Reviewerly/exHarmony huggingface dataset:
Memory Container: A Co-creation Model of Olympic Memories from Individuals to Groups

To discover the value of Olympic memory as the cultural heritage of the Olympics, we have studied the transformation and utilization of personal memory to group memory. Based on the theory of interactive memory, we have constructed a “memory container” scenario co-creation model, and then formed a memory transformation and the paradigm of delivery. As a kind of intangible Olympic cultural heritage, Olympic memory not only has the inheritance effect on the past Olympic culture, but also has an important enlightening effect on the future Olympic Games. The intangible nature of Olympic memory and its dependence on individual memory also make It becomes a legacy that is easy to lose. As the Winter Olympics is approaching, more Olympic memory legacy will emerge. How to store and transform it efficiently, and further realize its inheritance and development, has become an urgent problem to be solved. To break the limitations of personal memory and meet the public’s demand for Olympic cultural heritage, we have studied the transformation model from personal memory to group memory. Based on the theory of interactive memory system, this paper constructs a set of memory-based structured scenario co-creation design tools-memory container, to gather and refine the content of Olympic cultural heritage that everyone expects with group wisdom, promote the transformation of personal memory to group memory, and then Form a paradigm, and provide some theoretical basis for building a virtual interactive platform to construct a common memory.
NEGraf: A System for Power System Collapse Explanation using Graph Representation and Customized PageRank

Power flow simulation produces a colossal amount of complex, integrated, and diverse data. An analyst can get lost in those irregular data when understanding the undergoing of the system. Developing a well-performed analysis method and corresponding visualization techniques is essential. This study proposed NEGraf, a system prototype for transforming data into useful information to assist decision-making in power system monitoring. It can explain the system operating status to support the analyst in recognizing the specific phenomenon in collapse before the blackout, an abnormal period. NEGraf contains a node-edge graph module for data representation, a customized edge-weighted PageRank inference algorithm for data analysis, and a colored graph explanatory interface for information display. We ran a user study for a blackout recognition task. The results show that our interface can better explain intuitively dynamic features in collapse and improve accuracy and recall in predicting blackout than a traditional bar visualization interface.
New Trapdoor and Preimage Sampling on NTRU Lattice

Despite the importance of preimage sampling algorithm in lattice-based cryptography, its low efficiency limits its applications. In this study, we propose a family of gadget-based trapdoors on NTRU lattice. Our construction is compatible with existing efficient preimage sampling algorithms and offer compact secret. Comparison with two trapdoor schemes of [10], the hash-and-sign signature scheme based on our trapdoor scheme has better security and the sizes of secret and signature are reduced. In the first scheme, the sizes of public key and secret key are 6.25 kB and 5.0 kB for an estimation of 63.95-bit security. In the second one, they are 6.25 kB and 5.0 kB for an estimation of 75.92-bit security. Our proof-of-concept shows that the sizes of secret key and public key can be reduced to 0.88 kB and 3.06 kB for an estimation of 86.72-bit security.
Toward Out-of-Distribution Generalization Through Inductive Biases

State-of-the-art Machine Learning systems are able to process and analyze a large amount of data but they still struggle to generalize to out-of-distribution scenarios. To use Judea Pearl’s words, “Data are profoundly dumb” (Pearl & Mackenzie 2018); possessing a model of the world, a representation through which to frame reality is a necessary requirement in order to discriminate between relevant and irrelevant information and to deal with unknown scenarios. The aim of this paper is to address the crucial challenge of out-of-distribution generalization in automated systems by developing an understanding of how human agents build models to act in a dynamic environment. The steps needed to reach this goal are described by Pearl through the metaphor of the Ladder of Causation. In this paper, I support the relevance of inductive biases in order for an agent to reach the second rung on the Ladder: that of actively interacting with the environment.
Development of Component Recognition Applications and Labor Tools Based on Android and Tiny Yolo Network (Case Study: Signal and System Laboratory)

Practicum activities in the laboratory usually equipped with tools and components that must be prepared in advance. This study aims to develop an application for recognizing laboratory tools and components. The application is designed for Android-baced devices by utilizing the smartphone camera and developed using Tiny YOLO. The development follows System Development Life Cycle (SDLC) methodology using waterfall model. The system then tested by training data on 1,666 image objects obtained from Google in the form of laboratory tools and components such as Arduino, Raspberry Pi, HC-05 sensor, Esp-32 Module, Multimeter, Oscilloscope, and Function Generator. The results showed that the system can detect components and laboratory tools at an optimal distance of 25-35 cm and the accuracy of object detection is influenced by the light conditions in the. From several components tested, the object detection accuracy rate for Arduino Uno is 73.33%, Raspberry Pi is 82.5%, Bluetooth HC-05 module is 86.84%, Esp32 module is 84.37%, Multimeter is 80.6%, Oscilloscope is 76.31% and 80% function generator.
Bayesian Spatial Modeling of HIV Using Conditional Autoregressive Model

