Reviewerly/exHarmony huggingface dataset:
Effective Management of IoT Devices that can Withstand Attacks on Cloud Data

First, with regards to attribute-based encryption (ABE), it is an approach to access control that allows data to be encrypted and decrypted based on certain attributes, such as a user's role, location, or other characteristics. This approach provides granular control over who can access specific data, which is particularly useful for IoT applications where sensitive data is being generated by many devices. However, as I mentioned earlier, ABE can be computationally intensive, which may not be suitable for low-power IoT devices. One possible solution to this challenge is to use edge computing, where some of the computing tasks are performed at the edge of the network, closer to the devices generating the data. This can help reduce the amount of data that needs to be sent to the cloud and can improve overall system performance. Another challenge with ABE is that it does not provide protection against key sharing. If a user shares their decryption key with an unauthorized party, that party could potentially gain access to sensitive data. To address this challenge, it's important to have strong access controls in place to prevent unauthorized sharing of keys. In terms of data storage security, while outsourcing to cloud servers can certainly help with complex computing tasks, it's still important to implement sophisticated security measures. This might include encrypting the data at rest and in transit, implementing access controls, and monitoring the system for potential security breaches. Finally, it's important to follow regulations and best practices for key sharing to prevent unauthorized access to sensitive data. This might include policies around key management, user authentication, and data governance.
Susceptibility to instant messaging phishing attacks: does systematic information processing differ between genders?

The purpose of this research is to fill gaps in the literature by focusing on gender differences in perceptions of phishing susceptibility among Malaysian university students. Based on the Heuristic-Systematic Model of information processing, a theoretical model of victimization by instant message phishing was developed. Smart PLS-SEM was used to analyse 386 valid responses from an anonymous online survey. The findings reveal significant gender differences in systematic phishing processing, with male students emphasizing argument quality and female students emphasizing message involvement. This study discovered that persuasive phishing messages increase the likelihood of instant messaging phishing victimization in university students. When a phishing message has a high level of message involvement, Internet users have intents (a favourable attitude) to share personal information online. The findings provide insights that should be communicated to students early on to help them scrutinize message authenticity and fine-tune their security knowledge.
Approximating Max Function in Fully Homomorphic Encryption

This study focuses on efficiently finding the location of the maximum value for large-scale values encrypted by the CKKS (Cheon—Kim—Kim–Song) method. To find the maximum value, logM+1 comparison operations and logM rotation operations, and 2logM+3 additions and 2logM+1 multiplications are required. However, there is no known way to find a k-approximate maximum value, i.e., a value with the same most significant k-bits as the maximum value. In this study, when the value range of all data in each slot in the ciphertext is [0, 1], we propose a method for finding all slot positions of values whose most significant k-bits match the maximum value. The proposed method can find all slots from the input ciphertexts where their values have the same most significant k-bits as the maximum value by performing 2k comparison operations, (4k+2) multiplications, (6k+2klogM+3) additions, and 2klogM rotation operations. Through experiments and complexity analysis, we show that the proposed method is more efficient than the existing method of finding all locations where the k MSB is equal to the maximum value. The result of this can be applied to various privacy-preserving applications in various environments, such as IoT devices.
Automatic detection of semantic primitives using optimization based on genetic algorithm

In this article, we propose a method for the automatic retrieval of a set of semantic primitive words from an explanatory dictionary and a novel evaluation procedure for the obtained set of primitives. The approach is based on the representation of the dictionary as a directed graph with a single-objective constrained optimization problem via a genetic algorithm with the PageRank scoring model. The problem is defined as a subset selection. The algorithm is fit to search for the sets of words that should fulfil several requirements: the cardinality of the set should not exceed empirically selected limits and the PageRank word importance score is minimized with cycle prevention thresholding. In the experiments, we used the WordNet dictionary for English. The proposed method is an improvement over the previous state-of-the-art solutions.
An Infrastructure Cost and Benefits Evaluation Framework for Blockchain-Based Applications

Blockchain is currently a core technology for developing new types of decentralized applications. With the unique properties of blockchain, unique challenges and characteristics are introduced to the system. Among these characteristics, the infrastructure costs and benefits of the system are critical to evaluate the feasibility of any system and have yet to be addressed in the current literature. This work presents a framework for evaluating blockchain applications’ infrastructure costs and benefits. The framework includes a taxonomy to classify the related transactions, a model to evaluate the infrastructure costs and benefits in applications using public or private blockchains, and a methodology to guide the use of the model. The model is based on simple parameters that describe the systems, and the methodology helps to identify and estimate these parameters at any stage of the application life cycle. We quantitatively analyze three real use cases to demonstrate the framework’s merit. The analyses highlight the model’s accuracy by achieving the same results presented in the use cases. Furthermore, the use-case analyses emphasize the framework’s potential to evaluate different scenarios across the entire life cycle of blockchain-based applications.
Machine Translation Systems Based on Classical-Statistical-Deep-Learning Approaches

