Reviewerly/exHarmony huggingface dataset:
FPGA-based UAV Heterogeneous Computing Platform Architecture

The development of the UAV field is changing with each passing day, especially a lot of research has been done on the road of autonomous and controllable. At present, the control module has become the mainstream design. Because it can perform high-performance real-time image processing, which is popular, but this design has the disadvantages of high power consumption, slow processing and low response speed. With its high-performance concurrent computing mode, FPGA has excellent performance in signal processing, graphics deduction, control and other fields, and has low power consumption. The embedded architecture on the edge side has attracted more and more attention. Small UAVs have limitations such as small size, low power load, and high timeliness. The GPU-ARM architecture cannot meet the requirements of small drones. In order to solve the above problems, this paper proposes a new UAV architecture based on FPGA-ARM-GPU for the use of small UAV.
Research on Mobile Web Front-end Design Based on HTML5 Technology

The rapid development of society today makes HTML5 technology more mature and complete, and is widely used in various fields, including Web front-end design. Based on this, this article first gives a brief overview of HTML5 technology, and then designs a Web front-end model through demand analysis, and develops a mobile Web front-end to provide Internet users with better online services.
Multi-view Human Parsing for Human-Robot Collaboration

In human-robot collaboration, perception plays a major role in enabling the robot to understand the surrounding environment and the position of humans inside the working area, which represents a key element for an effective and safe collaboration. Human pose estimators based on skeletal models are among the most popular approaches to monitor the position of humans around the robot, but they do not take into account information such as the body volume, needed by the robot for effective collision avoidance. In this paper, we propose a novel 3D human representation derived from body parts segmentation which combines high-level semantic information (i.e., human body parts) and volume information. To compute such body parts segmentation, also known as human parsing in the literature, we propose a multi-view system based on a camera network. People body parts are segmented in the frames acquired by each camera, projected into 3D world coordinates, and then aggregated to build a 3D representation of the human that is robust to occlusions. A further step of 3D data filtering has been implemented to improve robustness to outliers and segmentation accuracy. The proposed multi-view human parsing approach was tested in a real environment and its performance measured in terms of global and class accuracy on a dedicated dataset, acquired to thoroughly test the system under various conditions. The experimental results demonstrated the performance improvements that can be achieved thanks to the proposed multi-view approach.
Safe incomplete label distribution learning

Label Distribution Learning (LDL) is a popular scenario for solving label ambiguity problems by learning the relative importance of each label to a particular instance. Nevertheless, the label is often incomplete due to the difficulty in annotating label distribution. In this mixing label case with complete and incomplete labels, it is often expected that the learning method can achieve better performance than the baseline method merely utilizing complete labeled data. However, the usage of incomplete labeled data may degrade the performance in real applications. Therefore, it is vital to design a safe incomplete LDL method, which will not deteriorate the performance when exploiting incomplete labeled data. To tackle this important but rarely studied problem, we propose a Safe Incomplete LDL method (SILDL), which learns a classifier that can prevent incomplete labeled instances from worsening the performance. Concretely, we learn predictions from multiple incomplete supervised learners and design an efficient solving algorithm by formulating it as a convex quadratic program. Theoretically, we prove that SILDL can obtain the maximal performance gain against the best one of the multiple baseline methods with mild conditions. Extensive experimental results validate the safeness of the proposed approach and show improvements in performance.
Validation of a Stewart platform inspection system with an artificial neural network controller

The use of thermographic cameras attached to Stewart platforms may become a valuable approach to build non-destructive inspection systems. However, achieving successful inspections requires positioning the platform that holds the camera very accurately, especially when the camera lacks of autofocus capabilities. Thus, if the distance from the lenses to the inspection surface is in range, sharp quality images may be obtained, otherwise, images may suffer defocusing producing poor inspection results. This work presents a new Artificial Neural Network (ANN) control aimed at improving the accuracy of the Stewart platform that holds a camera, hence improving the quality of the inspection system. The proposed ANN architecture positions the platform more accurately than the vendor provided Proportional Integral (PI) control strategy. The system kinematics and dynamics are analyzed in order to design the ANN model for the system. The developed control scheme is based on the inverse ANN model with an additional integral control. The advantages of this novel approach are that (1) the controller considers the interactions among the actuators and (2) includes a training process that continuously fine-tunes the ANN weight matrix. The proposed control scheme was validated experimentally, and the performance compared with tuned PI control, taken as the reference, for two different inspection trajectories. The results show that the ANN control is much more accurate and efficient than the reference PI (above 40% error reduction in the actuator positioning with the same power consumption), hence performing the inspections successfully.
Combining Active Semi-supervised Learning and Rare Category Detection

