Reviewerly/exHarmony huggingface dataset:
Borrowed Capabilities: Flexibly Enforcing Revocation on a Capability Architecture

Capability machine ISAs offer a security primitive called capabilities: unforgeable tokens that represent authority over memory, and the authority to invoke other components. To efficiently implement temporary authority delegation, i.e. capabilities which can be revoked between distrusting components, ISA extensions have been proposed and/or implemented, based on forms of garbage collection, local capabilities or linear capabilities. Each of these mechanisms has specific advantages and disadvantages. In this paper, we explore borrowed capabilities: a novel mechanism that combines some of the advantages of linear capabilities without requiring linear treatment of revocable pointers. Inspired by borrowing in substructural type systems like Rust, the idea is to reinterpret capability sealing as a form of borrowing. The seals behave as a form of lifetime identifier and can be matched against a separate lifetime token, which represents the lifetime. This short paper offers a first exploration of the idea, by defining an extension of the CHERI-RISC-V ISA with borrowed capabilities and a variety of features like mutable and immutable borrows, fractional lifetimes and reborrows. We extend the LLVM assembler for CHERI and experiment with example programs.
JomAR: Reality Purchasing Experiences

In the modern world, online shopping is a common activity that people do. They buy all things even large-size products such as furniture. People used to measure the product traditionally using a tape measure. However, people can take advantage of virtual reality products such as augmented reality. This study used a mixed methodology involving 105 respondents. The augmented reality will have the benefit to reduce the cost since the customer does not need to measure traditionally and to increase efficiency to buy the product. Additionally, the customer will have more trust in the seller. The augmented reality can help users to understand the product and match the product with the user's surroundings. This augmented reality will potentially grow and adapt to the user needs as the user might explore more with the image projection.
Mind Mapping Method in Foreign Language Education: Transformative Effects of a Productive Approach

The study establishes the connection between the need to form soft skills in students and critical thinking skills. The objective of the study was to examine how productive methods of developing soft skills and critical thinking affect the learning and structuring of abstract concepts. The study argue that the formation of soft skills and the development of critical thinking can be effective in foreign language teaching for structuring information and developing creativity. Also, the article is devoted to the research of effectiveness of productive teaching methods originally designed to develop critical thinking (mind maps) in teaching foreign language at university. To find out how the mind mapping method affects the perception, structuring of scientific information and creativity a special study was conducted. In addition, the method of statistical analysis was used in the study. Based on the results of the changes in the level of formation of ideas about scientific concepts are analyzed. Critical thinking in the study appears in a dual role: it is thinking skills, which are supposed to develop, and the scientific concept, which was used to create the research material. Analysis showed a different, more personalized, structuring of scientific information when using the productive method of learning (mind mapping). The results prove that using of productive teaching methods in the foreign language teaching significantly affects the level of adoption and structuring of scientific information, as well as the level of creative thinking.
RCES

To assist the COVID-19 focused researchers in life science and healthcare in understanding the pandemic, we present an exploratory information retrieval system called RCES. The system employs a previously developed EVE (Explainable Vector-based Embedding) model using DBpedia and an adopted model using MeSH taxonomies to exploit concept relations related to COVID-19. Various expansion methods are also developed, along with explanations and facets that collectively form rapid cues for a valuable navigational and informed user experience.
BJTU’s Submission to CCMT 2021 Translation Evaluation Task

This paper presents the systems developed by Beijing Jiaotong University for the CCMT2021 evaluation tasks. We joined four translation tasks of Chinese-English, English-Chinese, Uyghur-Chinese, Tibetan-Chinese. In all directions, we build our system based on transformer architecture and Dynamic-Conv. Additionally, we apply Byte Pair Encoding (BPE) to all translation tasks to resolve the out-of-vocabulary (OOV) problem. We also adopt some techniques that have been proven effective recently in academia, such as data augmentation, finetuning, model ensemble and reranking. Experiments show that our machine translation systems achieved high accuracy on all directions.
Multi-level Discriminator and Wavelet Loss for Image Inpainting with Large Missing Area

