Reviewerly/exHarmony huggingface dataset:
The Lightweight Anchor Dynamic Assignment Algorithm for Object Detection

Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current object detection models do not consider the aspect ratio of the ground-truth boxes in anchor assignment and are not well-adapted to objects with very different shapes. Therefore, this paper proposes the Lightweight Anchor Dynamic Assignment algorithm (LADA) for object detection. LADA does not change the structure of the original detection model; first, it selects an equal proportional center region based on the aspect ratio of the ground-truth box, then calculates the combined loss of anchors, and finally divides the positive and negative samples more efficiently by dynamic loss threshold without additional models. The algorithm solves the problems of poor adaptability and difficulty in the selection of the best positive samples based on IoU assignment, and the sample assignment for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample assignment algorithms, the LADA algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm, respectively, with the same model structure, which demonstrates the effectiveness of the LADA algorithm.
Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System

The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced.
A novel attack on McEliece's cryptosystem

This report proposes a new and novel attack on McEliece's cryptosystem that improves on the probability of attacks formerly proposed by Stern, and Lee and Brickell.Modern day encryption standards have been long since proven insecure to quantum attack, and quantum-resistant cryptosystems are now at the forefront of research. Since 2016, the National Institute of Standards and Technology (NIST) has presided over a public competition to establish new standards for public-key encryption that will secure our data in the post-quantum world. Now in its final round, one of the remaining candidates is McEliece's cryptosystem, a code-based cryptosystem proposed in 1978 by Robert J. McEliece. With a few minor alterations since its conception, McEliece's cryptosystem has, so far, proven resistant to quantum attacks, making it an ideal finalist candidate. The cryptosystem has not, however, escaped the attention of attack and, over the last four decades, a variety of algorithms have been proposed with the intention of exploiting it to recover the plaintext.This paper initially provides an overview of McEliece's cryptosystem and two existing attacks proposed by Stern, and Lee and Brickell in the 1980s. Observations are made on the shared probabilistic nature of Stern's algorithm, and Lee and Brickell's attack. It is noted that the first step of both algorithms involves the random selection of a subset of n indexes. In Stern's algorithm, n−k of n columns in a matrix H are chosen at random and, in Lee and Brickell's attack, k of n bits of the ciphertext are selected, also at random. This relationship is exploited to compound the two attacks and propose a new, novel attack. The complexity and probability of the new attack are discussed and an analysis is conducted to compare it against both Stern's algorithm and Lee and Brickell's attack.This analysis suggests that the probability of successful attack comes close to combining those of the two original attacks. Furthermore, the results suggest that the novel attack can successfully recover a message faster than Stern's algorithm. Improvements to the attack are suggested, concluding that further study should be conducted into fully analysing it and its implications on the security of McEliece's cryptosystem.
Research on Evaluation Model of Entrepreneurship Education Based on BP Neural Network

The evaluation system of entrepreneurial practice and curriculum teaching mostly depends on the evaluation of people. Qualitative evaluation lacking specific performance and quantitative measurement is carried out through small groups. This evaluation method lacks specific quantitative and weight basis. Therefore, the evaluation results often depend on the emotional bias and cognitive bias of individuals, and the subjectivity in the evaluation process is too strong. To solve this problem, BP neural network (BP-net) is introduced into the evaluation system of entrepreneurial practice and course teaching. The model has higher adaptability and nonlinear processing ability, so the evaluation results with higher objectivity and lower deviation rate can be obtained in the evaluation. According to the evaluation characteristics of entrepreneurial practice and course teaching, a combined update design is carried out, and a new momentum term is introduced to enhance the overall operation effect of the model. Finally, the model is tested by means of performance comparison and empirical analysis. The results show that the performance score of the model reaches 4.93 points of the highest value, and the R value of the regression fitting process is stable at more than 0.90. It can conduct a comprehensive and objective analysis for different index dimensions, and split out the advantages and disadvantages of entrepreneurial practice and course teaching. This model can evaluate the quality of entrepreneurial practice and course teaching, promote the self-improvement of colleges, and create a better innovation and entrepreneurship environment for students.
Image encryption application and security analysis based on current modulated edge-emitting semiconductor lasers with pulse packages embedded in the microcontroller

