Reviewerly/exHarmony huggingface dataset:
The use of data management technologies in the creation of historical Internet resources

In this article, special attention is paid to the problem of preserving and distributing the results of virtual reconstructions of historical and cultural heritage objects by creating specialized electronic reference books based on the Wordpress website builder. Using the example of the handbook on virtual reconstructions of estates in the Moscow region, the author shows how, with the help of modern data management technologies, it is possible to configure the system in such a way that its filling and administration take place with the least amount of time on the part of the authors of publications and the directory administrator. The main problem solved by such a system is the elimination of the need for manual entry of each article and subsequent approval of the publication in manual mode. The author of the study presents an approbation of the use of data management technologies in historical Internet resources. The systems presented in the article are used in industrial data management systems, but their availability allows them to be used in other fields of activity, including as an auxiliary tool in scientific research. Automatic transfer of data from the user's web form to a new directory page significantly speeds up the task of filling an Internet resource. The proposed version of the directory was created using the MySQL database, the Apache NiFi data orchestrator and the Wordpress website builder. All the listed tools are free and available for download on their official pages.
Self-Supervised Reinforcement Learning for Active Object Detection

Active object detection (AOD) offers significant advantage in expanding the perceptual capacity of a robotics system. AOD is formulated as a sequential action decision process to determine optimal viewpoints to identify objects of interest in a visual scene. While reinforcement learning (RL) has been successfully used to solve many AOD problems, conventional RL methods suffer from (i) sample inefficiency, and (ii) unstable outcome due to inter-dependencies of action type (direction of view change) and action range (step size of view change). To address these issues, we propose a novel self-supervised RL method, which employs self-supervised representations of viewpoints to initialize the policy network, and a self-supervised loss on action range to enhance the network parameter optimization. The output and target pairs of self-supervised learning loss are automatically generated from the policy network online prediction and a range shrinkage algorithm (RSA), respectively. The proposed method is evaluated and benchmarked on two public datasets (T-LESS and AVD) using on-policy and off-policy RL algorithms. The results show that our method enhances detection accuracy and achieves faster convergence on both datasets. By evaluating on a more complex environment with a larger state space (where viewpoints are more densely sampled), our method achieves more robust and stable performance. Our experiment on real robot application scenario to disambiguate similar objects in a cluttered scene has also demonstrated the effectiveness of the proposed method.
A Comprehensive Survey on Community Deception Approaches in Social Networks

Community detection techniques seek to find densely connected clusters within large networks. However, it raises privacy concerns, like the personal reveal or community member’s group information, and contradicts the desire of individual or group to remain anonymous. As a result, concealing a specific community in a network to avoid the finding by methods of community detection becomes critical. Some previous work focuses on hiding some sensitive communities in order to conceal the community association of the focused vertices. Community deception is achieved by changing the links between the vertices in a network minimally so that a specific community can hide as much as possible from a community detection algorithm. This article discusses a study on previously proposed community deception techniques, as well as a discussion of the performance measures used to evaluate community deception methods.
A new multi-spectral image registration algorithm

In this paper, an EOH based multi-spectral image registration algorithm is proposed, which is robust to rotation and scale changes. The key points of EOH descriptor have no main direction, and the neighborhood size of key points is fixed, so it is not robust to rotation change and scale change. The existing multi-spectral image registration methods mainly use the gradient features of the neighborhood of key points, but the gradient information between multi-spectral images is not stable, resulting in the limited improvement effect of these methods. The method proposed in this paper uses mutual information measure to calculate the relative rotation angle between images, so as to determine the main direction of key points, calculate the key point descriptor according to the main direction of key points, and make the size of key point neighborhood change with the scale of key points. Experimental results show that the proposed method in this paper is more robust to rotation and scale change.
Research on News Copyright Management and Protection Mechanism Based on Blockchain Technology

In the current digital background, infringement of news works has become a common phenomenon, and the traditional copyright protection measures have failed to meet the needs of the times. This paper will analyze the current research on blockchain in copyright protection at home and abroad, analyze the problems of news copyright protection in China, and explore a news copyright protection initiative suitable for China's national conditions.
Secure File Storage on Cloud Using Hybrid Cryptography

