Reviewerly/exHarmony huggingface dataset:
The Effect of Low Friction in Pavement Due to Floods and High-speed Vehicles in Increasing the Number of Rescue Vehicles' Driving Accidents

Background: The rescue operation is dangerous during floods when the street surface is wet and the rescue vehicles moving fast. Rural roads are known with a high percentage of run-off-the-road crashes and accidents, mostly affected by the unsuitable and inappropriate speed of the car and failure to keep a safe side distance from the highway alignment. Determining the length of the vertical curve and the stopping distance depends on the speed parameter in the geometric design. This parameter depends on the traffic flow and the vehicle driver. Materials and Methods: The calculation of distance visibility, especially in the vertical and horizontal curves, has a significant role in the geometric design of roads and the safety of roads and drivers. This study investigates changes in two critical parameters of friction surface of pavement and vehicles’ speed on the probability of accidents. In the present study, by changing the speed parameter, namely, increasing speed and decreasing friction, and reducing the distance of vision, the required vertical curve is calculated and compared with the existing curve length. Thus, when the curve length does not match the required arch length, the accident’s probability increases. Results: One of the study’s findings indicates that with a 5% decrease in the friction coefficient of the vertical curve, the probability of accident increases by about 20%. The results show that an approximately 10% increase in speed can raise the likelihood of a crash by 25% on average. Conclusion: The use of accident reduction-related strategies for rescue vehicles and ambulances will help the victims affected by natural disasters. The results showed that choosing the right speed is very important in flooded wet streets. Driving fast to speed up the relief operation not only increases the risk of traffic accidents but also slows down the relief of natural disasters. The results indicate the proper performance of the model created by the probabilistic method. Besides, this model’s use has the advantage of correctly identifying the factors affecting the increase in risk. Also, it helps to take the right measures to increase safety in the relief of natural disasters, especially floods.
Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets

The complex and changeable structures of ancient Chinese characters result in the decreasing accuracy of their image retrieval. To resolve this problem, a new retrieval method based on dual hesitant fuzzy sets is proposed. Dual hesitation fuzzy sets that can express uncertain information more comprehensively are employed in the feature extraction process of directional line elements. The multiattribute evaluation index of adjacent grids for the current grid and its corresponding membership and nonmembership functions are established, and the weight of each attribute is calculated by the dual hesitation fuzzy entropy, such that the proposed features can fully reflect the topological structure of ancient Chinese characters. Using the dual hesitation fuzzy correlation coefficient to measure the similarity between the ancient Chinese character images to be retrieved and the candidate images, the retrieval of ancient Chinese character images is realized. Experiments show that when the t0hreshold value of the correlation coefficient is 0.9, the average retrieval accuracy is 90.4%.
A parallel mechanism designed for the sun-tracking system

In this paper, a parallel mechanism designed for the sun-tracking system was designed and presented. The system adopts the parallel mechanism, which is realized by connecting a fixed plate and a moveable plate with the spherical joints. The linear actuators, which consist of step motors and ball screws, are used to drive the spherical joints and realize the solar tracking movement. The whole system has a size of 1495mm×1495 mm, while the height is 327mm. The flat shape allows installation in different directions. The weight of the sun tracking system is approximately 40 kg. The solid angle of the sun tracking system is 4/3 while the whole sky is 2. Therefore, it can effectively track the sun for at least 8 hours in a day, considering season varying and weak sunlight at sunrise and sunset. Besides, the system has the advantages of a small enveloping space and high stiffness. The feasibility of solar tracking based on the parallel mechanism was verified.
Long-term Behaviour Recognition in Videos with Actor-focused Region Attention

Long-Term activities involve humans performing complex, minutes-long actions.Differently than in traditional action recognition, complex activities are normally composed of a set of sub-actions, that can appear in different order, duration, and quantity.These aspects introduce a large intra-class variability, that can be hard to model.Our approach aims to adaptively capture and learn the importance of spatial and temporal video regions for minutes-long activity classification.Inspired by previous work on Region Attention, our architecture embeds the spatio-temporal features from multiple video regions into a compact fixed-length representation.These features are extracted with a 3D convolutional backbone specially fine-tuned.Additionally, driven by the prior assumption that the most discriminative locations in the videos are centered around the human that is carrying out the activity, we introduce an Actor Focus mechanism to enhance the feature extraction both in training and inference phase.Our experiments show that the Multi-Regional fine-tuned 3D-CNN, topped with Actor Focus and Region Attention, largely improves the performance of baseline 3D architectures, achieving state-of-the-art results on Breakfast, a well known long-term activity recognition benchmark.
An Architecture for Human Action Recognition in Smart Cities Video Surveillance Systems

Facial recognitionLlaurado-Fons, J. M. systems are part of our daily lives.Martinez, Ana The face is already used to unlock mobile phones, withdraw cash at ATMs, pay at establishments,Pujol, Francisco A. perform checks at airports or identify suspects at large events such as football matches or concerts.Mora, Higinio In Smart Cities there is a large amount of information about the environment, so it is very interesting to apply techniques to characterize the different domains and the detection of events and certain situations. In this work we propose a system for access control through facial recognition and data extraction of the individual through an identification card and deep learning. The architecture will be easily scalable and adaptable to new situations and fields of application. Therefore, our system will identify human features in video surveillance sequences using deep learning techniques to support video monitoring systems. The results obtained with different databases provide accuracies over 90%, which proves the validity of our proposal.
Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features

Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e.g., similarity with respect to query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel query recommendation method that uses a comprehensive set of features to find similar queries. We combine query text and search result features with bipartite graph modeling of user clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo search engines show the superiority of the proposed method in improving the precision of query recommendation, e.g., by more than 20% in terms of p@10, compared with some well-known algorithms.
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy

The techniques based on the theory of differential privacy (DP) has become a standard building block in the machine learning community. DP training mechanisms offer strong guarantees that an adversary cannot determine with high confidence about the training data based on analyzing the released model, let alone any details of the instances. However, DP may disproportionately affect the underrepresented and relatively complicated classes. That is, the reduction in utility is unequal for each class. This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class. We cast the learning procedure as a two-stage optimization problem, which integrates differential privacy with fairness. FairDP establishes a self-adaptive DP mechanism and dynamically adjusts instance influence in each class depending on the theoretical bias-variance bound. Our experimental evaluation shows the effectiveness of FairDP in mitigating the disparate impact on model accuracy among the classes on several benchmark datasets and scenarios ranging from text to vision.
Deductive learning approaches for Face Recognition by using Gabor Feature

Artificial Intelligence (AI) offers a vital role in refining the well-being of human in many areas.Recently in the field of face recognition, these have some limitations, exclusively when faces are in different poses or have different levels of illumination, or when the face is blurred.This research focuses to produce the optimal solutions for the face recognition.The parameter k is 6 the model produces the highest accuracy value which is 84.67% accuracy level.The highest precision value is 84.67% while applying the parameter k is 1.The highest recall value is 86.68% which is produced by while applying the parameter k=6, the lowest recall value is 68.77% while applying the parameter k=9.The model takes more time to build the model when applying the parameter k=10 and very low time consumption model is k=8 which is recommended this system for optimal solutions.
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration

In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the same task (social perception), and ii) coordinate with the human-like agent to solve the task in an unseen environment as fast as possible (human-AI collaboration). For this challenge, we build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines. We evaluate the performance of AI agents with the human-like agent as well as and with real humans using objective metrics and subjective user ratings. Experimental results demonstrate that our challenge and virtual environment enable a systematic evaluation on the important aspects of machine social intelligence at scale.
Statistical evaluation of the geochemical data for prospecting polymetallic mineralization in the Suoi Thau – Sang Than region, Northeast Vietnam

In Northeast Vietnam, Suoi Thau-Sang Than is considered as a high potential area of polymetallic deposits. 1,720 geochemical samples were used to investigate polymetallic mineralization; thereby polymetallic ore occurrences in this study region were discovered and the statistical and multivariate analysis helps to define geochemical anomalies in some northeastern regions, namely Suoi Thau, Sang Than, and Ban Kep. The statistical method and cluster analysis of geochemical data indicate that the Cu, Pb, and Zn elements are good indicators, and most of them comply with the lognormal or gamma distribution. Based on the third-order threshold, the geochemical anomalies of the content of the Cu, Pb, and Zn elements reflect the concentration of copper forming ore bodies in the mineralized zone, and clearly show the concentration in three distinct zones. The trend surface analysis which was employed to determine spatial variations and relationships among these good indicator elements and anomalous areas revealed relative changes in the content of the indicator elements, and they can be considered as regular. Moreover, the goodness of fit obtained trend functions of Pb and Zn, and Cu elements is a third-degree trend surface model. These results indicate that the models can be useful in studying geochemical anomalies and analyzing the tendency of the concentration of indicator elements in the Suoi Thau-Sang Than region. Additionally, it is suggested that the statistical analysis shows a remarkable potential to use the bottom river sediments in the region to investigate polymetallic mineralization. Moreover, geochemical data can help to evaluate geochemical anomalies of the pathfinder elements and potential mineral mapping of the Suoi Thau-Sang Than region in Northeast Vietnam.
ENGINEERING, GEOLOGICAL AND PETROGRAPHIC FEATURES OF ANTHROPOGENIC LAYERS IN THE TERRITORY OF SAMARA

The study of engineering and geological properties of anthropogenic deposits, widespread in the city of Samara. Their main properties have been identifi ed and a classifi cation based on the genetic principle has been developed. The results of a petrographic study of samples from cultural layers taken at an archaeological site are presented. Archaeological excavations were carried out on the territory of Khlebnaya Square, located in the oldest part of the city, in 2019. The object represents the cultural layers that were formed in the XVIII-XIX centuries at the site of the alleged location of the second Samara fortress. Petrographic studies of the samples were carried out under a binocular microscope at 8.75 times magnifi cation, in transmitt ed light of a polarizing microscope at 72 times magnifi cation and under a digital microscope (USB DIGITAL) at 10 times magnifi cation. It has been determined that the pebble fraction is represented by quartz ite and jasper fl int, the sandy and silty fractions are angular quartz fragments with an admixture of undecomposed organic remains. The organic cultural layers, which reach 7-8 m and more in the ancient part of the city, are especially powerful. Their presence is a limiting factor in modern urban construction.
Ciphertext-Only Attack on RSA Using Lattice Basis Reduction

