Reviewerly/exHarmony huggingface dataset:
A language modeling-like approach to sketching

Sketching is a universal communication tool that, despite its simplicity, is able to efficiently express a large variety of concepts and, in some limited contexts, it can be even more immediate and effective than natural language. In this paper we explore the feasibility of using neural networks to approach sketching in the same way they are commonly used in Language Modeling. We propose a novel approach to what we refer to as "Sketch Modeling", in which a neural network is exploited to learn a probabilistic model that estimates the probability of sketches. We focus on simple sketches and, in particular, on the case in which sketches are represented as sequences of segments. Segments and sequences can be either given - when the sketches are originally drawn in this format - or automatically generated from the input drawing by means of a procedure that we designed to create short sequences, loosely inspired by the human behavior. A Recurrent Neural Network is used to learn the sketch model and, afterward, the network is seeded with an incomplete sketch that it is asked to complete, generating one segment at each time step. We propose a set of measures to evaluate the outcome of a Beam Search-based generation procedure, showing how they can be used to identify the most promising generations. Our experimental analysis assesses the feasibility of this way of modeling sketches, also in the case in which several different categories of sketches are considered.
Flexible Data Augmentation in Off-Policy Reinforcement Learning

This paper explores an application of image augmentation in reinforcement learning tasks - a popular regularization technique in the computer vision area. The analysis is based on the model-free off-policy algorithms. As a regularization, we consider the augmentation of the frames that are sampled from the replay buffer of the model. Evaluated augmentation techniques are random changes in image contrast, random shifting, random cutting, and others. Research is done using the environments of the Atari games: Breakout, Space Invaders, Berzerk, Wizard of Wor, Demon Attack. Using augmentations allowed us to obtain results confirming the significant acceleration of the model’s algorithm convergence. We also proposed an adaptive mechanism for selecting the type of augmentation depending on the type of task being performed by the agent.
Extraction of Form Attribute and Query Interface Integrating from Deep Web

In this paper we describe a new strategy based on internal code and visual unit to extract query interface schema. In the process of extraction, there are still some aspects that should be improved. In order to access multiple network databases in deep Web, which is a time-consuming work, how to integrate a unified query interface is one potential problem. Integration is a process of matching attributes that are in the query forms based on domain ontology. It can save a lot of time and energy to visit abundant network databases. Experimental results also show that our method outperforms the state-of-art integrator from the perspective of traditional performance metrics.
Efficient Unsupervised Monocular Depth Estimation with Inter-Frame Depth Interpolation

To alleviate the need of expensive depth annotations, some existing works resort to unsupervised learning methods for depth estimation using monocular videos. To improve the accuracy of the prediction with the relationship of the inter-frame, we present a new unsupervised learning algorithm for monocular depth estimation. In contrast to most existing works, our method predicts depth maps for both target and source frames in each iteration. An inter-frame depth interpolation module is designed, which learns to infer the depth map of the target frame based on those of the source frames. A temporal consistency loss is also proposed to penalize the discrepancy of the predicted depth maps across frames, which not only enforces the coherence of video depth prediction but also provides supervision to source view depth estimation. Similar to the idea of the mutual learning, our method takes full advantages of both target and source views for depth estimation learning. Each module of our proposed method is differentiable, allowing end-to-end training of the whole system. Experiments on the popular KITTI dataset have been conducted and shown that our method performs favorably against state-of-the-art approaches.
Local Intrinsic Dimensionality Signals Adversarial Perturbations

The vulnerability of machine learning models to adversarial perturbations has motivated a significant amount of research under the broad umbrella of adversarial machine learning. Sophisticated attacks may cause learning algorithms to discover decision functions or make decisions with poor predictive performance. In this context, there is a growing body of literature that uses local intrinsic dimensionality (LID), a local metric that describes the minimum number of latent variables required to describe each data point, for detecting adversarial samples and subsequently mitigating their effects. The research to date has tended to focus on using LID as a practical defence method, often without fully explaining why LID can detect adversarial samples. In this paper, we derive a lower-bound and an upper-bound for the LID value of a perturbed data point and demonstrate that the bounds, in particular the lower-bound, have a positive correlation with the magnitude of the perturbation. Hence, we demonstrate that data points that are perturbed by a large amount would have large LID values compared to unperturbed samples, thus providing a theoretical justification for the use of LID in prior literature. Furthermore, our empirical validation demonstrates the utility of the bounds on benchmark datasets.
Visual Elements Mining in the Packaging Design of Children's Products based on OpenGL and SVM

