Reviewerly/exHarmony huggingface dataset:
Modified Histogram Equalization for Improved CNN Medical Image Segmentation

This research aims to improve the performance of convolutional neural network (CNN) in medical image segmentation that will detect specific parts of the body's anatomical structures. Medical images have drawbacks, such as the image's variability, quality, and complexity. We developed image preprocessing scenarios using Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and the hybrid approaches (HE-CLAHE and CLAHE-HE). We propose CNN with image enhancement for image segmentation and evaluate its performance on Lung CT-Scan and Chest X-ray datasets, which totaled 267 and 3616 images, respectively, and had ground truth. The experimental results indicate that the optimal cumulative distribution function (CDF) value of HE is 0 to 39, and the clip limit of CLAHE is 0.01. CNN produces the best segmentation with the addition of the CLAHE-HE approach. This method can increase the accuracy by 1.23 percentage points (training) and 3.22 percentage points (testing) for Lung CT-Scan images. Meanwhile, for Chest X-ray images, the training and testing accuracy increased by 1.58 and 0.96 percentage points. In addition, the proposed medical image segmentation approach using the CNN method with CLAHE-HE obtained the values of comparative coefficients DSC (dice similarity coefficient), and SSIM (structural similarity index measurement) of only about 0.92 and 0.97, respectively.
NEOLOGISMS IN THE ITALIAN LANGUAGE AS A SOCIOLINGUISTIC PHENOMENON

The continuous development of the language contributes to the emergence of new lexical units in it. The article is devoted to neologisms of Italian language. During the last time, a lot of new neological units appeared in the Italian language, which was due to the development of new technical terms or lexical units of social networks. Pretty often there are words in the language with a synonymous meaning, but borrowings and neologisms make the description more accurate or modern. The article describes studying the sociolinguistic aspect of Italian neologisms. The peculiarities of the classification of neologisms are indicated, and the options of the origin of neologisms are given. It is also noted that some neologisms are borrowed from English or other languages, while others are formed using word formation methods. The article examines two main groups of neologisms – combinatorial and semantic. The examples for each of the groups are given and explained. Moreover, it is mentioned how neologisms appear in dictionaries and become official lexical units.
Institutions to constrain chaotic robots: why generative AI needs blockchain

Generative AI is a very powerful new computing technology, but the problem of how to make it economically useful (Alice: “hello LLM, please send an email to Bob”) is limited by its inherent unpredictability. It might send the email, but it might do something else too. As a consequence, the large language models that underpin generative AI are not safe to use for most economically useful and valuable interactions with the world. This is the ‘economic alignment’ problem between the AI as an ‘agent’ and the human ‘principal’ who wants the LLM to interact in the world on their behalf. The answer we propose is smart contracts that can take LLM outputs and filter them as deterministic constraints. With smart contracts, LLMs can interact safely in the real world, and can unlock the vast economic opportunity of economically aligned and artificially intelligent agents.
Correlation between Socio-Psychological Adaptability and Tolerance to Uncertainty among Psychology Students

The problem of correlation between socio-psychological adaptability and tolerance to uncertainty among graduates from psychology faculties of higher institutions has obvious theoretical and practical relevance. The variety of approaches to understanding of the nature and structure of socio-psychological adaptability of personality provides new opportunities for considering this phenomenon as integral psychological construct. The purpose of the study is to develop scientific ideas about the correlation between various aspects of the adaptability of personality among young psychologists and tolerance to uncertainty in situations of interpersonal and professional interaction. The sample consisted of 60 graduate students of psychological and psychological-pedagogical university faculties studying under bachelor's degree programs. The diagnostic complex included tools for studying the cognitive, emotional and behavioral components of socio-psychological adaptability, as well as an individual's general and interpersonal tolerance / intolerance to uncertainty. The quantitative processing of the empirical data obtained using the Spearman correlation coefficient made it possible to detect significant correlation between various components of adaptability and manifestations of tolerance to uncertainty. The scientific novelty of the research lies in the formulation of the author's approach to the structure of the construct of socio-psychological adaptability of the individual and the possibilities of diagnostic and correctional work that open up in this regard. The conducted research has shown the need for the introduction of psychological and pedagogical support programs for students of helping professions in the process of studying at the university, aimed at the development of socio-psychological adaptability of the individual in connection with tolerance to uncertainty. In conditions of increasing social and economic instability, the study of these and other factors affecting the development of professional self-awareness will optimize the process of labor socialization of young professionals.
Exploration on load balancing data aggregation algorithm in wireless sensor network based on big data artificial intelligence

