Reviewerly/exHarmony huggingface dataset:
Motion Primitive Path Planning Under Uncertainty for High-Speed Vehicles

Motion primitives provide an approach to kinodynamic path planning that does not require online solution of the equations of motion, permitting the use of complex high-fidelity models. The path planning problem with motion primitives is a Markov Decision Process (MDP) with the primitives defining the available actions. Uncertainty in evolution of the primitives means that there is uncertainty in the state resulting from each action. In this work, uncertain motion primitive planning is demonstrated for a high speed glide vehicle. A nonlinear 6-degree-of-freedom model is used to generate the primitive library, and the motion primitive planning problem is formulated so that the transition probabilities in the MDP may have a functional dependence on the state of the system. Single-query solutions to planning problems incorporating operational envelope constraints and no-fly zones are obtained using AO* under chance constraints on the risk of mission failure.
Performance–energy trade-offs of deep learning convolution algorithms on ARM processors

Abstract In this work, we assess the performance and energy efficiency of high-performance codes for the convolution operator, based on the direct, explicit/implicit lowering and Winograd algorithms used for deep learning (DL) inference on a series of ARM-based processor architectures. Specifically, we evaluate the NVIDIA Denver2 and Carmel processors, as well as the ARM Cortex-A57 and Cortex-A78AE CPUs as part of a recent set of NVIDIA Jetson platforms. The performance–energy evaluation is carried out using the ResNet-50 v1.5 convolutional neural network (CNN) on varying configurations of convolution algorithms, number of threads/cores, and operating frequencies on the tested processor cores. The results demonstrate that the best throughput is obtained on all platforms with the Winograd convolution operator running on all the cores at their highest frequency. However, if the goal is to reduce the energy footprint, there is no rule of thumb for the optimal configuration.
A cost-sensitive logistic regression model for breast cancer detection

The diagnosis of breast cancer (BC) with a machine-learning model is a classification problem where the model involves training a model to identify the class of a given observation. However, real-world Wisconsin’s BC diagnostic dataset, which is widely employed to implement a model for BC detection, consists imbalanced class. The benign class outnumbers the malignant class. The implementation of a model for BC detection with an imbalanced dataset leads to biased classification towards the majority class leading to lower accuracy and precision of malignant class. Thus, this research proposes a cost-sensitive logistic regression model for BC detection. During the training phase, benign and malignant class is weighted to influence the classification bias toward begin class. The study compared the model with standard logistic regression. The experimental result appears to prove that the proposed model outperforms as compared to standard logistic regression. The model has receiver characteristic curve area value AUC = 99.99.
Using Automatic Programming to Improve Gradient Boosting for Classification

In this paper, we present our new and automatically tuned gradient boosting software, Classifium GB, which beats its closest competitor, H2O, for all datasets that we ran. The primary reason that we found it easy to develop Classifium GB is that we employed meta machine learning, based on evolution, to automatically program its most important parts. Gradient boosting is often the most accurate classification algorithm for tabular data and quite popular in machine learning competitions. However, its practical use has been hampered by the need to skilfully tune many hyperparameters in order to achieve the best accuracy. Classifium GB contains novel regularization methods and has automatic tuning of all regularization parameters. We show that Classifium GB gives better accuracy than another automatically tuned algorithm, H2O, and often also outperforms manually tuned algorithms such as XGBoost, LightGBM and CatBoost even if the tuning of these is done with exceptional care and uses huge computational resources. Thus, our new Classifium GB algorithm should rapidly become the preferred choice for practically any tabular dataset. It is quite easy to use and even say Random Forest or C5.0 require more skilled users. The primary disadvantage is longer run time.
Implementation of Social Media Data Collection Modules in Python

At the moment, the most popular source of information is the Internet, where social networks are of great importance, whose influence on modern life is growing. Since social networks are a large database in which there is a variety of information about millions of people, it attracts specialists from different fields of the scientific community.Currently, there are many ways to collect information from social media for its further use. Considering ready-made collection methods, we can distinguish three main ways: the use of web page parsers, the use of API resources and the use of ready-made monitoring systems.Usually, programs that use the API allow you to get some information about users and social network communities in an already structured form. However, off-the-shelf software solutions are mainly designed to handle brand/company mentions and promotion on the web. Own software solution can be used for various tasks, not necessarily related to those mentioned above.Since each API is unique and requires a detailed study of the documentation, this paper discusses the implementation of collection modules in the Python programming language. It offers a description of programs written to collect from social media, such as VK, YouTube and OK.
Acquiring an awareness of the latest regulatory developments concerning digital assets and anti-money laundering

