Reviewerly/exHarmony huggingface dataset:
Mixed Reality User Interface for a Hybrid Operation Room

Simultaneous use of multiple complex devices for imaging and intervention purposes in a Hybrid operation room creates new challenges to the medical staff, such as avoiding collisions of different systems. Augmented reality presents a potential solution to this challenge. By visually overlaying real devices with their digital twin, users can see anticipated movements of the medical devices in a head-mounted display. With this information they can identify potential collisions before moving the device. This paper focuses on the design of a novel mixed reality user interface for a Hybrid operating room, which enables users to control multiple devices virtually. Two versions of the mixed reality user interface with different user input approaches are compared in a within-subject study regarding their usability and their suitability in steering medical devices virtually. Users were significantly faster completing study tasks with the input module press and hold button than with a virtual joystick. Additionally user experience was rated higher for the press and hold button.
A BERT-based Heterogeneous Graph Convolution Approach for Mining Organization-Related Topics

Mining organization-related topics is helpful to analyze the information dissemination situation. Existing methods based on graph neural networks mainly consider the association between words and documents, they ignore the semantic interactions between documents, and do not consider the heterogeneity of edges which are difficult to solve the challenge of blurred topic boundaries in real scenarios, resulting in performance loss. This paper proposes a BERT-based Heterogeneous Graph Convolution Network (BERT-HGCN) approach for semi-supervised topic mining that comprehensively considers multi-semantic relations between words and documents. It deeply combines the advantages of transductive learning with pre-training models. We model documents as graph-structured data and capture multiple semantic dependencies among word-word, word-doc, and doc-doc via information propagation mechanism. During the model learning process, a two-stream encoding mechanism is used to learn the structural and semantic representations, which combines a hierarchical graph convolution network (HGCN) and a BERT-based auto-encoder. It considers both edges heterogeneity and semantics of original documents. Finally, a dual-supervision loss is used to train the classifier based on graph nodes and semantic representations for topic mining. We empirically evaluate the performance of the proposed model on a real-world organization-related dataset, and the experimental results demonstrate the efficacy of the model.
Complex Task Assignment of Aviation Emergency Rescue Based on Multiagent Reinforcement Learning

Emergency rescue is a powerful countermeasure to disasters, among which Aviation Emergency Rescue (AER) is irreplaceable thanks to its unique aviation attribute. However, traditional optimization methods are not capable of the dynamic task allocation of AER. This study performs a Multiagent Reinforcement Learning (MARL) model to handle the complex task assignment problem faced in AER, carries out a detailed analysis of the problem, and do comparative experiments with the Nearby policy and Best-fit policy. The result shows that the MARL model outperforms other simple models in AER.
Privacy Risk and Data Utility Assessment on Network Data

In the modern Internet era the usage of social networks such as Twitter, Instagram and Facebook is constantly increasing. The analysis of this type of data can help us understand interesting social phenomena, because these networks intrinsically capture the new nature of user interactions. Unfortunately, social network data may reveal personal and sensitive information about users, leading to privacy violations. In this paper, we propose a study of privacy risk for social network data. In particular, we empirically analyze a set of privacy attacks on social network data by using the privacy risk assessment framework PRUDEnce. After simulating the attacks on real data, we first analyze how the privacy risk is distributed over the whole population. Then, we study the effect of high-risk users sanitization on some common network metrics.
DISCOV: Stimulating Physical Activity Through an Explorative Interactive Walking Experience

Aware of the consequences of their inactive lifestyles, many people still struggle to integrate enough physical activity into their busy lives. Interventions that nudge to reinforce existing active behaviour seem therefore more likely to be effective than those adding an activity to daily routines. To encourage people to increase their physical activity level, we designed Discov, a network of physical waypoints triggering people to lengthen their walks. Placed in a public park, Discov encourages people to explore their surroundings in a fun and challenging way by creating an interactive walking experience. Adopting a Research-through-Design approach, we explore the potential of the design of accessible infrastructures and human-environment interactions to impact public health by nudging citizens into being more physically active. We discuss insights gathered through this process and report on first user tests of this interactive walking experience.
Experience of Modeling the Information and Training Space in the Independent Work of the Student in the Preparation of the Graduate Work

