Reviewerly/exHarmony huggingface dataset:
Leveled Design of Cryptography Algorithms Using Cybernetic Methods for Using in Telemedicine Applications

The technology world is developing fast with the developments made in the hardware and software areas. Considering that privacy and security of telemedicine applications are among the main necessities of this industry, as a result, there is a need to use lightweight and practical algorithms to be used in applications in the field of telemedicine, while security have the least negative impact. The distinct and contradicting components in the design and implementation of the cryptography algorithm, to achieve various objectives in medicine-based applications, have made it a complicated system. It is natural that, without identifying the components, indices, and properties of each system component, the hardware and software resources are lost and a proper algorithm cannot be designed. Accordingly, this paper presents a leveled model of cryptography algorithms using the cybernetic method. First, the main objectives and measures in the design of the cryptography algorithms are extracted using the measure reduction methods, and some of the excess and overlapping measures are eliminated. Then, three general classes of the cryptography algorithm design and implementation measures, applications of cryptography algorithms, and cryptography implementation techniques are extracted. Since the complexity of the cryptography algorithm design is relatively high, the cybernetic methodology is used to present a supermodel to make the cryptography algorithm design objective. Such design prevents examining unnecessary details and establishes a bidirectional relationship between the main design and implementation process and the support process. This relationship provides the support requirements of the main process by the support process at each step. Finally, the Q-analysis tools are used to analyse the proposed method, and the efficiency results are represented.
Visual Teaching Design of Compile Principle Based on JFLAP

The course of compile principle is characterized by abstract theory and complex algorithm. It is relatively difficult for students to understand, which makes the effect of theoretical teaching poor. Therefore, the visual teaching tool JFLAP is introduced into the theoretical teaching of this course. For the difficulties of students’ feedback, this paper takes SLR (1) parsing as an example. In order to make full use of the visualization function of JFLAP, this paper presents a visual demonstration of the whole process of SLR (1) parsing, including calculation of FIRST set and FOLLOW set, construction of DFA, creation of parsing table and analysis of specific string. Practice shows that the use of JFLAP plays a great role for students to understand and master the compile principle of various algorithms and to improve the quality of classroom teaching.
Segmentation of Handwritten Mathematical Matrices Using the Area Voronoi Diagram

Robust handwritten mathematical expressions recognition systems are expected to provide support for different mathematical structures. In this paper, a mathematical matrix segmentation method based on area Voronoi diagram is proposed. The method utilizes only input strokes geometrical data and therefore can be used as the first stage of recognition system workflow. The main steps of the method describe building area Voronoi diagram, calculating weights of the resulting diagram edges, and finding edges paths that separate matrix rows and columns. The evaluation shows that the method works adequately, but has some substantial limitations. Apart from mathematical matrices, it can be used for other tabular structures segmentation, for example, tables and charts.
EDKE: Encoder-Decoder based Kernel Estimation for Blind Image Super-resolution

Deep neural networks (DNNs) have recently achieved wonderful results in single image super-resolution (SISR). These DNN-based SR methods are usually trained on supervised training datasets downscaled with a fixed kernel (e.g., bicubic), and suffer from the generalization problem on real world images due to the complexity of the degradation process. More recent works (e.g., KernelGAN [1]) use a generator network to realize kernel estimation from a single image. However, such methods neglect the anisotropy of kernel, thus limiting the estimation accuracy of kernel in practice. To address this issue, in this paper, we propose a novel encoder-decoder based kernel estimation (EDKE) framework by considering the anisotropy of kernel. In our EDKE framework, the input image is first fed into an encoder network to learn the degradation process, followed by a decoder network to map the encoder image into the original image itself. In this way, the encoder image contains more compact information for the degradation kernel. Besides, our EDKE can be plugged into other blind SR methods. Extensive experiments demonstrate the effectiveness of our method in kernel estimation, and show the superiority of our method over other state-of-the-art methods.
Post-quantum Cryptography

Cryptography is the art of writing secret text. Employing cryptography the human-readable text is converted into a ciphertext (coded text) by the means of some consistent algorithm. The essence of a good cryptosystem lies in consistent and easy to understand encryption and decryption algorithms. Before the advent of quantum computers, much of the cryptographic algorithms depend on the principles of number theory, abstract algebra, and probability theory mainly factorization problem and discrete logarithmic problems. With the advancements in the field of quantum computing, the classical cryptosystems are no longer secure and it poses a threat to global security. In this brief survey, we would look toward some of the prime classical cryptosystems, their fallacy, and the advancement of the post-quantum cryptography, especially of the hashed-based signature scheme. The review also looks toward the difference between post-quantum cryptography and quantum cryptography.
Global Object Proposals for Improving Multi-Sentence Video Descriptions

