Reviewerly/exHarmony huggingface dataset:
Limits of Polynomial Packings for $$\mathbb {Z}_{p^k}$$ and $$\mathbb {F}_{p^k}$$

We formally define polynomial packing methods and initiate a unified study of related concepts in various contexts of cryptography. This includes homomorphic encryption (HE) packing and reverse multiplication-friendly embedding (RMFE) in information-theoretically secure multi-party computation (MPC). We prove several upper bounds and impossibility results on packing methods for $$\mathbb {Z}_{p^k}$$ or $$\mathbb {F}_{p^k}$$ -messages into $$\mathbb {Z}_{p^t}[x]/f(x)$$ in terms of (i) packing density, (ii) level-consistency, and (iii) surjectivity. These results have implications on recent development of HE-based MPC over $$\mathbb {Z}_{2^k}$$ secure against actively corrupted majority and provide new proofs for upper bounds on RMFE.
Aviation Medical Simulation Training Based on Interactive Technology

China is one of the countries with the most serious natural disasters in the world. It is urgent to strengthen the construction of emergency medical rescue capacity. In recent years, more and more people realize the necessity of interaction in aviation medical rescue simulation training for major disasters. China's professional aviation medical rescue training is relatively late, and the training equipment and system are relatively backward, mainly using medical simulator training. This paper proposes a gesture recognition algorithm which combines aviation medical simulation training with virtual environment. Based on the existing joint point extraction technology, 22 joint points of the hand are obtained. Combined with the key operations of aviation emergency medical rescue, neural network is used for learning and action recognition, so as to realize natural interaction.
Application of Artificial Immune Algorithm in Evolutionary Creation

With the rapid development of modern information technology, the corresponding computer music is also produced. How to apply artificial intelligence technology in the field of music has become a new research field, which is a new challenge for music sociology. This paper first introduces the artificial immune algorithm, and then explains the application of artificial immune algorithm in the evolution of music creation, analyzes the trend and characteristics of this new way, hoping that through this kind of research, technology can better serve music.
ABOUT THE PROGRAM OF THE STAFF OF THE URFU STUDENT TEAMS ON THE FORMATION OF FUTURE SKILLS

The article emphasizes the importance of the skills of the future in the training of a modern specialist in the labor market. A list of relevant skills of the future is given. A research design is presented in the development of a program for the formation of the skills of the future in the environment of student teams. The article analyzes the scientific literature on the topic of this study, describes the portrait of a soldier of the student detachments of UrFU in the context of the skills of the future. Conclusions are made about the skills of the future that are in demand by the fighters of student teams. Technologies for the formation of the skills of the future in the environment of student teams are proposed. The article presents a program for the formation of the skills of the future among the student teams of the Ural Federal University. The program consists of 4 modules: an information module, which includes 4 lectures from the speakers of the UPI, Ural State University-Ural Federal University Alumni Association and the Ural Federal University's Center of Universal Competencies "4K", an interactive module consisting of four trainings, a project module including "Creative Laboratory". The practical module contains five activities aimed at building the skills of the future. The results of testing some of the elements of the program for the formation of skills in the environment of student teams are presented. The conclusion is made about the significant potential of the movement of student teams in the formation of the skills of the future.
Pandemic, Repair, and Resilience: Coping with Technology Breakdown during COVID-19

COVID-19 disrupted the existing ecosystem of technology repair and recycle in Bangladesh as visiting repair workshops became difficult and most repairers and e-waste workers had to temporarily close their businesses. Consequently, users were left with very few choices for fixing or recycling their devices. Based on our interviews with 30 repair and e-waste workers and 21 users of electronic devices we capture various aspects of this disruption and the corresponding coping mechanisms. This paper reports how the repair and e-waste worker communities adopted various changes to their work, provided remote services, and yet faced a decline in their business. On the other hand, end-users learned to fix their devices, collaborated with each other, and negotiated with partially broken devices to address this challenge. We further discuss the broader implications of our findings for HCI scholarship in HCI4D, resilience, and sustainability.
Improving the Accuracy of Ballot Scanners Using Supervised Learning