Cloud is used in various fields like industry, military, college, etc. for various services and storage of huge amount of data. Data stored in this cloud can be accessed or retrieved on the users request without direct access to the server computer. The rapidly increased use of cloud computing within the many organization and IT industries provides new software with low cost. Cloud computing is useful in terms of low cost and accessibility of knowledge. Cloud computing gives lot of advantages with low cost and of knowledge accessibility through Internet. Ensuring the safety of cloud computing may be a major think about the cloud computing environment, as users often store sensitive information with cloud storage providers, but these providers could be untrusted. So, sharing data in a secure manner while preserving data from an untrusted cloud remains a challenging issue. Our approach ensures the safety and privacy of client sensitive information by storing data across single cloud, using AES, triple DES and Blowfish algorithm.
Application of Virtual Image Technology in Badminton Teaching in Colleges and Universities

Virtual image refers to the unreal image projected by projector, mobile phone, and other projection equipment. Through virtual images, we can explore some scenes that are difficult to observe directly with the naked eye or express fictional images in our minds. At present, the technology is widely used in construction industry, tourism industry, and medical system. At present, after the development of efficient physical education in China, schools generally do not pay attention to the specific application of virtual image technology in badminton teaching in colleges and universities in China. This paper analyzes the representative badminton events in China’s physical education, improves the traditional algorithm, optimizes the effective segmentation and recognition algorithm of virtual image technology in motion capture, combines the improved virtual image technology with virtual reality glasses, and compares the accuracy of the virtual image technology proposed in this paper with that of the traditional image technology. The accuracy of the virtual image technology proposed in this paper always remains the first. The practical application in badminton teaching in colleges and universities has achieved good results.
On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN, where combination of the data modalities (tabular and time dependent) are performed at different stages (early vs. model fusion). Despite high data unbalance, the models reach average precision 0.96-0.98 (0.75-0.85), recall 0.96-0.98 (0.74-0.85), and F1-score 0.97-0.98 (0.79-0.83) on the non-hospitalized (or hospitalized) class. Performances do not significantly drop even when selected lists of features are removed to study model adaptability to different scenarios. However, a systematic study of the SHAP feature importance values for the developed models in the different scenarios shows a large variability across models and use cases. This calls for even more complete studies on several explainability methods before their adoption in high-stakes scenarios.
Early detection of breast cancer in histopathology images employing convolutional neural network (CNN)

Breast cancer is currently acknowledged as a serious disease that affects both men and women. Breast cancer could develop as a person ages, either as a result of an unhealthy lifestyle or due to heredity-related factor. As a result, early detection towards the presence of cancer is required for early prevention. Upon considering its enormous skills in modeling complex data such as images and sounds, deep learning has served as one of the most debatable subjects in the area of Machine Learning. The Convolutional Neural Network involves the Deep Learning approach that offers the best results in image recognition (CNN). Breast cancer diagnosis in this study was conducted by utilizing the Convolutional Neural Network (CNN) approach, which has proven to be accurate in various investigations, especially regarding the picture data. This study employed histopathology images data from Kaggle in the form of breast histopathology images. The total number of bytes is 549 further utilized to import data, examine data, preprocessing, define helper functions for the classification task, and assess classification models as the five stages of the research method. Implementing a modified CNN model, researchers were able to detect breast cancer in histopathology images with an accuracy of 80%.
DadmaTools: Natural Language Processing Toolkit for Persian Language

We introduce DadmaTools, an open-source Python Natural Language Processing toolkit for the Persian language.The toolkit is a neural pipeline based on spaCy for several text processing tasks, including normalization, tokenization, lemmatization, part-of-speech, dependency parsing, constituency parsing, chunking, and ezafe detecting.DadmaTools relies on fine-tuning of ParsBERT using the PerDT dataset for most of the tasks.Dataset module and embedding module are included in Dad-maTools that support different Persian datasets, embeddings, and commonly used functions for them.Our evaluations show that DadmaTools can attain state-of-the-art performance on multiple NLP tasks.
Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis

The Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME), a 1.7-millionword treebank that is an important resource for research in syntactic change, has several properties that present potential challenges for NLP technologies.We describe these key features of PPCEME that make it challenging for parsing, including a larger and more varied set of function tags than in the Penn Treebank, and present results for this corpus using a modified version of the Berkeley Neural Parser and the approach to function tag recovery of Gabbard et al. (2006).While this approach to function tag recovery gives reasonable results, it is in some ways inappropriate for span-based parsers.We also present further evidence of the importance of in-domain pretraining for contextualized word representations.The resulting parser will be used to parse Early English Books Online, a 1.5 billion word corpus whose utility for the study of syntactic change will be greatly increased with the addition of accurate parse trees.
Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data.A challenging problem is ambiguity between hate speech and offensive language, causing low performance both overall and specifically for the hate speech class.It can be argued that misclassifying actual hate speech content as merely offensive can lead to further harm against targeted groups.In our work, we mitigate this potentially harmful phenomenon by proposing an adversarial debiasing method to separate the two classes.We show that our method works for English, Arabic German and Hindi, plus in a multilingual setting, improving performance over baselines.
IoT Automated Pill Dispenser for Elderly Care