Over recent years, machine translation has achieved astounding accomplishments. Machine translation has become more evident with the need to understand the information available on the internet in different languages and due to the up-scaled exchange in international trade. The enhanced computing speed due to advancements in the hardware components and easy accessibility of the monolingual and bilingual data are the significant factors that have added up to boost the success of machine translation. This paper investigates the machine translation models developed so far to the current state-of-the-art providing a solid understanding of different architectures with the comparative evaluation and future directions for the translation task. Because hybrid models, neural machine translation, and statistical machine translation are the types of machine translation that are utilized the most frequently, it is essential to have an understanding of how each one functions. A comprehensive comprehension of the several approaches to machine translation would be made possible as a result of this. In order to understand the advantages and disadvantages of the various approaches, it is necessary to conduct an in-depth comparison of several models on a variety of benchmark datasets. The accuracy of translations from multiple models is compared using metrics such as the BLEU score, TER score, and METEOR score.
Identifying opinion leaders and narrative agency in the rejection omnibus law discourse

As a social media platform, Twitter can become an arena for civil citizens to contest the state discourse. The rejection of the Omnibus Law Bill proves the legitimacy of the massive resistance and mass mobilisation to reject policy products that contradict the public interest. This research was meant to provide a social media analysis of the issues of the plan of legitimation of the Omnibus Law Bill by employing two analysis methods: social network analysis and descriptive qualitative method. Twitter interactions during the Omnibus Law Bill rejection era were dominated by civil actors. The primary activity on Twitter was not only civil movements but also confronting individuals who could help to form public ideas. Within the agency’s narrative framework, Twitter interactions related to Omnibus Law Bill are communal and social endeavours as part of participation in digital activism. Other dictions were also visible within various hashtags constructed to assert the agent’s political position. Various content and digital media platform were produced as the media for transferring information and knowledge. They were put for the best use in a beautiful package. By opinion leaders’ agency, the narrative built by the State can be contested to influence others.
An Optimization-Based High-Precision Flexible Online Trajectory Planner for Forklifts

There are numerous prospects for automated unmanned forklifts in the fields of intelligent logistics and intelligent factories. However, existing unmanned forklifts often operate according to offline path planning first followed by path tracking to move materials. This process does not meet the needs of flexible production in intelligent logistics. To solve this problem, we proposed an optimized online motion planner based on the output of the state grid as the original path. Constraints such as vehicle kinematics; dynamics; turning restriction at the end of the path; spatial safety envelope; and the position and orientation at the starting point and the ending point were considered during path optimization, generating a precise and smooth trajectory for industrial forklifts that satisfied non-holonomic vehicle constraints. In addition, a new rapid algorithm for calculating the spatial safety envelope was proposed in this article, which can be used for collision avoidance and as a turning-angle constraint term for path smoothing. Finally, a simulation experiment and real-world tray-insertion task experiment were carried out. The experiments showed that the proposal was effective and accurate via online motion planning and the tracking of automated unmanned forklifts in a complicated environment and that the proposal fully satisfied the needs of industrial navigation accuracy.
HOW NEW FINANCIAL TECHNOLOGIES RESOLVE ECONOMIC STAGNATION: THE FINANCIAL PERSPECTIVE

This research explores how new financial technologies are able to resolve economic stagnation by considering two dimensions of financial technology’s spread: the spread to poor people and that to rural area people. Based on a sample of 109 countries, our findings indicate that the spread of new financial technologies to poor people not only benefits economic growth directly, but also generates an indirect positive influence on economic growth via the channel of financial institutions’ development. By comparison, the effect of spreading financial technologies to rural area people on stimulating economic growth is insignificant. Lastly, we offer some policy implications associated with the results.
Integrating Symbol Similarities with Knowledge Graph Embedding for Entity Alignment: An Unsupervised Framework