There has been significant progress in image captioning in recent years. The generation of video descriptions is still in its early stages; this is due to the complex nature of videos in comparison to images. Generating paragraph descriptions of a video is even more challenging. Amongst the main issues are temporal object dependencies and complex object-object relationships. Recently, many works are proposed on the generation of multi-sentence video descriptions. The majority of the proposed works are based on a two-step approach: 1) event proposals and 2) caption generation. While these approaches produce good results, they miss out on globally available information. Here we propose the use of global object proposals while generating the video captions. Experimental results on ActivityNet dataset illustrate that the use of global object proposals can produce more informative and correct captions. We also propose three scores to evaluate the object detection capacity of the generator. Comparison of captions generated by the proposed method and the state-of-the-art techniques proves the efficacy of the proposed method.
The Neverending Story: Memory Corruption 30 Years Later

Memory errors have been present in software for more than three decades now, leading to numerous security issues. Low-level languages like C and C++ that are prone to this class of errors are in widespread use, meaning that a large number of current systems are susceptible to attacks that target memory corruption vulnerabilities. Therefore, hardening programs and protecting against this type of attacks is of the utmost importance to build secure systems. This paper explains the basis of memory errors and their characteristics, analyzes the huge research effort that has been carried out in relation to memory error detection, exploitation and attack prevention, and depicts the most relevant approaches in this area.
Effectiveness of rough initial scan for high-precision automatic 3D scanning

Abstract Herein, we present an efficient method for the high-precision automatic 3D scanning of unknown objects for mechanical parts. Our method comprises two phases, namely a rough initial scan and a precision scan. The goal of the rough initial scan is to scan the rough shape rapidly and to provide scan data for the precision scan, thereby reducing the duration of the entire process. Researchers have attempted to provide rough information regarding an object before precision scanning, e.g. by building a rough three-dimensional (3D) model using 2D images or capturing the shape in advance using a low-accuracy scanner with a larger view frustum. However, our two-phase scanning method uses a single type of high-precision scanner for scanning the rough shape and also for the precision scan, which comes afterwards. In the rough initial scan phase, the next scanning view is determined based on the scan data captured by the latest view, eventually forming helical shape movement. In this study, we apply the two-phase scan method to 18 types of models in a virtual 3D scanning environment. For diverse configurations, the model size is adjusted from small to large relative to the virtual scanner’s view frustum, and the number of next best views calculated per iteration during the precision scan phase is adjusted. We demonstrate that the rough initial scan provides a certain amount of scan data rapidly regardless of the model size and shape. Furthermore, we demonstrate that the two scan methods complement each other, thereby reducing the overall process time and workload.
An efficient backbone network for target detection in aerial images

Abstract Target detection in aerial images by high altitude unmanned aerial vehicles is one of the hot research topics. A detection that is efficient and accurate is of a high value in the military and civilian fields. However, due to the irregular distribution of targets and their various scales shown in aerial images, it is difficult for existing backbone networks to effectively extract target features based on deep learning. To address these problems, in the paper, an efficient backbone network called AerNet is proposed, to which a local feature enhancement module (LFEM) is added to fully extract discriminative features of aerial targets via multi-scale convolutional layers. The network AerNet consists of 55 layers, which can keep high spatial resolution in deeper layers and maintain a large receptive field. Experimental results show that our AerNet has achieved a satisfying detection performance on the DOTA benchmark.
Sentiment Analysis: Choosing the Right Word Embedding for Deep Learning Model

Sentiment analysis is a field that helps data analysts to gauge public opinion, observe brand and product reputation, perform nuanced market research, and understand customer experiences. It is the process by which whether a particular text is positive, negative or neutral can be determined. This process is a combined application of techniques of natural language processing and artificial intelligence to assign weighted sentiment scores to the entities within a sentence or piece of text. Word embedding or the feature vectors are the vectors that can represent the text into the vector space. Embeddings solve the problem of representing very large non-sparse vectors into a lower-dimensional space. For deep learning models, it is very necessary to input the text data as feature vectors. Various methods of generating feature vectors are explored. The paper mainly focuses on the application of various pre-trained word embedding on deep learning model and compares them through various metrics calculated on them. The results obtained showed that Word2Vec outperformed other word embeddings.
Multi-Modal Meta Continual Learning

