Reviewerly/exHarmony huggingface dataset:
Prediction of Load Demand on Base Stations for application of Energy Conservation Using Deliberated Informer

With the growing popularity and accessibility of mobile smart phones in every nook and corner of the world, cellular traffic demand has seen an exponential increase. Consequently, more number of Base Station (BS) are required to cater to this ever increasing demand. But, a close observation leads us to a very interesting fact that load demand on base stations is not constant throughout the day, i.e., there are time periods in the day where usually load demand is at its peak and as day comes to an end, it is seen load demand decreases. But the base stations are continuously in the ON mode which leads to massive energy wastage and telecommunication companies have to pay a heavy financial cost. This forms the basis for our motivation to reduce energy consumption or in other words ‘Energy Saving’ in Radio Access Networks(RAN). Recent studies have confirmed the possibility of RAN energy savings by the dynamic on/off of various BSs. In order to determine the correct time for applying the sleeping mechanisms, we have to predict the off-peak traffic hours. In this work, we propose a novel open-source dataset which contains the load demand versus time data for base stations for 6000 hours to facilitate training of various deep learning models. We propose an efficient transformer based model named Deliberated Informer and evaluate its performance on our dataset which gives state of the art performance in the task of Long sequence time-series forecasting (LSTF).
Denoising Autoencoder for the Removal of Noise in Brain MR Images

Medical imaging methods like X-rays, ultrasound, Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRI) can show structures of the internal body in great detail. However, there is unavoidable and ubiquitous noise in images created by medical imaging equipment. The use of noise reduction methods in medical imaging has therefore become crucial. In this article, we are focusing primarily on eliminating noise from MRI images of the brain. In terms of revealing details about the location and size of tumors, MRI is quite effective. Here we are proposing a Convolutional Denoising Autoencoder for removing noise from these images. Convolutional autoencoders can gently extract the data while preserving the spatial information of the image data. As a result, we can denoise with greater accuracy while using less computation and data. Our model got an SSIM value of 0.85 and a PSNR value of 30 dB.
Identifying Texting Idiolects using Transformers

Nowadays internet anonymity has turned into a head-scratcher issue for most law enforcement agencies. As the technological developments in the digital field are still rising exponentially so are cyber crimes. In cyber forensics, digital profiling of anonymous tweets is done by analysing and comparing the suspect’s tweets texting idiolects the anonymous author’s writing style. Hence our project proposal is to develop a model using transformers and natural language processing (NLP) techniques for identifying the author of the anonymous chat.
A Survey on Integration of Blockchain in Data Trading Industry

With the development of computer and information technology, diverse types of data are being created daily. This data along with big data technologies could give a lot of useful information when used right. To make this possible, many data trading platforms exist now. But since these systems are based on a third party platform who collect and trade data, there are many privacy and security concerns. To solve these problems, blockchain technology have been being proposed. The decentralised and transparent nature of blockchain could help to solve many security concerns. In this work, we provide a survey on data trading systems which utilize blockchain as the primary entity for trust-enabling to avoid any intermediaries. We provide an overview of blockchain technology and discuss the working and limitations of the centralized data trading systems. Also, we provide insights into DApp and Ethereum blockchain.
Lucas Kanade based Optical Flow for Vehicle Motion Tracking and Velocity Estimation

Optical flow is a powerful application of image processing that is used in a variety of applications, primarily in object tracking and motion estimation. In this paper, we implement a system for vehicle motion tracking and velocity estimation using Lucas-Kanade (L-K) algorithm based optical flow method. The work includes two applications of optical flow: tracking the movement of the vehicle in the case of a fixed camera and velocity estimation of a vehicle with a camera mounted on it. Pre-processing steps include gaussian smoothing, and computing spatial and temporal gradients. This is followed by the further formulation of Lucas kanade equation in the form of matrices. The system of equations is then solved using the least square error criteria, and the flow vectors are obtained. Processes such as segmentation, blob analysis, camera calibration, and thresholding are further done which are used for velocity estimation as well as motion tracking. The functionality was tested and verified on video sequences obtained from the lab testing scenarios and real-world camera visuals taken from various sources.
Implementing a land registration system using non-fungible tokens to represent land in the system and side-chain for data storage