This paper investigates the dynamical characteristics of current-modulated edge-emitting semiconductor lasers (CMEESLs) and their image encryption application. The associated dynamical characteristic of the CMEESL is divulged in the two-dimensional coordinate space of the system parameters by altering the modulation's frequency and amplitude. The CMEESL divulges cyclical pulse packages preceded by cyclic-doubling cascade follow by complex pulse packages when the modulation current density frequency is limited below 1 GHz. The microcontroller implementation of the CMEESL is realized and its results find agreement compared with the numerical dynamics of the CMEESL. The chaotic equations describing CMEESL are first modeled in the LabVIEW program and random number generation (RNG) is performed. The obtained random numbers are subjected to the NIST 800–22 statistical standard package, proving its worth to be employed for encryption applications. Employing the random numbers gotten, the LabVIEW environment is used in encrypting the image. The encrypted image is subjected to histogram, correlation, number of pixel change rates (NPCR) - unified average changing intensity (UACI), and entropy analyses. The reliability of the method is proven by the success achieved in the encryption process.
Multiple UAVs on a shared tether: Use cases, modeling, and probabilistic path planning

We analyze the concept of multiple unmanned aerial vehicles (UAVs) on a shared tether (MUST), where the UAVs act as control nodes for the shape of the tether, enabling the system to maneuver around obstacles without tangling or colliding for increased flexibility compared with single-tethered UAVs. MUST use cases are gathered from stakeholders in the military, public safety, and commercial domains. We present a model for the tether shape to use in collision checking and a model for the interactions among tether weight, size, and power, which we exercise to determine the maximum tether segment sizes. We apply three probabilistic path-planning algorithms from the literature to MUSTs, using a novel local planner and constraint set. In simulation, we show that probabilistic planners are a feasible approach to path planning for MUSTs with curved tether segments. We also show the first manual piloting modality of MUSTs.
A Survey of Optimal Control Allocation for Aerial Vehicle Control

In vehicle control, control allocation is often used to abstract control variables from actuators, simplifying controller design and enhancing performance. Surveying available literature reveals that explicit solutions are restricted to strong assumptions on the actuators, or otherwise fail to exploit the capabilities of the actuator constellation. A remedy is to formulate hierarchical minimization problems that take into account the limits of the actuators at the expense of a longer computing time. In this paper, we compared the most common norms of the objective functions for linear or linearized plants, and show available numeric solver types. Such a comparison has not been found in the literature before and indicates that some combinations of linear and quadratic norms are not sufficiently researched. While the bulk of the review is restricted to control-affine plant models, some extensions to dynamic and nonlinear allocation problems are shown. For aerial vehicles, a trend toward linearized incremental control schemes is visible, which forms a compromise between real-time capabilities and the ability to resolve some nonlinearities common in these vehicles.
Effects of Exploration Weight and Overtuned Kernel Parameters on Gaussian Process-Based Bayesian Optimization Search Performance