Recently, meta learning methods able to provide multiple initializations have drawn much attention due to its capability of handling multi-modal tasks. However, the modal differences in multi-modal distributions aggravate the catastrophic forgetting. In this paper, we augment multi-initial meta learning for continual learning, which alleviates the forgetting issue while allowing beneficial transfer to previous tasks from multi-modal distributions. Specifically, we propose a multi-modal meta continual learning (M3CL) framework, which is able to mitigate forgetting via gradient episodic memory (GEM). Modulation network in M3CL has the capability of identifying the mode of each image from online multimodal distributions. In addition, in order to take advantage of both modulation network and GEM, we consider the entropy-based regularization to learn effective modulation network and penalize overfitting to a specific task. Experiments on multimodal datasets identify that the modal differences indeed aggravate the catastrophic forgetting, and demonstrate the strong performance of M3CL in comparison to the state-of-the-art.
Design of General Aircraft Health Management System

This paper studies the basic architecture of general aircraft health management, and analyzes the key technologies of general aircraft health management system, including data management technology and database technology, fault diagnosis technology, health assessment technology. Finally, a general aircraft health management software is developed and verified.
Towards Designing Enthusiastic AI Agents

Immersive virtual worlds are increasingly being used for education, training, and entertainment, and virtual humans that can interact with human users in these worlds play many important roles. Understating the emotional constructs of the user and generating multimodal forms of communications that are aligned with the user's needs and input is key to designing AI agents. Most virtual agents and communicative systems lack the ability to understand enthusiasm or generate multimodal enthusiastic communicative presentations. In this work, we argue for the importance of including enthusiasm in the design of human-AI collaboration and communication and review the existing datasets and models that can be used to bridge the gap in this area.
Promoting access to indigenous information in Africa: Challenges and requirements

The United Nations Sustainable Development Goal SDG16.10 advocates for ensuring public access to information, yet knowledge access and sharing are some of the key challenges in many countries, especially in Africa. The African Union Agenda 2063 strongly recommends access to African indigenous, cultural information to promote sustainable development but how might this be achieved? This paper shares findings from a project - NetDiploma - that built an international multi-stakeholder network of experts, professionals and various user communities to mobilize and share knowledge to explore the requirements for promoting access to cultural heritage and government information held in memory institutions, such as archives, for everyone in Africa. It identifies the key enablers and challenges associated with the long-term goal of making African government, indigenous and cultural heritage information accessible online in the form of a Digital Public Library of Africa (DPLAf) and the research and development required to realise this vision.
RHE: Relation and Heterogeneousness Enhanced Issue Participants Recommendation

Open source software (OSS) platform users frequently join issue discussions in various repositories, and establish numerous co-talk (i.e. cross-issue reference and discussion) relationships between issues both within or cross repositories. In this work, we collect and analyze issue discussion data from GitHub to study the unique features of co-talk relationships, and discover that many participants play a versatile role during issue discussions across repositories. Based on the discovery, we enhance existing bug triaging technologies with the Relation and Heterogeneousness Enhance (RHE) method to include potential participants with cross-repository co-talk histories. RHE integrates co-talk relationship embedding and heterogeneous graph embedding for complex OSS communities. We conduct experiments with real-world data collected from GitHub to show the effectiveness and usefulness of RHE. The results suggest that RHE achieves an improved performance comparing to the baseline approaches.
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning

Learning state representations enables robotic planning directly from raw observations such as images. Several methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is often assumed and used as a proxy for estimating similarity between the underlying states of the system. However, observations commonly contain task-irrelevant factors of variation which are nonetheless important for reconstruction, such as varying lighting and different camera viewpoints. In this work, we define relevant evaluation metrics and perform a thorough study of different loss functions for state representation learning. We show that models exploiting task priors, such as Siamese networks with a simple contrastive loss, outperform reconstruction-based representations in visual task planning in case of task-irrelevant factors of variations.
An Optimized NL2SQL System for Enterprise Data Mart

