Reviewerly/exHarmony huggingface dataset:
AutoToolVCC: Variable consistency check tool for automotive software projects

Abstract Automotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s.
Characterising the Benefits of Multi-Modal Play in Virtual Reality

With the rising popularity of VR technologies, more people are experiencing what this medium has to offer. Right now, the most popular games are either single-player or online-multiplayer, leaving the people in the same room without a way of interacting with the HMD (Head Mounted Display) player. For VR to become mainstream, this problem has to be solved. A preliminary experiment was conducted in which two different ways of including a second person in the VR experience (through a PC or a Phone) were compared in terms of co-presence and immersion. Results showed that both ways are valid and can be used to add a second player --- the quantitative data gathered from two surveys (Networked Minds Measure of Social Presence for co-presence and iGroup Presence Questionnaire for Immersion) showed that there was no significant difference, and the qualitative data, which revealed 13 distinct themes divided into five categories, helped with understanding the survey results. The next steps are to concentrate on one of the categories (embodiment) and conducting a systematic review into ways of increasing it, followed by expert interviews to confirm the findings and create a definitive list of factors that affect embodiment. Finally, a second experiment will be conducted in order to confirm the validity of the factors.
Phasor Symbiotic Organisms Search Algorithm for Global Optimization

The symbiotic organisms search algorithm is a meta-heuristic algorithm based on the establishment of symbiotic relationships between populations in recent years. In this study, a novel phasor symbiotic organisms search algorithm (PSOS) based on phasor theory is proposed. Both the phase angle and the trigonometric function in the proposed PSOS are used to set parameters that can have the same or different directions, similar or diverse values. Different combinations of these parameters can better control the local exploitation and global exploration of PSOS. This makes the PSOS algorithm have stronger global optimization ability, avoiding premature algorithm or falling into local optimization. In order to verify the effectiveness of the algorithm, this study selected 23 benchmark test functions and a classic engineering optimization problem for testing. The results show that the PSOS algorithm has higher convergence accuracy and stronger robustness.
Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal [Formula: see text]-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.
The Interacting Multiple Model Filter on Boxplus-Manifolds

The interacting multiple model filter is the standard in state estimation where different dynamic models are required to model the behavior of a system. It performs a probabilistic mixing of estimates. Up to now, it is undefined how to perform this mixing properly on manifold spaces, e.g. quaternions. We present the proper probabilistic mixing on differentiable manifolds based on the boxplus-method. The result is the interacting multiple model filter on boxplus-manifolds. We prove that our approach is a first order correct approximation of the optimum. The approach is evaluated in a simulation and performs as good as the ad-hoc solution for quaternions. A generic implementation of the boxplus interacting multiple model filter for differentiable manifolds is published alongside with this paper.
Privacy Prediction of Lightweight Convolutional Neural Network

The growing popularity of cloud-based deep learning raises a problem about accurate prediction and data privacy. Previous studies have implemented privacy prediction for simple neural networks. Since more complex neural networks require more computational overhead, existing privacy prediction schemes are inefficient. To tackles the above problem, this paper introduces a privacy prediction method for lightweight convolutional neural network (CNN) that can be applied to encrypted data. Firstly, the complex CNN is pruned into a lightweight network without compromising the original accuracy, which can realize secure prediction efficiently. Secondly, the FV homomorphic encryption scheme is adopted to encrypt the user’s sensitive data and each layer in CNN is calculated on the ciphertext, so as to protect user’s data privacy. Finally, the security analysis and experiment results demonstrate the privacy-preserving property and practicability of the proposed scheme, where the complex CNN on the MNIST data set can achieve more than 98% accuracy.
Stance Detection Using Transformer Architectures and Temporal Convolutional Networks