This project is fabricated to ease medication consumption, especially for the elderly that always forget the proper time to consume their prescribed medicine. Thousands of cases have been recorded throughout the decade of wrong consumption of medicine. This may lead to serious issue as improper medicine dose is ineffective for the sickness that they suffer. This paper aims to develop an automated pill dispenser that triggers the alarm to the elderly, records the medication drop data using ThingSpeak, and notifies the caretaker through the Blynk application. This paper uses Arduino ATmega 2560 as the microcontroller for the device. It will be connected to a Wi-Fi module to transmit the data for the ThingSpeak platform and notifies the caretaker through the Blynk application. The paper will surely help eliminate the underdose and overdose issue, especially among the elderly and ease guardian’s worries about their absentness at the moment of medicine consumption. It is hoped that this paper can be used to replace hand-held pillbox device that is a hassle toward the elderly.
Buoyancy energy driven archimedes optimization algorithm based on Lévy flight and tangent flight

Archimedes optimization algorithm (AOA) is a metaheuristic algorithm inspired by the Archimedes physical law. It simulates the principle of buoyancy applied upward to partially or completely submerged objects. The decay energy of buoyancy, Lévy flight and Tangent flight are introduced into AOA. The buoyancy energy is adopted as the judgment condition of global search and local search. Then the location updating methods based on Lévy flight and Tangent flight are proposed so as to enhance its ergodicity and unrepeatable, improve the convergence speed and accuracy. Finally, through a large number of simulation experiments on 25 benchmark functions in CEC-BC-2017, the improved AOAs are compared to show their advantages and disadvantages. On the other hand, two engineering design problems (pressure vessel design and spring design problem) are optimized. The experimental results show that the AOA based on buoyancy energy mixed Lévy flight and Tangent flight can solve the function optimization and engineering optimization problems well. It has the strong balance between exploration and exploitation, fast convergence speed and high search accuracy.
Act for Your Duties but Maintain Your Rights

Most of the synthesis literature has focused on studying how to synthesize a strategy to fulfill a task. This task is a duty for the agent. In this paper, we argue that intelligent agents should also be equipped with rights, that is, tasks that the agent itself can choose to fulfill (e.g., the right of recharging the battery). The agent should be able to maintain these rights while acting for its duties. We study this issue in the context of LTLf synthesis: we give duties and rights in terms of LTLf specifications, and synthesize a suitable strategy to achieve the duties that can be modified on-the-fly to achieve also the rights, if the agent chooses to do so. We show that handling rights does not make synthesis substantially more difficult, although it requires a more sophisticated solution concept than standard LTLf synthesis. We also extend our results to the case in which further duties and rights are given to the agent while already executing.
Automatic Synthesis of Dynamic Norms for Multi-Agent Systems

Norms have been widely proposed to coordinate and regulate multi-agent systems (MAS) behaviour. We consider the problem of synthesising and revising the set of norms in a normative MAS to satisfy a design objective expressed in Alternating Time Temporal Logic (ATL*). ATL* is a well-established language for strategic reasoning, which allows the specification of norms that constrain the strategic behaviour of agents. We focus on dynamic norms, that is, norms corresponding to Mealy machines, that allow us to place different constraints on the agents' behaviour depending on the state of the norm and the state of the underlying MAS. We show that synthesising dynamic norms is (k + 1)-EXPTIME, where k is the alternation depth of quantifiers in the ATL* specification. Note that for typical cases of interest, k is either 1 or 2. We also study the problem of removing existing norms to satisfy a new objective, which we show to be 2EXPTIME-complete.
A Dataset of Stationary, Fixed-wing Aircraft on a Collision Course for Vision-Based Sense and Avoid

