Reviewerly/exHarmony huggingface dataset:
Quantum Rényi-2 entropy power inequalities for bosonic Gaussian operations

We derive quantum Rényi-2 entropy power inequalities for Gaussian operations of the beam-splitting and squeezing type. We first show that known quantum von Neumann entropy power inequalities generalize straightforwardly to quantum Rényi-2 entropy power inequalities for Gaussian states but fail to do so for non-Gaussian states. We then derive quantum Rényi-2 entropy power inequalities that provide lower bounds for the Gaussian operations for any state. The inequality for the squeezing operation is shown to have applications in the generation and detection of quantum entanglement.
Tunnel Parsing with Ambiguous Grammars

Abstract The article proposes an addition to the tunnel parsing algorithm that enables it to parse grammars having countable repetitions and configurations of grammar elements generating empty words without refactoring the grammar. The equivalency of trees built by the use of ambiguous grammar is discussed. The class of the ε-ambiguous grammars is defined as a subclass of the ambiguous grammars relative to these trees. The ε-deterministic grammars are then defined as a subclass of the ε-ambiguous grammars. A technique for linearly parsing on the basis of non-left recursive ε-deterministic grammars with the tunnel parsing algorithm is shown.
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions

We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social bias in training datasets impact test-time predictions. The dataset multiplicity framework asks a counterfactual question of what the set of resultant models (and associated test-time predictions) would be if we could somehow access all hypothetical, unbiased versions of the dataset. We discuss how to use this framework to encapsulate various sources of uncertainty in datasets’ factualness, including systemic social bias, data collection practices, and noisy labels or features. We show how to exactly analyze the impacts of dataset multiplicity for a specific model architecture and type of uncertainty: linear models with label errors. Our empirical analysis shows that real-world datasets, under reasonable assumptions, contain many test samples whose predictions are affected by dataset multiplicity. Furthermore, the choice of domain-specific dataset multiplicity definition determines what samples are affected, and whether different demographic groups are disparately impacted. Finally, we discuss implications of dataset multiplicity for machine learning practice and research, including considerations for when model outcomes should not be trusted.
Presentation Layer

The presentation layer consists of various methods of presenting and interacting with your data. In Power Platform, there are several tools with which to do this, such as Power Apps, Power BI, and Power Pages. Designing an interface for your data is one of the more challenging aspects of designing a solution because it is where users interact with data. It needs to be built in such a way that users can intuitively find and navigate data structures. In my experience, this part of the process involves numerous iterations to find a balance between functionality and what makes sense to users.
Research on Route Recommendation of Tourist Attractions Based on Improved Apriori Algorithm

It is a problem to be solved in the current tourism recommendation system to screen out the information aimed at tourists' interests from the huge tourism information. This paper analyzes the shortcomings of Apriori algorithm, a traditional data mining technology, in the management platform of tourist attractions. In order to better explore the potential information hidden in tourists' information such as age, whether they have children or not, and gender, an improved Apriori algorithm based on Hash table is proposed. It provides a reference for the improvement of tourism recommendation algorithm and system.
A Fused FCN and Superpixel Algorithm for Image Semantic Segmentation

Image semantic segmentation technology is a basic preprocessing method in artificial intelligence and computer vision research areas, which has been intensively studied in the past two decades. Many significant algorithms have been proposed and exhibiting their excellent abilities in the semantic segmentation areas. In this paper, we make some minor changes to the Full Convolution Network (FCN) algorithm to improve the semantic segmentation capability and the superpixel segmentation algorithm is also resorted to refine the segmentation result from Full Convolution Network (FCN) based on the Superpixel Edge Optimization Criteria(SEO). Extensive experiments are designed to evaluate its semantic segmentation performance on the public semantic segmentation datasets and the segmentation experiments results are compared with the state-of-art algorithms. The comparison results demonstrate that our proposed algorithm yields significant performance gains over its competitor algorithms on the PASCAL VOC 2012, Cityscapes and ADE20k datasets.
A Comprehensive Study on the Pillar Size of OTS-PCM Memory with an Optimized Process and Scaling Trends Down to Sub-10 nm for SCM Applications

