Reviewerly/exHarmony huggingface dataset:
Understanding regulatory trends: digital assets &amp; anti-money laundering

Purpose To evaluate the global anti-money laundering regulation of digital assets and cryptocurrencies. Design/methodology/approach This article provides an analysis of macro trends in digital asset money laundering regulation and explores the regulatory frameworks in some of the leading international crypto markets. Findings As the popularity and public adoption of digital assets have grown, global regulators have turned their attention to the risks of anti-money laundering. Monitoring the evolving international regulatory landscape is essential for organizations looking to successfully take advantage of digital asset-related investment opportunities. Practical implications Market participants should understand all applicable laws and procedures before they decide to enter the digital asset market. These considerations can become even more complex as businesses interact with multiple international regulators. Originality/value This article is designed to help investors understand the global anti-money laundering regulatory landscape regarding digital assets, particularly for those institutions interested in diversifying with crypto-related investment opportunities.
Hardware security without secure hardware: How to decrypt with a password and a server

Hardware security tokens have now been used for several decades to store cryptographic keys. When deployed, the security of the corresponding schemes fundamentally relies on the tamper-resistance of the tokens – a very strong assumption in practice. Moreover, even secure tokens, which are expensive and cumbersome, can often be subverted. We introduce a new cryptographic primitive called Encryption schemes with Password-protected Assisted Decryption (EPAD schemes), in which a user's decryption key is shared between a user device (or token) on which no assumption is made, and an online server. The user shares a human-memorizable password with the server. To decrypt a ciphertext, the user launches, from a public computer, a distributed protocol with the device and the server, authenticating herself to the server with her password (unknown to the device); in such a way that her secret key is never reconstructed during the interaction. We propose a strong security model which guarantees that (1) for an efficient adversary to infer any information about a user's plaintexts, it must know her password and have corrupted her device (secrecy is guaranteed if only one of the two conditions is fulfilled), (2) the device and the server are unable to infer any information about the ciphertexts they help to decrypt (even though they could together reconstruct the secret key), and (3) the user is able to verify that the device and the server both performed the expected computations. These EPAD schemes are in the password-only model, meaning that the user is not required to remember a trusted public key, and her password remains safe even if she is led to interact with a wrong server and a malicious device. We then give a practical pairixng-based EPAD scheme. Our construction is provably secure under standard computational assumptions, using non-interactive proof systems which can be efficiently instantiated in the standard security model, i.e., without relying on the random oracle heuristic.
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks

A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically.This paper presents GradTS, an automatic auxiliary task selection method based on gradient calculation in Transformer-based models.Compared to AU-TOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0.33% to 17.93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks.GradTS is also time-saving since (1) its gradient calculations are based on single-task experiments and (2) the gradients are re-used without additional experiments when the candidate task set changes.On the 8 GLUE classification tasks, for example, GradTS costs on average 21.32% less time than AUTOSEM with comparable GPU consumption.Further, we show the robustness of GradTS across various task settings and model selections, e.g.mixed objectives among candidate tasks.The efficiency and efficacy of GradTS in these case studies illustrate its general applicability in MTL research without requiring manual task filtering or costly parameter tuning.
Aircraft Rotated Boxes Detection Method Based on YOLOv5

Aiming at the problem of arbitrary alignment direction of aircraft targets in remote sensing images, a rotation frame detection method for aircraft targets is proposed based on YOLOv5. First, the circular smoothing label is introduced in the network classification regression to process the target angle information to avoid the abrupt change of angle value; then, the convolution operation in the backbone network is replaced by involution to reduce the number of network parameters; finally, Coordinate Attention is introduced after the Neck part to remove the background information from the features. The improved network is trained and tested on the aircraft target in the DOTA dataset, and the AP value is 90.68%, which is higher than other rotating frame detection algorithms, verifying the effectiveness of the network in this paper.
Spatially Conditioned Graphs for Detecting Human-Object Interactions

