Reviewerly/exHarmony huggingface dataset:
Actions you can handle: dependent types for AI plans

Verification of AI is a challenge that has engineering, algorithmic and programming language components. For example, AI planners are deployed to model actions of autonomous agents. They comprise a number of searching algorithms that, given a set of specified properties, find a sequence of actions that satisfy these properties. Although AI planners are mature tools from the algorithmic and engineering points of view, they have limitations as programming languages. Decidable and efficient automated search entails restrictions on the syntax of the language, prohibiting use of higher-order properties or recursion. This paper proposes a methodology for embedding plans produced by AI planners into the dependently-typed language Agda, which enables users to reason about and verify more general and abstract properties of plans, and also provides a more holistic programming language infrastructure for modelling plan execution.
Offline Marathi Character To Text Conversion

India is a country with rich literature and historical importance. Most of this literature is written in Devanagari script. It is important to preserve this literature and pass the knowledge to the other generation. Marathi is the 15th most spoken language in the world. The most challenging research area is handwritten optical character recognition, because of its difficulty in segmenting the character that grows due to modifiers and compound characters due to Marathi Script Marathi handwritten character to text conversion is a system which provides the user the Optical Character Recognition functions on Marathi handwriting. Offline Marathi conversion character recognition system has mainly four stages: Pre-processing, segmentation, recognition and documentation. It uses the Convolutional Neural Network to train the dataset and uses Image Processing techniques to recognize user image via local storage. The good image quality is taken as input by the user to do further processing to get best results. This system uses python programming language and different hardware platforms, software platforms to make the system as a whole. The implemented system has an accuracy of 99.2%.
Research on the Influence of Computer Network Teaching Platform Application on the Teaching Process of Higher Mathematics

Abstract With the rapid development of computer, great changes have taken place in China’s higher education, which has gradually changed to online education. With the construction of online teaching platform, it has become more and more convenient for college students. With the increasing demand of online education market, we need to combine education and Internet constantly, which will become the frontier direction of education innovation, especially higher mathematics (hereinafter referred to as HM) course. Advanced mathematics is a basic subject, including advanced mathematics, linear algebra, probability theory and mathematical statistics, which is very difficult for some students. Therefore, through the computer network teaching platform (hereinafter referred to as TCNTP), college students can better understand the HM course, which will gradually change the traditional teaching process. First of all, this paper puts forward the important role of TCNTP. Then, this paper puts forward the SWOT analysis of the influence of TCNTP on the teaching process of HM. Finally, some suggestions are put forward.
ProofViz: An Interactive Visual Proof Explorer

We introduce ProofViz, an extension to the Cur proof assistant that enables interactive visualization and exploration of in-progress proofs. The tool displays a representation of the underlying proof tree, information about each node in the tree, and the partially-completed proof term at each node. Users can interact with the proof by executing tactics, changing the focus, or undoing previous actions. We anticipate that ProofViz will be useful both to students new to tactic-based theorem provers, and to advanced users developing new tactics.
A Novel Approach to Heuristic Evaluation

Heuristic evaluation (HE) is one of the most popular usability evaluation methods. Despite its popularity, it has a major downside in that novice evaluators perform worse than expert evaluators when conducting it. Usability heuristics are general and could be open to different interpretations that make understanding them and implementing them difficult for novices. To make the usability heuristics clearer and more purposeful, they should be mapped to the overall goal of the evaluation. There are generally two main goals. One is to enable users to accomplish their goals in a given system, and the other is to enable them to accomplish their goals easily. In this work, we mapped Nielsen's usability heuristics to the aforementioned goals.
3D Modeling for Kitchen Scene in a Hospital

Abstract This research focused on hospital diet management to explore the design of 3D models for kitchen scenes and the facilities of a hospital. Hospital diet management has standard operating procedures, but most people have never seen the kitchen environment within a hospital. Hence, the ongoing research highlights the foodservice procedure and makes a live-action film to complete 3D modeling and animated scenes within a hospital. This research applied the 3ds Max and other multimedia technologies in the medical and healthcare fields. The presentation of the virtualized scene and facilities is realistic and detailed. This research illustrates that the diet management process can be done quickly by 3D modeling and animation. It can also be applied to healthcare education and train new employees who need to understand the carrier’s details as soon as possible.
Leveraging Transfer Learning for Binary Classification of Images with CNN