Recent image inpainting works have shown promising results thanks to great advances of generative adversarial networks (GANs). However, these methods would still generate distorted structures or blurry textures for the situation of large missing area, which is mainly due to the inherent difficulty to train GANs. In this paper, we propose a novel multi-level discriminator (MLD) and wavelet loss (WT) to improve the learning of image inpainting generators. Our method does not change the structure of generator and only works in the training phase, which thus can be easily embedded into sophisticated inpainting networks and would not increase the inference time. Specifically, MLD divides the mask into multiple subregions and then imposes an independent discriminator to each subregion. It essentially increases the distribution overlap between the real images and generated images. Consequently, MLD improves the optimization of GANs by providing more effective gradients to generators. In addition, WT builds a reconstruction loss in the frequency domain, which can facilitate the training of image inpainting networks as a regularization term. Consequently, WT can enforce the generated contents to be more consistent and sharper than the traditional pixel-wise reconstruction loss. We integrate WLD and WT into off-the-shelf image inpainting networks, and conduct extensive experiments on CelebA-HQ, Paris StreetView, and Places2. The results well demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance and generates higher-quality images than the baselines.
Self-Amplificated Network: Learning fine-grained learner with few samples

Abstract Training deep neural networks from only a few examples has been an interesting topic that motivated few shot learning. In this paper, we study the fine-grained image classification problem in a challenging few-shot learning setting, and propose the Self-Amplificated Network (SAN), a method based on meta-learning to tackle this problem. The SAN model consists of three parts, which are the Encoder, Amplification and Similarity Modules. The Encoder Module encodes a fine-grained image input into a feature vector. The Amplification Module is used to amplify subtle differences between fine-grained images based on the self attention mechanism which is composed of multi-head attention. The Similarity Module measures how similar the query image and the support set are in order to determine the classification result. In-depth experiments on three benchmark datasets have showcased that our network achieves superior performance over the competing baselines.
The frame system as an interlingual representation for parallel texts

Abstract One of the key problems in comparative studies based on frame semantics is the question whether frames can become an interlingua. This paper argues that not only single frames, but their systems or frame semantic domain representations consisting of frames and their relations are also useful in comparative studies. Such a system of frames helps one explain why seemingly unrelated expressions in different languages find a common denominator in higher-order frames, thus becoming semantic-pragmatic equivalents. To support this argument, an analysis of Polish, English and German lease agreements as parallel texts is conducted and the benefits of this approach to comparative studies are presented. The study is in line with the recent FrameNet initiatives, such as the Global FrameNet and automatic translation studies. However, it differs in some methodological aspects. Instead of using FrameNet as the given lexical resource, domain specific frames are defined starting from common general concepts of the analyzed semantic domain. A text-based approach rather than a comparison of bi-sentences or phrases is adapted. The work thus introduces a new approach to comparative studies based on frame semantics and frame semantic research. It also follows the recent research trend of adding a pragmatic dimension to frame semantic analysis by analyzing frames in context.
Research on fault analysis expert system of a weapon control system

The system is based on the control of faults and hidden dangers of the armed control system, using a hybrid FMECA and FTA integrated fault analysis method, the specific calculation of the hazard degree of the system, and according to the FMECA table, programming in C++, analysis and discussion of the technical difficulties, the intention and prospect prediction of the extension application.
Apriori Algorithm with Dynamic Parameter Selection and Pruning of Misleading Rules

In the field of knowledge discovery in databases (KDD), the effectiveness of association rules is important. Association rules are a technique of data mining, wherein we identify the relationship between one item to another. For mining, the association rules Apriori algorithm is widely used. The idea of the Apriori algorithm is to find the frequent sets from a transactional database. Through the frequent sets, association rules are obtained, and these rules must satisfy the minimum confidence threshold. This paper presents an improved method for deciding an optimum minimum support threshold and minimum confidence threshold, pruning of rules based on a contingency table, and finally the decision about whether to go for lift or confidence to get rid of uninteresting, misleading, and confusing association rules.
Study on the Stretching Experiment of Micro Flexible Fresnel Lens of PDMS Material

This study proposes an experimental stretching method and its effect using a flexible micro Fresnel lens made from polydimethylsiloxane (PDMS) material with 25 mm diameter, 1mm thickness. The PDMS lens is strained by a four-degree-of-freedom (DOF) stretching device to change the lens’s curvature. This stretching device consists of four linear stepping motors, placed concentric symmetry at the center of the PDMS lens. Each motor is operated via an A4988 stepper driver that receives the speed signal from the Arduino UNO controller. The simulation and experimental results are implemented to analyze and validate the performance of the stretched lens. The results indicated an increment of about 8% of the diameter and 12.3% of the imaging change after the stretching process.
Solving Second-Order Nonlinear Ordinary Differential Equations Based on Improved Genetic Algorithm