Purpose The purpose of this study is to analyze the anti-money-laundering (AML) policies in place around the world for digital assets and cryptocurrencies. Design/methodology/approach This study examines the regulatory frameworks of some of the most prominent international crypto markets and provides an analysis of macro trends in digital asset money laundering legislation. Findings As widespread use of cryptocurrencies has increased, authorities around the world have begun to focus on the associated money-laundering concerns. Organizations that want to take advantage of investment opportunities related to digital assets must keep an eye on the ever-changing worldwide regulatory landscape. Practical implications Before entering the digital asset market, potential participants should familiarize themselves with the relevant regulations and processes. In cases where enterprises interface with several international regulators, the complexity of these factors can increase. Originality/value This essay is written for financial institutions who are considering crypto-related investment opportunities as a means of diversification to better comprehend the worldwide AML regulatory framework pertaining to digital assets.
A Comparative Analysis of Collaborative Robots for Autonomous Mobile Depalletizing Tasks

Mobile manipulators are attractive in industrial warehouses as they are capable of interacting safely with humans, they can navigate in operational spaces and they can be reconfigured for different tasks, thus overcoming limitations of fixed robot cells. This paper presents a comparative analysis of collaborative robots mounted on a mobile base for depalletization tasks in tight spaces due to storage racks. An experimental evaluation is reported in a simulated environment using a geometric motion planner with different robot configurations. Results indicate that the choice of the most appropriate robot to perform a depalletization task is not trivial as it depends on many factors. Therefore, conducting a simulation using motion planning is an effective strategy to evaluate the performance of different robots.
A Deterministic Model to Predict Execution Time of Spark Applications

This work proposes a graph-based, deterministic analytical model that predicts the execution time of spark applications. It conceptualizes the structure of the spark application as a monolithic Directed Acyclic Graph (DAG) of stages capturing the precedence relationship among all the stages of the application. The model processes every stage of the DAG using a graph traversal algorithm, combined with a fixed scheduling policy of the spark platform in context (spark platform refers to the cloud that hosts the spark cluster). We validate our model against the measured execution time obtained by running a big data query (Query-64 of TPC-DS benchmark) that involves parallel execution of a large number of stages. The query is executed on the spark cluster of Google Cloud. Our model resulted in an execution time that is at 2.85% error in comparison to the measured execution time.
Model selection for maternal hypertensive disorders with symmetric hierarchical Dirichlet processes

Hypertensive disorders of pregnancy occur in about 10% of pregnant women around the world. Though there is evidence that hypertension impacts maternal cardiac functions, the relation between hypertension and cardiac dysfunctions is only partially understood. The study of this relationship can be framed as a joint inferential problem on multiple populations, each corresponding to a different hypertensive disorder diagnosis, that combines multivariate information provided by a collection of cardiac function indexes. A Bayesian nonparametric approach seems particularly suited for this setup, and we demonstrate it on a dataset consisting of transthoracic echocardiography results of a cohort of Indian pregnant women. We are able to perform model selection, provide density estimates of cardiac function indexes and a latent clustering of patients: these readily interpretable inferential outputs allow to single out modified cardiac functions in hypertensive patients, compared to healthy subjects, and progressively increased alterations with the severity of the disorder. The analysis is based on a Bayesian nonparametric model that relies on a novel hierarchical structure, called symmetric hierarchical Dirichlet process. This is suitably designed so that the mean parameters are identified and used for model selection across populations, a penalization for multiplicity is enforced, and the presence of unobserved relevant factors is investigated through a latent clustering of subjects. Posterior inference relies on a suitable Markov chain Monte Carlo algorithm, and the model behaviour is also showcased on simulated data.
Natural Language Processing of South African Online Question-Answering Health Forum HIV and AIDS Dataset Using Topic Modeling

HIV/AIDS is a major problem affecting South Africans, with 19.5% of the population within ages 15–49 living with the virus, with implications for the average life expectancy for the country. There is an online Question-and-Answer Health Forum on HIV/AIDS with a volume of questions that poses a challenge for medical doctors to provide quick and adequate answers. The vast dataset supplied by this forum presents an opportunity to employ machine learning-based natural language processing (NLP) information extraction techniques in addressing this challenge. This paper proposes a method to explore and analyze these questions using natural language processing and a topic modeling method to extract the most relevant information for chatbots. Topic modeling is employed in using the online health forum on Health24.com , to validate the proposed method. This provides interesting insight into the forum dataset with potential uses in future research work on developing chatbot-enabled automated question-answering systems.
Cryptographic Primitives with Hinting Property

A hinting PRG is a (potentially) stronger variant of PRG with a “deterministic” form of circular security with respect to the seed of the PRG (Koppula and Waters, CRYPTO 2019). Hinting PRGs enable many cryptographic applications, most notably CCA-secure public-key encryption and trapdoor functions. In this paper, we study cryptographic primitives with the hinting property, yielding the following results:
Towards Practical Topology-Hiding Computation