Abstract Based on BD‐AI(Big Data Artificial Intelligence) technology, an efficient LB data collection algorithm was designed and implemented for experimental verification and performance evaluation. The results showed the average load of algorithms A and B tested five times was between 44–48 and 60–64, respectively; the algorithms A and C were tested five times with data transmission speeds between 10–11Mbps and 4–5Mbps, respectively. The LB data aggregation algorithm based on BD‐AI in WSN is more efficient than traditional LB algorithms and traditional data aggregation algorithms. It can effectively help solve problems such as imbalanced data exchange, data redundancy, and energy waste among wireless sensor nodes, thereby improving the data collection efficiency and energy conservation of sensor networks, laying the foundation for implementing application scenarios such as intelligent perception and remote monitoring. The load balancing data aggregation algorithm of wireless sensor networks based on Big data artificial intelligence is faced with some challenges and difficulties. For example, wireless sensor networks usually produce a large amount of data, which needs real‐time aggregation and processing; Nodes in wireless sensor networks typically have limited computing, storage, and communication resources, among other issues.
A semantics-enabled approach for personalised Data Lake exploration

Abstract The increasing availability of Big Data is changing the way data exploration for Business Intelligence is performed, due to the volume, velocity and uncontrolled variety of data on which exploration relies. In particular, data exploration is required in Data Lakes that have been proposed to host heterogeneous data sources, given their flexibility to cope with cumbersome properties of Big Data. However, as data grows, new methods and techniques are required for extracting value and knowledge from data stored within Data Lakes, aggregating data into indicators according to multiple analysis dimensions, to enable a large number of users with different roles and competencies to capitalise on available information. In this paper, we propose PERSEUS (PERSonalised Exploration by User Support), a computer-aided approach for data exploration on top of a Data Lake, structured over three phases: (1) the construction of a semantic metadata catalog on top of the Data Lake, leveraging tools and metrics to ease the annotation of the Data Lake metadata; (2) modelling of indicators and analysis dimensions, guided by an openly available Multi-Dimensional Ontology to enable conformance checking of indicators and let users explore Data Lake contents; (3) enrichment of the definition of indicators with personalisation aspects, based on users’ profiles and preferences, to make easier and more usable the exploration of data for a large number of users. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.
Strategic planning and digital competencies in public educational institutions San Vicente, Cañete

Purpose: The objective of the study was to determine the relationship between strategic planning and digital competencies in teachers of educational institutions.&#x0D; &#x0D; Theoretical framework: The conceptual approach according to David (2013), strategic planning refers to the development of three main stages, which are the formulation, implementation and evaluation of the strategy. In relation to digital competencies, connectivism should be highlighted, since this comes from educational technological knowledge, which have been favorable within an effective learning approach (Gašević et al., 2015; Brooks, 2015), this encourages the use of tools, to improve the research process; as well as teaching that is based on the interconnectivity of the participants.&#x0D; &#x0D; Method/design/approach: This research uses a quantitative approach. The subjects of this study are 170 teachers from educational institutions in San Vicente, Cañete - Peru. The variables of this study are strategic planning and digital competencies. The level of study is correlational, as it seeks to establish the existence of correlations between the study variables (Sánchez &amp; Reyes, 2017). Being necessary to know the level of association between the study variables. Spearman's Rho test analysis techniques were used to test the hypothesis.&#x0D; &#x0D; Results and conclusion: The result of this study was that there is a correlation between the variables of Strategic planning and digital competencies in teachers of public educational institutions of San Vicente, Cañete-Peru being rho=0.357 and p=0.000&lt;0.05.&#x0D; &#x0D; Implications of the research: This research contributes to the understanding of strategic planning that is determinant for the good performance of successful educational institutions based on the development of digital competencies in teachers of public educational institutions.&#x0D; &#x0D; Originality/value: The results obtained in this study are relevant and coherent in a socio-educational context that demands the adoption of actions that contribute decisively to the improvement of educational practices and the engagement of the school community.
Learning Interpretable Style Embeddings via Prompting LLMs