We present a scaling study on phase change memory (PCM)-ovonic threshold switches (OTS) memory cells for storage class memory (SCM) applications. For the first time, the device size effect on the electrical characteristics of OTS-PCM cells is experimentally characterized with simulation support. It is found that the SET threshold voltage (VtS) of OTS-PCM cells increases as the device area scales down because of processinduced defects, as observed from experimental measurements and demonstrated with simulations. An optimized process is proposed and experimentally verified to minimize the VtS shift issue based on wafer level characterization. More importantly, combining the measurements and simulation, the device scaling trends down to sub-10 nanometers are provided for future high density cross-point PCM (XPCM) chips.
Automatic Recognition System of Landscape Portrait Photography on Account of AI

Under the prevailing research of using automation technology to develop video image recognition system, according to the development requirements of recognition system, Visual Basic, Matlab and Microsoft Word software development platform are determined, and tasks are assigned to the development platform. Several problems that should be considered in the establishment of Matlab automatic object are analyzed, and the idea and result of image recognition based on Matlab image processing toolbox function are given. The development practice shows that the use of automation technology can not only shorten the development cycle, but also perfect the system function. This article studies a series of theories and knowledge of automatic recognition system of landscape portrait photography based on AI, reveals the principle of automatic recognition system of landscape portrait photography based on AI, and carries on a systematic analysis of the system. Through the effect analysis of actual video images, the automatic recognition system of landscape portrait photography based on AI is studied. The test results show that the automatic recognition system of landscape portrait photography based on AI reaches 81.32% and 91.01% in image recognition, differentiation line recognition, data reorganization and system automation performance 92.35%, 97.10%. This paper helps to achieve new heights and new strategies for the integration of AI and photography in China in the new era, reflecting the value of the integration of A1 and photography, as well as the role of AI.
Data Collaboratives with the Use of Decentralised Learning

The endeavor to find appropriate data governance frameworks capable of reconciling conflicting interests in data has dramatically gained importance across disciplines and has been discussed among legal scholars, computer scientists as well as policy-makers alike. The predominant part of the current discussion is centered around the challenging task of creating a data governance framework where data is ‘as open as possible and as closed as necessary’. In this article, we elaborate on modern approaches to data governance and their limitations. It analyses how propositions evolved from property rights in data towards the creation of data access and data sharing obligations and how the corresponding debates reflect the difficulty of developing approaches that reconcile seemingly opposite objectives – such as giving individuals and businesses more control over ‘their’ data while at the same time ensuring its availability to different stakeholders. Furthermore, we propose a wider acknowledgement of data collaboratives powered by decentralised learning techniques as a possible remedy to the shortcomings of current data governance schemes. Hence, we propose a mild formalization of the set of existing technological solutions that could inform existing approaches to data governance issues. Our proposition is based on an abstractive notion of collaborative computation as well as on several principles that are essential for our definition of data collaboratives. By adopting an interdisciplinary perspective on data governance, this article highlights how innovative technological solutions can enhance control over data while at the same time ensuring its availability to other stakeholders and thereby contributing to the achievement of the policy goals of the European Strategy for Data.
Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models, and it is also difficult to obtain a substantial number of images of electrical equipment in public. In this paper, we propose a novel distillation method that compresses the knowledge of teacher networks into a compact few-shot classification network, employing a global and local knowledge distillation strategy. Central to our method is exploiting the global and local relationships between the features exacted by the backbone of the teacher network and the student network. We compared our method with recent state-of-the-art (SOTA) methods on three public datasets, and we achieved superior performance. Additionally, we contribute a new dataset, namely, EEI-100, which is specifically designed for electrical equipment image classification. We validated our method on this dataset and demonstrated its exceptional prediction accuracy of 94.12% when utilizing only 5-shot images.
Self-Concept Used by Autonomous Multilingual Learner for Improving Speaking Ability