Background: In the spatial analysis, the conventional method for disease modeling and mapping is based on a log-linear relationship between relative risk and local variation, while the covariates are ignored. On the other hand, the general assumption in spatial modeling is the stationarity of the mean, which implies the associations between the relative risk and some set of covariates, which is constant over regions. In reality, the comparative risk modeling usually infringes on this stationarity assumption because of spatial dependencies. Thus, non-stationarity of the mean can be employed using the Spatially Varying Coefficients (SVCs) model. Method: In this study, we propose a generalized linear model (GLM) with Bayesian inference to build the SVC model and compared it with the stationary model. The SVC model is used to relax the stationarity assumption in which nonlinear effects of age are captured through the random walk of order two and by allowing the covariates to vary spatially using a conditional autoregressive model. This study aimed to profile people living with HIV in Nigeria. In this chapter, identical spatial regression models are fitted for Bayesian approach, using General Household Survey (GHS) data for the year 2015. Result and Conclusion: The finding of this study highlights a nonlinear relationship between the incidence of HIV and age. Among others, this study highlights areas where women are at higher risk of HIV infection across the six regions of Nigeria. The modeling of the socio-demographic predictors of HIV infection and spatial maps provided in this study could aid in developing a framework to alleviate HIV and identify its hotspots for urgent intervention in the endemic regions.
A Weight-Reload-Eliminated Compute-in-Memory Accelerator for 60 fps 4K Super-Resolution

Convolutional neural networks (CNNs) have significantly enhanced the quality of image super-resolution (SR). However, deploying CNN with deeper layers for real-time 60fps and higher resolution SR, e.g., 4K, brings higher performance requirement, incurring huge area and power overhead as well. In this brief, a high performance compute-in-memory (CIM) accelerator with high energy and area efficiency is proposed for deep-layer CNN-based SR (CNNSR). It adopts alternate time division serial (ATS) dataflow to guarantee the high performance, which eliminates frequent and time-consuming weight reload operation of CIM macros. Moreover, considering the extremely high ratio of computation quantity to weight amount in the deep-layer CNNSR scenario, an energy and area efficiency enhanced digital CIM macro with high computation-to-memory ratio (CMR) is proposed. The proposed accelerator achieves 33.3TOPS peak performance, supporting 4K SR at no less than 60fps. It is evaluated under TSMC 28nm process, achieving 13.96TOPS/W energy efficiency and 1.85TOPS/mm2 area efficiency, which are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.79\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula> better than the state-of-the-art CNNSR accelerator, respectively.
Label Distribution Learning Based on Square Root Pearson

Label distribution learning is a gradually emerging field of learning that can address label ambiguity. In a sense, label distribution learning includes common single-label learning and multi-label learning with broader applicability. For each instance, label distribution learning uses the label distribution to reflect the individual label importance. Previous studies have shown that label distribution learning is an effective method to address label ambiguity. In order to strengthen the learning performance and make the prediction of the distribution more accurate, this paper proposes a label distribution learning algorithm called SRPLDL. We propose a metric called the Square Root Pearson, and then we build our learning model on the basis of this metric. Comparative experiments on 11 real label distribution datasets prove that the SRPLDL algorithm can solve the label distribution learning problem and outperforms other algorithms.
Research on the Whole Process Quality Control Method of Water Conservancy and Hydropower Construction Based on BP Neural Network

The traditional quality control method of the whole construction process is lack of process quality evaluation, which leads to the low overall quality score of the project after the completion of the project construction. A whole process quality control method of water conservancy and hydropower construction based on BP neural network is designed. First, set up water conservancy and hydropower construction site monitoring, and design data acquisition equipment for the whole construction process according to different construction positions; According to the acquisition equipment set above, collect the water conservancy and hydropower construction data at different locations and use them as the control data; On this basis, the framework structure of BP neural network is established, the control network level is determined, the back propagation function is determined, and the collected data of the whole process of water conservancy and hydropower construction are trained to complete the whole process quality control of water conservancy and hydropower construction. The example analysis results show that the whole process quality control of the project using the design method can effectively improve the construction quality.
Image-to-Image Translation with Semantic Decoupling Algorithm