Visual elements mining in the packaging design of children's products based on OpenGL and SVM is studied in this paper. Using the semantics of images to retrieve images based on people's habit of understanding and describing images is an ideal image retrieval method, hence, this paper selects the SVM as the theoretical basis for the study of the visual elements. For the implementation of the system, the OpenGL is the choice for the systematic optimization. The operator directly manipulates the robot, and records some key information and operation mode of the target by adjusting the position of the robot. After the comprehensive implementation of the system, the elements analysis for the packaging design is simulated.
Substructural fixed-point theorems and the diagonal argument: theme and variations

This article re-examines Lawvere's abstract, category-theoretic proof of the fixed-point theorem whose contrapositive is a `universal' diagonal argument. The main result is that the necessary axioms for both the fixed-point theorem and the diagonal argument can be stripped back further, to a semantic analogue of a weak substructural logic lacking weakening or exchange.
SinTM - LDA and RAKE based Topic Modelling for Sinhala Language

The advancement of technology increased the usage of textual information in the world. Growing of such numerous types of unstructured and heterogeneous text data become hard to manage. Topic modelling is a technique that retrieves abstract topics from a collection of documents and this technique is highly important to discover hidden and useful information from huge unstructured and heterogeneous text data. Sinhala is the native language in Sri Lanka and primarily spoken by Sinhalese. This paper presents a novel approach called SinTM to analyze a single Sinhala text document by combining topic modelling and keyword extraction techniques. The results were benchmarked with the well-known topic modelling algorithm Latent Dirichlet Allocation (LDA) and the SinTM was tested with prominent topic model evaluation matrices likelihood, r-squared, perplexity and coherence. We show that the SinTM can perform better results for Sinhala than the LDA.
Decoding Reed–Solomon Skew-Differential Codes

A large class of MDS linear codes is constructed. These codes are endowed with an efficient decoding algorithm. Both the definition of the codes and the design of their decoding algorithm only require from Linear Algebra methods, making them fully accessible for everyone. Thus, the first part of the paper develops a direct presentation of the codes by means of parity-check matrices, and the decoding algorithm rests upon matrix and linear maps manipulations. The somewhat more sophisticated mathematical context (non-commutative rings) needed for the proof of the correctness of the decoding algorithm is postponed to the second part. A final section locates the Reed-Solomon skew-differential codes introduced here within the general context of codes defined by means of skew polynomial rings.
Experimental Higher-Order Interference in Quantum Mechanics Induced by Optical Nonlinearities

The double-slit experiment is a direct demonstration of interference between quantum objects. Of course, interference is also observed in experiments using multiple slits. However, according to quantum theory, the interference patterns produced using such multi-slits can all be explained in terms of interference between pairs of slits [1] , [2] . Interestingly, there are proposed versions of quantum theory—so-called higher-order theories—wherein this is not possible [3] . In such theories the "third-order" interference (with three or more slits open) is genuinely different from second-order interference. Here we show that standard quantum mechanics actually allows for apparent high-order interference, if one considers nonlinear effects. Note, that another mechanism for this has also recently been shown based on so-called looped trajectories [4] – [6] .
Anti-Swing Trajectory Planning for Quadrotor Transportation Systems with Double-Pendulum Swing Effects

The quadrotor transportation system has attracted numerous attention on different occasions due to its broad adaptability. Nevertheless, the “double” underactuated property of the system brings various challenges in practical control, which is caused by the underactuation of the quadrotor and that the suspended payload cannot be controller directly. In addition, most existing methods are designed based on the hypothesis of neglecting the hook’s motion. In fact, the quadrotors translation will also cause the hook’s relative motion with respect to the payload. In this paper, a trajectory generation method for the precise positioning of the quadrotor and the suppression of the double-pendulum swing motion is proposed. By injecting the artificially constructed swing elimination term into the basic positioning reference trajectory, a novel anti-swing trajectory is designed. Lyapunov based stability analysis is provided to prove the performance of the trajectory. Simulation results are concluded to validate the effectiveness of the planning scheme.
Approach to Image-Based Recognition of User Face in Setting of Partial Face Occlusion by Personal Protective Equipment

