Reviewerly/exHarmony huggingface dataset:
Relying on multi-modal contextual cross-cultural communication ability training big data analysis

Language is a carrier of culture. In the current development of intercultural communication and intercultural communicative competence, it is necessary to effectively combine the language of intercultural communicative competence with cultural education, learn language from culture, and introduce culture into language. This article analyzes the data of cross-cultural communicative competence in a multi-modal context, explores the methods of determining multi-modal changes and cultural introduction in the training of cross-cultural communicative competence, and analyzes the effects of multi-modality in the training of cross-cultural communicative competence. The influence of cultural introduction, and finally the use of multi-modal reasonable determination and selection, is to improve the cultivation of cross-cultural communication skills.
Selective variance based kinship verification in parent's childhood and their children

Based on two facial image appearances estimating their kinship is the main aim of the kinship verification. Age progression-based kinship verification is one of the obscure parts in this research. The similarities in facial features between parent and their children will be numerous in their childhood. As age progress, child facial features are varied and dispersed from parent facial features. It becomes a challenging task to estimate their kinship. So, a new dimensional database with parent in childhood and their child images is collected. This paper proposes and trains a metric to ensure that the model can predict whether the given pair images are kin or non-kin. In training module, differences of Histogram of Gradient (HoG) features for all combinations of pairs are computed and each pair absolute differences are calculated. Further, selective minimum variances are used to assess the kin similarity features. A global threshold is computed to classify kins and non-kins. After this comprehensive training, testing is also done in a similar way. The computed global threshold in training module is effectively used to estimate kinship verification in testing module. Experimental results are presented and out performed with an accuracy of 82%.
Well-Calibrated and Sharp Interpretable Multi-Class Models

Interpretable models make it possible to understand individual predictions, and are in many domains considered mandatory for user acceptance and trust. If coupled with communicated algorithmic confidence, interpretable models become even more informative, also making it possible to assess and compare the confidence expressed by the models in different predictions. To earn a user's appropriate trust, however, the communicated algorithmic confidence must also be well-calibrated. In this paper, we suggest a novel way of extending Venn-Abers predictors to multi-class problems. The approach is applied to decision trees, providing well-calibrated probability intervals in the leaves. The result is one interpretable model with valid and sharp probability intervals, ready for inspection and analysis. In the experimentation, the proposed method is verified using 20 publicly available data sets showing that the generated models are indeed well-calibrated.
Automatic Joint Optimization of Algorithm-Level Compression and Compiler-Based Acceleration with Reinforcement Learning for DNN in Edge Devices

More accurate machine learning models often require more memory cost and more software-hardware co-adaption efforts for deployments on resource-constrained devices. Model compression techniques and deep learning compiler are developed to reduce the memory cost and latency. However, current methods require tremendous engineering efforts to optimize the model manually. This paper introduces a jointly learning based framework to perform the compression task and the acceleration task simultaneously. The joint optimization method auto-tunes the algorithm-level compression and compiler-based acceleration with reinforcement learning. The experiment results demonstrate that we compress the model by a factor of 2 or 8, and accelerate the optimization up to 30 times using our learning framework.
IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis

In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as reverse engineering and plays a pivotal role in software engineering, computer security, but also most recently in interpretability. In contrast to prior work, we do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program. We assess the performance of our approach on the Karel dataset. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases – even exceeding prior work that had privileged information on the black-box.
Security Analysis of Image Scrambling Cipher Based on Compound Chaotic Equation

As digital image has become one of the most important forms of expression in multimedia information, the security of digital image has become a concern. Because of its large amount of data and high redundancy, there are many security hidden dangers in ordinary image encryption methods. Aiming at the problems of low flexibility and poor anti-interference of traditional image scrambling technology, this paper proposes to select the scrambling diffusion encryption structure in the process of chaotic digital image encryption, which can produce relatively better encryption performance than single scrambling and diffusion scrambling. The composite chaotic operation used in this paper masks the distribution characteristics of chaotic subsequences. Based on the composite chaotic mapping model, the image scrambling password under the two-dimensional chaotic equation is established by scrambling the image in space and frequency domain. Several rounds of experiments show that the algorithm has a large scrambling scheme, further expands the key space of Arnold algorithm, and effectively resists the common computer brute force attack cracking and image decryption cracking methods such as exhaustive, differential attack and known plaintext attack. The improved encryption algorithm can realize the key avalanche effect, is very sensitive to the initial key and has high key security performance, and solves the security problem in the process of image transmission. Several performance syntheses show that the algorithm has high security performance and is suitable for image encryption scheme.
Augmented Reality-Based Product Validation to Support Collaborative Engineering of Complex Technical Systems