Blockchain Technology has been increasing in popularity in recent times due to its decentralized nature, immutability, transparency, high security and transparency. Several private and public organisations have been using it to develop new and innovative projects like cryptocurrency, NFTs, Metaverse, etc., and also to upgrade and improve old technologies like supply chains in agriculture and government voting systems. Land Registration is one such sector that can be upgraded using blockchain because in the current system there are a lot of middlemen involved and data is centralized and not secure, these things make the current system vulnerable to frauds and corruption. In this paper we have proposed a new implementation for the existing land registration system in the country that uses Non-Fungible tokens in the blockchain to represent a property and a sidechain like IPFS for storing its data in a decentralized manner. With our implementation we can overcome the flaws in the old system and make land registration secure, transparent and trustless. Here by "trustless" we mean that users do not have to trust any centralized authority or intermediary to verify transactions or maintain the integrity of the system. Instead, trust is placed in the system’s consensus mechanism and cryptographic protocols.
A Comprehensive Analysis of Underwater Image Processing based on Deep Learning Techniques

Underwater image processing has been an active research topic over the past few years as interest in marine observation and the use of ocean resources has increased. Different from conventional images, marine ecosystems are frequently subjected to challenging conditions such as underwater turbulence, low contrast, and high colour distortion as a result of the light's non-uniform attenuation as it passes through the water. To overcome these challenges, a good amount of work in conventional and deep learning based underwater image processing has been published over a period of time. Deep learning has demonstrated excellent performance improvement than the conventional approaches on the challenging vision tasks. In this survey, important underwater image processing methods using deep learning have been discussed. The major underwater metrics, common datasets, and challenges are also presented.
Mass-Customised Fashion in a Smart Holistic Wear-Care Business Model

Fashion is the second largest consumer good and the corresponding industry is highly relevant in the social discourse on the demand, global production routes, use, and recycling of raw materials. To extend the product life cycle, sustainability requires a personal bond to the fashion product, as well as high-quality materials and workmanship. Our research scenario uses RFIDs to digitise the service processes. With a personalised customer login, the customer can access all information about the individual product development and possible washing and repair services. The interactive added value creates a personal bond.
Extraction of Actionable Threat Intelligence from Dark Web Data

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of Cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based Cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making Cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities.
RISWMCI: Robust Imperceptible Semi-blind Watermarking Scheme for Medical Color Images

Color medical images are widely used in medical diagnosis for e-healthcare applications, and preserving their integrity and authenticity is a big problem. This paper presents a robust and imperceptible semi-blind color medical image watermarking scheme, which can be used for e-healthcare applications. The method uses a hybrid technique using Discrete Cosine Transform-Lower Upper factorization. The performance is evaluated using different assessment matrices for various test color images from distinct modalities. The method obtained an average PSNR of 51.99 dB, an SSIM of 0.9991, and an NC of an ideal value of 1. Its average embedding and extraction time are 0.3435 seconds, and 0.2223 seconds, respectively. The approach performs well in all respects when compared to cutting-edge methods.
Reconfiguration of Thermoelectric Generation Systems based on Artificial Bee Colony Algorithm

Thermoelectric generation (TEG) is regarded as one of the most promising green power generation methods. However, the complex and changeable environment often causes the temperature difference array to operate under the condition of non-uniform temperature difference (NTD), the output power is low, and the power-voltage (power-voltage, P-V) curve has obvious multi-peak characteristics, which is not conducive to the system stable and efficient operation. Traditional strategies are difficult to completely suppress and eliminate this adverse effect, so a modular temperature difference array reconstruction technology based on artificial bee colony (ABC) is proposed, which aims to fundamentally solve the problem of various uneven temperature distributions to TEG system. Firstly, a (15×15) modular TEG array reconstruction model is built by MATLAB/Simulink, the entire TEG array is divided into three array modules, and various dynamic reconstructions can be completed only by configuring two switch matrices, reducing the construction and operation costs of TEG system. Then, ABC is guided to perform reconstruction optimization with the goal of maximizing output power. The simulation results show that ABC can effectively smooth the P-V curve, increase the output power, reduce the voltage unbalance, fully tap the power generation potential of TEG array, and ensure the safe and stable operation of the system.
Reliability Analysis and Design of Surveying and Mapping Geographic Information Data Center System