In scientific research and production, researchers often use ordinary differential equations as mathematical modeling tools. Ordinary differential equations are often used to describe the laws of dynamic systems and have been widely used in many fields. However, there are many types of ordinary differential equations, and they are affected by many factors. In order to solve many problems encountered in the process of finding ordinary differential equation models, this paper takes the second-order nonlinear ordinary differential equations as the object to explore the improved genetic algorithm in solving the second-order nonlinear ordinary differential equations. This article first summarizes the status quo of the derivation of ordinary differential equations at home and abroad, and outlines the basic theories of genetic algorithms and ordinary differential equations. On this basis, combined with improved genetic algorithms to solve ordinary differential equations. This article systematically explained the steps of improving the genetic algorithm to solve the nonlinear equations, and used comparative analysis method, observation method and other research methods to carry out experimental research on the subject of this article. Studies have shown that when the number of individuals in the population is too large, the search space will also increase, which will directly cause the algorithm to slow down and the efficiency of the algorithm will also deteriorate.
Research on Heuristic Deployment and Scheduling Strategy of Cloud Resources Oriented to Cost Optimization

In the context of cloud computing, the cloud service of computing resources has become a trend, but often due to various objective reasons, redundant cloud resources often cause waste of resources, leading to increased enterprise operating costs and reduced resource utilization. In response to this situation, this paper proposes a cost-optimized deployment and scheduling strategy for cloud resources combined with heuristic algorithms. The strategy first selects the initial deployment plan through the FFD algorithm, and then selects the most suitable server type for deployment of virtual machines based on the greedy algorithm for scheduling migration, and finally obtains the optimized cloud resource deployment scheduling method. Experimental results show that this strategy can reasonably plan and operate cloud resources and achieve a significant reduction in enterprise costs.
Stein Variational Probabilistic Roadmaps

Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately resolve the topology of a given environment, many sampling-based motion planners resort to coarse, heuristically-driven strategies which often fail to generalize to new and varied surroundings. Further, many of these approaches are not designed to contend with partial-observability. We posit that such uncertainty in environment geometry can, in fact, help drive the sampling process in generating feasible, and probabilistically-safe planning graphs. We propose a method for Probabilistic Roadmaps which relies on particle-based Variational Inference to efficiently cover the posterior distribution over feasible regions in configuration space. Our approach, Stein Variational Probabilistic Roadmap (SV-PRM), results in sample-efficient generation of planning-graphs and large improvements over traditional sampling approaches. We demonstrate the approach on a variety of challenging planning problems, including real-world probabilistic occupancy maps and high-dof manipulation problems common in robotics. Video, additional material and results can be found here: https://sites.google.com/view/stein-prm.
Modelling Video Frames for Object Extraction Using Spatial Correlation

Object extraction forms a critical part of the object-based video processing. However, most of the techniques available concentrate only on surveillance and tracking. Normal video sequence does not have steady background and hence these techniques cannot be applied to them. In our work, we propose an elegant method to model background and foreground based on histogram data. We use 2D continuous wavelet transform to spatially localize object and create object mask to approximate silhouette. With available histogram for object pixels and background pixels, we obtain probability density function by normalizing the area under histogram. In order to retain smoothness in our density function, we use curve-fitting techniques to approximate the probability density function.
Optimizing Small BERTs Trained for German NER

Currently, the most widespread neural network architecture for training language models is the so called BERT which led to improvements in various Natural Language Processing (NLP) tasks. In general, the larger the number of parameters in a BERT model, the better the results obtained in these NLP tasks. Unfortunately, the memory consumption and the training duration drastically increases with the size of these models. In this article, we investigate various training techniques of smaller BERT models: We combine different methods from other BERT variants like ALBERT, RoBERTa, and relative positional encoding. In addition, we propose two new fine-tuning modifications leading to better performance: Class-Start-End tagging and a modified form of Linear Chain Conditional Random Fields. Furthermore, we introduce Whole-Word Attention which reduces BERTs memory usage and leads to a small increase in performance compared to classical Multi-Head-Attention. We evaluate these techniques on five public German Named Entity Recognition (NER) tasks of which two are introduced by this article.
Natural Language Processing in Language and Literature Based on Target Detection Algorithm

Chinese language and literature is broad and profound, which contains a long history and profound cultural connotation of the Chinese nation, is the treasure of the national culture of the Chinese nation. Understanding and learning Chinese language and literature can enrich our knowledge system, expand our knowledge, and improve our aesthetic level and cultural connotation. This is very helpful for the development of humanistic education and aesthetic education. Based on the target detection algorithm, this paper summarizes the aesthetic factors of Chinese language and literature, so as to further explore the learning path of knowledge points and aesthetic factors in Chinese language and literature.
The Impact of Decentralized Technologies on Social Media Megacorporations