Machine learning and deep learning approaches are widely used for various real-world applications. For the conventional machine learning methodologies, datasets from the same domain are generally used, viz., the training and test data for keeping the same input function space and the same data distribution characteristics. However, this approach is not very effective in scenarios where the cost of training data is high, or they are hard to access or gather. Hence, there is a requirement for trained high-performance learners on the data that is easily accessible in different domains. This technique is called learning transfer, relaxing the hypothesis of an independent and identically distributed dataset for test and training data and leading us to solve the problem of inadequate training data. Transfer of knowledge improves learning performance by drastically avoiding costly data labelling efforts. This work is the study and investigation of modern CNN architecture models used through transfer learning and checking the accuracy and efficiency with new image dataset. We compared the most widely used CNN transfer learning models available and observed that results were quite comparable with decreasing complexity of the latest highly dense neural network models. We also drew a comparison of models in terms of both training and validation accuracy based on model parameters and the layers present in the model.
Maze search relaxation method hardware high-level synthesis language on FPGA by NSL

We designed the shortest path search system for the maze. We implemented the shortest path search system on FPGA and CPU. Proprietary FPGA hardware is calculated up to 8.8 times faster than CPU. We report the implementation system results.
Pros and cons of distance education during the coronavirus

The article presents an overview of the positive and negative aspects of distance education for teachers and students in the period of coronavirus infection and identifies the problems and main directions of online learning development. English version of the article on pp. 160-167 is available at URL: https://panor.ru/articles/pros-and-cons-of-remote-education-during-the-coronavirus-pandemic/65458.html
Trump's January 6 Address: Hate Speech or Freedom of Speech? A Transdisciplinary Study

Purpose This research is a critical discourse analysis (CDA) of Trump's speech on January 6, 2021, which results in his supporters' storming the US Capitol in order to challenge certifying Biden's victory. The Democrats accused Trump of incitement of insurrection. Consequently, Trump was impeached. This article investigates Trump's speech to label it as hate speech or free speech. Design/methodology/approach Analytical framework is tri-dimensional. The textual analysis is based on Halliday's notion of process types and Huckin's discourse tools of foregrounding and topicalization. The socio-cognitive analysis is based on Van Dijk's ideological square and his theory of mental models. The philosophical dimension is founded on Habermas's theory of discourse. These parameters are the cornerstones of the barometer that will be utilized to reach an objective evaluation of Trump's speech. Findings Findings suggest that Trump usually endows “I, We, You” with topic positions to lay importance on himself and his supporters. He frequently uses material process to urge the crowds' action. He categorizes Americans into two conflicting poles: He and his supporters versus the media and the Democrats. Mental models are created and activated so that the other is always negatively depicted. Reports about corruption are denied in court. Despite that, Trump repeats such reports. This is immoral in Habermas's terms. The study concludes that Trump delivered hate speech in order to incite the mob to act in a manner that may change the election results. Originality/value The study is original in its tri-dimensional framework and its data of analysis.
Saving Nine Without Stitching in Time: Integrity Check After-the-fact

Electrical substations transform voltage from high to low, or low to high for distribution and transmission, respectively, and are a critical part of our electricity infrastructure. The state of a substation is continuously measured for monitoring, controlling and protection purposes, using synchrophasor measurements. The IEC 61850 standard defines communication protocols for electrical substations, including transmission of synchrophasor measurements. However, IEC 61850 does not properly address cyber security, leaving this critical infrastructure highly vulnerable to cyber attacks. This paper describes the development and testing of a novel mechanism for delayed integrity check for synchrophasor measurements. The results show that the solution manages to detect when integrity of the synchrophasor transmission is compromised, without adding any delay to the time-critical synchrophasor transmission itself.
Research and design of elderly health monitoring and anti-fall positioning alarm device based on STM32