Topology-hiding computation (THC) enables n parties to perform a secure multiparty computation (MPC) protocol in an incomplete communication graph while keeping the communication graph hidden. The work of Akavia et al. (CRYPTO 2017 and JoC 2020) shown that THC is feasible for any graph. In this work, we focus on the efficiency of THC and give improvements for various tasks including broadcast, sum and general computation. We mainly consider THC on undirected cycles, but we also give two results for THC on general graphs. All of our results are derived in the presence of a passive adversary statically corrupting any number of parties. In the undirected cycles, the state-of-the-art topology-hiding broadcast (THB) protocol is the Akavia-Moran (AM) protocol of Akavia et al. (EUROCRYPT 2017). We give an optimization for the AM protocol such that the communication cost of broadcasting $$O(\kappa )$$ bits is reduced from $$O(n^2\kappa ^2)$$ bits to $$O(n^2\kappa )$$ bits. We also consider the sum and general computation functionalities. Previous to our work, the only THC protocols realizing the sum and general computation functionalities are constructed by using THB to simulate point-to-point channels in an MPC protocol realizing the sum and general computation functionalities, respectively. By allowing the parties to know the exact value of the number of the parties (the AM protocol and our optimization only assume the parties know an upper bound of the number of the parties), we can derive more efficient THC protocols realizing these two functionalities. As a result, comparing with previous works, we reduce the communication cost by a factor of $$O(n\kappa )$$ for both the sum and general computation functionalities. As we have mentioned, we also get two results for THC on general graphs. The state-of-the-art THB protocol for general graphs is the Akavia-LaVigne-Moran (ALM) protocol of Akavia et al. (CRYPTO 2017 and JoC 2020). Our result is that our optimization for the AM protocol also applies to the ALM protocol and can reduce its communication cost by a factor of $$O(\kappa )$$ . Moreover, we optimize the fully-homomorphic encryption (FHE) based GTHC protocol of LaVigne et al. (TCC 2018) and reduce its communication cost from $$O(n^8\kappa ^2)$$ FHE ciphertexts and $$O(n^5\kappa )$$ FHE public keys to $$O(n^6\kappa )$$ FHE ciphertexts and $$O(n^5\kappa )$$ FHE public keys.
Authenticated Encryption with Key Identification

Authenticated encryption with associated data (AEAD) forms the core of much of symmetric cryptography, yet the standard techniques for modeling AEAD assume recipients have no ambiguity about what secret key to use for decryption. This is divorced from what occurs in practice, such as in key management services, where a message recipient can store numerous keys and must identify the correct key before decrypting. To date there has been no formal investigation of their security properties or efficacy, and the ad hoc solutions for identifying the intended key deployed in practice can be inefficient and, in some cases, vulnerable to practical attacks. We provide the first formalization of nonce-based AEAD that supports key identification (AEAD-KI). Decryption now takes in a vector of secret keys and a ciphertext and must both identify the correct secret key and decrypt the ciphertext. We provide new formal security definitions, including new key robustness definitions and indistinguishability security notions. Finally, we show several different approaches for AEAD-KI and prove their security.
Instantiability of Classical Random-Oracle-Model Encryption Transforms

Extending work leveraging program obfuscation to instantiate random-oracle-based transforms (e.g., Hohenberger et al., EUROCRYPT 2014, Kalai el al., CRYPTO 2017), we show that, using obfuscation and other assumptions, there exist standard-model hash functions that suffice to instantiate the classical RO-model encryption transforms OAEP (Bellare and Rogaway, EUROCRYPT 1994) and Fujisaki-Okamoto (CRYPTO 1999, J. Cryptology 2013) for specific public-key encryption (PKE) schemes to achieve IND-CCA security. Our result for Fujisaki-Okamoto employs a simple modification to the scheme. Our instantiations do not require much stronger assumptions on the base schemes compared to their corresponding RO-model proofs. For example, to instantiate low-exponent RSA-OAEP, the assumption we need on RSA is sub-exponential partial one-wayness, matching the assumption (partial one-wayness) on RSA needed by Fujisaki et al. (J. Cryptology 2004) in the RO model up to sub-exponentiality. For the part of Fujisaki-Okamoto that upgrades public-key encryption satisfying indistinguishability against plaintext checking attack to IND-CCA, we again do not require much stronger assumptions up to sub-exponentiality. We obtain our hash functions in a unified way, extending a technique of Brzuska and Mittelbach (ASIACRYPT 2014). We incorporate into their technique: (1) extremely lossy functions (ELFs), a notion by Zhandry (CRYPTO 2016), and (2) multi-bit auxiliary-input point function obfuscation (MB-AIPO). While MB-AIPO is impossible in general (Brzuska and Mittelbach, ASIACRYPT 2014), we give plausible constructions for the special cases we need, which may be of independent interest.
Face Mask Recognition System for Epidemic Prevention and Control Based on Multi-algorithm Fusion