Stance detection can be defined as the task of automatically detecting the relation between or the relative perspective of two pieces of text- a claim or headline and the corresponding article body. Stance detection is an integral part of the pipeline used for automatic fake news detection which is an open research problem in Natural Language Processing. The past year has seen a lot of developments in the field of NLP and the application of transfer learning to it. Bidirectional language models with recurrence and various transformer models have been consistently improving the state-of-the-art results on various NLP tasks. In this research work, we specifically focus on the application of embeddings from BERT and XLNet to solve the problem of stance detection. We extract the weights from the last hidden layer of the base models in both cases and use them as embeddings to train task-specific recurrent models. We also present a novel approach to tackle stance detection wherein we apply Temporal Convolutional Networks to solve the problem. Temporal Convolutional Networks are being seen as an ideal replacement for LSTM/GRUs for sequence modelling tasks. In this work, we implement models to investigate if they can be used for NLP tasks as well. We present our results with an exhaustive comparative analysis of multiple architectures trained on the Fake News Challenge (FNC) dataset.
PLSGAN: A Power-Law-modified Sequential Generative Adversarial Network for Graph Generation

Characterizing and generating graphs is essential for modeling Internet and social network, constructing knowledge graph and discovering new chemical compound molecule. However, the non-unique and high-dimensional nature of graphs, as well as complex community structures and global node/edge-dependent relationships, prevent graphs from being generated directly from observed graphs. As a well-known deep learning framework, generative adversarial networks (GANs) provide a feasible way, and have been applied to graph generation. In this paper, we propose PLSGAN, a Power-Law-modified Sequential Generative Adversarial Network to address aforementioned challenges of graph generation. First, PLSGAN coverts graph topology to node-edge sequences by sampling based on biased random walk. Fake sequences are produced by trained generator and assembled to get target graph. Second, power law distribution of node degree is taken into consideration to modify the learning procedure of GANs. Last, PLSGAN is evaluated on various datasets. Experimental results show that PLSGAN can generate graphs with topological features of observed graphs, exhibit strong generalization properties and outperform state-of-the-art methods.
Elimination of Incorrect Depth Points for Depth Completion

Commodity-level scan cameras generally capture RGB-D image with depth missing or incorrect depth points if the surface of the object is transparent, bright, or black. These incorrect depth points are generated randomly and limit the downstream applications of raw RGB-D images. In this paper, we propose a coarse-to-fine method to detect and eliminate the incorrect depth points via RGB semantics. In our flowchart, deep learning-based networks are applied to predict the potential regions with incorrect depth points and the normals of the point cloud. Then we develop a three-step elimination method to remove the incorrect depth points in the regions. Experimental results show that our method leads to great improvements for downstream applications of RGB-D images, especially in depth completion application.
We Asked 100 People

While robotic proficiency excels in constrained environments, the demand for vast amounts of world knowledge to cover unforeseen circumstances, constellations and tasks prevents sufficiently robust real-world application. Human computation has shown to provide successful advances to close this reasoning gap and accumulate knowledge, yet being greatly reliant on the quality of the provided data. In this paper, we introduce the game with a purpose Tool Feud that collects popularity rankings of object choices for robotic everyday activity tasks and evaluate an approach for classifying malicious responses automatically.
Small-Sample Information Diffusion and Applications

The small-sample problems in scientific research can be settled by the information diffusion in one-dimensional Euclidean space. In this paper, the diffusion in two-dimensional Euclidean space has been developed, according to the author's previous study. It has also made obvious achievements in the applications of the two-dimensional technology to the two practical cases in healthcare.
Design of Optimizing System for Advertisement on Vertical Types Based on Neural Network

The target users of different information flow advertising channels have different preferences for vertical ads. The neural network training model is used to predict the advertising results through the proportion of different types of vertical ads, so as to improve advertising efficiency and reduce advertising costs.
한국어 언어문화 교육을 위한 수업 모형 연구

The purpose of this study is to present a class model using video media for Korean language culture education. To learn Korean, Korean language learners need to understand the language culture inherent in the Korean language. However, cultural background of Korea becomes an element that makes it harder to understand Korean language because Korean culture and culture of Korean language learners are different. Therefore, this study investigated what Korean language culture has to offer and suggested the use of video media to teach that. Video media is accompanied by actual discourse situations and can be considered as an appropriate media that allows learners to directly see the spoken words of Korean. Among them, sitcoms are effective in educating language culture because the length of the video is relatively short and contains conversations that are almost the same as the actual conversation than drama or movie. Therefore, this study is meaningful in helping Korean language learners to build background knowledge on Korean language culture and to lead to actual discourse by suggesting a language culture class model using sitcoms. However, there is a limitation that there are strict consideration and standards when selecting visual media. Various media usage methods and class models for teaching Korean language culture will have to be presented in future research.
Under-Actuation Modelling in Robotic Hands via Neural Networks for Sign Language Representation with End-User Validation