With the economic development and the improvement of living standards, the aging problem has become more and more serious, and the health and safety of the elderly have also attracted much attention. In order to detect and rescue the elderly when they fall and monitor the vital signs of the elderly in real time, this paper designs an elderly health monitoring and fall prevention positioning alarm device. The device is based on STM32 single chip microcomputer system and adopts modular design. It is equipped with SIM800 C mobile communication module and GPS positioning module. When the old man falls unexpectedly and does not stand up in a certain period of time, the device automatically calls the bound guardian’s cell phone and sends SMS to display the location and state of the old man. Sensors such as body temperature, heart rate and step number are integrated to detect the health data of the elderly in real time, and alarm quickly when the data are abnormal. At the same time, cloud monitoring app is developed based on E4A platform to monitor the physical state of the elderly in real time. The experimental results show that the device can correctly identify most fall states, and the false alarm rate and missed alarm rate are low. It can effectively distinguish fall and daily life behavior. The test accuracy reaches 92 %, and has high stability and reliability.
A Multi-task Deep Network for video-based Person Re-identification

Video-based person re-identification is one of the significant areas of computer vision, and it has huge application potential in video surveillance. Many existing methods address this difficulty by extracting only the characteristics of one person in the network. This paper proposes a Pose-Attribute network (MBNet) through multi-task learning of embedded pose representation, which combines pedestrian attribute recognition with ReID. MBNet extracts 18 human body keypoints and 12 pedestrian attributes that contain gender, shoes, etc. from the image, and simultaneously learns the local and global features. Conclusion of experiments is on two datasets, including the DukeMTMC-VID and MARS. The results show that the mAP of MBNet is higher than the mAP of pose-based or attribute aware for Person re-identification. The experimental results show that the proposed network is better than other video-based people re-identification approaches.
The Profession of a Communicator: Contemporary Thoughts The reference on the book: The Professional Competences of a Specialist in Communications of the XXI Century / ed. A. D. Krivonosov. St. Petersburg, UNECON Publ., 2021. 211 p.

The reference gives a characteristic to the new monography titled “The professional competences of a specialist in communications of the XXI century”, which is the result of a long-term group study of the Communication Technologies and PR Department of St. Petersburgh State Economic University. The monography has been edited by the Head of the Department, professor A. D. Krivonosov. The analysis of the research strategy put into the basis of the monography is provided: from the study of specific features of a contemporary public environment to the determination of professional competences of specialists in communication sphere demanded in the society and further to the development of the model of a foresight approach to forming communicative competences and skills. The obvious advantage of the monography is a close connection between the theoretical foundations and the actual reality of the development of Russian education in communication sphere. The study topicality, sequence in the solution of the set tasks, presentation clarity, scientific value as well as theoretical significance of the monography, the validity of the conclusions have been analyzed. The recommendations for the practical use of the work are given.
Single Haze Image Restoration Under Non-Uniform Dense Scattering Media

We demonstrate an efficient restoration method based on sky segmentation and block optimized transmission (SSBOT) to restore the visibility of single haze image under non-uniform scattering medium. The SSBOT method uses sky region segmentation to determine the haze-opaque region, and estimates the global atmospheric light using quadtree-decomposition. The proposed SSBOT estimates the transmission in every sub-images of the degraded image with dark channel method with coefficient correction. The experimental results show that SSBOT can efficiently restore images degraded by non-uniform dense scattering medium. The proposed method can be used for various security and surveillance applications.
Double Discriminative Graph Regularized Semantic Auto-Encoder for Zero-shot Learning

research-article Double Discriminative Graph Regularized Semantic Auto-Encoder for Zero-shot Learning Share on Authors: Debao Tai Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Zhonghao Zhang Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 134Pages 1–7https://doi.org/10.1145/3469213.3470337Online:18 August 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Neural Dense Captioning with Visual Attention

