divergence

In rank data modeling, the classical distance-based approach has been extended by many authors in order to capture certain ranking features. Here, we study the effect on the Marginals matrix by generalizing the Mallows model via the Cressie-Read power divergence. Marginals matrices, induced by different ranking models, are extensively compared and their symmetric property over the class of Hoeffding distances is rigorously proved. Furthermore, the presented results are illustrated on a real data example for several special distances and models.
Current and Historical Publication Trends of State Library Association Journals and Newsletters

Librarians have reflected on their literature since state library association journals and newsletters began publication. This trend peaked in the 70s and 80s, and there was a virtual gap in the literature in the following decades. It is crucial to reassess publication trends due to recent changes in the library profession, such as declining membership and ease of publishing online. Print publication trends in state library association journals and newsletters will be examined using OCLC MARC records retrieved in Connexion and compiled in an Excel spreadsheet and graph. In addition, electronic publishing trends will be analyzed by assessing state association websites for the presence of a journal or newsletter, its format, and whether it is open source. Results from the study show that there were changing trends in the frequency of print publishing over the years, and most state associations presently publish an online journal in open-access PDF format. However, OCLC users should update OCLC records to reflect accurate publication dates and make separate records for an electronic format to depict publication trends accurately. State associations should also create a retention plan for electronically published material to avoid losing a historical archive.
A Novel Post-quantum Piekert’s Reconciliation-Based Forward Secure Authentication Key Agreement for Mobile Devices

Lattice-based cryptography plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. However, it is not easy to design quantum resistant password-based protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a novel post-quantum key exchange based on a variant of lattice assumption, the ring learning errors. This protocol uses better unbiased Piekert’s reconciliation with respect to even q, but reconciliation in Ding’s protocol is biased. This protocol ensures both authentication and key agreement. This protocol needs just two messages in exchange for authentication and key agreement. This protocol ensures both security against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications.
A Decade Long View of Internet Traffic Composition in Japan

The Internet traffic composition has changed considerably over the last decade and continues to evolve as a consequence of several factors. In this paper, we showcase this change taking a holistic view derived from the deployment of new and emerging protocols (QUIC, encrypted DNS), shifting user behavior towards a (more) secure Web, natural incidents (Covid-19 pandemic), and effects of peering with large content delivery hyper-giants (Google) that tend to have both short- and long-lasting impact on Internet traffic. Knowledge of such changing trends is essential for better management of networks and services. To this end, we analyze >6.6 TiB of data collected at a large backbone link in Japan (MAWI dataset) and investigate traffic composition across several months and years. We observe that IPv6 traffic in 2019 represents a volume comparable to that of the total traffic observed in 2007 and is (now) increasingly used to carry Web traffic. Over IPv4, we observe significant growth in encrypted Web traffic with HTTPS-to-HTTP ratio evolving to 2-to-1 in 2019 compared to less than 2% HTTPS share in 2007. Meanwhile, we witness (for the first time) an alteration of the traffic composition on the educational network as a result of the Covid-19 pandemic. We observe a vanishing weekday-weekend pattern and a shift towards increased usage of OpenVPN and rsync traffic due to increased remote work. Finally, we also study the impact of Google as a peering entity in the routing ecosystem and observe a significantly increased traffic share of QUIC over both address families, and overall a larger HTTP-to-HTTPS ratio in terms of traffic volume.
Multiobjective evolutionary search of the latent space of Generative Adversarial Networks for human face generation

This article presents an explicit multiobjective evolutionary approach for synthetic human face image generation, exploring the latent space of generative adversarial networks. The approach considers the similarity to a target image and the race attribute. The evolutionary search explores the real-coded latent space of Style-GAN3 and applies DeepFace for similarity and race evaluation. Realistic images are generated, properly exploring the search space and the Pareto front of the problem. The generated images pose a challenge to the automatic detection system in DeepFace. Results are applicable to enhance the security of face recognition systems.
Semantic-Edge Interactive Network for Salient Object Detection in Optical Remote Sensing Images

Despite salient object detection in natural images has made remarkable progress, it is still an emerging and challenging problem to detect salient objects from optical remote sensing images [remote sensing image salient object detection (RSI-SOD)]. To improve RSI-SOD based on fully convolutional networks (FCNs), attention and edge awareness have been used separately to aid integration and refinement of multilevel features for effective decoding. Although they have been shown to semantically enhance salient features and reduce fuzzy boundaries, the correlation between the semantic-enhanced salient features and edge features is rarely explored, which has inspired the development of a new model to enable close interaction between semantic and edges for fully activating the advantages of attention and edge awareness, and led to the semantic-edge interactive network (SEINet) presented in this article. The proposed model consists of two interacting decoding branches based on the U-shaped network to achieve salient object detection (SOD) and salient edge detection (SED), and the multiscale attention interaction (MAI) module is proposed to provide edge-enhanced semantic for SOD and semantic-enhanced edge for SED interactively between the two branches. Moreover, to alleviate the problem of semantic dilution, the semantic-guided fusion (SF) module is proposed and deployed at the end of the SOD branch. From the extensive quantitative and qualitative comparison of the proposed model against the FCN-based models with and without incorporation of attention and edge awareness, the proposed model obtains the most stable scores at different thresholds of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F$</tex-math></inline-formula> -measure curve and outperforms 18 state-of-the-art methods.
Colossal Encipher Isolation Conserve Classifier