The subject of the research is the modeling of the educational space, increasing the efficiency of the student's independent work in preparing a thesis. Electronic educational resources are a means of learning and act to transfer its quality. One of the objects of introduction into the educational practice of educational organizations of the Ministry of Internal Affairs of Russia continues to be the organization of independent work. The level of the completed thesis, defense performances and the final grade are directly related to the effectiveness of managing students' independent work. The scientific novelty of the study lies in the fact that it describes in detail the method of using the capabilities of the electronic information and educational environment of the institute to provide methodological and organizational assistance to students performing final qualifying work. This experience of implementing the student's independent work made it possible to identify a number of advantages in comparison with traditional forms. The use of the presented model of managing students' independent work contributed to the achievement of the learning objectives.
Identifying Spammers by Completing the Ratings of Low-Degree Users

Along with the rapid development of e-commerce, a large number of spammers disrupt the fair order of the e-commerce platform. The false ratings rated by these spammers do not match the quality of items, confusing the boundaries of good and bad items and seriously endangering the real interests of merchants and normal users. To eliminate the malicious influence caused by these spammers, many effective spamming detection algorithms are proposed in e-commerce platforms. However, these algorithms are ineffective in judging how trustworthy a user with insufficient rating data. In order to address this issue, we take inspiration from traditional recommender systems by completing the missing ratings of low-degree users to improve the efficiency of spamming detection algorithms when approaching those users. User similarity is used in this paper to predict the missing ratings of users. A novel reputation ranking method $$IOR\_LU$$ is proposed. We then test our improvements compared with DR, IGR, and IOR. Experimental results on three typical data sets suggest that our method combined with IOR has improved by at least $$9.68\%$$ , $$3.29\%$$ , and $$0.21\%$$ in dealing with malicious spammers, respectively. As for results on detecting random spammers, our method improves by a least $$5.06\%$$ , $$21.12\%$$ and $$4.46\%$$ , respectively.
Investigating the Effect of Machine-Translation on Automated Classification of Toxic Comments

This paper discusses the research findings on the performance of automated toxic comment classification following machine translation. We tested Google Perspective API first on comments from non-English Wikipedia talk pages in five languages, and then on their English translation (generated with Google's Cloud Translate API). In addition to giving baselines on the current performance of Perspective in five languages, this allows for comparison on how machine-translation alters the classification. We show that the level of disagreement between pre- and post-translation classification is heavily dependent on the language used. The comments come from a Kaggle dataset and we filter them to ensure monolingual comments with simple punctuation. Results show above 84% of the French, Italian and Spanish comments received the same class pre- and post-translation, while Portuguese and Russian performed the worst of the five languages tested, with F-scores below 0.6.
Automated information system for teaching programming languages

The article presents an automated training information system that implements the author’s methodology for learning programming languages. The results of the study of a number of languages show the general classes of their constructions: branches, loops, data input and output constructions, creation of functions and / or methods, constructions for setting constants or variables. This, as well as other features of modern programming languages, make it possible to successfully teach them on the basis of theory and examples containing constructions of the studied and formal language. The system uses modern free software and original modules, developed and tested a database of training examples and tasks, interpreters for the C # and Python programming languages. A script for checking the correctness of the task execution result has been implemented, including the stages of text parsing and task execution in the interpreter. The system allows you to form and track the trajectory of individual training. A comprehensive check of the level of knowledge of the target language is possible with additional control of decisions for originality. The system is used in teaching a number of software engineering disciplines at the university.
Design and implementation of perfect secure block cipher and its security research

