Reviewerly/exHarmony huggingface dataset:
Cyber Borderlines: Exploring the Interplay Between E-Commerce and International Trade Law

This review delves into the intricate interplay between e-commerce and international trade law, shedding light on the dynamic challenges and opportunities presented by the digital age. As e-commerce transcends traditional geographical boundaries, it reshapes global trade dynamics, necessitating a reevaluation of existing regulatory frameworks. The paper navigates through the complexities of jurisdiction, data privacy, intellectual property rights, and trade facilitation in the digital era. It critically examines the role of e-commerce platforms and explores the potential for harmonizing international trade rules. By exploring these dimensions, this review contributes to the understanding of how e-commerce intersects with international trade law and the implications for shaping the future of global commerce.
Linear Combination of Hamiltonian Simulation for Nonunitary Dynamics with Optimal State Preparation Cost

We propose a simple method for simulating a general class of nonunitary dynamics as a linear combination of Hamiltonian simulation (LCHS) problems. LCHS does not rely on converting the problem into a dilated linear system problem or on the spectral mapping theorem. The latter is the mathematical foundation of many quantum algorithms for solving a wide variety of tasks involving nonunitary processes, such as the quantum singular value transformation. The LCHS method can achieve optimal cost in terms of state preparation. We also demonstrate an application for open quantum dynamics simulation using the complex absorbing potential method with near-optimal dependence on all parameters.
Light in quantum computing and simulation: perspective

A summary is given of recent progress in photonic quantum simulation and computation. Non-error-corrected machines performing specialised tasks have already demonstrated a quantum advantage over the best algorithms running on conventional computers, and practical applications for such machines are being explored. Meanwhile, designs for error-corrected fault-tolerant quantum computers based on light are reducing the performance requirements for individual components and systems, although the engineering challenges are severe. Light also plays a central role in other platforms for quantum computing and simulation, from control of individual atomic qubits to remote entanglement of separate processing nodes, along with an important role in communications and other long-distance networks.
Hate Speech and Offensive Language Detection Using an Emotion-Aware Shared Encoder

The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusive content.Therefore, automatically detecting this content is essential for banning inappropriate information, and reducing toxicity and violence on social media platforms.The existing works on hate speech and offensive language detection produce promising results based on pre-trained transformer models, however, they considered only the analysis of abusive content features generated through annotated datasets.This paper addresses a multi-task joint learning approach which combines external emotional features extracted from another corpora in dealing with the imbalanced and scarcity of labeled datasets.Our analysis are using two well-known Transformer-based models, BERT and mBERT, where the later is used to address abusive content detection in multi-lingual scenarios.Our model jointly learns abusive content detection with emotional features by sharing representations through transformers' shared encoder.This approach increases data efficiency, reduce overfitting via shared representations, and ensure fast learning by leveraging auxiliary information.Our findings demonstrate that emotional knowledge helps to more reliably identify hate speech and offensive language across datasets.Our hate speech detection Multi-task model exhibited 3% performance improvement over baseline models, but the performance of multi-task models were not significant for offensive language detection task.More interestingly, in both tasks, multi-task models exhibits less false positive errors compared to single task scenario.
FSNet: Frequency Domain Guided Superpixel Segmentation Network for Complex Scenes

Existing superpixel segmentation algorithms mainly focus on natural image with high-quality, while neglecting the inevitable environment constraint in complex scenes. In this paper, we propose an end-to-end frequency domain guided superpixel segmentation network (FSNet) to generate superpixels with sharp boundary adherence for complex scenes by fusing the deep features in spatial and frequency domains. To utilize the frequency domain information of the image, an improved frequency information extractor (IFIE) is proposed to extract the frequency domain information with sharp boundary features. Moreover, considering the over-sharp feature may damage the semantic information of superpixel, we further design a dense hybrid atrous convolution (DHAC) block to preserve semantic information via capturing wider and deeper semantic information in spatial domain. Finally, the extracted deep features in spatial and frequency domains will be fused to generate semantic perceptual superpixels with sharp boundary adherence. Extensive experiments on multiple challenging datasets with complex boundaries demonstrate that our method achieves the state-of-the-art performance both quantitatively and qualitatively, and we further verify the superiority of the proposed method when applied in salient object detection.
Effect of Digital Financial Innovation on the Financial Performance of Small and Medium Enterprises in Nairobi City Centre, Kenya