Although image-to-image translation has made some progress in achieving both multi-feature and multi-modal tasks. However, due to the independence and correlation between image features, existing works cannot implement fine-granularity control. In this paper, we propose a model based on cycle consistency and semantic decoupling algorithms, called InterpretableGAN, which aims to learn the cross-mapping relationships among the source, condition and target domain to solve conditional image-to-image translation tasks. We extract the domain-common and domain-style features from the source domain and the target style code from the condition domain, then use the target style code to manipulate the domain-style features to get the target style features. Finally, the generator uses the target style and domain-common features to synthesize translation results. Both qualitative and quantitative results on the CelebA dataset demonstrate the ability of our proposed model.
Enhancing Education Process Supported by Virtual Reality

In the higher education system, the introduction of information technologies is part of the modern unit of education. At the present time, when universities commonly use the form of distance and mixed education, this method places the student at the center of the entire educational system and among all other components related to this system. Supporting the development of skills, competencies, critical thinking, communication, and expressing ideas and concepts naturally, in a free form, stimulate the student. The motivation of individuals has a great influence on education and even on learning itself. These information technologies can implicitly greatly enhance motivation and also education through innovative teaching, learning, and assessment technologies. Motivation is increased through interactivity, dynamics, and effects provided by information technology. Virtual reality (VR) is the most interactive of these technologies. The contribution is devoted to the use of virtual reality in the educational process, where the basis of the use of virtual reality is the effort to display spatial models and scenes as faithfully as possible, their manipulation, the creation of the real world, a certain part of it with all its regularities and rules, movement in three-dimensional space and that all in real-time. In doing so, basic procedures from the field of computer graphics are used.
Active Short-Long Exposure Deblurring

Mobile phones can capture image bursts to produce high quality still photographs. The simplest form of a burst is two frame short-long (S-L) exposure. S-L exposure is particularly suitable in low light conditions where short exposure frames are sharp but noisy and dark, and long exposure frames are affected by motion blur but have better scene chromaticity and luminance. In this work, we take a step further and define active short-long exposure deblurring where the viewfinder frames before the burst are used to optimize the S-L exposure parameters. We introduce deep architectures and data generation for active S-L exposure deblurring. The approach is experimentally validated with realistic data and it shows clear improvements. For the most difficult scenes (worst 5%) the PSNR is improved by +1.39dB.
A Secure and Efficient Heterogeneous Signcryption Scheme for IIoT

With the development of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), as an extension of the IoT, has become more and more widely used in the industrial field. It collects various data in the IIoT environment through sensors. For security reasons, these data cannot be presented in plaintext. To ensure the security of these data, a signcryption primitive is presented. In practice, numerous sensors and servers are not in the same environment, so we need a heterogeneous signcryption scheme to solve this problem. In this paper, we introduce a heterogeneous signcryption scheme with the critical characteristic of allowing devices in certificateless cryptography (CLC) environments send messages to devices in identity-based cryptography (IBC) environments. In the random oracle model (ROM), for its security aspects, we present the relevant proofs. We also compared it with other schemes in terms of communication overhead. A comprehensive analysis has shown that our scheme improves efficiency and also increases security.
An Evaluation Framework for Blockchain-Based Public Key Infrastructures

The X.509 Public Key Infrastructure (PKI) is one of the most important trusted infrastructures for establishing a secure connection among participants on the Internet. Nevertheless, this infrastructure is far from perfect and various attacks in the past decade successfully exploited its weaknesses. A common idea for the creation of a more resilient PKI is the use of distributed ledger technology such as blockchain. Multiple proposals attempting to make use of this technology to create a more reliable PKI have been published. However, most of them are still conceptual and fail to properly consider the feasibility. In this paper, we propose a set of requirements which can be used to evaluate and compare the merits of a certain design. This framework is put to the test by a review of twelve published blockchain-based PKI replacements or extensions. We find that there are major differences between the different proposals and their fulfillment of the requirements. To address these issues and achieve a design which is practically usable, we recommend to integrate the most promising ideas put forward by different authors. By doing so, a holistic and scalable solution can be created.
Lower Bounds on the PTF Weight of ODD-MAXBIT Function

We show that every polynomial threshold function that sign-represents the ODD-MAXBITn function has total absolute weight 2Ω(n1/3). The bound is tight up to a logarithmic factor in the exponent.
Convolutional Attribute Mask with Two-step Attention for Fashion Image Retrieval