In order to achieve perfect security and unconditional security of block cipher, this paper develops and implements the perfect security block cipher based on Boolean algebra and multi-branch conditional selection logic. A 2n-bit perfect secure block cipher has the following technical characteristics: 1. Every reversible encryption transformation is a bijection mapping from the plaintext space <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{0,1\}^{2\mathrm{n}}$</tex> to the ciphertext space <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{0,1\}^{2\mathrm{n}};2$</tex> . Any plaintext can be encrypted into any same ciphertext by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{2\mathrm{n}}$</tex> transformations, and any ciphertext can be decrypted into any same plaintext by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{2\mathrm{n}}$</tex> transformations; 3. Any plaintext-ciphertext pair can be produced under <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{2\mathrm{n}}$</tex> keys. The above characteristics make the perfect secure block cipher algorithm not only have perfect security, but also have unconditional security. Perfect security lies in that the probability of any two plaintexts encrypting into any ciphertext is equal. Because the key length 4n is a variable, the perfect secure block cipher can encrypt a plaintext of any length into any same ciphertext in theory, thus its key and plaintext cannot be cracked. Unconditional security lies in that if the plaintext has no semantics, for any ciphertext of any length, this ciphertext can be produced under any keys and can be encrypted from at least <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{2\mathrm{n}}$</tex> different plaintext into it. After the opponent captures the ciphertext, the probability of guessing the key correctly is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\ /\ 2^{4\mathrm{n}}$</tex> , and the probability of guessing the plaintext is less than or equal to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\ /\ 2^{2\mathrm{n}}$</tex> . Comparing with other block cipher, such as AES, SM4 etc., perfect security block cipher algorithm has the advantages of simple algorithm, variable block length, easy software and hardware implementation, perfect security and unconditional security.
Online Planning for Interactive-POMDPs using Nested Monte Carlo Tree Search

The ability to make good decisions in partially observed non-cooperative multi-agent scenarios is important for robots to interact effectively in human environments. A robust framework for such decision-making problems is the Interactive Partially Observable Markov Decision Processes (I-POMDPs), which explicitly models the other agents' beliefs up to a finite reasoning level in order to more accurately predict their actions. This paper proposes a new online approximate solver for I-POMDPs, called Interactive Nested Tree Monte-Carlo Planning (I-NTMCP), that combines Monte Carlo Tree Search with the finite nested-reasoning construction of I-POMDPs. Unlike existing full-width I-POMDP planners, I-NTMCP focuses planning on the set of beliefs at each nesting level which are reachable under an optimal policy and uses sampling to construct and update policies at each nesting level, online. This strategy enables I-NTMCP to plan effectively in significantly larger I-POMDP problems and to deeper reasoning levels than has previously been possible. We demonstrate I-NTMCP's effectiveness on two competitive environments. The results indicate that I-NTMCP can generate substantially better policies up to more than 50× faster than I-POMDP Lite - one of the fastest I-POMDP solvers today. In the pursuit-evasion domain, we show I-NTMCP can plan effectively in a complex problem with over 88K states, which is two orders of magnitude larger than existing I-POMDP planning benchmark problems.
Toward Cleansing Backdoored Neural Networks in Federated Learning

Malicious clients can attack federated learning systems using compromised data during the training phase, including backdoor samples. The compromised global model will perform well on the validation dataset designed for the task, but a small subset of data with backdoor patterns may trigger the model to make a wrong prediction. In this work, we propose a new and effective method to mitigate backdoor attacks in federated learning after the training phase. Through federated pruning method, we remove redundant neurons and "backdoor neurons", which trigger misbehavior upon recognizing backdoor patterns while keeping silent when the input data is clean. The second optional fine-tuning process is designed to recover the pruning damage to the test accuracy on benign datasets. In the last step, we eliminate backdoor attacks by limiting the extreme values of inputs and neural network neurons’ weights. Experiments using our defenses mechanism against the state-of-the-art Distributed Backdoor Attacks on CIFAR-10 show promising results; the averaged attack success rate drops more than 70% with less than 2% loss of test accuracy on the validation dataset. Our defense method has also outperformed the state-of-the-art pruning defense against backdoor attacks in the federated learning scenario.
Comparative Analysis of Algorithms for Mobile Robots in Performing Certain Tasks

In this paper algorithms for line following and overcoming previously undefined obstacles for mobile robots with differential drive and mobile robots with Mecanum wheels are presented and analyzed. The hardware of the robots is described. Software has been developed for implementation of the described methods. Experiments related to these methods have been conducted. The results of the experiments are presented.
Detecting Compromised Architecture/Weights of a Deep Model

