Reviewerly/exHarmony huggingface dataset:
Big Data-Enabled Solutions for Covid-19

In the time of the pandemic like CORONA, Covid-19, everyone is ftghting against this deadly virus. Besides, governments are looking for a barrier that stops spread of virus until the vaccine is made. In modern era, technology plays an important role. This paper brings the way by using a powerful technology called Big data. Big data know for handling a large amount of data and provide powerful insights into the data. Big data integrated with Artificial Intelligence is a powerful tool to ftght against this pandemic. Many countries like Taiwan, China with the use of Big Data stop this pandemic up to some extent. But the collection of data itself comes up with the big challenge of PRIVACY AND SECURITY. In the recent times, the world has seen the effect of data leaking whether by Facebook or by Google. Many European countries due to this big challenge will not be able to use this technology.
Automation Mangrove Identification with Case Based Reasoning Process

Mangroves are ecosystems with unique functions in the environment. Because of its physical properties, mangroves are able to play a role as a wave retardant as well as retaining intrusion and abrasion of the sea. Mangroves themselves have various types of species that are spread throughout Indonesia and not yet widely known to people in general. In identifying the mangrove species itself cannot be done arbitrarily, it requires an expert who truly understands the mangrove species. This research was conducted with the aim of adopting the knowledge of mangrove experts to identify mangrove species into expert systems. The method used is case based reasoning method using the KNN algorithm which is used to calculate the similarity value between cases that will be applied to the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu. This system is built using HTML, CSS, Javascript, Php, and Mysql programming languages and is designed using UML diagrams. The results of this study itself are, it has been successfully applied the case based reasoning method in the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu
An ontology proposal for a corpus of letters of Vincenzo Bellini: formal properties of physical structure and the case of rotated texts

In this paper the formal OntoBelliniLetters ontology is described concerning the corpus of Vincenzo Bellini's letters kept at the Belliniano Civic Museum of Catania. This ontology is part of a wider project - the BellinInRete project - one of whose aims is the development of a more general and complete ontology for the whole of Vincenzo Bellini's legacy preserved in the museum. The main concepts and relations building up the ontology knowledge base are described and discussed and some formal properties of them are presented. The ontology schema is inspired by the CIDOC Conceptual Reference Model (CIDOC CRM).
Research on Navigation Algorithm of Unmanned Ground Vehicle Based on Imitation Learning and Curiosity Driven

The application of deep reinforcement learning (DRL) for autonomous navigation of unmanned ground vehicle (UGV) has the problem of sparse rewards, which makes the trained algorithm model difficult to converge and cannot be transferred to real vehicles. In this regard, this paper proposes an effective exploratory learning autonomous navigation algorithm Double I-PPO, which designs pre-training behaviors based on imitation learning (IL) to guide UGV to try positive states, and introduces the intrinsic curiosity module (ICM) to generate intrinsic reward signals to encourage exploratory learning strategies. Build the training scene in Unity to evaluate the performance of the algorithm, and integrate the algorithm strategy into the motion planning stack of the ROS vehicle, so as to extend to the actual scene for testing. Experiments show that in the environment of random obstacles, the method does not need to rely on prior map information. Compared with similar DRL algorithms, the convergence speed is faster and the navigation success rate can reach more than 85%.
Development of Violin-Playing Robot

We are developing an anthropomorphic robot that can play the violin to enhance the communication ability with humans through its expressive performance. The bow force was not controlled in our previous report, which will cause alternation of the bow force and reduce the sound quality. This report introduces a robot’s right hand and its bow force control system. Each four fingers except the thumb have a chamber covered with a silicone membrane, which inflates when the air pressure inside the chamber increase. Air pressure of each finger can be controlled independently. By altering the air pressure in the index finger during the down bow, we can control the bow force. Experimental result shows that the system can reduce the fluctuation of the bow force from the target.
Twin K-Shuffle Based Audio Steganography