This program has numerous applications, one of which is directly related to agricultural and produce areas. Since no participation is required during computation, the main limits are large cypher text growth and latency. We develop MAS- Encryption, a novel cryptographic tool that allows secure computing on the multiply-add structure while still supporting real-value input, to get around these limitations. By connections between the Rise, yields, and farming-related empowerment, this application will give farmers some suggestions on how to increase agricultural cultivation yields. For the needs of the farmer, it will also provide more details on yields and fertilizer. In our program, MAS encryption is used to protect user information from prying eyes and the administrator, client information will not be displayed to the user. Our tests' results demonstrate that the MASE technique we've suggested and MASE-based classifiers are efficient in achieving the maximum achievable trade-off between computational performance and communication interactions.
Mining Multi-View Clustering Space With Interpretable Space Search Constraint

Multi-view clustering can cluster signal samples from multiple views into groups. Currently, multi-view clustering fuses the information of different views into a low-dimensional space for clustering. However, the direct reduction of high-dimensional information to a very low-dimensional space leads to the loss of a lot of sample semantic information, while a higher dimension after dimensionality reduction may blur the clustering structure of samples. To tackle these problems, we propose a novel framework called Mining Multi-view Clustering Space with Interpretable Space Search Constraint to explore the clustering structure while preserving the low-dimensional space semantic information. Our method maps samples from the raw space to a low-dimensional space separated into consensus and private features. This allows us to explore the interpretability of samples in the low-dimensional space to achieve representative representations. After assembling these representations, guided fusion is carried out and a search constraint is imposed to achieve a more reasonable clustering structure. Finally, by dynamically screening positive and negative samples, the clustering performance of the clustering space is maximized by contrastive learning. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.
Cryptocurrency Trading Using Machine Learning

Education on cryptocurrency is essential for individuals to make informed decisions regarding foreign investment in digital assets. In recent years there is an exponential increase in the price of cryptocurrency due to its easy trading especially in developing countries so the trend of financial institutions buying cryptocurrency into their portfolios has grown in the past decade which results in economic growth. The first completely digital assets that asset managers have included are cryptocurrencies. Traders have a unique opportunity to forecast price swings due to social media’s impact on cryptocurrency prices. Trading using Al and Machine Learning has drawn more attention in recent years. One could investigate the above hypothesis to determine if it is feasible to capitalize on the Bitcoin market’s inefficiency for the purpose of generating unusually high profits. The advanced machine-learning techniques enable straightforward trading strategies to exceed conventional benchmarks. The findings demonstrate how basic computational processes might assist predict the near-term development of the bitcoin market. Further, there are prediction and comparison prices using SVM and Random Forest algorithms on the basics of efficiency while changing the number of days.
Privacy-Preserving Data Sharing Scheme for E-Health Systems

With the constant threat of cyber attacks and their associated risks, it is more important than ever to have a secure system for uploading, viewing, and sharing patient health data. Hospitals, doctors, nurses, and technicians all have a moral and legal obligation to protect the confidentiality of the patients trusted with their care. For this obligation to be met, there must be a method for both patients and doctors to conduct secure searches over encrypted health records. The scheme proposed in this paper allows doctors and patients to securely search over multiple encrypted files while maintaining the confidentiality and privacy of the patient’s health data. Our proposed scheme utilizes a lightweight modified k-Nearest Neighbor (kNN) algorithm that relies on simple computations rather than complex data transformations, which affords our scheme the functionality of searching over encrypted data while maintaining a low computational overhead. Our performance analysis shows that our scheme is lightweight regarding computation and communication overheads. Additionally, our security and privacy analysis further indicates that our proposed scheme efficiently achieves our security and privacy design goals.
APLIKASI PENYEDIA JASA LAUNDRY ONLINE BERBASIS WEB MENGGUNAKAN PHP DAN MYSQL

As we know that technology is currently growing rapidly in almost all parts of the world, all circles almost use it. The use of internet is very essential in everyday life as people cannot escape from it. The internet is funtioned today by some people to provide information as a communication tool. Beside that, people also utilize the internet as a media for online laundry service providers. This website has been successfully created to make it easier for consumers who have little time because they are busy with work in terms of washing consumer clothes. This paper will discuss the creation of the website application, with application design using dreamweaver and photoshop as the interface engine and PHP and MySql as the backend database.
A sizeable empirical investigation of bug attributes in Blockchain structures