Loveland, Rohan Kaplan, NoahThe task of exploring data sets that are too large to allow individual examination of all instances necessitates new tools to identify and group interesting classes. This work investigates a hybrid system that performs both rare category detection and active semi-supervised clustering using the previously developed Farpoint algorithm. The prior algorithm focused solely on rare category detection, while the current research incorporates a capability for simultaneously obtaining high clustering accuracy. We investigate two methods of constrained clustering. One method is a constrained variant of K-Means that favors compact clusters, while the other is a novel variant of hierarchical agglomerative clustering that determines a cut-off threshold based on constraint violation. Using artificially skewed MNIST data we empirically compare the relative performance of the two new methods with the original Farpoint algorithm. Results show substantial improvement in clustering accuracy.
Taking the Confusion Out of Multinomial Confusion Matrices and Imbalanced Classes

Classification is a fundamental task in machine learning, and the principled design and evaluation of classifiers is vital to create effective classification systems and to characterise their strengths and limitations in different contexts. Binary classifiers have a range of well-known measures to summarise performance, but characterising the performance of multinomial classifiers (systems that classify instances into one of many classes) is an open problem. While confusion matrices can summarise the empirical performance of multinomial classifiers, they are challenging to interpret at a glance—challenges compounded when classes are imbalanced.We present a way to decompose multinomial confusion matrices into components that represent the prior and posterior probabilities of correctly classifying each class, and the intrinsic ability of the classifier to discriminate each class: the Bayes factor or likelihood ratio of a positive (or negative) outcome. This approach uses the odds formulation of Bayes’ rule and leads to compact, informative visualisations of confusion matrices, able to accommodate far more classes than existing methods. We call this method confusR and demonstrate its utility on 2-, 17-, and 379-class confusion matrices. We describe how confusR could be used in the formative assessment of classification systems, investigation of algorithmic fairness, and algorithmic auditing.
Statistical analysis of multi‐day solar irradiance using a threshold time series model

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Recently, new statistical models have been proposed to provide stochastic simulations of high‐resolution data to downscale and forecast solar irradiance measurements. Most of the existing models are linear and highly depend on normality assumptions. However, solar irradiance shows strong nonlinearity and is only measured during the day time. Thus, we propose a new multi‐day threshold autoregressive model to quantify the variability of the daily irradiance time series. We establish the sufficient conditions for our model to be stationary, and we develop an inferential procedure to estimate the model parameters. When we apply our model to study the statistical properties of observed irradiance data in Guadeloupe island group, a French overseas region located in the Southern Caribbean Sea, we are able to characterize two states of the irradiance series. These states represent the clear‐sky and non‐clear sky regimes. Using our model, we are able to simulate irradiance series that behave similarly to the real data in mean and variability, and more accurate forecasts compared to linear models.
Simultaneous measurement of 3D surface and thickness of large area and thickness transparent materials based on polarization model and TOF correction

The field of 3D reconstruction of transparent objects has always been a difficult problem. Due to its reflection and refraction effects in the propagation process and its susceptibility to the surrounding ambient light, traditional methods cannot achieve the three-dimensional reconstruction of the transparent surface. Therefore, the research on the 3D reconstruction of transparent targets has important theoretical significance and practical value. This paper proposes a method for 3D reconstruction of transparent objects based on laser continuous wave modulation and polarization characteristics, which can measure 3D surface and thickness at the same time. This method uses the normal map obtained by combining laser continuous wave modulation to correct the normal map obtained based on polarization characteristics. Compared with the two methods alone, the reconstruction accuracy has a more accurate reconstruction effect. We evaluate this method by experimenting with 30 mm transparent cylinder glass. Experiments show that the reconstruction effect is good, and the relative error of the three-dimensional reconstruction of the transparent cylinder can be as low as 0.86%, which achieves the expected goal.
The role of inclusive education in governance for inclusive economic participation: gender evidence from sub-Saharan Africa