Most U.S. voters cast hand-marked paper ballots that are counted by optical scanners. Deployed ballot scanners typically utilize simplistic mark-detection methods, based on comparing the measured intensity of target areas to preset thresholds, but this technique is known to sometimes misread “marginal” marks that deviate from ballot instructions. We investigate the feasibility of improving scanner accuracy using supervised learning. We train a convolutional neural network to classify various styles of marks extracted from a large corpus of voted ballots. This approach achieves higher accuracy than a naive intensity threshold while requiring far fewer ballots to undergo manual adjudication. It is robust to imperfect feature extraction, as may be experienced in ballots that lack timing marks, and efficient enough to be performed in real time using contemporary central-count scanner hardware.
A discrete optimisation approach for target path planning whilst evading sensors

In this paper we deal with a practical problem that arises in military mission planning. The problem is to plan a path for one, or more, agents to reach a target without being detected by enemy sensors. Agents are not passive, rather they can initiate actions which aid evasion. They can knockout sensors. Here to knockout a sensor means to completely disable the sensor. They can also confuse sensors. Here to confuse a sensor means to reduce the probability that the sensor can detect an agent. Agent actions are path dependent and time limited. By path dependent we mean that an agent needs to be sufficiently close to a sensor to knock it out. By time limited we mean that a limit is imposed on how long a sensor is knocked out or confused before it reverts back to its original operating state. The approach adopted breaks the continuous space in which agents move into a discrete space. This enables the problem to be formulated as a zero–one integer program with linear constraints. The advantage of representing the problem in this manner is that powerful commercial software optimisation packages exist to solve the problem to proven global optimality. A heuristic for the problem based on successive shortest paths is also presented. Computational results are presented for a number of randomly generated test problems that are made publicly available. • We consider a novel practical problem that arises in military mission planning. • We find paths for agents to reach a target in minimal time whilst evading sensors. • Formulated as a zero–one integer program with linear constraints. • Shortest path based constraints considerably reduce the size of the problem. • A heuristic based on successive shortest paths is also presented.
Few Pains, Many Gains: Fast On-device Image Compression through Super Resolution

Mobile terminals have been accepted as main stream tools for online sociality, from which people upload photos and videos to share their lives. However, the huge expense of intensive bandwidth occupation boosts the running cost of service vendors. Conventional image compression methods alleviate this problem but fail to guarantee the visual quality and operation efficiency simultaneously. Therefore, in this paper, we propose a lightweight encoder that introduces super resolution methods to save bandwidth. In the pipeline, images are downsized at sources, transmitted with less bandwidth requirements, and reconstructed at sinks. The downscaling operation is computational friendly for most mobile terminal processors while the complex reconstructing calculation is assigned to powerful servers of the sharing platform. To accelerate the process and improve reconstructing quality, we propose an edge feature fusion super resolution method embedded in an efficient deep neural network for high- resolution image upload. Our method eliminates the heavy computation brought by the encoding process while guaranteeing reconstruction quality. Quantitative and qualitative evaluations on benchmarks demonstrate that in the same encoding approach, our performance surpass the state-of-the-art while largely reducing parameters and computations.
Basketball Action Behavior Recognition Algorithm Based on Dynamic Recognition Technology

Behavior recognition is an important research topic in the field of artificial intelligence and computer vision. In daily life, smart devices with behavior recognition capabilities are widely used in video retrieval, video surveillance, human-computer interaction and other fields, and they play an important role in the landscape. This paper studies the concept, function and application of dynamic recognition technology. At the same time, the basketball action recognition is summarized, and the saliency detection algorithm is proposed. The results show that the ucf-101 data set of 25 iterations has the largest proportion, 23.4%; the hmdb-51 data set of 10 iterations has the smallest proportion, 23.6%. As the number of iterations increases, the accuracy continues to improve. Finally, based on stability, the number of iterations basically no longer increases. At the same time, considering the issue of training time, the final number of iterations is selected as 10 times.
News Article Name Disambiguation Model Based on Reinforcement Learning

With the advent of information media society, people are facing classification problem that whether two news articles describe the same event. This helps readers identify fake news and better accept social media information. An important point is to judge whether the names in the article are the same person by the name disambiguation method. In this paper, based on the disambiguation model of reinforcement learning, the author proposes an improved solution for news articles name disambiguation problem. Solution contains entity recognition algorithms based on news article, entity disambiguation model with paragraph training method and self-adjusting method based on reinforcement learning. The proposed solution is expected to have good availability and the HR@1 value of the system based on reinforcement learning is highly improved compared with the traditional method, which is indirectly proved by experiment.
Development of Multifactor Forecasting Model based on Fuzzy Time Series