Entity alignment refers to discovering identical entity pairs in 2 knowledge graphs, which is a significant task in knowledge fusion. Early automated entity alignment techniques are based mainly on similarity calculation and comparing symbolic features, i.e., entity names, between entities. Nevertheless, such methods’ performance would reduce significantly when the difference between knowledge graphs is enormous because of relying on predefined comparison rules. Recently, embedding-based methods calculate the similarity between entity pairs through vector embeddings and thus can deal with different knowledge graphs. However, embedding-based methods mostly require humans to annotate data, which is laborious. Therefore, we learn from each other to propose an unsupervised entity alignment framework in this work, which can generate initial alignment seeds automatically by considering symbolic similarities. It can effectively avoid the waste of human resources and is suitable for handling multiple types of knowledge graphs. In addition, we investigate improving the quality and quantity of initial alignment by integrating multiple symbolic similarity features of entities and dealing with the situation of entity information missing better. Experimental results on 3 real datasets demonstrate its state-of-the-art performance.
Grand Challenges in environmental geochemistry

SPECIALTY GRAND CHALLENGE article Front. Geochem., 05 April 2023Sec. Environmental Geochemistry Volume 1 - 2023 | https://doi.org/10.3389/fgeoc.2023.1154473
Vision-based automatic order check method for online medicine dispensing cabinet under incomplete data

In recent years, computer vision technology has been increasingly used in the retail industry, especially in unmanned retail. In the online medicine dispensing cabinet where the order images are captured along with the customer’s online order information, an automatic order check method is needed to verify whether the picked items match the order information and avoid the mis-dispensing of medicines. Well-built images dataset and annotations are needed for the task. However, it is difficult to construct datasets with fine annotations and keep the images up-to-date in the application scenario. Automatic order check is facing the problems of small samples, diverse and dynamic categories, weak supervision, and domain gaps. To deal with the order check task under incomplete data in real scenarios, we carried out the following works: (1) We proposed a framework for automatic order check (AOC), and collected an incomplete dataset in natural scenes. (2) We proposed a foreground detection method based on conventional image processing. Image matching methods based on local descriptors and text recognition were designed for classification. (3) We proposed a data simulation pipeline for rotated object detection model training with weak supervision. Our methods showed promising results on our challenging dataset.
The Intellectual Property Law System and Data

Generally, the political consensus at the beginning of the internet era was that platforms should have only limited liability under intellectual property law for content that users uploaded on these platforms.1 Now, however, the platforms are the center of gravity for the Internet, drawing in (for technical reasons and owing to network effects) all data streams in the respective ecosystems, for the benefit of the system leader controlling the platform. Furthermore, the contracts that system leaders conclude with business users, and which control their business relationship, not only normally grant the exclusive right to data generated on the platform to the platform provider but also generally neutralize any intellectual property rights held by the business user. Indeed, the system leaders are regulators of their respective ecosystems and use their system of contracts to control the ecosystems and exclude the use and importance of intellectual property rights. The platform or cloud provider contractually secures the right from the platform or cloud user, not only to store the data but also to analyze it and make use of it for the provider’s own benefit, and for the benefit of others in the ecosystem, on all connected markets. The platform providers thus become the masters of their respective data ecosystems; they do indeed hoard the data and generally do not trade or share the data.
Exploring the use of emoji in museum social network sites

This paper aims to explore how emoji make meaning in interaction with other semiotic resources in museum social media posts. In so doing, it examines to what extent emoji are changing the way museums communicate with their audiences. The analysis is based on a corpus of museum social posts and grounded in Systemic Functional Linguistics. Findings show that emoji make meaning not only in interaction with language, working as a paralinguistic resource, but also in interaction with visual content. The analysis highlights the presence of image-driven emoji, which create a convergence of meaning in association with details of the post image, limiting or excluding the textual component of the caption from the intermodal semiosis. Image-driven emoji often lead to playful effects and can be exploited by museum communicators for engagement purposes, to create quizzes and games. The study therefore suggests that emoji can contribute to add an informal component to museum communication, according to the 'post-museum' style, which is characterised by a more direct interactional attitude towards the audience.
Information and communication space and pedagogical ideologies of the beginning of the 21st century: the Ukrainian aspect