Secure communication is most effective when it is covert. In the realm of covert communication, steganography conceals secret message within a cover medium. This ensures that adversaries who have access to this carrier medium are unaware of the existence of the secret message. This paper proposes a novel twin K-Shuffling and embedding technique that scrambles and hides secret message inside audio samples. The scrambling phase of the proposed technique consists of bit and character shuffling. The bit-shuffling scrambles the bit-string of each character in the secret message into cipher-text via K-Shuffle. The characters of the resulting cipher-text are then shuffled by another K-Shuffle technique to yield chaotic cipher-text. At the embedding phase, the scrambled cipher-text is randomly planted into the carrier audio samples. The novelty in this proposed technique is the provision of a three-layer protection for secret messages; bit, character, and encoding layers. Results and analyses show that this technique satisfied both embedding and encryption requirements of steganographic systems.
Multi-functional motion control and simulation of a large unmanned mobile manipulator for autonomous grasping scenes

Mobile manipulator can perform complex operation tasks in a larger workspace, and has great application potential in practical production. Aiming at a large unmanned mobile manipulator for autonomous grasping scenes, this paper realizes its multi-functional motion control and simulation. Firstly, this paper analyzes the kinematics models of the manipulator and mobile base, and describes them with D-H parameters; The whole-body kinematics model of the mobile manipulator is obtained by integrating the mobile base and the manipulator. Secondly, in order to meet the requirements of path planning and motion simulation of mobile manipulator in different scenarios, this paper designs three different simulation modes and realizes the simulation in virtual environment, the simulation combining actual controller and virtual robot and the simulation of digital twin system. Finally. different experiments are designed to verify the feasibility and effectiveness of the three simulation methods.
Image classification algorithm based on social network metadata

The images in online social networks usually carry a large amount of social network metadata, such as labels, users, picture groups, locations and comments. These social network metadata, which include rich image semantic information, can help users distinguish the content of imagesand communicate with each other. An image classification algorithm based on multiple social networks, MSNet, is proposed in this paper. Firstly, MSNet collects the social network metadata of images and constructs relationship networks of images based on the metadata. Then, a network embedding algorithm is used to learn the representation vectors of images in each network. Finally, a neural network classifier is trained to classify the images by using the visual features and network representation of images. The experimental results on PASCAL、MIR、CLEF and NUS image data sets show the superiority performance of MSNet in comparison with CNN-neighbor and kernel canonical correlation analysis (KCCA).
Proof of a Conjectured 0-Rényi Entropy Inequality With Applications to Multipartite Entanglement

Characterizing the relations among the three bipartite reduced density operators <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \rho _{AB}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \rho _{AC}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \rho _{BC}$ </tex-math></inline-formula> of a tripartite mixed state <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \rho _{ABC}$ </tex-math></inline-formula> has been an open problem in quantum information. One of such relations has been reduced by [Cadney et al, LAA. 452, 153, 2014] to a conjectured inequality in terms of matrix rank, namely <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r(\rho _{AB}) \cdot r( \rho _{AC})\ge r( \rho _{BC})$ </tex-math></inline-formula> for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \rho _{ABC}$ </tex-math></inline-formula> . It is denoted as open problem 41 in the website “Open quantum problems-IQOQI Vienna”. We prove the inequality, and thus establish a complete picture of the four-party linear inequalities in terms of the 0-entropy. Our proof is based on the construction of a novel canonical form of bipartite matrices under local equivalence. We extend our result to inequalities in multipartite systems, as well as the condition when the inequality is saturated.
A Survey on Phishing Website Detection Using Deep Neural Networks

Phishing is a social engineering attack, where an attacker poses as a legitimate individual or institution and convinces a victim to divulge their details through human interaction. There has been a steep rise in phishing cases across the globe. A report by Cisco [1] shows that phishing was the reason for 90% of data breaches in 2021. Various detection models have been proposed in the past to counter such attacks. Some proposed models work on improving the detection rate of phishing URLs while others focus on reducing their detection time. Authors have used machine learning, deep learning, and various other novel mechanisms in feature selections that result in high algorithm performance. This study is a systematic analysis of recent work utilizing deep learning for phishing detection, highlighting the research methods, algorithms, programming tools, and datasets used in such studies. This study further proposes some guidelines for future research, which include standardizing documentation and performance reporting. These guidelines may help researchers in their quest to replicate others’ work and compare newly proposed methods with previously developed systems.
A mathematical perspective of ultrasound image representations and image quality criteria

