Reviewerly/exHarmony huggingface dataset:
On Kernel Search Based Gaussian Process Anomaly Detection

Anomaly detection becomes more important with increasing automation. Especially for time series data, prevalent in industry, there are numerous methods that have been well researched. In this work we provide a proof of concept for a novel approach using the interpretability of Gaussian processes. To detect an abnormal section, the data is split into equally sized segments which are then interpreted individually using separate kernel searches. The resulting kernels can then be compared and clustered by one of multiple presented methods. The segments that contain an anomaly end up in their own cluster. To test all possible configurations of our proposed approach, we applied them to a subset of the SIGKDD 2021 anomaly dataset mutliple times and evaluated the results. Almost all configurations were able to succeed, although not yet reliably reproducible. The results of our performance evaluation indicate that kernel searches are in principle applicable to anomaly detection in univariate time series data.
The DAIS-C: A Small, Specialised, Spoken, Schizophrenia Corpus

Despite an increase in corpus linguistic applications, no British English corpus of speech in schizophrenia prior to the DAI-C existed. A reference schizophrenia corpus would be a phenomenal undertaking, one exceeding the scope of a thesis. A specialised corpus built to explore linguistic creativity in this population offers a useful first step, nonetheless. The next sections review best practices in the design of small, specialised, and spoken corpora. These are synthesised to form a set of requirements for the DAI-C. A description of how I approached these requirements follows before a summary of the DAI-C’s main characteristics.
Traffic Sign Repositories: Bridging the Gap Between Real and Synthetic Data

Creating a traffic sign dataset with real data can be a daunting task. We discuss the issues and challenges of real traffic sign datasets, and evaluate these issues from the perspective of creating a synthetic traffic sign dataset. A proposal is presented, and thoroughly tested, for a pipeline to generate synthetic samples for traffic sign repositories. This pipeline introduces Perlin noise and explores a new type of noise: Confetti noise. Our pipeline is capable of producing synthetic data which can be used to train models producing state of the art results in three public datasets, clearly surpassing all previous results with synthetic data. When merged or ensemble with real data our results surpass previous state of the art reports in three datasets: GTSRB, BTSC, and rMASTIF. Furthermore, we show that while models trained with real data datasets perform better in the respective dataset, the same is not true in general when considering other similar test sets, where models trained with our synthetic datasets surpassed models trained with real data. These results hint that synthetic datasets may provide better generalization than real data, when the testing data is outside of the distribution of the real data.
RecomAlly: dynamic ally recommendation on Twitter based on rhetorical structure theory and valence shifters

Microblogs like Twitter emerged as a significant means for instantaneous information sharing on the web and forming the communities of similar interests by recommending the correlated information like tiny URL's, hashtags, friends or ally's, etc. to the target users. Existing systems do not capture the dynamic change in the user's interest over the time while recommending the potential user to the target user. Also, while computing the content similarity of the tweets, the existing systems just instinctively considers the total number of matching words in the two tweets, without considering the semantic similarity or discourse relation between them. In this work, we propose a dynamic and personalised ally recommendation system that computes the user's interests dynamically and considers the larger matching of semantic orientation of tweets on common topics which are computed based on content similarity of tweets using rhetorical structure theory.
Sentiment analysis on pandemic poems using TextBlob

Pandemic poetries are those poems written to document the pandemic poetically, created with an aim to connect people and help each other in the times of isolation. Interpreting poems largely reside with the readers and there is always the underlying communication of the author too. A highly subjective area for interpretation and understanding, marked with contextual meanings, poems offer diverse thought processes. Analysing the sentiments of the written work cannot always be white and black; there are grey areas to be explored also. How convergent or divergent will be the results of interpretation when analysed through machine sentiments? Through this study we aim to analyse two poems published during this pandemic situation using Textblob. It can be deduced that sentiment is influenced only by the literal meaning of words, rather than by the context of the whole sentence/paragraph which brings in divergence in interpretation especially in the word-list-based method.
Finding All Graphic Sequences and Generating Random Graphs

Graphs are basic requirement of research in many fields like electrical circuits, computer networks, Genome sequencing, traffic flow, compiler design and cryptography to name a few. There are three fundamental issues one has to address in respect of an undirected graph. First, given a number of nodes, how many unique degree sequences can be there to handle? Second, out of these unique degree sequences, how many are graphic? And third, how to generate random graphs for a degree sequence? This paper presents working solutions for all these issues. An efficient algorithm to find all unique degree sequences for a given number of nodes is presented. An algorithm based on Havel-Kasami algorithm is presented which is more efficient than Erdos-Gallai algorithm to test the connectivity. Finally, an algorithm to generate random graphs is also presented.a
Internet of Things and Digital Forensics: Recent Studies and Challenges