Within the course of a research project, a digital collaboration platform has been developed to exploit the potentials of Additive Manufacturing (AM). Potential users of AM and technology experts are digitally guided through the whole development process of individual AM components on the platform. This paper presents an Augmented Reality-based solution for interactive product validation across locations that supports the collaboration via the platform. It enables the efficient integration of the customer into the development. The solution consists of an AR application for the Microsoft HoloLens 2 for the customer and a visualization application for the expert. To enable a collaborative AR visualization, the two applications are networked and synchronized. 3D models of the developed components can be interactively visualized and experienced at the future application site. The expert can immerse into the customer’s view and explain important aspects via audio communication and through interactive animations and further functionalities. On the other side, the customer can give feedback to the 3D model using gestures and speech control and point out aspects to be revised. The solution is integrated into the collaboration platform and the development processes. Based on use cases of two companies from different industries the solution has been evaluated and optimized. This ensures the practical applicability and a functional scope that meets the needs of the industry. Especially in times of Corona, Augmented Reality-based product validation is one way to let customers and developers work together efficiently across locations.
Performance evaluation of independent global solar radiation estimation models for different climatic zones: A case study

Rapid technological advancement in solar applications requires accurately determined daily global solar radiation (DGSR). The independent models to predict DGSR are super cited as they do not require any other climatological or geographical parameter. In this study, nine day-of-the-year based (DYB) independent models are developed to estimate daily global solar radiation in Pakistan. The meteorological data from the year 2000–2017 is used while covering thirty locations from five climatic zones of Pakistan. The models’ performance is statistically assessed in terms of normalized mean bias error (nMBE) and normalized root mean square error (nRMSE). The results indicate that the two Gaussian function model estimates DGSR with a satisfactory performance at most stations in different climatic zones. On average, the correlation coefficient and index of agreement between day-of-the-year and DGSR are 0.934 and 0.956, respectively. The two Gaussian function model is then further compared with four existing best claimed models found in literature. Results show that the nMBE and nRMSE ranges from 0.87 to 3.00 and 0.95 to 3.21, respectively. Thus, it shows that the existing models overestimate the DGSR and are not applicable for long-term planning. In comparison, for the present study, nMBE and nRMSE take values between −0.05 to 0.17 and 0.09 to 0.24, respectively. Furthermore, Global Performance Indicator (GPI) is used to rank the models based upon their prediction performance which ranges between −3.5626 and 4.1998. The study revealed that DYB models are best suited for hot climatic regions of Pakistan as compared to cold climatic zones. Also, the locations within the China Pakistan Economic Corridor (CPEC) highway network have maximum accuracy. This study can help energy system designers to predict DGSR values at any location in Pakistan.
LOKI: Development of an interface for task-based, privacy-friendly smart home control through LOCal Information Processing

English – Current control systems for smart home devices such as Amazon Echo rely primarily on central data processing on the company’s own servers. Since smart home devices collect potentially sensitive data, this often leads to privacy concerns on the part of users. A privacy-friendly alternative is to process smart home data locally, which would further protect smart home systems from hacking attacks. To this end, in an iterative “human-in-the-loop” process, we developed LOKI, an interface that enables control of a smart home using local information processing. In addition, LOKI provides the option to specify certain routines, enabling task-based control that provides a more natural interaction with smart home devices than device-based control.
Image Recognition Method for Pitching Fingers of Basketball Players Based on Symmetry Algorithm