This paper presents a study on under-actuation modelling applied to robotic hands aimed at sign language representation. Prior studies using a simulated TEO humanoid robot for representing sign language have shown positive comprehension and satisfaction responses among the deaf and hearing impaired community. The under-actuated mechanics of the robotic fingers were not contemplated in the simulated model, thus the correspondence problem arises as the previous joint space positions cannot be directly sent to the physical system. In addition to the 3:1 and 2:1 ratio of the under-actuation of the finger mechanisms, tendons and springs involve stiffness and elasticity that are difficult or unfeasible to model, and justify the need for a data-driven approach. Three motor command generators using three different neural network models are analysed and evaluated. Two of the generators are trained in a supervised fashion, and the third involves variational self-supervision and a transformation upon the latent space. The simulated joint space positions are translated into motor commands for the physical embodied robot to represent a sign language dactylology, which is in turn evaluated by deaf and hearing impaired end-users.
Steady and optimal entropy squeezing for three types of moving three-level atoms coupled with a single-mode coherent field*

The entropy squeezing properties of different types of moving three-level atoms coupled with a single-mode coherent field are studied. The influences of the moving velocity and initial states of the three-level atom on the entropy squeezing are discussed. The results show that, the entropy squeezing properties of the three-level atom depend on its initial state, moving velocity, and the type. A stationary three-level atom can not obtain a steady entropy squeezing whatever initial conditions are chosen, while a moving three-level atom can achieve a steady and optimal entropy squeezing through choosing higher velocity and appropriate initial state. Our result provides a simple method for preparing squeezing resources with ultra-low quantum noise of the three-level atomic system without additional any complex techniques.
Maturity Differentiation of Colon Polyp Based on Endoscopic Images Using Machine Learning and Spatial Feature

Colon cancer is a dangerous type of degradative disease after lung cancer. Colon cancer start with the presence of polyps on the colon. Early preventive measures are important anticipatory steps to prevent the development of polyps into cancer, as well as being an important point in determining the treatment that must be undertaken by patients. Early detection of polyps based on medical images is very challenging because it involves instance factors and the technical factors used. This study aims to produce an automation system classification of polyp maturation so that it can facilitate pathologists and health practitioners in diagnosing polyps in the human intestine and determine the level of maturation early. This study based on a colonic endoscopic dataset utilizing spatial feature extraction to extract important features from the polyp image and feedforward neural network backpropagation classifier function with a hidden layer to determine its maturation class. Based on experiments conducted, it found that the proposed system was able to classify polyp maturation classes with an accuracy level of 93.94% and a false-positive rate (FPR) level of 6.05%.
Angle-Based Clustering

The amount of data increases steadily, and yet most clustering algorithms perform complex computations for every single data point. Furthermore, Euclidean distance which is used for most of the clustering algorithms is often not the best choice for datasets with arbitrarily shaped clusters or such with high dimensionality. Based on ABOD, we introduce ABC, the first angle-based clustering method. The algorithm first identifies a small part of the data as border points of clusters based on the angle between their neighbors. Those few border points can, with some adjustments, be clustered with well-known clustering algorithms like hierarchical clustering with single linkage or DBSCAN. Residual points can quickly and easily be assigned to the cluster of their nearest border point, so the overall runtime is heavily reduced while the results improve or remain similar.
Automation of a Decision Tree Conversion into a Fuzzy Inference System Using ANTLR