Within this research, aimed to solve the problem of face recognition in setting of partial face occlusion by personal protective equipment an approach was proposed, which assumed training of a neural network model FaceNet on a specialized dataset Masked VGGFace2. This paper presents a strategy of training dataset augmentation based on the convolutional neural network model MTCNN and MaskTheFace software. Using this suite, based on the VGGFace2 dataset, an extended dataset Masked VGGFace2 was established, including images of human faces, partially occluded by personal protective equipment. Within this research, a series of experiments was performed, aimed to qualitative assessment of predictions, obtained with this solution, in comparison to the FaceNet model, which was trained on the original VGGFace2 dataset. The performed experiments revealed, that the proposed solution demonstrates a significantly higher recognition quality of partially occluded faces (AP = 0.9488)—recognition accuracy increase as per average precision (AP) metric was over 24%, compared to the original model. On test subset from the original dataset VGGFace2, the value of the AP metric was 0.9635, what outperformed the respective metric of the original model by 2.8% and justified a high genericity of the proposed solution in terms of face recognition problem.
Motivation as a Core Aspect of the Development of Professional Skills and Abilities in the Period of Distance Learning of Art Students

The relevance of the study is that distance learning completely changes the relationship between teacher and student, offering modern opportunities for independent development. However, this type of knowledge acquisition requires self-discipline and high motivation, since the interaction of subjects is gradually changing and involves the overall development of the individual and the shift of emphasis to independent cognitive activity. Unfortunately, a large number of students are not ready to study the material in remote conditions, which is confirmed by a lack of self-discipline and a tendency to disobedient actions. The inappropriate organisation of distance learning for subjects of art disciplines, in particular vocal lessons, makes the educational material uninteresting and contributes to the loss of motivation. For effective work it is important to set tasks correctly and clearly, to cover them with characteristic significance, which should become internally accepted by the student in the future. The purpose of the study is to cover and review the core aspects that are part of the motivation of students of art disciplines, particularly during vocal lessons in distance learning. Learning motivation in the remote form is a prerequisite for productive learning activities. The need for access to quality education with the use of information and electronic means is a basic requirement of the modern educational system. With the rapid change of environment and requirements for distance learning, the lack of information on the impact of motivational elements is becoming an urgent issue. An important area of the innovative integration structure of educational institutions is the information support of students aimed at obtaining quality results. The practical value lies in the search for mechanisms to develop the motivation of art students in distance learning, highlighting the main practical recommendations for increasing motivation in this format of information reproduction, which can be used in further activities
Application of Big Data Analysis and Image Processing Technology in Athletes Training Based on Intelligent Machine Vision Technology

With the rapid development of machine vision technology in recent years and the rapid increase of various video data, image processing and behavior recognition based on video data have become one of the hot research topics nowadays. This paper mainly studies the application of big data analysis and image processing technology in athlete training, the time-segmenting two-stream network based on sparse sampling adopted in this paper can better express the long-term motion characteristics. Firstly, the continuous video frame data is divided into several segments, and each segment of video frame sequence is randomly sampled to form a short sequence data containing user actions, and then the feature extraction is carried out by using double-stream network. In this paper, the proposed algorithm model is simulated and compared with other algorithms. The experimental results show that the recognition rate of the proposed model is the best among several algorithms.
Analysis of Promotion/Inhibition Factor Model Based on Teacher’s Attitude Survey for Programming Education at Elementary School

An attitude survey for teachers was administered to derive extracted inhibition factors and promotion factors using exploratory factor analysis to clarify concrete measures to promote programming education in elementary schools. From the results of 434 respondents, 3 inhibition factors were identified: “lack of teaching materials”, “recognition of inequality”, and “resistance to ICT utilization”. Three promotion factors were also identified: “promotion system”, “provision of information”, and “human support”. In order to clarify the relationships among the six extracted factors, covariance structure analysis was conducted. As a result, the three collaborations of “promotion system”, “provision of information”, and “human support” led to improvements in “lack of teaching materials” and “resistance to ICT utilization”. Furthermore, it was shown that there is a relationship leading to the resolution of the disparities in the skills and knowledge of teachers and students.
Computer Graphic Study of Advanced Manufacturing Technology with Chaos Characteristic Analysis

Abstract This paper uses the characteristics of computer graphics to study advanced production technology and carry out chaotic characteristics. AMT applied to the scope of the whole manufacturing process, especially the goals and effects achieved. All reflect that this is a practical technology that is applied to manufacturing and plays a major role in the development of manufacturing and national economy. With the rapid development of computer technology, the research progress of advanced manufacturing technology is also very fast. Chaos is being combined with many disciplines, resulting in new edge disciplines. Scholars gradually use the graphical characteristics of computers to analyze the chaotic characteristics. This paper further researches and uses the computer's imaging features to analyze the chaotic characteristics of advanced manufacturing technology.
Video Playback Quality Evaluation Based on User Expectation and Memory