We address the problem of detecting human-object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in different messages going to neighbours of the same node. To this end, we explore various ways of applying spatial conditioning under a multi-branch structure. Through extensive experimentation we demonstrate the advantages of spatial conditioning for the computation of the adjacency structure, messages and the refined graph features. In particular, we empirically show that as the quality of the bounding boxes increases, their coarse appearance features contribute relatively less to the disambiguation of interactions compared to the spatial information. Our method achieves an mAP of 31.33% on HICO-DET and 54.2% on V-COCO, significantly outperforming state-of-the-art on fine-tuned detections.
A Comprehensive Transformative Effect of IoT, Machine Learning, and Blockchain in Computing Technology

In today’s scenario, every organization is dependent on the use of computers for automating business and work. The data generated by the organization is huge and has great significance in analyzing the processes. It can be rightfully described as the lifeblood of any organization. The world of computing requires three technologies: IoT, machine learning and blockchain. The confluence of these technologies is inevitable in the future due to the benefits which are involved in future applications. There is barely any activity today which cannot apply the use of these three technologies; e.g. healthcare, automation, education, and the Internet of Things (IoT) can be defined as interconnection of various autonomous devices which are capable of communicating with each other. IoT requires an intermittent Internet connection and address for every device. The user of these devices can remotely monitor and manage by retrieving the information on a handheld device such as a cell phone. This way, the devices are connected 24/7 and continuously generating data. Challenges of IoT involve security, connectivity problems, and huge amounts of data. The devices can be made capable of making intelligent decisions by incorporating artificial intelligence (AI) machine learning technology. Machine learning is a sub-branch of AI. It has huge potential to detect the patterns and anomalies in the data which is generated by the wireless sensor nodes in IoT. The advanced decision-making process of machine learning has already influenced our daily routines in, for example, healthcare, the banking sector, interactive gaming, transportation, and missions for exploration of space. Challenges faced by ML include data security, centralization, and limitations in resources. To cover the security aspect of these two technologies, blockchain technology is the perfect answer. It is a decentralized peer-to-peer network which stores the records and transactions in blocks which cannot be altered. This technology secures the communication by eliminating the need for any trusted third party. The blocks are stored in such a manner that it is impossible to hack or tamper with the data by taking control of device or capturing the records. This chapter will present a comprehensive and quantitative analysis of the existing research in these fields and how these technologies can be used for a transformative impact in accessing information by the users. The convergence of blockchain, machine learning, and IoT will provide scalability and high-level intellectual functioning which will be secure to bring a new paradigm of digital transformation.
Thermal imaging pedestrian detection algorithm based on attention guidance and local cross-level network

Pedestrian-related accidents are more frequent at night when visible (VI) cameras are inefficient. Compared with VI cameras, thermal cameras work better in this particular environment. Conversely, thermal images have several drawbacks, such as high noise, low-resolution, less detailed information, and susceptibility to ambient temperature. To overcome these shortcomings, an improved algorithm based on you only look once version 3 (YOLOv3) is proposed. First, the number and size of anchors are obtained using k-means++, which makes the shape of the anchors more suitable for detecting the target. Second, the attention module is added to the backbone network, which is helpful with extracting better feature maps from low-quality thermal images. Finally, the improved atrous spatial pyramid pooling module is added to the back of the backbone network to enable the extracted feature maps to contain more multi-scale information and context information. Experiments on the computer vision center-09 dataset show that the average precision is 86.1%, which is 3.5% higher than YOLOv3 and 0.8% higher than YOLOv4. The detection speed reaches 48 FPS. The results show that the improved algorithm has good accuracy and generalization.
Efficiency and Acceptability of Mixed Reality Application of Solar System in Teaching Science

