Reviewerly/exHarmony huggingface dataset:
Robot Path Planning Based on Improved RRT Algorithm

Path planning is an important research content of mobile robot. Because of the successful application of Rapidly-Exploring Random Tree (RRT) algorithm in robot path planning, it has been greatly studied and developed since it was proposed. In this paper, the robot path planning simulation experiment is carried out for two two-dimensional maze environment, whose sizes are 860 * 770 and 1250 * 1000 respectively. One of the mazes is circular, and the other is composed of many square obstacles. In the case of unknown maze environment, any two points in the maze are selected as the starting point and end point to plan the path between them. In this paper, RRT algorithm is used, and then the algorithm is improved. Dijkstra algorithm is used to find the optimal path from the beginning to the end. In this paper, the random search tree algorithm is used to start from the starting point and search the random number until the target point is reached. Then Dijkstra algorithm is used to search the shortest path from the target point to the end point. Record the nodes and paths of the fast search tree, and then find the optimal path through Dijkstra algorithm. Through the implementation of fast search tree algorithm by Visual Studio (VS), the optimal path is obtained. The optimal path is shown by Matlab.
Improved Image Enhancement of Natural Images with Median Mean-Based Sub-Image Clipped Histogram Equalization

This paper introduces a powerful image contrast enhancement algorithm that is developed based on the energy curve equalization technique. Instead of the histogram, an energy curve is used for considering spatial contextual information in the image. This is Improved Image Enhancement of natural images with median mean-based sub-image clipped histogram equalization. The algorithm consists of the following steps, first, obtaining the energy curve, secondly, calculating median and mean intensity values of the image, third, the energy curve is clipped using a threshold level which is its mean occupancy, and fourth, the clipped energy curve is divided into two halves based on median and then further partitioned into four subparts based on mean intensity values, all these four portions are equalized and then combined to form an enhanced image. This promotes natural enhancement and levies control over the rate of enhancement. The simulation conveys that the proposed method shows supremacy over the other previously existing methods. This shows an increase in entropy which is the information content held in the resultant image.
ALRS: An Adversarial Noise Based Privacy-Preserving Data Sharing Mechanism

Deep learning is data-hungry, and generally its performance highly depends on the amount of training data. Multiple parties can obtain better models by sharing their data and train models collaboratively. To privacy concerns, sensitive raw data of each entity can not be shared directly. In this paper, we propose a data sharing mechanism called ALRS (for Adversarial Latent Representation Sharing) that shares data representations rather than raw data, and applies adversarial example noise to protect shared representations against model inversion attacks, and achieve a balance between privacy and utility. Compared with prior collaborative learning works, ALRS requires no centralized control. We evaluate ALRS in different contexts, and the results demonstrate that our mechanism is effective against reconstruction and feature extraction attacks, while maintaining the utility of models at the same time.
Hierarchical Evaluation of Software Projects: An Experiment

A relevant goal of Software Engineering is to assure the quality of object oriented software. Among existing models for iterative and incremental development of software systems, Model Driven Architecture (MDA) has reached a leadership position. MDA enables model-driven software development which treats models as primary development artefacts. The present empirical study answers the following research questions: (RQ1) are there available in the literature hierarchical methods that can be adopted to assess the quality of methods, classes, and packages, as well as making an overall evaluation of software projects at the design level. (RQ2) What is the outcome of the adoption of one of those methods (if any) to measure the quality of software projects developed by adopting an MDA-oriented software development process and xGenerator? The latter is a Java technology platform for the creation of MVC Web applications, which implements the model-driven approach. To answer RQ1, the literature was investigated and, among available alternatives, a Multi Level Quality Analysis Model was selected to carry out an experiment. The outcome of the experiment, which was very satisfactory, provided the reply to RQ2.
Financing of research activities of Russian universities from foreign sources

The volume and sources of co-ﬁnancing by foreign partners of research activities of Russian universities for the period 2010-2020 are analyzed. Identiﬁed funding trends; shows the dynamics of changes in ﬁnancial ﬂows of countries that have made the maximum contribution to the co-ﬁnancing of science of Russian universities.
SAU’S Submission for CCMT 2021 Quality Estimation Task