The Video Quality Assessment (VQA) has been attracting attentions of image processing community due to its applications in evaluation and optimization of user experience during video playback. Until now, the researches on VQA mainly focus on short video sequences less than 10 s that have similar quality representations to images. In this work, we propose a VQA model for the medium-length video sequences by considering the user perception, expectation and memory. Firstly, we aggregate the popular perception-based VQA metrics to develop a hybrid metric with high consistency to subjective scores. Secondly, we exploit the impact of user expectation on quality evaluation and further utilize it to refine our model. Thirdly, the exponential decay law of user memory is introduced to generate the final model, namely, Expectation and Memory-based VQA (EM-VQA). Experimental results show that the proposed model achieves superior performance than the state-of-the-art VQA models.
Location Finder Mobile Application Using Android and Google SpreadSheets

In this paper, we introduce a mobile application (app) called Location Finder that is built with Java using Android Studio and the Google Spreadsheets. This application works semantically as different locations can be automatically detected. It is a complete native Android application to find different locations such as restaurant, hotel, shops, bus stops, ATM, universities, hospitals, gas station and many more. It provides advanced mapping systems that will show different places of interest for app users. This paper discusses the system architecture and details of the design. One of the advantages of the proposed mobile app is that it is easy to develop and based on interoperable Google Spreadsheets platform.
Leveraging Private Flows from Digital Technology to Finance SDGs in Africa: E-Business Revenue Opportunities Amidst IFF Challenges

The sixth session of the Africa Regional Forum on Sustainable Development seeks to “Deliver a Transformed and Prosperous Africa through the 2030 Agenda and Agenda 2063”. The forum seeks to explore, gain insights into, share and agree on collective regional efforts toward accelerating the implementation of these agendas. This policy brief supports targeted steps toward leveraging private flows from the digitalisation of the economy to achieve the goals of the agendas within the set time frame. It also cautions against the potential of digitalisation to enable illicit financial flows, if left unregulated. This policy brief presents specific discussions and action points to be considered at the forum.
A Pre-LN Transformer Network Model with Lexical Features for Fine-Grained Sentiment Classification

Sentiment classification is an important task of sentiment analysis, which aims to identify different sentiment polarity in subjective text. Although most existing models can effectively identify the extreme polarity (extremely positive, extremely negative), we find they cannot distinguish the intermediate polarity (generally positive, neutral, generally negative) clearly. Besides, the models based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) also have some problems, such as weak parallel computing power and poor long-distance dependence capacity. This paper proposes a new model based on Pre-LN Transformer and lexical features, which can improve fine-grained sentiment classification of online reviews. In this work, the Pre-LN Transformer encoder with multi-headed self-attention captures hidden features of different subspaces. Unlike the Post-LN Transformer, the Pre-LN Transformer places the normalization layer in the residual block to make the model more stable. On this basis, we reconstruct the Vader lexicon and further integrate sentiment lexical features extracted from the lexicon into the model. We perform sentiment classification tasks on two publicly available online review datasets. Experimental results show that our model achieves state-of-art performance while distinguishing fine-grained sentiment.
Feature Recognition and Modeling of Historical and Cultural Documentaries from the perspective of International Communication

As a carrier of “history” and “culture”, historical and cultural documentary not only shows rich cultural phenomena, but also contains profound cultural and spiritual connotations” “Cultural identity” shows the identity of a specific group to a certain culture. In this paper, the micro motion feature representation method of high resolution range change information is used to classify and recognize different human actions. Experimental results show that the average recognition accuracy of the proposed feature representation and recognition method for two types of 15 actions is more than 90%.
Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models

Practical needs of developing task-oriented dialogue assistants require the ability to understand many languages. Novel benchmarks for multilingual natural language understanding (NLU) include monolingual sentences in several languages, annotated with intents and slots. In such setup models for cross-lingual transfer show remarkable performance in joint intent recognition and slot filling. However, existing benchmarks lack of code-switched utterances, which are difficult to gather and label due to complexity in the grammatical structure. The evaluation of NLU models seems biased and limited, since code-switching is being left out of scope. Our work adopts recognized methods to generate plausible and naturally-sounding code-switched utterances and uses them to create a synthetic code-switched test set. Based on experiments, we report that the state-of-the-art NLU models are unable to handle code-switching. At worst, the performance, evaluated by semantic accuracy, drops as low as 15% from 80% across languages. Further we show, that pre-training on synthetic code-mixed data helps to maintain performance on the proposed test set at a comparable level with monolingual data. Finally, we analyze different language pairs and show that the closer the languages are, the better the NLU model handles their alternation. This is in line with the common understanding of how multilingual models conduct transferring between languages.