The constant changed in technology and the new innovations was undoubtedly contributed largely to education. Mixed Reality (MR), one of the newest technologies and a very promising tool that allows merging of physical reality and virtual environment to enhance teaching and learning process for better and engaging learning experiences. The researchers proposed a “Mixed Reality Application of Solar System” as an assistive tool by the teachers in teaching of the lessons faster through visualizing 3D models of planets in the solar system. In this paper, the researchers made use of descriptive-qualitative technique and a survey in gathering data. The study has undergone testing and evaluation with the help of five (5) IT experts as alpha testers and twenty (20) STEP students of Subic National High School as beta testers using the ISO 9126-1 software quality standards criteria. Alpha tests overall rated mean of 4.53, interpreted as “Excellent” on Functionality, 4.13 “Very Good” on Usability, 4.00 “Very Good” on Efficiency and 4.40 “Excellent” on Portability while beta tests overall rated mean of 4.78 “Excellent” on Functionality, 4.66 „Excellent” on Usability, 4.60 “Excellent” on Efficiency and 4.80 “Excellent” on Portability. Respondents were satisfied on the performance of the mixed reality application as manifested on the results of the survey, 4.15 “Very Good” on users‟ acceptance and 4.50 “Excellent: on experts‟ acceptance. Based on the data gathered from the respondents, the researchers recommended the use of the developed mixed reality application in teaching science. Respondents recommendations were mainly focused on the improvement of the graphics and user interface through adjustment of interactive buttons in the application. The researchers recommended for the future enhancement of the application to the future researchers the capability of the mixed reality application to be available in iOS and personal computers
Software Architecture for Modeling the Interval Static and Dynamic Objects

The aim of this paper is to represent a method and a software architecture implementation for modeling the interval static and dynamic objects. Also describes the specifics of the implementation of service-oriented software architecture, as one of the options for using the developed database of model experiments for use in various subject areas.
TRAINING METHOD OF SPORTS ATHLETES USING THE NONLINEAR SYSTEM OF MOVING HUMAN BODY COMPETITIVE ABILITY

The purpose is to optimize the training methods of athletes and improve their competitive ability. First, the corresponding research model is established by analyzing the influencing factors of the athlete training system and combining with the dynamic nonlinear theory. Next, based on the deterministic learning theory, the dynamic nonlinear state identification model of athletes is established to obtain the state information of athletes in the process of competitive sports training and competition. Then, based on the established nonlinear state model, the nonlinear staged athlete training mode is designed. The experimental results show that the designed gait recognition algorithm based on deterministic learning can effectively collect the nonlinear signals generated by the movement process; the accuracy of gait recognition is about 90%, and it is less affected by the training and test datasets. The nonlinear staged training mode can effectively improve the overall index of maximum strength and strength endurance of tennis players, and effectively improve the training effect of players. This exploration provides a reference for the study of nonlinear competitive ability training mode of sports athletes.
Improved Keyword Extraction for Persian Academic Texts Using RAKE Algorithm; Case Study: Persian Theses and Dissertations

Improved Keyword Extraction for Persian Academic Texts Using RAKE Algorithm; Case Study: Persian Theses and Dissertations
Effective Use of Drone in Elementary School Programming Classes

To solve problems such as lack of appropriate teaching materials in elementary school programming classes, the use of drones was considered. We gave classes using drone for 26 children. All children were able to create a drone program to fly the designated flight route and complete the tasks. In the questionnaire after the class, it was confirmed that there was a high level of interest in programming and that it was effective in understanding unlearned content and active learning.
Do Image Classifiers Generalize Across Time

We study the robustness of image classifiers to temporal perturbations derived from videos. As part of this study, we construct two datasets, ImageNet-Vid-Robust and YTBB-Robust , containing a total 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB respectively and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions
Enabling Autonomous Medical Image Data Annotation: A human-in-the-loop Reinforcement Learning Approach

Deep learning techniques have shown significant contributions to several fields, including medical image analysis.For supervised learning tasks, the performance of these techniques depends on a large amount of training data as well as labeled data.However, labeling is an expensive and timeconsuming process.With this limitation, we introduce a new approach based on Deep Reinforcement Learning (DRL) to costeffective annotation in a set of medical data.Our approach consists of a virtual agent to automatically label training data, and a human-in-the-loop to assist in the training of the agent.We implemented the Deep Q-Network algorithm to create the virtual agent and adopted the method mentioned above, which employs human advice to the virtual agent.Our approach was evaluated on a set of medical X-ray data in different use cases, where the agent was required to create new annotations in the form of bounding boxes from unlabeled data.Results show that an agent training with advice positively impacts obtaining new annotations from a data set with scarce labels.This result opens up new possibilities for advancing the study and implementing autonomous approaches with human advice to create a costeffective annotation in data sets for computer-aided medical image analysis.
Leveraging cloud environment flexibility to smoothen the transition to remote teaching during COVID-19 pandemic – a case study