The emerging global market for unmanned aerial vehicle (UAV) services is anticipated to reach USD 58.4 billion by 2026, spurring significant efforts to safely integrate routine UAV operations into the national airspace in a manner that they do not compromise the existing safety levels. The commercial use of UAVs would be enhanced by an ability to sense and avoid potential mid-air collision threats however research in this field is hindered by the lack of available datasets as they are expensive and technically complex to capture. In this paper we present a dataset for vision based aircraft detection. The dataset consists of 15 image sequences containing 55,521 images of a fixed-wing aircraft approaching a stationary, grounded camera. Ground truth labels and a performance benchmark are also provided. To our knowledge, this is the first public dataset for studying medium sized, fixed-wing aircraft on a collision course with the observer. The full dataset and ground truth labels are publicly available at https://qcr.github.io/dataset/aircraft-collision-course/.
Combining Artificial Intelligence and Edge Computing to Reshape Distance Education (Case Study: K-12 Learners)

Nowadays, the use of distance learning is increasing, especially with the recent Covid-19 pandemic. To improve e-learning and maximise its effectiveness, artificial intelligence (AI) is used to analyse learning data stored in central repositories (e.g. in cloud). However, this approach provides time-lagged feedback and can lead to a violation of user privacy. To overcome these challenges, a new distributed computing paradigm is emerging, known as Edge Computing (EC), which brings computing and data storage closer to where they are required. Combined with AI capabilities, it can reshape the online education by providing real-time assessments of learners to improve their performance while preserving their privacy. Such approach is leading to the convergence of EC and AI and promoting AI at the Edge. However, the main challenge is to maintain the quality of data analysis on devices with limited memory capacity, while preserving user data locally. In this paper, we propose an Edge-AI based approach for distance education that provides a generic operating architecture for an AI unit at the edge and a federated machine learning model to predict at real-time student failure. A real-world scenario of K-12 learners adopting 100% online education is presented to support the proposed approach.
Secure Covid-19 Electronic Health Records Management for Telediagnosis and Travel Ticket Assistant System Using Cryptographic Approaches

Presently, the whole world is suffering from the Covid-19 pandemic. In this harmful situation, using information and Internet technology is mandatory for the government and medical practitioners. After the lockdown, the government needs to take important decisions to allow passengers to travel through air, rail, and land. In the present situation, people need to get a medical report from the hospitals to travel through various modes of transport. In this regard, the Covid-19 history of the passengers plays an important role in issuing tickets to the passengers. Hence, in this paper, a novel authentication method using InterPlanetary File System (IPFS) is suggested to retrieve the Covid-19 history of all passengers to determine whether to issue tickets and allow people to travel through various modes of transport. The government can share the Covid-19 status of passengers with the ticket issuing authority. The medical practitioners can share medical reports and medical images of such people for telediagnosis. To provide security, a novel privacy-preserving storage and sharing of Covid-19 records using secure authentication and image cryptosystem are proposed using chaos, cryptographic hash (SHA-256), Paillier cryptosystem, and IPFS. Security analysis shows that the system can withstand various kinds of attacks.
A partial least squares regression model based on variational quantum algorithm

Abstract Partial least squares regression (PLSR) is an essential multivariate correlation analysis method in machine learning field. In this paper, we propose a variational quantum algorithm for partial least regression (VQPLSR). By exploring the relationship between standard basis states and optimization, we design a cost function that can train regression parameters and weight vectors simultaneously. The VQPLS requires only one copy of variables as input, which reduces the complexity of quantum circuit implementation. Compared with PLSR, the VQPLSR achieves an exponential speed-up in the independent variable dimension n and dependent variable dimension w . Simulation results show that regression parameters and weight vectors can be constructed with the error ∼10 −5 for 4 × 2 dimensional variable matrix. This algorithm inspires us to explore more quantum applications in machine learning.
A secured transaction based on blockchain architecture in mobile banking platform

Blockchain technology makes the transfer of money possible and makes transactions electronically easier and safer than the conventional payment system. Its common awareness that blockchain can make financial data security easier. Technology, however, also poses certain difficulties in fulfilling some financial data protection needs. Therefore, this paper recommends a framework founded on blockchain to protect and secure financial data transfer on mobile banking platform. A multi-level authentication was used to secure the mobile banking and a two-factor authentication protocol to generate a time based one-time password (TOTP) for secure funds transfer. The proposed system addresses the requirements of building a secure application for mobile banking because of the increased security and privacy of clients on mobile banking applications. The secured blockchain mobile banking can be employed to enhance the business transaction on mobile banking platform, make the system more effective, and help respond quickly to any transaction on the platform.