The study's objective is to determine how digital financial innovation has affected financial performance of SMEs in Nairobi City Centre, Kenya. The research was focused on evaluating the effect of digital payment systems, mobile banking, and online lending on SMEs' financial performance. The research was grounded on Technology Acceptance Model (TAM), Schumpeter's Theory of Innovation, and Diffusion of Innovation (DOI) theories to provide a conceptual framework for understanding the effect of digital financial innovation on SMEs performance. A descriptive research design was employed to analyze the data gathered from the target population, which consisted of SME owners in the retail industry located in Nairobi City Centre. The sample size was 300 SMEs, selected through a simple random sampling technique to ensure a representative representation of businesses based on their size and industry. The primary research instrument used will be a questionnaire, which allows the collection of quantitative and qualitative data on SMEs' utilization and experiences with digital financial innovations. The results revealed that digital payment systems, mobile banking, and agent banking did not have statistically significant relationships with SMEs' financial performance, as evidenced by their respective p-values (0.773, 0.090, and 0.405). These findings suggest that the adoption and utilization of these digital financial services alone do not significantly influence SMEs' financial outcomes. In contrast, online lending was found to have a positive and statistically significant effect on financial performance, with a p-value of 0.042. This implies that SMEs in the retail sector in Nairobi City Centre can potentially improve their financial performance by actively engaging in online lending activities. However, it is important to note that the overall model's R-squared value was low at 5.2%, indicating that the independent variables collectively explained only a small proportion of the variance in financial performance. The F-statistic (1.231) was statistically insignificant with a p-value of 0.303, suggesting that the predictors had an insignificant combined effect on explaining financial performance. In conclusion, while online lending shows promise as a means to enhance financial performance, SMEs should adopt a comprehensive financial management approach that considers various factors beyond just digital financial services to achieve sustained improvements in their financial outcomes.
Toward Less Constrained Macro-Neural Architecture Search

Networks found with neural architecture search (NAS) achieve the state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, most NAS methods heavily rely on human-defined assumptions that constrain the search: architecture's outer skeletons, number of layers, parameter heuristics, and search spaces. In addition, common search spaces consist of repeatable modules (cells) instead of fully exploring the architecture's search space by designing entire architectures (macro-search). Imposing such constraints requires deep human expertise and restricts the search to predefined settings. In this article, we propose less constrained macro-neural architecture search (LCMNAS), a method that pushes NAS to less constrained search spaces by performing macro-search without relying on predefined heuristics or bounded search spaces. LCMNAS introduces three components for the NAS pipeline: 1) a method that leverages information about well-known architectures to autonomously generate complex search spaces based on weighted directed graphs (WDGs) with hidden properties; 2) an evolutionary search strategy that generates complete architectures from scratch; and 3) a mixed-performance estimation approach that combines information about architectures at the initialization stage and lower fidelity estimates to infer their trainability and capacity to model complex functions. We present experiments in 14 different datasets showing that LCMNAS is capable of generating both cell and macro-based architectures with minimal GPU computation and state-of-the-art results. Moreover, we conduct extensive studies on the importance of different NAS components in both cell and macro-based settings. The code for reproducibility is publicly available at https://github.com/VascoLopes/LCMNAS.
Multi-view brain tumor segmentation (MVBTS): An ensemble of planar and triplanar attention UNets

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with attention to segmentation. We tested the performance of these variants on the BraTS2020 training and validation datasets. The best dice similarity coefficients for enhancing tumor, whole tumor, and tumor core over the training set are 0.712, 0.897, and 0.837, while they are 0.699, 0.875, and 0.782, over the validation set, respectively (obtained through BraTS model evaluation platform). The scores are at par with the leading 2D and 3D BraTS models. Therefore, the proposed approach with fewer parameters (almost 3× less) demonstrates comparable performance to that of a 3D model, making it suitable for brain tumor segmentation in resource-limited settings.
Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging

In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.
The Influence of the Social Environment on Personality Formation