We propose a method to learn multiple latent spaces for attribute-specific fashion image retrieval. Our network learns multiple deep image features for a given set of fashion attributes through convolutional attribute masks and two-step attention. The masks promote our network to learn image features for a specific attribute. The two-step attention helps our network collect important spatial and channel information for the fashion attribute using spatial or channel attention. We visually show that our network correctly attend to essential regions for a given fashion attribute and learns well-distanced embeddings in latent spaces. We achieve state-of-the-art performance for attribute-specific fashion image retrieval.
Fuzzyfication of Statistical Parameters for Term Selection Approach in Automatic Query Expansion

Query Expansion (QE) is not just about adding terms but its about enhancing the query so user will land to relevant documents fast enough. QE is also a technique for narrowing down a user's search to the phrases that are most relevant to them. It gets rid of all the words that aren't needed. Appropriate selection of term is crucial in this process. This work uses evolutionary algorithms like Fuzzy logic and Genetic algorithms. The proposed approach is a hybrid word selection technique is based on fuzzy logic with statistical methods such as RSV and KLD. Local analysis of query is performed using pseudo-relevance feedback. Documents retrieved are ranked using okapi-bm25. A weighted method from several term selection strategies is used to apply fuzzy similarity reasoning for term selection. Performance of proposed approach is measured on parameters such as F-score. This evaluation has been done on two benchmark datasets, i.e., CACM, CISI.
Detection of Breast Cancer Through the Machine Learning Techniques

The loss of life through cancer is one of humanity's serious issues in the growing world. India alone has witnessed 30% of the instances of these diseases at some stage in the previous few years and its miles possibly to increase. Breast cancer in India debts that one girl is identified each minute and every nine minutes, one girl dies. Early recognition and prognosis can save the existence of infected patients. By considering this reason we have tried to create a machine learning model which will help to predict breast cancer in a woman. We have taken 3 machine learning algorithms, instance, logistic regression, random forest, and XGBoost. The reason behind taking 3 machine learning algorithms is to find the best suitable algorithm with maximum precision. Also, used a dataset containing the cells report of women around the world. The dataset contains 30 highlights like mean range, mean surface, add up perimeter, add up the region, add up minimization, add up concavity, etc., and this dataset is utilized to prepare and test our model. All examinations are executed inside a recreation environment and led in the Anaconda platform. We have carried out this research to anticipate, identify and examine the precision for detection of breast malignant growth. Future studies may be performed to anticipate different factors, and breast malignant growth examination can be sorted in light of these variables.
Semantic Parsing of Interpage Relations

Page-level analysis of documents has been a topic of interest in digitization efforts and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a multi-page document. To this end, we formalize the task as semantic parsing of interpage relations and we propose an end-to-end approach for interpage dependency extraction, inspired by the dependency parsing literature. We further design a multi-task training approach to jointly optimize for page embeddings to be used in segmentation, classification, and parsing of the page dependencies using textual and visual features extracted from the pages. Moreover, we also combine the features from two modalities to obtain multimodal page embeddings. To the best of our knowledge, this is the first study to extract rich semantic interpage relations from multi-page documents. Our experimental results show that the proposed method increased LAS by 41 percentage points for semantic parsing, increased accuracy by 33 percentage points for page stream segmentation, and 45 percentage points for page classification over a naive baseline.
Alexa! Examine privacy perception and acceptance of voice-based artificial intelligence among digital natives

Technological advancements, globalization, and digitalization have initiated various innovations across the globe transforming consumer needs and their ways to interact with brands and technology. Voice-based Artificial Intelligence, for the past few years, has been widely accepted and used by consumers worldwide for routine and special purposes. Nowadays, its presence can be felt across the majority of sectors like Healthcare, Education & Training, Entertainment, Automobile, and Retail. With its exciting unique features and convenience, there is no denial of voice being the future. Consumers have the power to decide the fate and success of an innovation. Thus, it is essential for organizations, innovators, and marketers who wish to increase technology adoption rates and reduce the barriers to acceptance of voice-based artificial intelligence among digital natives to evaluate the influence of numerous variables. This study aims to identify the motivators and inhibitors of the adoption of voice assistants among digital natives along with understanding their privacy perceptions about voice-based artificial intelligence. To examine the acceptance of voice-based artificial intelligence and its usage by youngsters, the researchers have proposed a comprehensive model to examine the relationship between various variables affecting consumer behaviourial intention. The study will be a significant addition to the previous literature as very limited studies have tried to understand the acceptance of voice assistants taking digital natives as consumers.