Against the backdrop of the ongoing COVID-19 pandemic, We propose FMRS-CFR (Face mask recognition system-Centerface Resnet), a mask recognition system for epidemic prevention and control based on multi-algorithm fusion to adapt to multi-scenario applications. In this work, Centerface face key point detection and Resnet50 classification model were used. Built a system that maintains multi-adaptation with the dynamics of external scenarios and ported the system to the Atlas 200 Developer Kit, And quantitative evaluation of videos in more than a dozen different scenarios. Experimental results show that the FMRS-CFR system can achieve a recognition accuracy rate of 99.88%, which greatly improves the recognition rate of not wearing a mask or wearing the correct one to a certain extent, and achieves the purpose of effectively assisting epidemic prevention and control.
FREQUENCY PROPERTIES OF THE LEXIS OF SCIENTIFIC TEXTS AND ZIPF’S LAWS OF HIGHER ORDERS

The statistical properties of the lexis of texts in natural languages have attracted the close attention of many mathematicians and linguists over the past hundred years. The basic law of the dependence of the word frequency on its rank, which is known as Zipf’s law, is that the product of the frequency of a word and its rank is approximately constant and is a marker of the text language. It should be noted that numerous analogs of Zipf’s law in other subject areas have great practical significance for describing the functioning of various sociotechnical systems: Auerbach’s law of the distribution of cities by population, Pareto’s law of the distribution of material goods in society, Bradford’s law of the distribution of scientists by productivity, Lotka law of the distribution of publications in bibliographic sources. The article considers the latent frequency characteristics of the lexis of scientific texts on various topics by the array of articles in the multidisciplinary open access journal “Young Scientist”. The paper formulates the concept of a higher-order frequency table and empirically investigates the dependence of frequencies in such tables on their ranks. Power, hyperbolic, and other twoparameter models of the dependence of frequencies on ranks in tables of frequencies of higher orders were constructed for all articles from the corpus of texts under consideration. The constructed models are generalizations of the well-known Zipf models and have high quality indicators. In the paper, new predictors are obtained, which can be useful for solving problems of classifying scientific texts.
Disaster Management with Resource Optimization Supported by Drone Applications

Besides the military and commercial applications of drones, there is no doubt in their efficiency in case of supporting emergency management. This paper evaluates some experiences and describes some initiatives using drones to support disaster management. Method: This paper focuses mainly on operational and tactical drone application in disaster management using a time-scaled separation of the application, like pre-disaster activity, activity immediately after the occurrence of a disaster and the activity after the primary disaster elimination. Paper faces to some disasters, like dangerous material releases, floods and forest fires. Author gathered international examples and used own experiences in this field. Results and discussion: In case of hazardous material leakage drone can be a very effective or can be the only one tool for supporting disaster management. Floods are typical for a slow onset disaster. In contrast, managing floods is a very complex and difficult task. It requires continuous monitoring of dykes, flooded and threatened areas. Drone can help managers largely keeping an area under observation. Forest fires are disasters, where the tactical application of drone is already well developed. Drone can be used for fire detection, intervention monitoring and also for post-fire monitoring.
Hardware Solution for Cybersecurity of Unmanned Systems Critical Infrastructure

This paper describes different vulnerabilities in the infrastructure of unmanned systems. Unmanned systems and wireless communication are prone to software and hardware attacks. Security is an important issue during system design. This study shows that hardware security implementation of FPGA-based design in the unmanned system and provides a robust security design method. The use of logic encryption technique is implemented to change the value of the input signal based on the security key value. The increase in the number of bits in keys protects against different cyberattacks but it increases the complexity of the design depending on the application and the level where it is applied. This security method is implemented in the Zedboard device and is used to ensure hardware security for the application.
Crowd-Aided Anomaly Detection in Surveillance Videos

Surveillance cameras are increasingly being installed to detect public anomalies and prevent crimes. However, such additional surveillance cameras require a larger workforce to monitor videos, inducing heavy costs. Various methods have been proposed to automate anomaly detection in surveillance videos, including deep-learning-based methods. Unfortunately, state-of-the-art methods based on deep learning have not yet achieved a high level of anomaly localization. Our previous study proposed a crowdsourcing-based anomaly detection method which utilizes human cognitive abilities on demand. In an evaluation experiment using shoplifting videos, our method demonstrated high accuracy. In this study, we extended the application of our crowdsourcing-based method to anomaly detection, which is a broader application area. Because a method that relies entirely on crowdworkers is costly, we propose a new crowd-aided anomaly detection method that supports deep-learning-based methods by employing a crowdsourcing-based method, which provides high anomaly localization ability at a low cost.