The revolution of the Internet of Things (IoT) is increasing dramatically, where everything has become smart, and this new technology has helped facilitate plenty of things for humanity and made life easier in terms of applications and machines that think like humans using artificial intelligence. Currently, many applications of the Internet of Things affect our daily lives. Although the Internet of Things has brought us ease of life, it has brought many challenges related to security. However, solving these issues and challenges requires a high degree of skills. The approach that addresses the increment of cybercrimes is IoT Forensics. IoT forensics is a call for investigating and mitigating these cybercrimes. In this study, we overview the basics of IoT and present an illustrative study of digital forensics and IoT Forensics, then discussing that with some differences between IoT Forensics, Digital Forensics, and IoT Security, and an overview of the Process of IoT Forensics have been discussed. In addition, this work focuses on recent research work from 2018 onwards in terms of IoT Forensics models, frameworks, analysis, and use cases; finally, IoT and Digital Forensics Challenges and open issues have been discussed.
Artificial Intelligence (AI) based Nutrition Advisorusing an App

There is a growing awareness of a good lifestyle among people today. People tend to follow different diets and exercise. However, consulting a nutritionist is not for everyone. Diet Advisor is an artificial intelligence application about human nutrition. Act as a nutritionist, just like a real nutritionist. This system is very similar to that of a nutritionist. Individuals, due to busy schedules, people in the modern period are unable to focus on their welfare. As a result, a diet should be suggested to manage health. A balanced diet is required to grow. A machine with intelligence who specialises in health is called an AI nutritionist. supplies providing its consumers with a suitable diet plan based on some evaluation parameters. This AI powered technology could really create a nutrition plan that emphasises a balanced, healthy diet. Fruits, vegetables, grains, skim milk and other low-fat dairy products, beans, lean protein sources like chicken and tuna, and lentils are examples of nutritious foods. Mealsthat have been suggested by experts. However, since each user appears to have a distinctive eating intake varying medical problems, a nutritionist generates an eating plan tailored to each individual. The web-based virtual nutritionist is an artificial intelligence-powered human implementation focused on diet. It functions as a nutrition advisor, much like an actual health professional. Participants who want a nutrition plan must give different information, like their height, BMI, and work schedules. The system saves all of the subscriber’s data and uses it to send the nutrition plan to the subscriber. As a result, the subscriber can receive the necessary diet with just one click and avoid having to see a nutritionist, which makes the process quicker.
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution

Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance. In order to further improve the reconstruction capability of current methods of compressed image super-resolution, a circular Swin2SR (CSwin2SR) approach is proposed. The CSwin2SR contains a serial Swin2SR for initial super-resolution reestablishment and circular Swin2SR for enhanced super-resolution reestablishment. Simulated experimental results show that the proposed CSwin2SR dramatically outperforms the classical Swin2SR in the capacity of super-resolution recovery. On DIV2K valid dataset, the average increment of PSNR is greater than 0.18 dB and the related average increment of SSIM is greater than 0.004.
Live Demonstration: An Integrated Computing and Communication Platform for Vehicle-Infrastructure Cooperative Autonomous Driving

Perception, computing and communication are usually decoupled in today’s vehicle-road coordination applications, which significantly adds to the system delay and cost. In contrast, we showcase a platform that integrates perception, communication and computing to provide timely roadside bird-eye-view (BEV) maps to vehicles for vision fusion. A neural processing unit and a cellular vehicle-to-everything (C-V2X) wireless baseband are both implemented on FPGA.
Dynamic Virtual Machine Allocation and Load Balancing methods for Heterogeneous Task Allocation in Public Cloud