This study investigates the relevance of inclusive education in moderating the effect of good governance on female economic inclusion in sub-Saharan Africa. First, inclusive tertiary education modulates: (i) government effectiveness to induce a positive net effect on female labour force participation; (ii) political stability and corruption-control to induce negative net effects on female unemployment; (iii) government effectiveness for a positive net effect on female unemployment and (iv) regulation quality and the rule of law for positive net impacts on female employment. Second, inclusive secondary education moderates: (i) corruption-control for a positive net effect on female labour force participation; (ii) “voice and accountability”, government effectiveness and corruption-control for negative net impacts on female unemployment; (iii) the rule of law for a positive net effect on female unemployment; (iv) “voice and accountability”, government effectiveness and corruption-control for positive net effects on female employment. Policy implications are discussed. Inclusive education thresholds for complementary policy policies are also computed and discussed. At these thresholds, inclusive education becomes a necessary but not a sufficient condition to complement governance in order to promote female economic inclusion.
Natural Scene Text Localization and Removal: Deep Learning and Navier-Stokes Inpainting Approach

Algorithm utilizes adaptive gaussian binary thresholding, text localization, dynamic masking and TELEA/Navier-Stokes Inpainting Algorithm. An algorithm is proposed that provides effective text removal from scenes, real world images containing text. It directly predicts and localizes scene text in any given image. A number of examples on real world images containing scene text demonstrate the effectiveness of the algorithm in removing large as well as smaller detectable texts which uses the ‘An Efficient and Accurate Scene Text Detector’ Deep Learning Model.
Low-Data Cryptanalysis On SKINNY Block Cipher

Abstract At CRYPTO 2021, Dong et al. proposed an automatic method of Meet-in-the-Middle (MITM) key-recovery attacks. In this paper, we further extend it to a new automatic model which can be used to find low-data complexity attacks. With the help of the automatic model, we propose MITM attacks against reduced-round versions of all the six members of the SKINNY family with extremely low-data complexity. More precisely, we present MITM attacks against 19-round SKINNY-$n$-$3n$, 15-round SKINNY-$n$-$2n$, 11-round SKINNY-$n$-$n$ with three, two, one plaintext-ciphertext pairs, separately. In addition, we can attack two more rounds and three more rounds with no more than $2^8$ and $2^{32}$ data complexity, respectively.
Mobile Learning and Social Media Applied in Entrepreneurship Education Courses in a Higher Vocational College

social media is a toolkit for daily life as well as education for mobile learning. With the convenience and accessibility of smart devices to date, educators from higher education institutes provide social media as a collaborative and learning management system for learners across fields. Lacking empirical research in entrepreneurship education, this research adopted a survey method to get 505 responses enrolling in a Chinese higher vocational college. Their attitudes towards the effectiveness of social media for entrepreneurship education are from three aspects of exploring opportunities, mobilizing resources, and taking action. In addition, the critical function of social media-communication-was taken into consideration in detail. The results showed that learners are positive about the combination of learning’ tools and entrepreneurship education for collaborative learning. While the responses reflected that the effectiveness of social media in the identification of entrepreneurial opportunities, which is a difficult point for entrepreneurship education. The possible demographic factors which influence the attitudes towards social media and communication were discussed. This research tried to get first-hand feedback from higher vocational learners about social media for entrepreneurship education. Scholars and educators might get inspiration from this study for their own researches or teaching activities.
Accurately Quantifying under Score Variability

The quantification objective is to predict the class distribution of a data sample. Therefore, this task intrinsically involves a drift in the class distribution that causes a mismatch between the training and test sets. However, existing quantification approaches assume that the feature distribution is stationary. We analyse for the first time how score-based quantifiers are affected by concept drifts and propose a novel drift-resilient quantifier for binary classes. Our proposal does not model the different types of concept drifts. Instead, we model the changes that such changes cause in the classification scores. This observation simplifies our analysis since distribution changes can only increase, decrease or maintain the overlap of the positive and negative classes in a rank induced by the scores. Our paper has two main contributions. The first one is MoSS, a model for synthetic scores. We use this model to show that state-of-the-art quantifiers underperform in the occurrence of any concept drift that changes the score distribution. Our second contribution is a quantifier, DySyn, that uses MoSS to estimate the class distribution. We show that DySyn statistically outperforms state-of-the-art quantifiers in a comprehensive comparison with real-world and benchmark datasets in the presence of concept drifts.
Visualization of Tawang Alun Temple Based Augmanted Reality as a Representation of Ancient Relics in Sedati Sidoarjo