Gaussian process-based Bayesian optimization (GPBO) is used to search parameters in machine learning, material design, etc. It is a method for finding optimal solutions in a search space through the following four procedures. (1) Develop a Gaussian process regression (GPR) model using observed data. (2) The GPR model is used to obtain the estimated mean and estimated variance for the search space. (3) The point where the sum of the estimated mean and the weighted estimated variance (upper confidence bound, UCB) is largest is the next search point (in the case of a maximum search). (4) Repeat the above procedures. Thus, the generalization performance of the GPR is directly related to the search performance of the GPBO. In procedure (1), the kernel parameters (KPs) of the GPR are tuned via gradient descent (GD) using the log-likelihood as the objective function. However, if the number of iterations of the GD is too high, there is a risk that the KPs will overfit the observed data. In this case, because the estimated mean and variance output by the GPR model are inappropriate, the next search point cannot be properly determined. Therefore, overtuned KPs degrade the GPBO search performance. However, this negative effect can be mitigated by changing the parameters of the GPBO. We focus on the weight of the estimated variances (exploration weight) of the UCB as one of these parameters. In a GPBO with a large exploration weight, the observed data appear in various regions in the search space. If the KP is tuned using such data, the GPR model can estimate the diverse regions somewhat correctly, even if the KP overfits the observed data, i.e., the negative effect of overtuned KPs on the GPR is mitigated by setting a larger exploration weight for the UCB. This suggests that the negative effect of overtuned KPs on the GPBO search performance may be related to the UCB exploration weight. In the present study, this hypothesis was tested using simple numerical simulations. Specifically, GPBO was applied to a simple black-box function with two optimal solutions. As parameters of GPBO, we set the number of KP iterations of GD in the range of 0–500 and the exploration weight as {1,5}. The number of KP iterations expresses the degree of overtuning, and the exploration weight expresses the strength of the GPBO search. The results indicate that, in the overtuned KP situation, GPBO with a larger exploration weight has better search performance. This suggests that, when searching for solutions with a small GPBO exploration weight, one must be careful about overtuning KPs. The findings of this study are useful for successful exploration with GPBO in all situations where it is used, e.g., machine learning hyperparameter tuning.
Extending the OSLC Standard for ECA-Based Automation

Workflow automation is taking over software development systems, helping businesses increase efficiency, accelerate production, and adapt quickly to market changes. Combined with agile principles, it has given birth to the DevOps paradigm. However, practitioners often face an important issue known as vendor lock-in caused by the cost of tool replacement or migration to different platforms. This issue could be addressed by standardising service interfaces to facilitate their integration. Linked Data is an attractive choice for implementing such a standard without sacrificing versatility. Following this approach, the Open Services for Lifecycle Collaboration (OSLC) proposal aims to build an environment where services can interoperate using standard models. Therefore, this article proposes an extension of the existing OSLC specification, based on the Event-Condition-Action (ECA) model, for event-based interoperable automation. This extension enables a new path in the field of semantic automation for OSLC services, which allows the self-interaction of services among them and with human users. The article presents the key concepts of the proposed model and exemplifies its application in an automation scenario with two real-life services. The validation of the proposal has been carried out using established ontology evaluation methods, such as coverage and similarity metrics and competency questions.
Controllable face image editing in a disentanglement way

The prevalence of deep learning has attracted interest in the face image manipulation domain, especially in face editing with disentanglement representation. However, how to realize controllable disentangled representation of face images still remains challenging. Current methods require extensive supervision and training, or images will have a significantly impaired quality. We present an approach that learns how to represent data in an ideal disentangled way, with minimal supervision. Specifically, we use a swapping autoencoder with identity and attribute branches to learn identity and attribute representations, respectively. In addition, we separate the process of disentanglement and synthesis by an advanced pre-trained unsupervised StyleGAN2 image generator to make the entire network structure focus on learning data disentanglement. The identity and attribute vectors from different images are combined into a new representation that is mapped by a linear mapper into the generator’s latent space to generate a new hybrid image. In this way, we take advantage of StyleGAN2’s most advanced quality and its expressive latent space without the pressure of training a decoder. Experimental results prove that our method successfully separates the identity and other attributes of face images, outperforms existing methods, and requires less training and supervision.
A Comparative Study of Traditional, Ensemble and Neural Network-Based Natural Language Processing Algorithms