Blockchain systems' credibility is severely damaged by flaws. A thorough understanding of blockchain vulnerabilities is crucial for developing effective methods for preventing, detecting, and remediating them. In this study, we employ statistical methods to compare the bug populations of eight popular public blockchain projects. The first step is a human review of 500 bug reports to see what sorts of problems have been reported. Second, we utilize card sorting to divide the reported problems into seven distinct categories that can all be found in blockchain-based software. Here, we take a look at the many causes of errors in software development and programming. As a last step, we look at how different problems have different average timeframes to fix. The findings show that (1) conceptual defects are the most common type of real-time issue, (2) bug frequency distributions follow consistent patterns across applications and languages, (3) fixing security vulnerabilities takes the longest median time, and (37.33%) fixing performance bugs takes longer than a year.
Association Rule Extraction and Correlation Analysis Framework for Dominant Feature Selection in Thyroid Disease Data

Thyroid related illnesses are one of the most common among disorders of the endocrinal glands. This paper is an attempt at extracting the principal features of thyroid disease dataset. These relevant features trained on a suitable machine learning model can successfully diagnose thyroid diseases in a machine learning pipeline. In this pa-per, we propose a framework of iterative Association Rule Mining and Correlation Analysis, which integrates associa-tion rule mining algorithm and subsequent correlation-based analysis of the result set to extract dominant features in the dataset. Apriori and FP Growth algorithms, the two most popular association rule mining algorithms, were used to extract the major attributes which causes thyroid related sicknesses. The association rule mining is used in dimen-sionality reduction of the dataset. We have used a thyroidi-tis dataset for this study, which is first preprocessed to pre-pare it for association rule mining. Preprocessing is a signif-icant step in our method. Next step is the splitting of the dataset into n data subsets. The Apriori and FP growth algorithms are applied on these multiple data subsets to create frequent k-itemsets. The most relevant of the associa-tion rules are selected by a majority voting from the result set generated after the application of association rule mining. The result set, comprising of the features which are selected as the principal attributes are again filtered using correlation analysis. The results show that gender factor is the most dominant of all features. It shows a predominance in association of women with Thyroid diseases than men. In the proposed method, the dataset undergoes a two-step process of refinement and consequently, association rules generated gives valuable insights into the thyroid related disease’s causative factors. The experimental results show that the performance of the proposed framework helps in principal feature selection, and thus can be used in dimen-sionality reduction of a dataset in the pre-processing step of classification and prediction of thyroid diseases. This framework can be used in the pipeline of thyroid disease prediction systems.
A single point of failure? AI and network detection and response

Artificial intelligence is creeping into everything, including network detection and response (NDR). But is faith in the new technology blinding us to some serious deficiencies and possible vulnerabilities?
Analysis of hybrid combination of NLP and and AI for an any kind of applications

The relationship of AI and the mental process is a problem that has plagued science for a very long time. Communication, formerly an inherent and unique ability of the mind, is today expressed in an infinite variety of ways that surpass verbal speech. There are programs that can participate in a conversation that is somewhat compassionate in addition to understanding what is intended by what is spoken. They operate in a very orderly way, which may be explained by using language ideas. Luc Serrated knives' Fluid Fabrication Linguistics is the primary idea that underlies this topic. It is concentrated on the procedures of parser and segmenting any speech, which are essential to AI's comprehension and creation of communication. This theory has provided crucial insights into differences in the conceptions of communication in the artificial and natural worlds, in as well as the five primary features of tongues (morpheme, grammatical, semantics, syntax, and operational). Even if they have certain characteristics, the writer's conclusion will focus on the differences among two nearby capacities. The purpose of this study is to outline potential problems that might arise from the use of intelligent speech patterns while also illuminating the workings of AI speech recognition interpreters employing consistently apply ideas.
Assessment of a mixed reality smart home controller: HoloHome pilot study on healthy adults

Abstract This work presents HoloHome, a Mixed Reality application that aims to provide a new means of interaction to control the Smart Home components while providing continuous support for the inhabitants, including older adults and people with limited mobility and cognitive skills. HoloHome integrates several technological paradigms such as Mixed Reality, Internet of Things, and Ambient Assisted Living to maximize the tailored domestic comfort for the Smart Home’s inhabitants while promoting their comfort, safety, and independence in performing daily activities. It is designed and implemented on Microsoft HoloLens, ensuring the connection of the Mixed Reality environment with the distributed network of domestic sensors through the WiFi-enabled microcontrollers to present an innovative method of interaction with the Smart Home and IoT network. Additionally, HoloHome provides unique services and assistive technologies mainly designed for older adults and people with short-term memory deficits to provide continuous assistance, support, and instruction for household activities. This paper conducts the assessment of HoloHome, where the proposed system's usability, cybersickness, and effectiveness are investigated. The usability of HoloHome is validated on a sample of healthy adults to highlight desirable modifications before further assessments of older adults and people with disabilities. This pilot study demonstrated a good level of perceived usability (71.5%) among healthy adults, leading toward the best possible adjustments prior to proposing the system to the more fragile users.
An Efficient QAOA via a Polynomial QPU-Needless Approach

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem's expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentially-increasing memory usage when dealing with large problems requiring more qubits. These limitations make today's QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some k-bounded pseudo-Boolean problems, optimizing the exact problem's expectation value can be done in polynomial time using a classical computer.