English for good communication among countries due to the related phenomenon, nowadays, there are increasingly diverse of society from their ways of spoken languages or expressing their life. There are several difficulties in learning foreign languages such as insecurity of dual abilities, comments from other people, unwillingness to learn, fear of taking a risk for correcting other people’s oversight, difficulty to get the facilities, and getting rid of boredom. The purpose of this research is to analyze the self-concept used by Fiki Naki as an autonomous multilingual learner. To be more detailed, the present research analyzes the self-concept or the ways of autonomous language learning that Fiki Naki uses by utilizing social technology such as Fiki Naki’s Youtube, Podcast, and Maxstream for improving his speaking ability. This research used a document analysis as a qualitative research method and Fiki Naki’s YouTube videos will be the research object for completing the analysis. There is three steps model in data analyzing such as; data reduction, data presentation, and conclusion drawing /verification. The findings describe some ways that he used to master the basics of various languages. Later on, this research will be turned into a reference for language researchers to develop the concept of autonomous language learning.
Interactive Learning Media Based on Character Education in Indonesian Subjects for Grade 1 Elementary Schools

The impact of the development of technology and information certainly encourages educational institutions to continue to strive for programs that are by the abilities of students, technological advances, the environment and situations, conditions and needs of students. With the rapid development of information technology, it certainly has a major influence on the personality development of students, both positive and negative. For this reason, it is very important to re-arrange the pattern of personality education to balance and fortify the negative factors resulting from these developments. This study aims to develop interactive learning media products based on character education in the material of invitation sentences and response sentences in Indonesian subjects. This type of research is research and development of the ADDIE model design, which consists of five stages: Analyze, Design, Development, Implementation and Evaluation. The subjects of this study were 28 students of class I SD. Data collection methods and instruments used in this study were questionnaires and tests. This study found significant differences in Indonesian learning outcomes before and after using interactive learning media based on character education in Indonesian class I SD subjects. This study concluded that character education-based interactive learning media has proven effective in significantly increasing Indonesian learning outcomes.
A Study on the Scale Analysis of Youth Training Facilities by Accommodation Capacity and Facility Area Based on the Facility Status in 2022

(Research Purpose) In 2017, Previous studies on the size and area of facilities related to youth activities were investigated, such as the total area, site area, and per capita area required for construction. Accordingly, based on the new data updated in 2022, this study aims to examine the changes in the status of youth training facilities nationwide after COVID 19, follow-up studies by region, public/private to obtain implications, and derive the latest data. (Research Method) Based on the status data of youth training facilities from 2017 to 22, the accommodation quota and area analysis were derived. The main indicators used were the total floor area, site area, and accommodation capacity in relation to building facilities. (Results of the study) 1. The youth training center increased by 0.31㎡ from 10.29㎡ to 10.60㎡ per non-accommodation capacity, and the site area decreased by 7.25㎡ from 42.23㎡ to 34.98㎡, 2. Youth culture houses increased by 1.36㎡ from 7.35㎡ to 8.71㎡ per non-accommodation capacity, and the site area decreased by 15.94 from 44.78㎡ to 28.84 ㎡, 3. The youth center -accomodation- increased 51.16㎡ from 15.40㎡ to 66.56㎡ per person, and 109.71㎡ from 189.60㎡ to 299.31㎡, 4. The floor area of Youth campsites decreased by 4.76㎡ from 9.98㎡ to 5.22㎡ per person, and The site area increased by 15.85㎡ from 133.27㎡ to 149.12㎡, 5. Youth hostels increased by 5.63㎡ from 14.24㎡ to 19.87㎡ per person, and the site area increased by 45.69㎡ from 47.49㎡ to 93.18, 6. The total floor area per person for specialized youth facilities increased slightly by 0.03㎡ from 9.36㎡ to 9.39㎡, and the site area decreased by 2.53㎡ from 82.34㎡ to 79.81㎡. (Conclusion) It is expected that the overall area per person in youth facilities will increase, and the comfort of youth's space use will increase. Although the number of outdoor activity places for teenagers has decreased significantly, the area per person provided by the facility has increased, which is expected to be positive in terms of the size of the activity space.
A Linear Recurrence-Based Pseudorandom Number Generator Optimized for Detector Emulators