Objective: This scientific article aims to identify and analyze the multidimensional and structural features of personality formation. The study explores the interplay between content and evaluation components within personality formation and examines its temporal dimension. Additionally, the research investigates the theoretical aspects of personality formation, distinguishing between personal and social dimensions, and considers its ontogenetic development in relation to social categorization processes.&#x0D; &#x0D; Methods: The study employs a comprehensive approach to understanding personality formation, drawing on psychophysiological, personal, social, and deep characteristics. Various types and influences of the social environment on personality formation are categorized based on parameters such as the presence of formation crises, decision-making strength, openness to new choices, and more. This classification serves as a framework for analyzing the diverse expressions and substantive characteristics of personality formation.&#x0D; &#x0D; Results: The research reveals that personality formation is a dynamic, lifelong process that can undergo both progressive and regressive changes. It is shown that personality formation is rooted in social interactions and language assimilation, with alterations in the individual's social environment leading to corresponding changes in formation. Various types of personality formation, including ego-formation, group formation, and positive and negative forms, are identified and examined in detail.&#x0D; &#x0D; Conclusions: The study concludes that an integral approach, encompassing psychophysiological, personal, social, and deep aspects, offers a promising avenue for comprehending personality formation. This research contributes to a deeper understanding of the complexity and diversity of personality formation processes, moving beyond surface-level classifications. Furthermore, it underscores the significant role of social interactions and environments in shaping personality formation, providing valuable insights for psychological research and practice.
EviewsR: An R Package for Dynamic and Reproducible Research Using EViews, R, R Markdown and Quarto

EViews is a software designed for conducting econometric data analysis. There exists a one-way communication between EViews and R, as the former can run the code of the latter, but the reverse is not the case. We describe [EviewsR](https://CRAN.R-project.org/package=EviewsR), an R package which allows users of R, R Markdown and Quarto to execute EViews code. In essence, [EviewsR](https://CRAN.R-project.org/package=EviewsR) does not only provide functions for base R, but also adds EViews to the existing [knitr](https://CRAN.R-project.org/package=knitr)'s knit-engines. We also show how EViews equation, graph, series, and table objects can be imported and customised dynamically and reproducibly in R, R Markdown and Quarto document. Therefore, [EviewsR](https://CRAN.R-project.org/package=EviewsR) seeks to improve the accuracy, transparency and reproducibility of research conducted with EViews and R.
Automatic and Authentic eAssessment of Online Database Design Theory Assignments

One of the main reasons MCQ assessments are popular is that tests using text understanding is difficult and often erroneous. The emergence of large language models such as ChatGPT is not mature enough to help grading engineering assignments yet. Since MCQ tests are not well suited for summative assessment, scaling up eLearning for large number of students is difficult using non-MCQ tests. In this paper, we introduce a new eAssessment tool for database design courses that uses graphical conversations to understand learner’s mental model of cognitive state. We show that the model is capable of substituting NLP for authentic assessment for eLearning.
Mendeley Software improves students' scientific writing: Mentorship and training

Accredited and global journals require Mendeley-based article writing. Most authors of scientific papers cite and create a bibliography manually and need to learn Mendeley. Students need help with a bibliography when writing scientific papers. Avoid plagiarism by including a bibliography. With the many references needed for scientific articles, some students will need help manually creating a bibliography. Many students need help using Mendeley and learning its applications. To create a bibliography automatically, Mendeley is required. This community service activity seeks to improve students' scientific work. The research method was chosen as the lecture, discussion, and technical guidance (BIMTEK) method. Participants' understanding and ability to utilize the software increased after the activity. This article will explain the installation process and student skills.
Possibilities of deterioration of artificial intelligence

The following article covers the modern technology that changed the known world for the humans dramatically. Its influence is only increasing and its adaption rate by industries is moving parallel. This technology might provide some solid results and transformation in one field and might have negative influence on the other parts. Therefore, this article will be aimed to illustrate what is AI technology and its common application and therefore its impact on them. This article also covers future perspectives of the technology.
Getting Better Dialogue Context for Knowledge Identification by Leveraging Document-level Topic Shift