The problem of developing a multifactor forecasting model using the tools of fuzzy set theory has been considered. It is proposed to represent each of the analyzed factors, which have the same source of occurrence, but different trajectories of evolution, in the form of a fuzzy time series. The groups of fuzzy logical dependencies on the basis of fuzzy time series for each trajectory have been created. The multifactor forecasting model is developed with the use of these groups of fuzzy logical dependencies, taking into account the operations of union and intersection. Optimal parameter values of the forecasting model, providing a minimum value of the average forecasting error rate, have been determined using a genetic algorithm. An example which confirms the effectiveness of the proposed approach to the development of a multifactor forecasting model has been given.
Privacy-Enhancing Group Signcryption Scheme

In the last decades, several signcryption schemes have been proposed for different privacyenhancing purposes.In this paper, we propose a new privacy-enhancing group signcryption scheme that provides: unforgeability, confidentiality, ciphertext and sender anonymity, traceability, unlinkability, exculpability, coalition-resistance, and unforgeable tracing verification.It is important to notice that the proposed scheme allows a signer to anonymously signcryt a message on the group's behalf (i.e., sender's anonymity).Security analysis of the scheme is also provided.Our proposal is proven to be strongly existentially unforgeable under an adaptive chosen message attack, indistinguishable under an adaptive chosen ciphertext attack, and to provide ciphertext anonymity under an adaptive chosen ciphertext attack.Furthermore, the scheme is extended to work in a multi-receiver scenario, where an authorized group of receivers is able to unsigncrypt the ciphertext.The experimental results show that our scheme is efficient even on computationally restricted devices and can be therefore used in many IoT applications.Signcrypt protocol on smart cards takes less than 1 s (including communication overhead).The time of Unsigncrypt protocol on current ARM devices is negligible (less than 40 ms).
Our Future Arrived: Diffusion of Human-Machine Communication and Transformation of the World for the Post-Pandemic Era

The world is getting into a new phase in history. For the first time, humans are verbally communicating and developing meaningful relationships with non-living objects. AI is a wormhole to open a gateway to the new world, and the COVID-19 pandemic prepared the world to transform its system to be an open system that responds to, communicates with, and utilizes the remnants coming out of the wormhole of the new world. Now, we urgently need to create a holistic discourse on how we can recognize, develop, or shape the identities of communicable machines as people develop a partnership with them. Based on the emerging questions and discourses about human-machine communication, this special issue strives to investigate the present and future of advanced human-machine communication.
Effectiveness of Graph Neural Networks for User-User-Item Recommendation Systems

Graph Neural Networks are an efficient and effective framework for graph representation learning. The versatility and representational power of graph neural networks are quickly making them the preferred tool for many machine learning tasks. The same is true of recommender systems: graph neural networks allow us to easily incorporate new user, item, and contextual features. Recommender systems take a set of existing features as input to get embedding of user's. Standard high-performance deep architectures assume particular data symmetries which are not satisfied in recommender systems. So, this research work has proposed a Graph neural network (GNN) based recommendation system, where interactions of user-item are taken into consideration. Both interactions and opinions are encoded to build a user-item graph in the proposed approach. To obtain strong recommendations trust information has an important role. Also, this research work has developed a trust circle to get the nearest neighboring users for item recommendations in order to take social relations into consideration. Extensive experiments on real-world datasets have been conducted to demonstrate the superiority of the proposed model and it significantly outperforms baseline methods.
Gray Scale Image Colorization for Human Faces

Image colorization is an eclectic topic of research. Former approaches to the gray scale image colorization problem rely on manual methods with human intervention that produced some de-saturated results that are not likely to be true colorizations. To fully automate the process of colorization, enormous amount of computation power and datasets are required which is daunting. This paper proposes an approach to fully automate the task using an autoencoder model. This can be useful in colorizing old black and white pictures and movie snapshots. We used Convolution Neural Network (CNN) for colorization because of its ability to deal with image datasets. The experimental results demonstrated that this method outperforms the state of art technique.
Full-text search engine with suffix index for massive heterogeneous data