Tawangalun Temple is one of the heritage temples of the Majapahit Kingdom in Sidoarjo Regency which is located in Buncitan Village, Sedati District. Based on the analysis of problems in the field of historical construction, Tawang temple still has little information in digital form, so that little information is visually packaged in digital, resulting in a lack of public interest in accessing information. The system development research method used is an action research research design where the research is carried out by designing the Tawang Alun Temple AR application and implementing the design. This research will create augmented reality by recommending locations that can help all people, from school children to students and the general public, displaying the Tawang Alun Temple site. This application will be able to display 3D objects in the form of the Tawang Alun temple.
A Semantically Enhanced Deep Neural Network Framework for Reputation System in Web Mining for Covid-19 Twitter Dataset

With the web containing a huge amount of information, the extraction of application oriented understandable data has become easier with web mining. Web Mining is the area that is derived from data mining. Unlike data mining, web mining is used to extract interesting patterns from the information available on the web. When used with deep learning, the pattern recognition becomes much easier. Deep learning works in the same way, how a human brain works in terms of predicting the outcomes when a bulk of information is provided. It deals with mathematical models that recognize the patterns efficiently. The different types of web mining techniques, namely: web content mining (WCM), web structure mining (WSM) and web usage mining (WUM) persists. Researchers and economists around the globe are keen in knowing the impact of the pandemic on the society’s economic status; this work helps find the same using reputation system. As twitter is a hub of different opinions of public, we work with covid- 19 data set from twitter. A reputation system helps finding the socio economic status of the tweets regarding covid-19 dataset. This paper has proposed a framework in which the web mining is implemented using a semantic enhanced deep neural network technique for the reputation system.
Extracting Parallel Sentences from Low-Resource Language Pairs with Minimal Supervision

Abstract At present, machine translation in the market depends on parallel sentence corpus, and the number of parallel sentences will affect the performance of machine translation, especially in low resource corpus. In recent years, the use of non parallel corpora to learn cross language word representation as low resources and less supervision to obtain bilingual sentence pairs provides a new idea. In this paper, we propose a new method. First, we create cross domain mappings in a small number of single languages. Then a classifier is constructed to extract bilingual parallel sentence pairs. Finally, we prove the effectiveness of our method in Uygur Chinese low resource language by using machine translation, and achieve good results.
Computing Contingent Plan Graphs using Online Planning

In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. However, the translation may become very large, encumbering the task of the non-deterministic planner. We suggest a different approach—using an online contingent solver repeatedly to construct a plan tree. We execute the plan returned by the online solver until the next observation action, and then branch on the possible observed values, and replan for every branch independently. In many cases a plan tree can have an exponential width in the number of state variables, but the tree may have a structure that allows us to compactly represent it using a directed graph. We suggest a mechanism for tailoring such a graph that reduces both the computational effort and the storage space. Our method also handles non-deterministic domains, by identifying cycles in the plans. We present a set of experiments, showing our approach to scale better than state-of-the-art offline planners.
Sample size determination for comparing accuracies between two diagnostic tests under a paired design

With the progressive technology, many medical researches are aimed to develop diagnostic tests that can detect diseases faster and accurately. The assessment of the accuracy of the diagnostic test for classifying two groups is through the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). When a paired design is considered, the sample size determination requires the information about two AUC estimates and the corresponding variance and covariance of two AUC estimators. This paper derives the nonparametric estimators of the variance and covariance of two AUC estimators. The result is used to derive the sample size formula when the paired sample is planned. Since most of the results do not have a closed form, numerical results are provided under various scenarios.
Reading (Hidden) Dialogue of Organic Tectonics

This article explores the potential of organic material to conceptualize new means of future tectonic beyond the duality of construction technology and representation through a case study of indigenous craft making. Particularly, we propose the understanding of organic tectonic practice as the dynamic action among the human body, spaces, surface, materials, structural systems, and construction. This idea is demonstrated through the tectonic exploration of banana leaves wrapping practices. The practice has been culturally established as a common indigenous technique in Indonesia, and it has been transmitted across generations through the direct practical demonstration of “making.” The findings suggest that the essence of tectonic practice may be found not merely through its materiality but also within the dialogues that occurred during wrapping operation as an integrated set of action knowledge. The exploration of banana leaf wrapping typologies introduced the idea of tectonics as a whole process that considers the relationship between elements that are often separated. This study concludes that the knowledge of action is equally essential with material properties. Such knowledge must be maintained and promoted as a possibility of searching for good materials in contemporary design practices.