In the basketball game, the accuracy and standardization of pitching are directly related to the score. So it is very important to analyze the pitching figure movement to have a better positioning of the fingers. There are limited techniques to recognize the movement. The human motion recognition method is one of them. It utilizes the spatiotemporal image segmentation and interactive region detection to recognize images of pitching finger movement of basketball players. This method has a limitation that the symmetrical information of the human body and sphere cannot be excavated, which leads to certain errors in recognition effect. This paper presents a method of recognizing pitching finger movement of basketball players based on symmetry algorithm, constructs an acquisition model, carries out edge contour detection and adaptive feature segmentation of images of pitching finger movement of basketball players, and uses a fixed threshold to segment finger image to extract players’ hand contour and locate the middle axis of the finger. On this basis, the symmetry recognition method based on nematode recognition algorithm is used to recognize the symmetry of basketball pitching finger movement image and complete the accurate recognition of basketball pitching finger movement image. The experimental results show that the proposed method can effectively recognize the basketball player’s finger movement image. The average recognition accuracy is 98%, the growth rate of recognition speed is 98%, and the maximum recognition time is 12 s. The robustness of the proposed method is 0.45.
Research on Mobile Robot Vision Navigation Algorithm

Abstract Robot navigation is one of the key technologies of indoor robots and it is the primary solution to the problem of intelligent robot. In this paper, the visual navigation problem of mobile robot was analyzed and the robot vision navigation algorithm based on monocular vision was proposed. The algorithm obtained three-dimensional information of obstacle through data fusion, builder a three-dimensional model of obstacle, and optimized the navigation path by using the heuristic best search algorithm, and realized the drawing of the robot navigation path.
Blended Learning Platform: A Requirement Analysis

Learning during the pandemic and during the application of new habits in the context of implementing learning that implements the protocol for preventing the spread of Covid-19 has unique and uncertain needs. Unique means that the learning process has special characteristics from pre-pandemic learning. Meanwhile, the meaning of “nonpermanent” is a learning process that changes in learning planning, implementation, and evaluation. This causes the need for new innovations in learning platforms that are able to accommodate this unique and variable learning. This article provides an analysis of the requirements of a blended learning platform. Where changes in dynamic forms of learning give rise to various needs in learning. The method used is the survey, and analysis of responses of respondents using user story quality (USQ). The results found in this study are presented in the form of a table containing the item requirements for developing a blended learning platform.
Analysis of GEE with a mixture working correlation matrix for diverging number of covariates

The generalized estimating equations (GEE) method has been widely used for longitudinal data analysis. Wang [GEE analysis of clustered binary data with diverging number of covariates. Ann Stat. 2011;39:389–417] developed an asymptotic theory for GEE analysis of clustered binary data with diverging number of covariates. She suggested several moment estimators of the nuisance parameter in the working correlation matrix. However, these estimators might not exist when the working correlation structure is misspecified. When the number of covariates is finite, Xu et al. [A finite mixture model for working correlation matrices in generalized estimating equations. Stat Sin. 2012;22:755–776] proposed a mix-GEE method based on a finite mixture model to capture correlations among repeated measurements. In this paper, we develop an asymptotic theory for the mix-GEE estimator with diverging number of covariates. Simulation studies are used to demonstrate the performance of the mix-GEE with diverging number of covariates, which indicate this method is more numerically stable and has a higher efficiency than the GEE with a specified working correlation matrix in diverging number of covariates framework. Finally, a real dataset is used for illustration.
Research on the Emotional Polarity Classification of Barrage Texts

Abstract With the widespread application of multimedia technology, video barrage has become an important form of expressing opinions and emotions. The classification of Chinese barrage texts according to emotions is of great significance for mining users’ emotions. However, the large number of Internet buzzwords in the barrage texts brings certain difficulties to sentiment analysis. Therefore, in order to address the ambiguity problem of Internet buzzwords in the barrage texts and effectively classifying the video barrage texts, the BERT pre-training language model and Support Vector Machine are combined to classify the barrage texts of positive and negative emotions. In this paper, the word vectors of the barrage texts are firstly generated by the BERT model. The SVM model is used as a classifier, and Particle Swarm Optimization is introduced to optimize the parameters of the SVM model. This method is compared with the traditional methods on the barrage data sets of different video types. The experimental results show that the precision, recall and F1-score of the proposed method are higher than those of the traditional models.
Mediator-Based Effective Resource Allotment on Multi-Clouds