The paper discusses techniques of processing a sample of sets of numerical features of observations that relate to a certain subject area and belong to certain classes. Such techniques include well-known methods of constructing a decision tree and a fuzzy inference system. An isomorphism of the decision trees and a corresponding fuzzy inference system rule set is being justified. Algorithms of both methods are described in special languages. An approach to automated conversion of the decision tree to a fuzzy inference system using ANTLR, a tool for creating compilers, is proposed. The toolkit used, along with the creation of classes for lexical and parsing of a description in one language, allows generate a class for converting text from one language to another. The relevance of the approach is that with representing fuzzy classifying knowledge allows one to implement an expert system, allowing domain specialists to classify objects. Usage of the decision trees in expert systems is problematic. An example of applying this approach to a classification of leaf specimens originating from different plant species is given.
Agent Learning and Autoregressive Modeling

Relative entropy is used to investigate whether a sequence is memoryless or has memory and to discern the presence of any structure in the sequence. Particular emphasis is placed on obtaining expressions for finite sequence length N and autoregressive sequences with known and unknown autocorrelations. We relate our results to the terms entropy gain, information gain, and redundancy as defined in agent learning studies, and show that these terms can be bounded using the mean squared error due to linear prediction of a stationary sequence.
Research on Image Edge Detection Method Based on Improved Robinson Operator and Otsu Algorithm

Edge detection plays an important role in image processing. In this paper, we first discuss the Robinson edge detection operator and their improved algorithm, all of which will involve the determination of the optimal threshold. Then, by improving the classical maximum inter-class variance method, a threshold segmentation algorithm based on the mean weighted maximum inter-class variance in the neighborhood of gray histogram is proposed, this algorithm improves the accuracy and enhances the noise resistance by weighting the neighborhood mean of the maximum inter-class variance method. Experimental results show that the proposed new algorithm improves the speed and accuracy of edge detection compared with classical Otsu methods.
THE LOGIC OF BUILDING A THEORETICAL MODEL OF THE PROCESS OF PEDAGOGICAL SUPPORT OF PUPILS’ CHOICE OF PROFESSION BY THE DIGITAL EDUCATIONAL ENVIRONMENT RESOURCES

Исследуется проблема организации воспитательной работы школы в части педагогической поддержки профессионального выбора в условиях цифровизации образования. Поднимается вопрос теоретического осмысления и моделирования процесса педагогической поддержки профессионального выбора средствами цифровой образовательной среды. Представлена девятикомпонентная модель процесса педагогической поддержки профессионального выбора и логика ее создания. Каждому из компонентов (задачно-целевому, средовому, субъектному, методологическому, мотивационно-деятельностному, содержательному, диагностическому, результативному и рефлексивному) дана характеристика, а также обозначена специфика связи между компонентами модели. The education system faces the issues of online education and the effectiveness of career guidance programs in schools. The article deals with the problem of organizing pedagogical support for profession choice in the school digital educational environment. The author rises the question of theoretical understanding and modeling of the process of pedagogical support of professional choice by the digital educational environment resources. The author presents the 9-componet model and the logic of its construction. The model presents characteristics of each of the following components: the task, the environmental component, the subjective component, the methodological component, the motivational activity component, the diagnostic component, the results, the reflection as well as the specificity of the relationships between these components. The peculiarity of this model is the absence of a clear hierarchy changed for the interdependence of the components. This feature is characteristic of pedagogical models in the formation of a new concept of education. The author pays attention to the particularities of the usage of the approaches, principles and methods of the pedagogical support of the professional choice in the conditions of the digital educational environment. The author also considers the selection of the digital educational environment means to solve the problem of the pedagogical support of the schoolchildren’s professional choice.
Efficient Boolean SSE: A Novel Encrypted Database (EDB)

Searchable symmetric encryption (SSE) with increasing expressiveness was proposed to acquire optimal communication complexity, but it has poor efficiency compared to the single-word query. Many of the existing expressive SSE mechanisms use the inverted index to build the encrypted database (EDB) for the sub-linear search complexity. However, this inevitably brings about the problems of repeated storage and low efficiency of set operations. To this end, we introduced a new approach, using the boolean vectors (BV) instead of the inverted indexes to build EDB, which dramatically improves the search efficiency and reduces the storage space. While boolean operation must run on the closed space, we divided the full queried space into several disjoint closed sub-spaces and proved its correctness theoretically. Our construction supports arbitrary conjunctive, disjunctive, and boolean queries. The experiments prove that our system is more efficient in storage and search complexity than existing methods.