Paper starting from the possible risks of surveying and mapping geographic information data center, we analyzed the system architecture of data center first, and then we analyzed and designed the reliability of data center from five aspects: equipment redundancy, network reliability, virtual machine reliability and cloud desktop reliability. Finally, through the reliability mathematical model, the availability, system failure rate and mean time between failures of the surveying and mapping geographic information data center are calculated by using the series-parallel hybrid model.
Dataflow graphs as complete causal graphs

Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software design approaches make it difficult to track and discover such relationships at system scale, which leads to growing intellectual debt. In this paper we consider an alternative approach to software design, flow-based programming (FBP), and draw the attention of the community to the connection between dataflow graphs produced by FBP and structural causal models. With expository examples we show how this connection can be leveraged to improve day-to-day tasks in software projects, including fault localisation, business analysis and experimentation.
Prevalence of Code Smells in Reinforcement Learning Projects

Reinforcement Learning (RL) is being increasingly used to learn and adapt application behavior in many domains, including large-scale and safety critical systems, as for example, autonomous driving. With the advent of plug-n-play RL libraries, its applicability has further increased, enabling integration of RL algorithms by users. We note, however, that the majority of such code is not developed by RL engineers, which as a consequence, may lead to poor program quality yielding bugs, suboptimal performance, maintainability, and evolution problems for RL-based projects. In this paper we begin the exploration of this hypothesis, specific to code utilizing RL, analyzing different projects found in the wild, to assess their quality from a software engineering perspective. Our study includes 24 popular RL-based Python projects, analyzed with standard software engineering metrics. Our results, aligned with similar analyses for ML code in general, show that popular and widely reused RL repositories contain many code smells (3.95% of the code base on average), significantly affecting the projects’ maintainability. The most common code smells detected are long method and long method chain, highlighting problems in the definition and interaction of agents. Detected code smells suggest problems in responsibility separation, and the appropriateness of current abstractions for the definition of RL algorithms.
Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment

Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling, self-driving assistance, and source code understanding. With rapid exploration, more and more complex DNN architectures have been proposed along with huge pre-trained model parameters. A common way to use such DNN models in user-friendly devices (e.g., mobile phones) is to perform model compression before deployment. However, recent research has demonstrated that model compression, e.g., model quantization, yields accuracy degradation as well as output disagreements when tested on unseen data. Since the unseen data always include distribution shifts and often appear in the wild, the quality and reliability of models after quantization are not ensured. In this paper, we conduct a comprehensive study to characterize and help users understand the behaviors of quantization models. Our study considers four datasets spanning from image to text, eight DNN architectures including both feed-forward neural networks and recurrent neural networks, and 42 shifted sets with both synthetic and natural distribution shifts. The results reveal that 1) data with distribution shifts lead to more disagreements than without. 2) Quantization-aware training can produce more stable models than standard, adversarial, and Mixup training. 3) Disagreements often have closer top-1 and top-2 output probabilities, and Margin is a better indicator than other uncertainty metrics to distinguish disagreements. 4) Retraining the model with disagreements has limited efficiency in removing disagreements. We release our code and models as a new benchmark for further study of model quantization.
Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems

Computer Vision (CV) is used in a broad range of Cyber-Physical Systems such as surgical and factory floor robots and autonomous vehicles including small Unmanned Aerial Systems (sUAS). It enables machines to perceive the world by detecting and classifying objects of interest, reconstructing 3D scenes, estimating motion, and maneuvering around objects. CV algorithms are developed using diverse machine learning and deep learning frameworks, which are often deployed on limited resource edge devices. As sUAS rely upon an accurate and timely perception of their environment to perform critical tasks, problems related to CV can create hazardous conditions leading to crashes or mission failure. In this paper, we perform a systematic literature review (SLR) of CV-related challenges associated with CV, hardware, and software engineering. We then group the reported challenges into five categories and fourteen sub-challenges and present existing solutions. As current literature focuses primarily on CV and hardware challenges, we close by discussing implications for Software Engineering, drawing examples from a CV-enhanced multi-sUAS system.
The Role of Smartphone towards English Speaking and Listening Skills Enhancement: A Case of the University of Rwanda

This study sought to explore the role of smartphone applications to enhance English speaking and listening skills at the University of Rwanda. The study is a case study that used voluntary response sampling techniques with 20 students and 6 lecturers. Findings revealed that students used various smartphone applications such as YouTube, WhatsApp, Facebook, Tiktok and Instagram for English skills enhancement. Smartphone applications helped students to acquire speaking and listening skills in a variety of ways. Furthermore, it was found that various strategies such as recorded videos, playing games, audio songs and WhatsApp conversations were used to enhance their language skills mastery. The researchers recommend that the university authorities should ensure availability of internet so that students may use their smartphone applications effectively to enhance English skills. Lecturers should make use of smartphone applications too because the method is a powerful tool for effective learning.
Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform

For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practiceƒ For traditional software, ISO25000 and its predecessors have since long time been used to define and measure quality characteristics. Recently, quality models for AI systems, based on ISO25000, have been introduced. This paper applies one such quality model to a real-life case study: a deep learning platform for monitoring wildflowers. The paper presents three realistic scenarios sketching what it means to respectively use, extend and incrementally improve the deep learning platform for wildflower identification and counting. Next, it is shown how the quality model can be used as a structured dictionary to define quality requirements for data, model and software. Future work remains to extend the quality model with metrics, tools and best practices to aid AI engineering practitioners in implementing trustworthy AI systems.
Algorithm Debt: Challenges and Future Paths

Technical Debt (TD) is the implied cost of additional rework caused by choosing easier solutions in favour of shorter release time. It impacts software maintainability and evolvability, manifesting as different types (e.g., Code, Test, Architecture). Algorithm Debt (AD) is a new TD type recently identified as sub-optimal implementations of algorithm logic in scientific and Artificial Intelligence (AI) software. Given its newness, AD and its impact on AI-driven software remains a research gap. This poster aims to motivate reflective discussion on AD in AI software, by summarising findings, discussing its possible impact, and outlining future areas of work.
The Application of Virtual Reality Technology in the New Liberal Arts Environment

Under the background of new liberal arts construction, how to carry out new ideas, combine new technologies and implement new models in college curriculum teaching is the key to promote the major reform smoothly.By sorting out the optimization idea of international economic and trade course "international settlement", combined with the application status of virtual reality technology in enterprise production practice, this paper discusses the integration idea of virtual reality technology and international settlement teaching, and analyzes some problems that should be paid attention to in the application process, which has practical significance for cultivating high-level composite liberal arts talents required by the new era [1].
Design of WeChat Basketball Competition Mini Program and the Application of Sports Education Mode in Colleges and Universities

Objective: To design a WeChat basketball competition small program, including technical action video, basketball competition registration, basketball competition data record, basketball competition results storage and other functions, make basketball teaching more direct and convenient in colleges; Methods: Based on the wechat platform, design and develop small programs themed on basketball contests, and applied in the actual college basketball sports education mode, teachers experience whether the use of small programs as an auxiliary tool is effective and put forward suggestions for modification; Conclusion: This procedure is easy to use, it covers fewer functions, but it has been able to meet the requirements in the teaching of college courses, the teaching effect of the use class is good, received high praise from students and teachers.