Style representation learning builds content-independent representations of author style in text. To date, no large dataset of texts with stylometric annotations on a wide range of style dimensions has been compiled, perhaps because the linguistic expertise to perform such annotation would be prohibitively expensive. Therefore, current style representation approaches make use of unsupervised neural methods to disentangle style from content to create style vectors. These approaches, however, result in uninterpretable representations, complicating their usage in downstream applications like authorship attribution where auditing and explainability is critical. In this work, we use prompting to perform stylometry on a large number of texts to generate a synthetic stylometry dataset. We use this synthetic data to then train human-interpretable style representations we call LISA embeddings. We release our synthetic dataset (StyleGenome) and our interpretable style embedding model (LISA) as resources.
LogiCoT: Logical Chain-of-Thought Instruction Tuning

Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation

End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine translation has been established as a powerful pretraining objective on text as it enables the model to capture high-level semantics of the input utterance and associations between different languages, which is desired for speech models that work on lower-level acoustic frames. Motivated particularly by the task of cross-lingual SLU, we demonstrate that the task of speech translation (ST) is a good means of pretraining speech models for end-to-end SLU on both intra- and cross-lingual scenarios. By introducing ST, our models reach higher performance over baselines on monolingual and multilingual intent classification as well as spoken question answering using SLURP, MINDS-14, and NMSQA benchmarks. To verify the effectiveness of our methods, we also create new benchmark datasets from both synthetic and real sources, for speech summarization and low-resource/zero-shot transfer from English to French or Spanish. We further show the value of preserving knowledge for the ST pretraining task for better downstream performance, possibly using Bayesian transfer regularizers.
VIP5: Towards Multimodal Foundation Models for Recommendation

Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resulting in disparate modeling and engineering methodologies. This has impeded the ability for these fields to directly benefit from each other’s advancements. With the recent development of foundation models, large language models have emerged as a potential general-purpose interface for unifying different modalities and problem formulations. In light of this, we propose the development of a multimodal foundation model (MFM) considering visual, textual, and personalization modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5), to unify various modalities and recommendation tasks. This will enable the processing of multiple modalities in a shared architecture for improved recommendations. To achieve this, we introduce multimodal personalized prompts to accommodate multiple modalities under a shared format. Additionally, we propose a parameter-efficient training method for foundation models, which involves freezing the P5 backbone and fine-tuning lightweight adapters, resulting in improved recommendation performance and increased efficiency in terms of training time and memory usage. Code and data of VIP5 are available at https://github.com/jeykigung/VIP5.
CoLT5: Faster Long-Range Transformers with Conditional Computation

Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontanon, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Toward Realization of Universal Quantum Teleportation Using Weak Measurements

Abstract In this manuscript, universal quantum teleportation in the presence of memory or memory‐less dynamics with applications of partial collapse measurement operators is analyzed. These results show that the combined effects of memory or non‐Markovianity, and weak measurements can lead to universal quantum teleportation (UQT). This study involves noise models of physical importance having characteristic Markovian and non‐Markovian regions, allowing one to observe a transition in quantum properties as one switches from non‐Markovian to Markovian dynamics. For this, the effects of different types of non‐Markovianity are characterized for efficient UQT, both due to the retention of correlations for a longer duration and due to information backflow. The memory effects arising from a correlated channel with or without weak measurements are analyzed further. Interestingly, this analysis for a correlated amplitude damping channel shows that memory effects are of significant advantage in minimizing the fidelity deviation. The presence of weak measurements further enhances the realization of UQT in the presence of memory. The ability of memory effects to achieve zero fidelity deviation at non‐zero time is interesting and of experimental importance.
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes

A long standing goal in the data management community is developing systems that input documents and output queryable tables without user effort. Given the sheer variety of potential documents, state-of-the art systems make simplifying assumptions and use domain specific training. In this work, we ask whether we can maintain generality by using the in-context learning abilities of large language models (LLMs). We propose and evaluate Evaporate, a prototype system powered by LLMs. We identify two strategies for implementing this system: prompt the LLM to directly extract values from documents or prompt the LLM to synthesize code that performs the extraction. Our evaluations show a cost-quality tradeoff between these two approaches. Code synthesis is cheap, but far less accurate than directly processing each document with the LLM. To improve quality while maintaining low cost, we propose an extended implementation, Evaporate-Code+, which achieves better quality than direct extraction. Our insight is to generate many candidate functions and ensemble their extractions using weak supervision. Evaporate-Code+ outperforms the state-of-the art systems using a sublinear pass over the documents with the LLM. This equates to a 110X reduction in the number of documents the LLM needs to process across our 16 real-world evaluation settings.
Data Sampling In Federated Learning: Principles, Features And Taxonomy

Federated learning collects data from various devices, analyzes it locally, aggregates it, and then finds meaningful insights from it. Data sampling works the same way by dividing the larger data set into smaller parts and applying computation to those data sets, which reduces the time taken to do the work. Data sampling in federated learning aims to find the ideal mixture of selecting data sets for training purposes to improve training accuracy while staying within the maximum capability of the device and network. In this article, we present an overview and analysis of recent data sampling techniques for federated learning. The list includes sampling approaches suitable for federated learning environments such as clustering, dynamic sampling, adaptive sampling, probabilistic sampling, and many more. The feature analysis is comprised of a description of the procedure, the criteria, and other relevant parameters for sampling. The efficiency of the sampling technique is analyzed via comparison of claimed accuracy and convergence rate with respect to the used dataset.
Ariadne: a Privacy-Preserving Network Layer Protocol

This paper presents Ariadne, a privacy-preserving network layer communication protocol using source routing to avoid relying on trusted third parties. In Ariadne, a source node willing to send anonymized network traffic to a destination uses a path consisting in nodes with which it has pre-shared symmetric keys through a setup phase. It uses temporary keys derived from those pre-shared keys to protect communication privacy using onion routing techniques. Ariadne enhances previous approaches by introducing two novelties. Firstly, the temporary keys used to process the packets on the path are referenced using mutually known encrypted patterns. This avoids the use of an explicit key reference that could be used to de-anonymize the communications. Secondly, the source route is encoded in a fixed size, sequentially encrypted vector of routing information elements, in which the elements' positions in the vector are pseudo-randomly permuted.
Metaverse through Blockchain and Intelligent Networking: A Comprehensive Survey

As technology has advanced, people's lives have been transformed by the virtual world, which has been created by technologies such as the Internet, computers, artificial intelligence, and hardware. The metaverse, a new social ecology connecting the physical and virtual worlds, is rapidly expanding as the demand for virtual reality grows. Privacy, security, high synchronization, and low latency have all been challenged as data volumes and value have grown and the metaverse continues to evolve. As blockchain and intelligent networking technologies continue to evolve, these challenges can be addressed and the metaverse's needs for trusted construction, continuous data interaction, and computing can be satisfied. In order to provide immersive experiences in the metaverse, a comprehensive review of blockchain's role and benefits is essential. The purpose of this survey is to discuss the metaverse's development trend, architecture, and characteristics. This review paper is novel in providing an overview of existing blockchain research, including overviews, applications, and challenges. Furthermore, we summarized the metaverse's applications, emphasizing their significance and areas of development. Using the survey, we are able to discuss open issues, challenges, and future research directions.
Evaluation of Healthprompt for Zero-shot Clinical Text Classification

This paper presents an evaluation of the Healthprompt, a prompt-based zero-shot clinical text classification framework. The lack of publicly available datasets and the expensive data annotation in the clinical domain make traditional NLP models difficult to train. To overcome this issue, Healthprompt utilizes Pre-trained Language Models (PLMs) and prompt-based Zero-Shot Learning (ZSL) to perform clinical NLP tasks without additional training data. However, the original Healthprompt paper missed the error analysis and ablation study of the framework. This paper conducts an error analysis and an ablation study on Electronic Health Records(EHR) notes to understand the capabilities and limitations of the Healthprompt framework for clinical text classification. The results provide insight into the potential and limitations of prompt-based zeroshot learning for clinical NLP tasks and offer suggestions for improvements to the Healthprompt framework, and for the future development of prompt-based ZSL.