Accurate data analysis is an important part of data-driven financial audits. Given the increased data availability and various systems from which audit files are generated, RCSFI provides a way for standardization on behalf of analysis. This research attempted to automate this hierarchical text classification task in order to save financial auditors time and avoid errors. Several studies have shown that ensemble-based models and neural-network-based natural language processing (NLP) techniques achieved encouraging results for classification problems in various domains. However, there has been limited empirical research comparing the performance of both of the aforementioned techniques in a hierarchical multi-class classification setting. Moreover, neural-network- based NLP techniques have commonly been applied to English datasets and not to Dutch financial datasets. Additionally, this research took the implementation of hierarchical approaches into account for the traditional and ensemble-based models and found that the performance did not increase when implementing the included hierarchical approaches. DistilBERT achieved the highest scores on level 1-2-3-4 and outperformed the traditional and ensemble-based models. The model obtained a F1 of 94.50% for level 1-2-3-4. DistilBERT also outperformed BERTje at level 1-2-3-4 despite BERTje being specifically pre-trained on Dutch datasets.
Improving Agent Decision Payoffs via a New Framework of Opponent Modeling

The payoff of an agent depends on both the environment and the actions of other agents. Thus, the ability to model and predict the strategies and behaviors of other agents in an interactive decision-making scenario is one of the core functionalities in intelligent systems. State-of-the-art methods for opponent modeling mainly use an explicit model of opponents’ actions, preferences, targets, etc., that the primary agent uses to make decisions. It is more important for an agent to increase its payoff than to accurately predict opponents’ behavior. Therefore, we propose a framework synchronizing the opponent modeling and decision making of the primary agent by incorporating opponent modeling into reinforcement learning. For interactive decisions, the payoff depends not only on the behavioral characteristics of the opponent but also the current state. However, confounding the two obscures the effects of state and action, which then cannot be accurately encoded. To this end, state evaluation is separated from action evaluation in our model. The experimental results from two game environments, a simulated soccer game and a real game called quiz bowl, show that the introduction of opponent modeling can effectively improve decision payoffs. In addition, the proposed framework for opponent modeling outperforms benchmark models.
Investigating the Impact of Education and Women in Agriculture on Human Capital Outcomes in Africa: An Interaction Analysis

Agriculture plays a crucial role in Africa's economy, and women constitute a significant proportion of the agricultural workforce in the region. Impact of women's role in farming and women education in Africa is significant for the human capital development. Furthermore, women's engagement in agricultural activities has increased with time, leading to improved contributions to food security and livelihood. Against this background, this study investigates the impact of women's participation in agriculture and women education interaction on human capital in Africa with the data obtained from the World Development Indicators (WDI) of the World Bank and Human Development Index (HDI) covering 33 African countries for the time frame 2000 to 2018. To control endogeneity that may be present in fixed effects model, the study applied the Generalised Method of Moments (GMM). The results show that, across African sub-regions, while women's participation in agriculture has a significant negative effect, education has a significant positive impact on human capital outcomes. Additionally, the interaction between women participation in agriculture and education has a significant positive impact on human capital across sub-regions in Africa. The findings underscore the need for policies that promote gender equality, as this has the potential to significantly increase human capital formation and sustainable development. The study, through the the interaction between women's in agriculture and education highlights the contribution of these factors to various goals, such as no poverty (SDG 1), zero hunger (SDG 2), quality education (SDG 4), gender equality (SDG 5) and decent work and economic growth (SDG 8).
Philosophy of Sustainable Development: Understanding the Significance of Gender Equality in Business Organisations

Sustainable development is a significant concern for practitioners, researchers, and activists. Literature on this area has shown that gender inequality is one of the biggest hurdles in the process of the Sustainable Development Goals (SDGs). Business leaders, policymakers, politicians, practitioners, and researchers from all fields should understand the significance of gender equality, without which it is impossible to achieve Sustainable Development Goals. The UN gives a call to countries and individuals to make some effort to achieve gender equality. It directly refers to the target of SDG5 and indirectly the other dimensions, including poverty, girl-child education, economy, child death ratios, forced marriage, violence against women, etc. Gender equality is an axiological appraisal to achieve Sustainable Development Goals. For that, this paper tries to determine the necessity of gender equality in achieving Sustainable Development Goals. It mainly focuses on two objectives first one is the necessity of gender equality, and the second one is the role of business organizations in achieving gender equality.