This paper describes our submissions to CCMT 2021 quality estimation sentence-level task for both Chinese-to-English (ZH-EN) and English-to-Chinese (EN-ZH). In this task. We follow TransQuest framework which is based on cross-lingual transformers (XLM-R). In order to make the model pay more attention to key words, we use the attention mechanism and gate module to fuse the last hidden state and pooler output of XLM-R model to generate more accurate prediction. In addition, we use the Predictor-Estimator architecture model to integrate with our model to improve the results. Experiments show that this is a simple and effective ensemble method.
Extensive Research on Adaptive Intelligence Cognitive and Machine Learning for Computing Technology

In this work, cognitive computing is introduced with all the definitions explanations used in the research works. The steps involved in cognitive systems have been outlined. Five key attributes of a cognitive system is listed and their relevance to the applications are also detailed out. Difference between artificial intelligence and cognitive computing systems is presented and the differences are explained in terms of various dimensions. Application of cognitive systems and technologies used in the applications has been outlined with examples. Reasons for choosing cognitive systems over artificial intelligence and machine learning have been discussed in detail. Finally, a brief literature review of definitions of cognitive systems and recent developments have been presented at the end.
Polarization Dehazing withDualImages Based on Dark Channel Quartering

For the polarization dehazing, we proposed the dark channel quartering to estimate the atmospheric light intensity, and four vertical polarization images to obtain the dual images based on Stokes parameter.
Relation-Algebraic Verification of Borůvka’s Minimum Spanning Tree Algorithm

Previous work introduced a relation-algebraic framework for reasoning about weighted-graph algorithms. We use this framework to prove partial correctness of a sequential version of Borůvka’s minimum spanning tree algorithm. This is the first formal proof of correctness for this algorithm. We also discuss new abstractions that make it easier to reason about weighted graphs.
Energy production modeling of 1 kWp solar power plant using SARIMA

Institut Teknologi Nasional Bandung (ITENAS Bandung) has installed a 1 kWp solar power plant (SPP) system since 2018, and many kinds of research have been performed regarding this system. The performance of the SPP can be evaluated by SPP's energy production, which is highly influenced by environmental conditions, i.e. irradiance and ambient temperature. Modeling of energy production is one of the important issues, and can be used as a forecasting tool in order to get energy production predictions at predetermined periods. In this paper, energy production modeling of 1 kWp SPP at ITENAS Bandung will be explained, using the seasonal autoregressive integrated moving average (SARIMA). The basis of this model is utilizing energy production data history of a 1 kWp SPP data for 1 year period (June 2020 – June 2021), and creating the model to predict the energy production. Based on modeling and evaluation, it can be found that the SARIMA model has an accuracy of 97.9%, compared to actual operational data.
From Alinsky to Zoom: Understanding Relational, Constituency-Based Organizing in 2021

Research across political science, communications, sociology, and human-computer interaction has explored how modern social movements are leveraging technologies and digital tools to mobilize for social change. While mass mobilization has influenced policy, triggered revolutions, and influenced cultures globally, mobilization is just one method of collective action. In contrast, the more structured, relationship-focused practice of community organizing can also enable mass societal change, such as in the US Civil Rights movement and in the development of tech-giant labor unions. While there is exploration into how we might research and design tools informed by organizing, specific frameworks and practices of community organizing - such as the People, Power, Change framework - also must evolve in response to the ever changing landscape of technology in society. In this workshop, we will bring together community organizers, technologists, designers, and researchers to explore what it might mean to organize and center relationships in the modern era with (or without) computer support, in what contexts we should prioritize organizing rather than mobilizing, and how we might iterate upon our existing framework to respond to our findings. We will produce an iterated draft of a traditional organizing framework.
Truncating the Coefficients of the Discrete Cosine Transform for Better Digital Image Compression Performance

This paper focuses on the quantization step of the JPEG digital image compression. The irreversible step of the JPEG process is the quantization operation that eliminates some of the coefficients. The JPEG is based on the discrete cosine transform (DCT), a transform that is capable of compacting the digital image energy into only a few coefficients. Quantization eliminates high frequency components of the image. Since quantization is followed by zig-zag scanning, the basic idea of this paper is to preserve a predefined number of diagonals from the quantization matrix, thus achieving optimal data for the Huffman coding process and make savings in the bitrate. Analysis is done for both uniform and non-uniform quantization. Since JPEG is a lossy compression scheme, the degradation is measured using objective measures such as the Peak signal to noise ratio (PSNR) and the Structuralsimilarity index (SSIM).
Stream Mining for Network Sensor Data Using Classification and Metaheuristic Techniques