What is an ultrasound “image”? Where an engineer or physicist may see a quantitative map of physical properties, a physician may see qualitative anatomical features and pathologies. In the past, each image type was evaluated appropriately within its respective context: quantitative image quality was measured with criteria like the contrast-to-noise ratio (CNR), and qualitative images underwent dynamic range transformations to optimize human perception of contrast. Recent non-linear beamforming methods have blurred this line: the produced images are transformed like qualitative images but evaluated with quantitative criteria like CNR. Here, we identify the mathematical flaws in reasoning and suggest rigorous alternatives.An image assigns values to spatial coordinates. Let X denote the set of all possible image values. The mathematical structure endowed on X determines the set of equivalence-preserving transformations. For instance, qualitative images treat X as a general topological space, where images are equivalent under homeomorphisms of X (dynamic range transformations). Quantitative images treat X as a metric space and have fewer equivalence-preserving transformations. These structures determine when criteria like contrast, CNR, generalized CNR, and spatial resolution can be used. To extrapolate these criteria to other contexts, one must use relevant structure-preserving isomorphisms (e.g., histogram matching to preserve information content).
PEACook: Post-editing Advancement Cookbook

Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing efforts. Training on APE models has made a great progress since 2015; however, whether APE models are really performing well on domain samples remains as an open question, and achieving this is still a hard task. This paper provides a mobile domain APE corpus with 50.1 TER/37.4 BLEU for the En-Zh language pair. This corpus is much more practical than that provided in WMT 2021 APE tasks (18.05 TER/71.07 BLEU for En-De, 22.73 TER/69.2 BLEU for En-Zh) [1]. To obtain a more comprehensive investigation on the presented corpus, this paper provides two mainstream models as the Cookbook baselines: (1) Autoregressive Translation APE model (AR-APE) based on HW-TSC APE 2020 [2], which is the SOTA model of WMT 2020 APE tasks. (2) Non-Autoregressive Translation APE model (NAR-APE) based on the well-known Levenshtein Transformer [3]. Experiments show that both the mainstream models of AR and NAR can effectively improve the effect of APE. The corpus has been released in the CCMT 2022 APE evaluation task and the baseline models will be open-sourced.
Project Management: University Leaders’ Willingness

This paper presents a study of the management system of Russian universities in the formation of the educational system of the Russian Federation. The research is presented by a mathematical model of professional interactions of university managers. Factor and cluster analyses of social profiles of Russian University managers were conducted. The causes of qualification deficits are identified and the educational needs of middle managers of Russian universities, as well as the motives for their occurrence, are identified. The directions of educational programs to meet the weak current competencies of university managers, as well as recommendations for the formation of project activities in the main areas of the University based on strong current competencies are proposed. The practical significance of the results of the research is to solve the problems of managerial accounting of the effectiveness of professional interactions of managers when switching to a project type of activity.
A robust and high-precision edge segmentation and refinement method for high-resolution images

Limited by GPU memory, high-resolution image segmentation is a highly challenging task. To improve the accuracy of high-resolution segmentation, High-Resolution Refine Net (HRRNet) is proposed. The network is divided into a rough segmentation module and a refinement module. The former improves DeepLabV3+ by adding the shallow features of 1/2 original image size and the corresponding skip connection to obtain better rough segmentation results, the output of which is used as the input of the latter. In the refinement module, first, the global context information of the input image is obtained by a global process. Second, the high-resolution image is divided into patches, and each patch is processed separately to obtain local details in a local process. In both processes, multiple refine units (RU) are cascaded for refinement processing, and two cascaded residual convolutional units (RCU) are added to the different output paths of RU to improve the mIoU and the convergence speed of the network. Finally, according to the context information of the global process, the refined patches are pieced to obtain the refined segmentation result of the whole high-resolution image. In addition, the regional non-maximum suppression is introduced to improve the Sobel edge detection, and the Pascal VOC 2012 dataset is enhanced, which improves the segmentation accuracy and robust performance of the network. Compared with the state-of-the-art semantic segmentation models, the experimental results show that our model achieves the best performance in high-resolution image segmentation.
Telling Us Your Needs with Your Eyes