Cloud-based computational environments can offer elastic and flexible services to wide audiences. Malopolska Educational Cloud was originally developed to support the day-to-day collaboration of geographically scattered schools with universities which organized online classes, led by university teachers, as an amendment to face-to-face teaching. Due to the centralized management and ubiquitous access, both the set of services provided by MEC and their usage patterns can be adjusted rapidly. In this paper we show how – during the COVID-19 pandemic – the flexibility of Malopolska Educational Cloud was leveraged to speed up the transition from in-class to remote teaching, both in the classes and schools which were already involved in the MEC project, and newly added ones. We also discuss the actions that were required to support the smooth transition and draw conclusions for the future. © 2021 The Author(s).
ABOUT THE CHILDREN'S SEGMENT OF MUSICAL MASS MEDIA CULTURE IN THE XXIST CENTURY

The children's segment of culture — "music about children" and "music for children", being formed in the media environment, under new conditions needs close attention of scientists and updating of analytical tools for research. Acquiring features and qualities that differ from those previously inherent, this area requires scientific comments on various parameters — in terms of dynamics, prevailing directions, etc., opening new horizons for studying musical culture of the information community. The article discusses the possibilities of using search engines (Google, Yandex) to review children's musical products in the media space of modern society. The changing environment and conditions for musical culture determine the necessity of new approaches, including those from marketing technology for researching the content of genre trends, studying consumer interest, obtaining statistical information, etc. With the help of Internet applications (Trends, Wordstat) there is a prospect to analyze musical and cultural reality of society in the web search mode, revealing the comparative degree of popularity of different objects, in this case, related to the children's mass media sphere. The results obtained allow to state the predominant demand for entertainment products, which is a general trend in cultural life, regardless of the age category of consumers.
Extractive Text Summarization Using Graph Based Ranking Algorithm And Mean Shift Clustering

Text summarization is the process of converting a huge text file into a summarized version preserving its meaning and context. The major aspect of any extractive text summarization technique is to provide precise and accurate summary by using any sentence ranking algorithms. The text is first tokenized and preprocessed. The sentences in the text are ranked according tothe corresponding graph based ranking algorithm and finally the top K-scored sentences are included in the summary. Combining the graph based ranking algorithm along with mean shift clustering enables us to efficiently summarize the articles increasing the precision of the system generated summary and also reduces the execution time. The efficiency of these approaches are evaluated using rouge metrics and execution time.
Dynamic Survival Analysis with Individualized Truncated Parametric Distributions.

Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.
Abnormal behavior detection in video by using convolutional neural network

Unusual behavior detection is critically important for visual surveillance. It is also a challenging research topic in computer vision. Although much effort has been devoted to tackle this problem, such detection task in a realistic and uncontrolled environment is still far from mature. The major difficulty lies in the ambiguous characteristic in differentiating normal and abnormal behaviors, whose definitions often vary according to the context of video's history. In this paper we propose a framework for detecting and locating abnormal activities in video sequences. The key aspect of our method is the pairing of the 2D and 3D spatial-temporal Convolutional Neural Networks (CNN) for anomaly detection in contiguous video frames. The Features from Accelerated Segment Test (FAST) detector has been used In order to increase the reliability in identifying the interest locations in entry frames of convolutional neural network model. These feature extracted only from volumes of moving pixels that reduce the computational costs. The architecture of CNN model allows us to extract spatial-temporal features that contain complicated motion features. We test our framework on popular benchmark dataset containing various human abnormal activities and situations. Evaluation results show that our method outperforms most of other methods and achieves a very competitive detection performance compared to state-of-the-art methods.