Existing popular search engines like Elasticsearch (ES) commonly use inverted indices to quickly retrieve source data matching a given set of queries. However, an inverted index may not find all of the matching results from data, particularly those that are hard to be segmented into words, such as data logs and scientific signals. This article presents our innovative technique for a true full-text search system called SAES by replacing the inverted index in ES with the suffix index to guarantee a 100% recall ratio. We designed a distributed suffix index scheme with online building and offline merging capable of scaling with the architecture of ES. The suffix index is dynamically constructed by several suffix array construction tools which adapt to the data size and available computing resources such as CPU cores, RAM, and disk capacities. Furthermore, it can be compacted to provide a trade-off between searching speed and index storage space. An experimental study was conducted to test the functions and performance of single- and multi-node SAES on realistic datasets of texts, logs, genomes, and signals. The systems performed well for both exact and approximate search queries defined on units of bytes or half-bytes. This work provides a feasible reference design for extending ES with suffix index to support true full-text searches over massive heterogeneous data.
On Construction and Estimation of Stationary Mixture Transition Distribution Models

Mixture transition distribution (MTD) time series models build high-order dependence through a weighted combination of first-order transition densities for each one of a specified number of lags. We present a framework to construct stationary MTD models that extend beyond linear, Gaussian dynamics. We study conditions for first-order strict stationarity which allow for different constructions with either continuous or discrete families for the first-order transition densities given a prespecified family for the marginal density, and with general forms for the resulting conditional expectations. Inference and prediction are developed under the Bayesian framework with particular emphasis on flexible, structured priors for the mixture weights. Model properties are investigated both analytically and through synthetic data examples. Finally, Poisson and Lomax examples are illustrated through real data applications. Supplementary files for this article are available online.
An Analytical System: Data Modelling Practices for Handling an Epidemic

For better business analytical processing, data warehouses are a helpful system. Most of the government departments, academic institutions and other businesses such as warehouse are necessary to promote their business. This study proposes some data modelling practices for handling a possible future epidemic. It shows the technical steps to develop the system like the processes of extraction, transformation and loading of data and generating an error event table. Several data are collected from concerned sources. It uses the cube format for modelling the data and viewing the data up to the granular level. It uses energy-efficient FP-growth algorithm to predict a new pattern for a possible ‘unknown epidemic. The energy-efficient FP algorithm uses a compressed representation of the database. It uses a ‘bag of jobs’ algorithm for quick execution of groups of operations. The study uses probabilistic clustering for forming the data marts on the demand of the users. It generates several reports to support the authority to handle such an epidemic in a better way. It may enable the users or policymakers to generate several reports to examine the real-time situation and manage policies towards a better future.
Visual Autopilot Decision System Based on Deep Learning

The development of artificial intelligence and automatic driving is in full swing, and the application of artificial intelligence technology in automatic driving technology is becoming more and more in-depth. Therefore, this paper proposes a visual automatic driving decision system based on deep learning, which can make automatic driving vehicles keep straight in the lane.
Sensitivity Analysis of a Multilayer Perceptron Network for Cervical Cancer Risk Classification

In recent times, deep learning and machine learning algorithms are being applied to aid in the diagnosis of cervical cancer to facilitate early diagnosis and reduce mortality rates. This paper concerns sensitivity analysis on an existing cervical cancer risk classification algorithm with respect to the number of epochs, number of neurons in the input layer (NIN), and number of neurons in the hidden layer (NNIHL). Sensitivity analysis is used to analyse the performance of the cervical cancer classification algorithm based on a Multilayer Perceptron Network when changes are made to the setup or architecture of the algorithm. Experimental results reveal that that the algorithm yields a high accuracy when it is trained at 500 epochs with eight input neurons and 100 or 300 or 500 neurons in the hidden layer. We also analyse the execution time of the algorithm under the varied parameters and discover that higher values for NIN, NNIHL and number of epochs all yield longer execution times. These results can aid in the successful application of deep learning for cervical cancer risk prediction.
Image Encryption Using Matrix Attribute Value Techniques

In this technological world encoding the images is one of the important activities to avoid monitor or hack by the third parties. Data protection becomes essential because the communication occurs frequently over open networks. This paper focuses on Binary model encryption using a form of stream ciphering. Due to the development of technology conversion of images into encoded format is more essential in any type of network. It helps to protect our data from the hacker who try to steel our information’s. Confidentially is particularly important for safe dissemination of the media over IP networks. While compression of image reduces the bandwidth, it is not safe to transmit compressed images alone. It is equally important to encrypt the images until they are transmitted to a network. Investigation shows that the methodology suggested is generating better results than the current procedures.