Cloud computing faces problems in conjunction with security, average overall performance monitoring, scaling, data lock-in problem, load balancing, electricity usage, beneficial useful resource scheduling and carbon emissions. In Cloud computing, the number of users has a direct relationship with the demand for resources. Therefore, in order to schedule a task a key challenge is to balance the load among resources. The volume of work in the cloud grows exponentially in proportion to the number of users. As a result, a large number of heterogeneous jobs of varying length is required to be scheduled across various heterogeneous resources. Therefore, this study proposes optimize scheduling method for efficient resource allocation and task scheduling that can assign VMs to host and distribute the task effectively.
Control of Overhead Crane Pendulation by Self-Tuning Interval Type-2 Fuzzy Controller

in this paper, the research proposes an online fuzzy self-tuning method for an interval type-2 fuzzy PD controller. The interval type-2 fuzzy PD controller that implements the footprint of uncertainty (FOU) is developed using the fundamental concept of the type-1 fuzzy PD strategy. The proposed method is applied to an overhead crane to control the load's pendulation and cart position for effective material transportation. The position and angle of an overhead crane are put forth to be controlled by a pair of distinct self-tuning interval type-2 fuzzy PD controllers (SIT2FPDC) in this work. The SIT2FPDCs modify the gain to the process requirements through modifying the output scaling factors in real-time. By manipulating the mass and length of the pendulation, this research undertakes an in-depth examination of the overhead crane. In comparison to alternative fuzzy controllers, the crane control strategy with the proposed controller is examined. Due of the crane system's high nonlinearity, this study does not employ a conventional controller.
Improved Mixed Discrete Particle Swarms based Multi-task Assignment for UAVs

Aiming at the problem of multi-task distribution for Unmanned Aerial Vehicle (UAV) swarm, a new type of multi-task distribution model for UAVs is established in this paper, various constraints are considered, such as bomb load and damage loss. To solve the multi-task allocation problem, an improved mixed discrete particle swarm optimization algorithm (IM-DPSO) is proposed, a two-dimensional particle coding matrix with task priority is designed, the genetic variation rules with particle update strategies are combined, and then the inertia weight and learning factors are optimized to enhance the algorithm's ability of solving the problem. Simulation results show that the improved algorithm can better solve the problem of multi-task allocation for UAVs under the distribution model.
Motion Recognition System in VR Training System for Train Attendants

With the increasing improvement of rail transit, the safety of railway traffic has received more and more attention, and efficient and accurate communication between train attendants is the basis of driving safety. Aiming at the gesture training of train attendants, this paper uses the MediaPipe module as the skeleton key point recognition framework on the basis of virtual reality technology, and designs a gesture recognition solution for train conductors by using skeleton information extraction technology and deep learning related methods. It can train train the conductor’s gestures, which has the characteristics of real-time, good interaction, initiative and flexibility.
Create Short Videos on Metaverse Platforms Based on Virtual Panoramic Cameras

At present, most short videos are presented on the monitors of mobile phones, computers, TVs and other media, produced by camera shooting or software video synthesis, and published on the short video APP platform. Physical cameras in reality cannot enter the metaverse world to shoot, so there are very few panoramic short video materials on the metaverse. Therefore, the virtual panoramic camera provides a new channel for shooting on the metaverse. This article analyzes the development trend of shooting short videos and the sources of existing short video materials. By comparing the advantages and disadvantages of virtual panoramic short videos and traditional short video shooting, it explains how to create panoramic short videos on the Metaverse platform to improve real-world camera shooting. Limitations, expounds the structure of the relevant technical route, and finally summarizes the feasibility and difficulties faced in the future of using virtual panoramic cameras to shoot panoramic short videos in the Metaverse world.
Research on Virtual Widening of Power Information Acquisition Channel Based on LoRa and Data Compression

Limited by the channel bandwidth and information rate of the communication network of the electric energy information acquisition system, the energy data of the power system terminal lacks a suitable access method response to the above problems, this article combines the wide coverage characteristics and data compression technology achieved by LoRa (Long Range Radio, LoRa) spread spectrum communication, and proposes a new method of access communication based on LoRa spread spectrum and improved data compression coding for embedded applications. This technology can not only take into account the advantages of low design cost, strong coverage, anti-interference, etc. But also increases the virtual bandwidth of the channel, optimizes the comlink, and lay a solid foundation for high-frequency data collection and large-scale data transmission.
Research on the Application of VR Technology in the Experience of Virtual Movable-Type Printing Museum

The traditional movable-type printing craft is confronted with the problem of difficult learning and inheritance. In order to enable the public to experience or learn the movable-type printing craft with the help of virtual platform and make the movable-type printing craft have more possibilities for future development, the VR experience project of virtual movable-type printing museum is designed and developed through data collection and modeling, 2D interface design, virtual interactive design and systematic development of visual platform. It can retain the integrity of the procedure of movable-type printing craft to the greatest extent, provide a freedom, efficient, safe and green simulation learning platform, rapidly promote the traditional movable-type printing culture in a large range, innovate the presentation of movable-type printing craft and promote the development of printing technology.
Comprehensive Learning Particle Swarm Optimization(CLPSO) based hybrid cloud task scheduling with predefined completion time constraints