Digital detector emulators (DDEs) are important tools for developing particle flux monitoring systems in the laboratory. However, as the dynamic range of modern particle flux monitoring systems, particularly neutron flux monitoring (NFM) systems, continues to rapidly increase, DDEs must also increase their maximum count rate to keep up. The pulse array generating module is the core of the DDE, which utilizes pseudorandom numbers generated by linear-feedback shift registers (LFSRs) to control the intensity and amount of simulated neutrons emitted in each clock cycle. However, the defects or limitations associated with pseudorandom number generators (PRNGs) can cause deviations from the ideal situation, which affects the quality of the output waveform. We propose a novel design that achieves the optimal quality of simulated pulse signals with the same computational cost compared to existing methods, along with a customized algorithm based on the property of linear transformation that can automatically generate the desired LFSR structure. We evaluate the simulated results of the PRNG and pulse array generator from multiple aspects to avoid any systemic errors in the design. In addition, we collect and analyze measured Campbell value data of the DDE. This study aims to contribute to the development of more accurate particle flux monitoring systems by improving the capabilities and reliability of DDEs, addressing the issue of PRNGs, and proposing a novel design that achieves optimal quality of simulated pulse signals.
A Local Attention-based Neural Networks for Abstractive Text Summarization

Text summarization is a process of generating a concise summary of a given text. It is a popular research topic, and many studies have used sequence-to-sequence deep neural network models such as Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) to tackle it. These models consist of two phases: encoding the input text and generating the summary. However, these models may lose information during the encoding phase, particularly when using deep layers, leading to inaccurate summaries. In this paper, we propose a bi-directional LSTM model with a Recurrent Residual Attention mechanism to address this issue. We tested our model on the Amazon Reviews dataset from the Stanford Network Analysis Project and found that it performed better than standard LSTM models and outperformed previous studies.
A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics?

This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions.
Underwater Image Restoration Method Based on Multi-Frame Image under Artificial Light Source

This paper studies the underwater image restoration problem in autonomous operation of AUV guided by underwater visual. An improved underwater image restoration method is developed based on multi-frame neighboring images under artificial light source. At first, multi-frame neighboring images are collected during AUV approaching the targets, and a transmittance estimation method is developed based on the multi-frame images to avoid the assumption of the known normalized residual energy ratio in the traditional methods. Then, the foreground and background regions of the images are segmented by locking the small area where the background light is located. Hence, the accuracy of background light estimation is improved for the underwater mages in turbid water to improve the accuracy of image restoration. Finally, the performance of the developed underwater image restoration method is verified by the comparative results in the pool environment.
A semantic segmentation scheme for night driving improved by irregular convolution

In order to solve the poor performance of real-time semantic segmentation of night road conditions in video images due to insufficient light and motion blur, this study proposes a scheme: a fuzzy information complementation strategy based on generative models and a network that fuses different intermediate layer outputs to complement spatial semantics which also embeds irregular convolutional attention modules for fine extraction of motion target boundaries. First, DeblurGan is used to generate information to fix the lost semantics in the original image; then, the outputs of different intermediate layers are taken out, assigned different weight scaling factors, and fused; finally, the irregular convolutional attention with the best effect is selected. The scheme achieves Global Accuracy of 89.1% Mean and IOU 94.2% on the night driving dataset of this experiment, which exceeds the best performance of DeepLabv3 by 1.3 and 7.2%, and achieves an Accuracy of 83.0% on the small volume label (Moveable). The experimental results demonstrate that the solution can effectively cope with various problems faced by night driving and enhance the model's perception. It also provides a technical reference for the semantic segmentation problem of vehicles driving in the nighttime environment.
Differential perceptions of and reactions to incivil and intolerant user comments

Abstract Building on recent research that challenges the notion that norm violations in online discussions are inherently detrimental, this study relies on a distinction between incivil and intolerant user comments and investigates how online users perceive and react to these distinct forms of antinormative discourse online. Conducting a preregistered factorial survey experiment with a nationally representative sample of n = 964 German online users, we presented participants with manipulated user comments that included statements associated with incivil (profanity; attacks toward arguments) and intolerant discourse (offensive stereotyping; violent threats). The results show that intolerant statements consistently lead to higher perceptions of offensiveness and harm to society as well as an increased intention to delete the comment containing the statement, whereas incivil statements do not. An exploratory multiverse analysis further suggests that these effects remain robust across a variety of analytical decisions.
Quantum Graph Neural Network Models for Materials Search

Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.