Adversarial attacks perturb data to modify a model’s prediction. These perturbations can be crafted in a white-box or black-box setting, depending on whether the target model architecture/weights are known or unknown. Compromised architecture and weights of a model makes it vulnerable to the more powerful white-box attacks. In this work, we determine if a deep model is compromised by distinguishing white-box from black-box adversarial attacks. The proposed method utilizes the internal representations of the target model and a proxy model to increase the detector efficacy. Additionally, it employs a spatial smoothing module to control the strength of white-box attacks relative to black-box attacks, and a proxy module to aid in measuring the transferability of the attack. Both modules work in tandem to increase the contrast of the internal representations between white-box and black-box attacks for better discrimination. We perform a detailed ablation of our method to showcase the importance of the different modules, and show that the spatial smoothing and proxy defense techniques enable our framework to significantly outperform the simple classification baseline on common vision datasets.
Real-time Litter Recognition Using Improved YOLOv4 Tiny Algorithm

Littered roads have become a familiar sight in India. The main reason is the increasing population and inefficient waste disposal system. Since garbage collectors cannot pick litter in all the places, there is a need for an efficient way to detect it. Hence, a machine learning-based object detection model is used. In this, we have applied an improved YOLOv4-Tiny algorithm to detect the garbage, classify it and make the detection process easier on custom datasets. We have improved the algorithm in terms of the object prediction time, this is done by replacing a max pooling layer with one of two layers present in a fully connected layer. When an input is given, the algorithm detects the litter in the image with a bounding box around it along with the label and confidence score. The proposed model reduces the prediction time by 0.517 milliseconds less than the original algorithm employed which concludes that the object is predicted faster.
Validation of Security Variables for Audits in Wireless Wi-Fi Networks

Wi-Fi wireless networks are an integral part of everyday life and as such security, the protection of these networks according to their evolution over time has been a constant challenge due to their use and the information that is transmitted through it and changes in the purpose of their use and the current needs of electronic devices [1]. Currently, there are several sources of documentation that aim to guide both designers of Wi-Fi wireless networks and security managers who must perform audits to networks, and consequently with the need to incorporate the principle of defense in depth. This study presents an experimental review [2] to validate established security variables to measure their effects and obtain results on how these multivariables [3] relate to each other and affect the gap in the results of an information security audit.
Photo Analysis for Characterizing Food-Related Behavior and Photo Elicitation to Set-Up a Mixed Reality Environment for Social Eating Among Older Adults Living at Home

To be more successful in preventing malnutrition for older adults living at home, there is a need for better methods to characterize their food behavior, as well as there is a need for health-supporting technologies focusing more on individualized contextual preferences. This study reveals how photos can be used to characterize older adults’ food-related behavior and preferences, and how photo elicitation can be used to design an eating environment in mixed reality for older solitary adults. This study is based on a sample of 22 older adults, who took in total 153 pictures of their meals, and a workshop using photo elicitation with 16 older adults in a community center. The findings revealed how photos can be used as a self-monitoring process to create meaningful and rich in-depth information on food-related behavior of older adults living at home. Photo elicitation can be used as a supplement to characterize older adults’ food-related behavior and preferences in a mixed reality environment. Further, we outline both advantages and limitations of using photo elicitation in a context of human-computer interaction.
Notice of Removal: On the Inverses and Their Hamming Weights of Known APN, 4-Differentially Uniform and CPP Exponents Over F₂ⁿ

Removed.
Identification of Language Using ML Algorithms and Vectorization Techniques

Tongue classification is a method for identifying and classifying the vernacular employed in a speech or material. This work has examined and compared the usage of feature extraction methods and algorithmic machine learning to detect countries. We used three ml techniques, two feature extraction methods. The ml algorithms utilised are Nave Bayes, Reptree, and SVM, and the feature extraction methods have are Idf Documentary Repetition and Counting Vectorizer. This research evaluated and analyzed the aforementioned classification methods and segmentation techniques. It also involves website design.
Answering Questions About COVID-19 Vaccines Using ChatBot Technologies

Chatbots are a powerful tool to design and implement sophisticated computer systems able to interact with human users through natural language. Chatbots are considered more friendly to users than other sources of information, and consequently, they have been largely applied to various domains. In this work, we propose a chatbot application aimed at answering questions about COVID-19 vaccines. Besides the interesting application domain and the knowledge engineering behind this development, we also introduce a modular chatbot architecture based on an easy-to-update database and natural language templates in which new information (for example, new vaccines) can be added without the need for retraining the chatbot. Furthermore, in this paper, we provide an empirical evaluation of the proposed chatbot application.