This paper proposed comprehensive learning PSO based on deadline constraint task scheduling for a hybrid IaaS cloud environment. One of the significant challenges in managing cloud services is scheduling tasks to VMs in such a way as to optimize the objective (that may be a reduction in make span, idle time, and waiting time or increasing the profit, availability, and serviceability). In this study, profit maximization is taken as an objective. This study concentrates on the workload on a batch, i.e., a bag of independent tasks which can be a kind of image rendering process, simulation, data processing, or video rendering process. We have used CLPSO for task scheduling and fixed the fitness function as profit maximization. The particle is designed so that we can easily obtain the allocation sequence by ranking the particle's coordinates. We found that for the given set of VMs (private and external cloud), the optimal or best number of tasks that can be taken is 3 to 5, as the average profit for those tasks is higher than the other set of tasks. Similarly, for a different set of cases, we can run the same setup and find the best number of tasks that can be accommodated to increase the profit. This study can be extended by changing the objective and can be repeated to get the inference on the new objective.
Deep Residual Learning for Unmasking DeepFake

Recently, DeepFake has gained a lot of popularity, any multimedia output created with deep learning technology that appears realistic to viewers is referred to as "DeepFake." Despite the positive developments of DeepFake, it has been a significant contributor to threats to an individual's privacy because it allows for the indiscernible swapping of one person's face for another without that person's permission. Additionally, it is simple for hostile actors to influence public events like elections by disseminating false information and harming national security. Therefore, identifying such DeepFake is a critical yet difficult challenge. The separation of DeepFake contents from genuine ones using the human eye has always been a challenging process, but recent research has demonstrated the use of several technologies to provide positive results for the same, but with certain limits. In order to highlight the benefits and drawbacks of the various algorithms utilized for DeepFake production and detection, the paper provides a thorough analysis of the methods employed. The proposed work focuses on using Inception-Resnet V2 to detect deep fakes and packages those advantages of deep learning for this purpose. Here the frames are collected from the uploaded video and divide it into the required number of frames in order to detect deepfakes. The subject's face is then extracted from the video using python face recognition modules. The proposed model, which has been trained on a variety of frame sequences, to determine whether the video is real or a deep fake with 95% accuracy.
Handwritten trip sheet to digital conversion using OCR

Neural Networks have promising potential at recognizing handwritten texts. In this paper, neural networks have been utilized for converting the handwritten trip sheet into digital format. A trip sheet is a written record containing the details of a trip. It is always easier to maintain records in a digitized format rather than in a physical form where there are chances of the documents being lost or damaged. Converting them into digital format solves all these problems making it easier & safer to organize and work on the data. The model proposed here is based on a combination of EasyOCR and Regular Expressions that help convert the data on a trip sheet into a CSV format. The proposed model works by splitting the image into various boxes by using the intersection of the lines and tries to recognize the characters present in that space separately. All of the recognized texts are then stored individually and can be later converted into various formats. The proposed model was tested on many handwritten trip sheets that provided favorable results with 85% accuracy.
Research on Evaluating Teaching Quality of Accounting Courses Based on Improved K-nearest Neighbors Algorithm(KNN)

Considering the differences in clustering positions between classifiable attribute data with different occurrence times, we redefined the position of the clustering center and proposed an improved KNN clustering method. The proposed method effectively solved the problem of classifiable mixed type data. On this basis, the KNN method was applied to course teaching evaluation to study the common characteristics of different types of teaching and provide a basis for evaluating courses.
Research on Eye Movement Tracking and English Teaching Application Based on CNN-BLSTM Fusion Algorithm

The effectiveness of integrating tracking data for eye movement research in different environments is insufficient compared to traditional data fusion algorithms. Therefore, a new deep learning-based eye-movement tracking and data fusion algorithm is proposed in this research. This method adds effective artificial information to the position information of tracking data in original eye movement research for the first time. Then, the convolutional neural network and the bidirectional short-term memory network are fused to produce a new fusion architecture. The experimental results show that compared with six classic data fusion algorithms, this method performs better in centralized fusion. On this basis, the proposed eye-tracking technology is used to investigate the effect of repeated reading of English discourse by Chinese-speaking college students to improve their reading efficiency of English discourse.