Datastream mining comes to play an integral role in real-time applications. The main sources of the data stream flows are sensors, multimedia and social media. It contains remote sensors, stock markets, tweets, and video surveillance systems. The data stream has distinctive characteristics which include its high speed and very huge volume, in addition to its ability to change over time. It has many challenges, one of which is the concept drift that happens due to the continuous property of data stream. Traditional data mining techniques could not deal with or mine this big, rapid data. The aim of this work is to classify sensor data using metaheuristic and classification techniques. The metaheuristic approach is used to build the balanced chunks of data using Particle Swarm Optimization (PSO) to obtain a better classification accuracy. The meta model formed by the metaheuristic and classification techniques shows an improvement in accuracy performance at a high rate, as compared to non-metaheuristic models. Multiple classifiers have been chosen based on fuzzy function for each chunk to select the optimal one which gives the optimal results with high accuracy. The obtained results show a good performance in terms of classification accuracy for the neural network with 90% and low positive rate.
An efficient multimodal transport transaction settlement system based on block chain

In recent years, with the increase of world trade volume, multimodal transport has become the main mode of international trade transportation, and its development is facing a new stage. As an important part of multimodal transport, invoice affects the efficiency of multimodal transport. Electronic invoices are replacing traditional paper invoices as the main form of bill of lading. But due to the characteristics of paperless and intangible electronic, the electronic invoices, unlike traditional paper invoices, cannot be actually possessed. Considering the block chain technology possesses the characteristics of unchanged and traceability, it will apply into the electronic invoice. It provides shippers and carriers with freight information and pre-agreed service contract rates, enables shippers and carriers to conduct secure and transparent transactions, improves electronic invoice processing efficiency, and reduces shipper and carrier costs.
Causality and Its Applications

The diagnosis of cause–effect relations boosts the both predictive and prescriptive models and their analysis. The recent trend and growth in machine learning, leading the field of predictive analytics, base the certain case for causal analysis. Causal analysis supports the study of causes and effect as they are observed and their underneath relationship directing such trends, are analysed for predictive modelling. The relation in cause–effect provides much needed information on interdependency in the various features. The interdependency helps to identify the valuable or influential features which supports for any certain trend, which is of interest. In this chapter, machine learning and necessity for causal analysis is discussed in depth. The main focus point is to provide different scenario where causal integration into machine learning is useful but lacks the process of inference. A depth view is shown for different situations where causal analysis can produce and largely effect the standard industrial process of predictive modelling. Next to this trend, a more advanced step is prescriptive analysis. Going one step further of predicting what is expected, is prescriptive analytics focus on what to avoid to achieve the required target. In this case, causal analysis can be used to find feature dependency model, leading to the prescriptive future model. As seen in machine learning modelling, much effort is put upon setting up the predictive modelling. But most industry require a better understanding of feature dependency and a model leading to business success while avoiding losses, a prescriptive modelling. In deep learning, much effort is lost on building the most accurate predictive modelling. But neural networks never seen in the lights of models which are explainable. The weights produced in most accurate models of deep learning, does not provide interpretability or their effectiveness in producing final results. With the rise of explainable AI, there is now efforts put onto the explain ability of the deep networks. This new trend/flow now opened the windows for causal explanations in deep neural network models. The task to explain deep learning models, require understanding on hidden weights and their influence on final neural outputs. Causal influence of input features through the weights inform their contribution towards neural outputs. Through explainable neural network weights, the better models can be built to overpower or underpower the effects of input features. In this note, the chapter provide how causal influence is useful and their significance for causal influence analysis. The criteria for causal influence analysis in deep neural models are discussed with examples.
Gradual GRAM and secure computation for RAM programs1

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.
Calculation of the Work Done on the Structured Elements of the Subject Domain

In the article, the authors obtained a formula for calculating the work on structured elements of the subject area, performed by a non-material point. The formulas obtained in the article are new and relevant, because today the equations of force and work, all further definitions and values that depend on force and work are associated with the movement of a material point. For this, the generally accepted methods of calculating work through force were used. Were used the previous results of the study of the authors. The theory and formulas for calculating the dynamic coefficients of structuring were used to assess the subject area. According to the obtained formula for calculating the work on structured elements of the subject area, the dependence of the work on the number of structured elements of the subject area was built. To analyze the results obtained, we took into account the cognitive phenomenon of stinginess, which negatively affects the selection process in exploratory research, as well as the reflexive approach proposed by the authors. The research results showed the following. For structuring 64 elements, 4 g-calories were required. To perform mental work, the body needs to spend about 478 g-calories without using structural methods. As a result of our research, we have identified a significant savings in brain energy, which is equal to 474 g-calories. The structuring method proposed by the authors helps to eliminate mental distortions on 474 g-calories with a minimum expenditure of brain energy 4 g-calories when using the structuring algorithm.
Polarity Search of Ternary FPRM Circuit Based on DMMA Algorithm