Image captioning is the task of how to represent image information more effectively and efficiently. It uses both Natural Language Processing and Computer Vision for generating the caption. In the existing research, many methods for caption generation were proposed, but the information in the description is not so related to the image and also most of them uses the global information of the image. In order to solve this problem, in this paper we have described three image captioning model namely ResNet, Glove and VGG16.These are the pretrained models which uses Convolutional Neural Network (CNN) and Deep Recurrent Neural Network (RNN) based on Long Short Term Memory (LSTM). CNN has one or more convolutional layers and are used for image classification and features extraction. Only the meaningful area's features were extracted through CNN. For generating the caption LSTM uses over RNN because it has many layers so the predictions for generating the captions are more accurate in LSTM. The dataset use in our project is Flicker8k dataset. It contains total 8k images where 2k images are test dataset and 6k images are trained dataset. The Flicker8k dataset is used to demonstrate the proposed methodology using python as a language.
Research on the Application of YOLOv3-Tiny Algorithm in the Detection of Circuit Experiment Equipment

research-article Research on the Application of YOLOv3-Tiny Algorithm in the Detection of Circuit Experiment Equipment Share on Authors: Mingju Liang Guangdong Shunde Innovation, Design Institute, China Guangdong Shunde Innovation, Design Institute, ChinaView Profile , Zhenguo Wang School of Information Engineering Zhengzhou University and Guangdong Shunde Innovation Design Institute, China School of Information Engineering Zhengzhou University and Guangdong Shunde Innovation Design Institute, ChinaView Profile , Yang Wang Guangdong Shunde Innovation, Design Institute, China Guangdong Shunde Innovation, Design Institute, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 224Pages 1–6https://doi.org/10.1145/3469213.3470431Online:18 August 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Research about the Transmutation Trend of the Silk Road Documentary under Recurrent Neural Network

The Silk Road documentaries refer to documentaries related to the theme of the Silk Road. Its connotation, scope, and significance have a profound impact on the creation of Chinese documentaries, and become a major documentary genre with a unique style in the history of documentary creation in China. Although the production of documentary about "Silk Road" has achieved remarkable results in both quantity and quality, its development trend is seriously affected by the development iteration of research objects over time and national policies, resulting in the unknown future development of "Silk Road" documentary. Therefore, this paper first reviews and summarizes the theme background and historical development of the Silk Road documentary. Then, a long short-term memory neural network (LSTM) model with gate mechanism and memory unit is used to adaptively model its historical time series data. Finally, the long-term iterative forecasting method is used to gradually introduce the development trend of the number of "Silk Road" documentaries in the next few years. Under the circumstances that the national policy on the Silk Road in the next few years is unknown, this paper could better guide the future development trend of this type of documentary creation in the form of modeling, and provide reference for future scholars in this direction.
A Study on Training Course for Staffs to Solve Expertise Issues with Multiple Criteria Decision Making Methodology – a Case of PC-based Information Security Monitoring Tool Development

The development of a PC-based information security monitoring tool (PISMT) involves what events are required to monitor and what information is needed to collect. For developing staff, there is a multiple criteria issue. This study proposed a multiple criteria decision making (MCDM) process method (MCDMPM) to explore a PC-based information security monitoring tool (PISMT) development. The MCDMPM trains the developing staff to determine which functionalities receive implementation priorities in a PISMT. The MCDMPM adopted a literature survey, Delphi, and AHP to explore a PISMT development. First, the MCDMPM taught the developing staff to launch a thorough literature survey to have concise operational definitions for each functionality, which might implement in an ISMT. Second, the MCDMPM trained the staff to establish and confirm a criteria decision hierarchy with the Delphi method. Third, the MCDMPM assisted the staff to investigate the individual opinions of the experts using AHP. Finally, based on the individual opinions of the experts, the MCDMPM taught the staff to receive aggregated group opinions for each functionality under each construct/the total goal. With the result of the PISMT development, the developing staff implemented significant functionalities for development effectiveness. The empirical evidence for the effectiveness of the MCDMPM is shown from the PISMT development.
How Software Architects Focus Their Attention

As part of our software architecture research and practice we have found that a common difficulty for new architects is knowing where to focus their attention to maximise their effectiveness. This led us to wonder whether successful experienced architects have any common techniques or heuristics that they use to help them achieve this. In an earlier study where, having interviewed experienced architects, we found that in fact there were some common heuristics that they use, we created a simple model based on an analysis of their advice. In this paper we explain how we validated that model with a wider survey of experienced enterprise and software architects and, from the findings of that study, extended the model with an additional dimension. This resulted in our model having four primary guidelines, which are: focus on stakeholder needs and priorities, prioritise time according to risks, delegate as much as possible, and ensure team effectiveness.