In cloud computing, software and hardware are provisions. So, suppliers require QoS in allotment about provisions to cloud users. Cloud suppliers along with users are identity-invested individuals who would attempt to amplify their advantages. Current huge distributed framework permits clients to distribute and exchange provisions. In cloud computing, clients buy various provisions similar to network transmission capacity, computational power, and capacity framework from at least single cloud suppliers for a restricted timeframe with a variable or predetermined cost. Allotment of resources to cloud users is a difficult method because of the intricacy of best distribution of resources (productive allotment with restricted resources and greatest advantage). So, enhance resource allotment mediator-related resource allotment technique is utilized. In aforementioned strategy, the client does not request to know who is the service supplier as well as where resources live. The user obtains the provisions by the minimal cost. The cost about resources in cloud is resolved efficiently dependent on demand – deliver form otherwise statically dependent on fixed value form. We can resolve the resource allotment abnormality by utilizing a multimediator framework. In multimediator framework (MMF), suppliers and purchasers are mediators. Resources are scattered between numerous mediators, and the mentioned mediators will perform allotment procedure. The aforementioned process is usually recognized as multimediator resource allotment (MMRA). An extensive variety of problems like Grid computing, routing, can be resolved via MMRA. The multi-mediators are several times identified as “social government mediators”; it will provide greatest profits to suppliers as well as users. In our framework, we recommend agent-based multimediator system. The proposed framework has three sorts of mediators, in particular, client mediator, resource brokering mediator, and resource supplier mediator.
Primer on artificial intelligence

Artificial intelligence (AI) has induced considerable achievements in various fields such as computer vision, natural language processing, autonomous vehicles, and so on. The AI models refer to the technology presenting human intelligence through computer programs, which could efficiently address the complex optimization problems in the wireless network to enhance the communication quality. On the other hand, the wireless network could also assist the data collection for model training and simplify the deployment of inference models. In this chapter, we shall present the basic concept of various AI models, which contributes to comprehend the following chapters. The machine learning (ML) models are specified by presenting supervised learning, unsupervised learning, and reinforcement learning. Moreover, we particularly present various models of deep learning (DL), which exhibit complex structure and excellent performance.
YOLOv3 Target Detection Algorithm Based on Channel Attention Mechanism

Aiming at the problems of real-time target detection algorithm YOLOv3, such as insufficient positioning of bounding boxes and difficulty in distinguishing overlapping objects, a YOLOv3 detection algorithm with attention mechanism is proposed. First, a new channel attention module is proposed, which can automatically obtain the importance of each feature channel; then it is combined with the residual module of YOLOv3 to form a new skeleton network, which solves the imbalance of the detection frame distribution in the edge area Question: Finally, in order to improve the detection speed, the loss function of the original YOLOv3 detection network is further optimized. Comparing experiments on the Pascal VOC2007 data set, the experimental results show that compared with mainstream target detection models, the performance of this model is greatly improved, and it also has a good detection effect on overlapping objects. The average accuracy of the model on the test set is as good as The detection speed reached 93.50% and 47.58FPS, meeting the requirements of real-time detection.
Practical security of quantum key distribution in the presence of side channels

Abstract Security of quantum key distribution (QKD) is based on fundamental laws of quantum physics. However, the security of QKD implementations is at risk due to various deviations, called side channels, of the real device from the theoretical model. In this work, we discuss the practical ways to avoid the threat of side channels in QKD. We use interference visibility measurement for estimation of distinguishability of quantum signals, and show how to integrate it into the security proof. Our results paves the way towards the development of novel robust QKD schemes.
Distance Metric Learning with Prototype Selection for Imbalanced Classification

Distance metric learning is a discipline that has recently become popular, due to its ability to significantly improve similarity-based learning methods, such as the nearest neighbors classifier. Most proposals related to this topic focus on standard supervised learning and weak-supervised learning problems. In this paper, we propose a distance metric learning method to handle imbalanced classification via prototype selection. Our method, which we have called condensed neighborhood components analysis (CNCA), is an improvement of the classic neighborhood components analysis, to which foundations of the condensed nearest neighbors undersampling method are added. We show how to implement this algorithm, and provide a Python implementation. We have also evaluated its performance over imbalanced classification problems, resulting in very good performance using several imbalanced score metrics.