Through studying the Ternary FPRM expression and polarity conversion algorithm, combining with the mayfly optimization algorithm, this paper proposes a decomposition-based multi-objective mayfly optimization (Multi objective mayfly optimization algorithm based on decomposition, DMMA) algorithm solution. The DMMA algorithm decomposes the target space into multiple uniform subspaces, and each subspace independently retains a Pareto solution so as to improve the distribution of the population on the Pareto front; using the Chebyshev decomposition method, the optimal solution of the sub-problem is taken as the direction of population evolution so as to expand the search range of the population. On this basis, Ternary FPRM polarity conversion technology and DMMA algorithm are combined to search for the optimal polarity of the circuit. By testing 10 Benchmark reference circuits, and comparing the performance of DMMA algorithm with MODPSO and MODCPSO algorithms, it is effectively proved that DMMA algorithm has advantages in searching the optimal polarity of Ternary FPRM circuits.
A Hybrid Algorithm for Face Detection to Avoid Racial Inequity Due to Dark Skin

There has been significant development in the facial recognition technology during past few decades. This technology has been widely used by different organizations and governments for defense, security, and surveillance projects. Furthermore, it has now been incorporated into our daily usages, such as consumer applications, personal data protection, or cyber-security, particularly while using smartphones. Most of these systems work very efficient, however, there are some challenges related to the accuracy of results of facial recognition systems when tested on images of people with dark skin. As a matter of fact, various studies demonstrate higher accuracy when tested on data set with white skin personnel, while exhibit a much lesser accuracy when the same algorithms are tested on dataset of people with dark skin. This article highlights the variation in accuracy of existing facial recognition algorithms when applied to dark-skinned people. Furthermore, as a principal contribution it presents a hybrid algorithm based on Gaussian and Explicit rule model that improves the accuracy for face-detection for dark skinned people. Thorough experimental evaluation has been conducted with a data set of black faces by first identifying skin and non-skin regions and then applying skin segmentation. The results have been compared with existing face detection algorithms with a clear improvement in the accuracy of 89% for dark skin.
Pyramidal Semantic Correspondence Networks

This paper presents a deep architecture, called pyramidal semantic correspondence networks (PSCNet), that estimates locally-varying affine transformation fields across semantically similar images. To deal with large appearance and shape variations that commonly exist among different instances within the same object category, we leverage a pyramidal model where the affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed. Different from the previous methods which directly estimate global or local deformations, our method first starts to estimate the transformation from an entire image and then progressively increases the degree of freedom of the transformation by dividing coarse cell into finer ones. To this end, we propose two spatial pyramid models by dividing an image in a form of quad-tree rectangles or into multiple semantic elements of an object. Additionally, to overcome the limitation of insufficient training data, a novel weakly-supervised training scheme is introduced that generates progressively evolving supervisions through the spatial pyramid models by leveraging a correspondence consistency across image pairs. Extensive experimental results on various benchmarks including TSS, Proposal Flow-WILLOW, Proposal Flow-PASCAL, Caltech-101, and SPair-71k demonstrate that the proposed method outperforms the lastest methods for dense semantic correspondence.
Frontal Facial Expression Recognition Based on Convolutional Neural Network and Haar-Cascade Descriptors

In this paper, a frontal facial expressions recognition classifier is developed based on a deep convolutional model with a few numbers of learning parameters that make the proposed model applicable for mobile applications. In this, a high rate of accuracy and fast recognition is achieved. Firstly, contrast enhancement of the input images of the FER+ dataset is applied using the adaptive histogram equalization method. Then, the frontal facial images are only selected by using Haar-Cascade descriptors. Moreover, three different training methods are examined and the best recognition outcome of the presented CNN model is chosen, which outperforms the compared CNN models in terms of correctness and reduced number of parameters. The average accuracy of the proposed model reaches 94 % on validation and test sets. The acquired result can be considered promising in the domain of real-time communication with machines. Stating that feature selection and image enhancement have an effective impact on the precision of the emotion classifier. However, the shortage of datasets in any emotion class can defect the learning process and cause an instant decrease in the classifier accuracy.