Natural language interfaces to databases is a growing field that enables end users to interact with relational databases without technical database skills. These interfaces solve the problem of synthesizing SQL queries based on natural language input from the user. There are considerable research interests around the topic but there are few systems to date that are deployed on top of an active enterprise data mart. We present our NL2SQL system designed for the banking sector, which can generate a SQL query from a user's natural language question. The system is comprised of the NL2SQL model we developed, as well as the data simulation and the adaptive feedback framework to continuously improve model performance. The architecture of this NL2SQL model is built on our research on WikiSQL data, which we extended to support multitable scenarios via our unique table expand process. The data simulation and the feedback loop help the model continuously adjust to linguistic variation introduced by the domain specific knowledge.
Programming of a Lightweight Robot Using Function Blocks and Sequential Function Charts

This paper presents an approach to realize a universal robot programming language. Currently, numerous programming languages for industrial robots exit, depending on the manufacturer of the robot. Attempts to create a standardized language have failed in the past. The presence of several new providers of robots on the market increases the number of different programming languages and concepts. For this reason, it would be desirable to have a unified procedure as can be found in the field of programmable logic controllers. Here, a specification is valid, which defines certain types of programming languages. In this contribution, a lightweight robot was connected to a programmable logic controller, where the programming of the robot should be performed. For this purpose, the communication between these two partner devices was achieved using precast data structures. Concerning the programmable logic controller, function blocks were developed to operate the robot and its periphery. They include robot-based functionality, known from common robot programming languages. With regard to the robot, a client program, which interprets and executes the commands from the programmable logic controller, was implemented. Some small robot tasks were performed to show the simplicity of our approach to robot programming based on the languages of programmable logic controllers.
Multi-loss Spatial-Temporal Attention-Convolution Network for Action Tube Detection

The paper proposes a novel network model of the video action tube detection method based on a 3-dimension convolutional neural network and spatial-temporal attention mechanism. The network introduces a special module for extracting the location features of the video sequences, and an internal loss function is designed for the location network module. The location features are then squeezed to modify the scale to be the same as the classification network feature. They are then concatenated and input into a fusion network that receives feedback from the global loss function. The overall position deviation of the target in the video sequence is taken as the internal loss. This directly affects the squeezed location feature of the sequence, and improves the accuracy of action prediction under the attention mechanism. Experimental results indicate that our network model outperforms the existing methods. We further visualize the activation map which reveals the intrinsic reason for performance improvements.
Dynamic Pseudo Labels Sampling for Semi-Supersvied Person Re-Identification

Person re-identification approaches based on deep learning have recently advanced considerably. However, most of the existing re-ID studies focus primarily on closed-set applications and neglect realistic, open-world settings; this limitation restricts the application of person re-ID in practice. Generally, closed-set re-ID methods usually assume that annotated data is sufficient for use when training or testing a deep learning model. However, in open-set applications, it is unrealistic to collect and annotate a available number of training data. For this purpose, we proposed a semi-supervised learning person re-identification method by dynamic pseudo-labelled data sampling. Using the dynamic pseudo labels sampling framework to fully exploit labelled and unlabelled data, we train and optimize the model in an iterative process. The semi-supervised learning method labels only a subset of pedestrians, while the single-sample learning method labels only one data point for each pedestrian. Then, a pseudo label is generated for the unlabelled data, and the labelled data and a subset of the pseudo-labelled data are then used as an extended dataset for training. To make use of the unlabelled data, pseudo labels are generated from the unlabelled data with a dynamic strategy. Based on the predicted confidence, only use selected pseudo-labelled data for training, and the Convolution Neural Network (CNN) model is updated through retraining with an extended dataset. The effectiveness of this method is demonstrated by experiment on publicly available datasets. Compared with the state-of-the-art methods, the performance is improved.
Determination of Camera Position in Soccer Games

The automation of soccer video analysis is an area of recent interest, particularly the 3D reconstruction of soccer matches from broadcast video. One important task is the camera center determination. The camera center is calculated from a single soccer match image. It is assumed that a model of the soccer field with known dimensions is accessible. Two different approaches are presented: 1. Camera matrix determination; and 2. Homography matrix determination. It is shown that the homography matrix can be determined using mapped points and lines. Both methods are compared. To improve the numerical stability, a normalization procedure is used. Both methods with normalization are also compared. The results showed that the combination of points and lines with normalization improved the obtained results.