User feedback is valuable for the continued development of the system. Most of the user feedback analyzed today is explicit, such as app reviews. However, many users are reluctant to provide this explicit feedback, which costs them extra time. In this paper, we present an ongoing study, which investigates eye movement patterns with the purpose of automatically collecting users’ implicit feedback. Specifically, we establish the semantic connection between eye movements and users’ perceptions based on cognitive and empirical evidence. Our proposed approach can thus systematically assess the satisfaction of six non-functional requirements, quietly unveiling users’ needs for the software they use. Finally, we design an evaluation plan in order for assessing the effectiveness of our approach.
Password-Protected Lossless Visible Watermarking Technique for Digital Image

Sharing digital images through social networking and other means of the Internet has placed its ownership and copyright at the corner. Protection from unauthorized use of digital images is gradually becoming a challenging task. Digital watermarking is used as the shield against data theft and unauthorized use of digital images without the consent of their owner. In this paper, we propose a password-protected visible watermarking system (PPVWS) to protect against its use without proper citation or proper permission from the owner of the protected image. This method describes an effective way to watermark a visible image or logo to any digital image so that the watermarked image reflects the source/owner of the image. The watermarked image produced in this technique is password protected, and the original image is self-constructed from the watermarked image when the correct password is produced. The proposed algorithm is able to embed the watermark through a password, provided by the owner. The watermark removal algorithm, presented in this paper, is performed through the same password that was given at the time of embedding the watermark. The proposed technique has been shown to secure enough for cryptanalysis as well as bruit force.
Subtitle Translation Practice from the Perspective of Computer-Aided Translation: Takes the Comprehensive Application of Netease’s Website, Arctime, and Adobe Premier Pro as an Example

The era of purely manual subtitle translation has ended.Nowadays, with the help of computeraided translation (CAT) software and programs, the efficiency of subtitle translation has been greatly improved.While CAT has outstanding advantages in accuracy and creativity, it involves more than the use of machines that can not be entirely depend on.At the same time, for contemporary translators, it is necessary to learn something in this field.This paper is based on a practice of subtitle translation using CAT software and programs which includes NetEase's website, Arctime, and Adobe Premier Pro.Three subtitle translation methods were explored, and problems and solutions in the translation process were discussed as well.
Prediction of national strategic commodity prices based on multivariate nonparametric time series analysis

Artificial Neural Network (ANN) or often referred to as artificial neural networks is a method inspired by the awareness of the complex learning system in the brain consisting of sets of neurons that are closely interconnected. While the Fourier series is a trigonometric polynomial function that has a very high degree of flexibility to overcome data that has a repeating pattern. In the time series data, both models can be used for nonparametric approaches that have many advantages, one of which is that they are more flexible and not tied to certain classical assumptions. In this study, a comparative study will be carried out between the ANN model and the Fourier series model to obtain forecasting results on national strategic food commodity prices simultaneously, with initial commodity prices referring to the website of Pusat Informasi Harga Pangan Strategis (PIHPS). The selection of the best model is selected based on the model that results in the smallest rate of prediction error in a commodity price prediction data by comparing the Mean Absolute Percentage Error (MAPE) values. The results of this test get the smallest MAPE value on the ANN model of 0.05974244, while the smallest MAPE value on the Fourier series model is 0.000325423. The results show that the Fourier series model is the best model for predicting the price of strategic commodities in Indonesia.
Income Sustainability Of Private Universities In A Sub-Saharan African Country: Ghana

Tertiary institutions, both public and private, play a vital role in global socio-economic development through the production of human capacity.However, an area of research that has gotten relatively little academic attention in the Ghanaian higher education milieu is private universities' strategic responses to their financial sustainability.The purpose of this study, which used an exploratory research methodology, was to examine the various sources of income generation by private universities and strategic responses adopted to sustain their financing in Ghana.With an incorporated case study design, the study used a qualitative research methodology.The main findings, which were supported by data from semi-structured interviews conducted in five private universities and documentary evidence, showed that the primary source of pressure on private tertiary institutions comes from the limited funding that prevents them from achieving their missions.The findings also showed that private universities had taken certain strategic steps to counteract the effects of their restricted funding.The options highlighted included sustaining tuition fees through an aggressive admission strategy, income diversification strategy by expanding non-traditional sources of income, commercialising, and research.For the universities, cost-cutting through increased budgetary efficiency also represented a more important strategy.Although private universities in Ghana continue to haphazardly rely on inadequate funding, the survey found that they are actively engaged in numerous varied and alternative income-generating activities in order to survive.