To build a goal-oriented dialogue system that can generate responses given a knowledge base, identifying the relevant pieces of information to be grounded in is vital. When the number of documents in the knowledge base is large, retrieval approaches are typically used to identify the top relevant documents. However, most prior work simply uses an entire dialogue history to guide retrieval, rather than exploiting a dialogue’s topical structure. In this work, we examine the importance of building the proper contextualized dialogue history when document-level topic shifts are present. Our results suggest that excluding irrelevant turns from the dialogue history (e.g., excluding turns not grounded in the same document as the current turn) leads to better retrieval results. We also propose a cascading approach utilizing the topical nature of a knowledge-grounded conversation to further manipulate the dialogue history used as input to the retrieval models.
3-Party Secure Computation for RAMs: Optimal and Concretely Efficient

A distributed oblivious RAM (DORAM) is a method for accessing a secret-shared memory while hiding the accessed locations. DORAMs are the key tool for secure multiparty computation (MPC) for RAM programs that avoids expensive RAM-to-circuit transformations. We present new and improved 3-party DORAM protocols. For a logical memory of size N and for each logical operation, our DORAM requires $$O(\log N)$$ local CPU computation steps. This is known to be asymptotically optimal. Our DORAM satisfies passive security in the honest majority setting. Our technique results with concretely-efficient protocols and does not use expensive cryptography (such as re-randomizable or homomorphic encryption). Specifically, our DORAM is 25X faster than the known most efficient DORAM in the same setting. Lastly, we extend our technique to handle malicious attackers at the expense of using slightly larger blocks (i.e., $$\omega ((\lambda + b)\log N)$$ vs. $$\lambda +b$$ where $$b=\varOmega (\log N)$$ is original block size). To the best of our knowledge, this is the first concretely-efficient maliciously secure DORAM. Technically, our construction relies on a novel concretely-efficient 3-party oblivious permutation protocol. We combine it with efficient non-oblivious hashing techniques (i.e., Cuckoo hashing) to get a distributed oblivious hash table. From this, we build a full-fledged DORAM using a distributed variant of the hierarchical approach of Goldreich and Ostrovsky (J. ACM ’96). These ideas, and especially the permutation protocol, are of independent interest.
Transparent conductive oxides and low-loss nitride-rich silicon waveguides as building blocks for neuromorphic photonics

Fully CMOS-compatible photonic memory holding devices hold a potential in the development of ultrafast artificial neural networks. Leveraging the benefits of photonics such as high-bandwidth, low latencies, low-energy interconnect, and high speed, they can overcome the existing limits of electronic processing. To satisfy all these requirements, a photonic platform is proposed that combines low-loss nitride-rich silicon as a guide and low-loss transparent conductive oxides as an active material that can provide high nonlinearity and bistability under both electrical and optical signals.
Hybrid Cryptography and Steganography Method to Provide Safe Data Transmission in IoT

IoT, the upcoming phase of the information innovation, refers to the condition in which millions and millions of people and appliances are associated to improve the interchange of enormous volumes of data from various regions, necessitating the resultant requirement for intelligent data aggregation. But along with the many advantages and uses it offers, the Internet of Things also introduces a fresh difficulty element in the form of several intrinsic inconveniences, particularly security issues during data transmission phases, which mainly affect data confidentiality and integrity aspects. A combined technique of insubstantial cryptography and steganography (LSB Replacement) approach is employed for data communication between IoT tool and home server and following integrated technique of cryptography and steganography (suggested MSB–LSB Replacement approach) for data transmission stages between home server and IoT device in this paper’s security scheme, which addresses both the aforementioned issues.
How to Ensure the Quality of the University Education in the Era of Change

In connection with the new geopolitical and socio-economic situation, the article considers the question of the new design of the national higher education system and quality assurance of the educational outcomes in these circumstances. The article remodels the international experience of the higher education systems’ construction. The key issues in respect of which must be found socio-professional consensus, are substantiated. The article presents the results of the network study dedicated to the quality problems of higher education as well as the experience of the organization of the “University National Initiative of Education Quality” (UNIEQ) as the subject of transformation. The article begins a series of materials (cases) written by the UNIEQ developers on specifi c aspects that may improve the quality of education: tools for increasing demand and manageability of higher education, and achieving “content excellence”. The article is intended for researchers, managers of higher education, and developers of programs for the transformation of education.