We use lattice basis reduction for ciphertext-only attack on RSA. Our attack is applicable in the conditions when known attacks are not applicable, and, contrary to known attacks, it does not require prior knowledge of a part of a message or key, small encryption key, e, or message broadcasting. Our attack is successful when a vector, comprised of a message and its exponent, is likely to be the shortest in the lattice, and meets Minkowski's Second Theorem bound. We have conducted experiments for message, keys, and encryption/decryption keys with sizes from 40 to 8193 bits, with dozens of thousands of successful RSA cracks. It took about 45 seconds for cracking 2001 messages of 2050 bits and for large public key values related with Euler’s totient function, and the same order private keys. Based on our findings, for RSA not to be susceptible to the proposed attack, it is recommended avoiding RSA public key form used in our experiments
Formal Verification of Stochastic Systems with ReLU Neural Network Controllers

In this work, we address the problem of formal safety verification for stochastic cyber-physical systems (CPS) equipped with ReLU neural network (NN) controllers. Our goal is to find the set of initial states from where, with a predetermined confidence, the system will not reach an unsafe configuration within a specified time horizon. Specifically, we consider discrete-time LTI systems with Gaussian noise, which we abstract by a suitable graph. Then, we formulate a Satisfiability Modulo Convex (SMC) problem to estimate upper bounds on the transition probabilities between nodes in the graph. Using this abstraction, we propose a method to compute tight bounds on the safety probabilities of nodes in this graph, despite possible over-approximations of the transition probabilities between these nodes. Additionally, using the proposed SMC formula, we devise a heuristic method to refine the abstraction of the system in order to further improve the estimated safety bounds. Finally, we corroborate the efficacy of the proposed method with simulation results considering a robot navigation example and comparison against a state-of-the-art verification scheme.
The problem of communication in education

The article considers the connection of education with teaching as a mean for its realization and the new type of socio-historical inheritance at the end of the XX century. This is related to the changes in the nature of scientific knowledge itself, with the understanding that education is a complicated complex in which, along with teaching, education is present as a process of cooperation and co-creation between teachers and students. And this, in turn, raises the question of changes in value attitudes ​​and new cultural realities in the process of communication / so far understood narrow-mindedly as a converse /, which builds a new type of holistic personalities, whose mentality unites culture and education.
Where to look at the movies: Analyzing visual attention to understand movie editing

In the process of making a movie, directors constantly care about where the spectator will look on the screen. Shot composition, framing, camera movements, or editing are tools commonly used to direct attention. In order to provide a quantitative analysis of the relationship between those tools and gaze patterns, we propose a new eye-tracking database, containing gaze-pattern information on movie sequences, as well as editing annotations, and we show how state-of-the-art computational saliency techniques behave on this dataset. In this work, we expose strong links between movie editing and spectators gaze distributions, and open several leads on how the knowledge of editing information could improve human visual attention modeling for cinematic content. The dataset generated and analyzed for this study is available at https://github.com/abruckert/eye_tracking_filmmaking
Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels. We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.
STRATEGIES FOR THE DEVELOPMENT OF CRITICAL THINKING IN PRIMARY SCHOOL STUDENTS IN THE COURSE "I’M IN THE WORLD"

Among the skills that modern people need for self-realization there are creativity, critical thinking, communication with people, working with information, computer literacy, striving for self-improvement, flexibility, openness to innovations. To navigate the turbulent flow of information, each individual needs developed critical thinking. Critical thinking skills should be developed and refined right from the school desk, from the early school age. Children are open to innovations. These qualities should be used to their maximum potential in the educational process. The article presents separate methods for the development of critical thinking, which a primary school teacher can use in teaching process of the subject "I am in the World". Specific examples of using the Aquarium methods are presented: "Fish bone", "Six hats" and others at the lessons in the 3rd form of comprehensive schools. The emphasis is made on the importance of students’ critical thinking, which promotes orientation towards success in activity, self-realization and self-development. Mastering the strategies of critical thinking development is considered as an important task of educational systems in many countries of the world, including Ukraine. Critical thinking is a complex and multifaceted phenomenon. Facilitating its development in students requires a teacher of high professionalism. In this situation attention should be paid to the systematic and thorough study of the theory and methodology of teaching, the experience of using various strategies of other teachers, methodologists, accumulation of their own experience. There are a lot of methods for the development of critical thinking. Each of them has its own specifics, structure, stages (although it is not forbidden to make certain adjustments, taking into account the age of pupils, the studied subject). Proper use of such methods in the course "I am in the World”, which is interdisciplinary and shapes the outlook of students, will help the primary school teacher to teach children to think independently, to see and evaluate the problem, to look for several possible solutions, to make decisions and to interact effectively with others.