Social media is a mega-industry built by systematically monetizing the exploitation of human emotions, reactions, and biases. The authors explain how this industry became so profitable by creating a fear of missing out (FOMO) to command our attention, blending news and content in one feed to keep users 'in-app', and using powerful algorithms to promote more provocative posts, filter content, and trigger the reward centres of our brains. The authors examine how decentralized technologies, including cryptocurrencies, tokenization, and blockchain are being developed and deployed into new social media applications. The authors speculate on how these blockchain-backed startups could challenge the status quo and appeal to new expectations of user privacy, tighter regulation, and a more equitable monetization system.
Non-Gaussian Model-based Clustering

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Detecting and Preventing Faked Mixed Reality

Virtualized collaboration can significantly increase remote management of critical infrastructures. Crises such as the current COVID-19 pandemic push the technology: they require remote management to keep our infrastructures running. Mixed Reality (MR) prototypes enable remote management in diverse fields such as medicine, industry 4.0, energy systems, education, or cyber awareness. However, the evolution of virtualized collaboration is still in the beginning. By design, MR is fake: its reality is generated from models. This makes detecting attacks very difficult. Many MR-attacks result from well-known cybersecurity threats. This paper identifies classic attack surfaces, vectors, and concrete threats that are relevant for MR. It presents mitigation methods that can help to secure the underlying data exchanges. However, distributed systems are often heterogeneous and under different management authorities, making securing the entire virtualized remote management stack difficult. The paper therefore also introduces considerations towards an MR-client-based attack detection, i.e., MR-forensics, including relevant features and the use of machine learning.
Malaysian Coins Recognition Using Machine Learning Methods

Coins have become essential to our daily lives. As a legal tender, identifying, validating, classifying, and sorting coins is a complex process. In essence, automated coin detectors should identify deteriorated or older coins and distinguish between genuine and fake coins. Nonetheless, we identified limited literature examining different Machine Learning (ML) approaches for detecting Malaysian coins. This study investigates machine learning approaches and identifies the most efficient and accurate for Malaysian coin recognition. The model was trained on 311 images of coins and classified into four object categories: 5, 10, 20, and 50 cents. Six classifiers are used to test the training model. For the Grey-Level Co-occurrence Matrix (GLCM) feature extraction, AdaBoost classifiers were the most accurate, whereas K-Nearest Neighbors (KNN) classifiers were the least accurate. Moreover, the Artificial Neural Networks (ANN) classifier had the highest accuracy in the Histogram of Oriented Gradients (HOG) feature, while the Linear Discriminant Analysis (LDA) classifier had the lowest. The study findings and future directions are discussed.
Students’ Preferences in Choosing the Form and Means of Interaction with Professors: Innovation or Tradition?

This article deals with the preferences of students in choosing forms of communication in the educational process, as well as ways to consult with professors. The relevance of the study is due to attempts to change traditional forms of education at universities on distance learning and to introduce creative corporate educational platforms for communication in the educational space. The purpose of the study is to investigate the opinion of the student audience about the transition to ICT for study and interaction in higher education and to identify the importance of personal contacts and consultations with professors. The study was conducted among students of leading Russian universities through an online sociological survey. Respondents (126 students who study mainly undergraduate and graduate programs) were asked to answer questions related to the most convenient channels of interaction with professors, preferred methods of passing exams and tests, assessment of work on corporate educational platforms, forms of creative study, etc. The integration of distance learning and corporate educational platforms is currently directive in nature, which in many ways leads to a chaotic process, where is underestimated for students the importance of traditional forms of education, including personal consultations, communication in contact student groups, lecturers’ comments during the work etc. Students prefer to use Internet technologies, such as e-mail, social networks and messengers, not for educational purposes, but for advisory purposes and perceive involvement of professors into such format as an element of creative approach in educational process in opposite to familiar forms.
Obstacle Detection via Air Disturbance in Autonomous Quadcopters

Autonomous drones can detect and avoid walls as the technology stands today, but they can only do this with camera, ultrasonic, or laser sensors. This paper highlights how data mining classification techniques can be used to predict which side of a drone an object is located from the air disturbance created by the drone being near such an object. Data was collected from the drone’s IMU, while it flew near a wall to its immediate left, right, and front. The IMU includes gyroscope, accelerometer, roll, pitch, and yaw data. Position and barometer data was also collected. The data was then fed to NearestNeighbor, GradientBoosting, and RandomForest classifiers.