Connections and relationships of pedagogical theories and pedagogical ideologies emerging on their basis are considered in the direction of modern philosophical approaches. Pedagogical ideology as an ethnocultural and cultural-historical phenomenon The modern information and communication space is a powerful mechanism of impact on the formation of public opinion and pedagogical consciousness, as well as a source and repository of pedagogical ideas, and therefore, pedagogical ideologues. Pedagogical ideologies, which make up the pedagogical content of the information and communication space of the beginning of the 21st century, form a polyphonic, polyparadigmatic, personally oriented discourse aimed at "overcoming the power" of the idealized political discourse.Is considered by many philosophers of education as a significant component of scientific and pedagogical thinking. Almost every culture is mythologemic, as it generates sociocultural matrices of various myths, in particular social and pedagogical ones, which affect the value-meaning aspects of pedagogically directed actions, the nature and content of the pedagogical process, mental processes of its participants. At the current stage of the development of education and pedagogical thought, the concept of "mythologeme" becomes close to "ideologeme", which strengthens socio-political influences on the course and development of scientific and pedagogical thought.At the current stage of development, the history of pedagogy includes many samples of the usage of the information and communication space, presented at different periods of times by various means of mass information, as a powerful mechanism of political and ideological influence on the development of the theory and practice of education. Thus, the problem of objective evaluation of the intellectual and spiritual heritage of world pedagogy, implementation of a modern reading of this valuable experience freed from ideological distortions arises.The relevance of the research is strengthened by the necessity to create a picture of the historical-pedagogical process exempted from ideological layers, which includes all the diversity of ideas of past pedagogical ideologies. Emancipated from political contexts, historical knowledge in the field of education determines the further development of pedagogical thought and becomes a scientific, philosophical and moral imperative for the formation of a modern pedagogical ideology.
Segmentation of cervical nuclei using convolutional neural network for conventional cytology

Although implementing the Pap smear has drastically reduced the mortality rates from cervical cancer, false positives and negatives are related to the quality of the analysis and the cytopathologist experience. An alternative is the insertion of digital cytology in the quality monitoring to assist the screening. However, conventional cytology is still a major challenge, as it presents a lot of cellular overlap and several epithelial structures that make it difficult to implement computational methodologies. This article compares the performance of U-net and SegNet neural networks for nuclei segmentation in cervical images. Experiments were performed with different activation functions, batch sizes, and datasets, ISBI (synthetic images from liquid cytology) and CRIC Cervix-Seg (conventional cytology real images). The models achieved a Dice coefficient of 0.9783 for ISBI2015 and 0.9429 for CRIC Cervix-Seg. These results suggest a methodology capable of segmenting real images of cervical nuclei with quality, even in situations of overlap and artefacts, advancing efforts towards the automation of tasks as part of the cytopathological analysis in the laboratory work routine.
PMA-Net: A parallelly mixed attention network for person re-identification

The objective of person re-identification (Re-ID) is to match unmistakable people across different settings and camera views. Although the use of convolutional neural networks (CNN) has been effective, they lose much significant data and ignore the correlation between the overall and partial features brought about by downsampling operators (e.g., pooling). This compromises the retrieval performance based on the Euclidean distance. We propose a parallelly mixed attention network (PMA-Net) to demonstrate the complementarity of spatial and channel information for Re-ID. First, we integrate self-attention with depthwise convolution in a parallel design, extracting different dimensions of information. Second, we propose two interactive branches to entwine spatial and channel information while extracting features. Then, we add a new additional embedding to incorporate non-visual inputs and reduce the feature bias toward camera variances. Finally, we perform additional maximizing the minimal pairwise angles (MMA) regularization on the features of PMA, which successfully encourages the angular diversity of feature vectors. We confirm the effectiveness of PMA-Net through an extensive set of ablation studies. The results of three benchmarks show the superiority of our model over current mAP/top1 accuracies of 91.3%/96.1% on Market-1501, 83.3%/91.7% on DukeMTMC-ReID, and 66.2%/85.1% on MSMT17.
Dual-branch deep image prior for image denoising

In this work, we propose a two-stage denoising approach, which includes generation and fusion stages. Specifically, in the generation stage, we first split the expanding path of the UNet backbone of the standard DIP (deep image prior) network into two branches, converting it into a Y-shaped network (YNet). Then we adopt the initial denoised images obtained with DAGL (dynamic attentive graph learning) and Restormer methods together with the given noisy image as the target images. Finally, we utilize the standard DIP on-line training routine to generate two complementary basic images, whose image quality is quite improved, with the help of a novel automatic iteration termination mechanism. In the fusion stage, we first split the contracting path of the standard UNet network into two branches for receiving the two basic images generated in the previous stage, and obtain a fused image as the final denoised image in a fully unsupervised manner. Extensive experimental results confirm that our method has a significant improvement over the standard DIP or other unsupervised methods, and outperforms recently proposed supervised denoising models. The noticeable performance improvement is attributed to the proposed hybrid strategy, i.e., we first adopt the supervised denoising methods to process the common content of images substantially, then utilize the unsupervised method to fine-tune the specific details. In other words, we take full advantage of the high performance of the supervised methods and the flexibility of the unsupervised methods.