Reviewerly/exHarmony huggingface dataset:
Smart Banknote Functionality: Critical Features and Production Challenges

A smart banknote is a physical banknote on a paper or polymer substrate that can communicate with an electronic network. A smart banknote is denominated and has the physical properties of a traditional banknote in size, feel, appearance, and etc. It is not a rigid, plastic card such as may be used in present credit or debit transactions. The purpose of a smart banknote is to act as a hybrid that can function either as a definitive or an electronic instrument, depending upon the immediate need. Such functionality will allow a smart banknote to act as a transitional device between traditional payment systems and electronic and crypto-based payment systems.
Endometriosis Laparoscopic Image Reconstruction Using PCA and IPCA

Principle component analysis is a “state-of-art” technique for handling medical-based image data. The Laparoscopic images of Endometriosis are in raw format and large volumes and hence the images cannot be used directly for further processing. The Principal Component Analysis (PCA) has been used to handle such a large volume of images without affecting pixel intensity. A algorithm has been proposed using PCA & IPCA to identify the exact components needed to compress the images. The size of the image has been reduced by implementing PCA Eigenvectors and Eigenvalues. The best component value identified through IPCA is 300 where the variance is 95 %.
Online Action Change Detection for Automatic Vision-based Ground Control of Aircraft

Classic action recognition algorithms require the user to pre-select the time window for the action by clipping the video or choosing the initial and final time frames. Recently, new deep learning algorithms have been developed to detect a key action from an untrimmed video. However, they are unsuitable for temporally segmenting continuous action sequences and are too computationally expensive for implementation on autonomous systems. This paper presents a fast and accurate online action change detection framework. Given a streaming RGB video, the algorithm is used to detect any changes in the sequence. If a new action is discovered, the action recognition module will be applied to classify the action. Compared to existing methods, this two-stage approach reduces computation cost by not applying the recognition algorithm on every time window and improves classification accuracy by precisely locating the starting time step of each action. In a simulated Airport environment created using Unreal Engine™, the framework is demonstrated and validated by detecting and recognizing sequential gestures from a ground crew, who sends gesture commands to control the movement of an autonomous aircraft. A hybrid optimal controller is developed to combine the visual information obtained from the framework and prior information, such as airport map and reference lines, to control the airport to safely navigate to a terminal gate.
Forecasting the User Prediction from Weblogs Using Improved IncSpan Algorithm

A weblog is a collection of patterns that appear every time a person visits a website. When the buyer retrieves information from the server, the server will collect the log. The record contains a stream of transactions that will aid in recognizing the individual buyer behavior. The proposed framework can be utilized to identify the buyer behavior. It consists of preprocessing, navigation pattern modeling, and classification to identify the buyer behavior. The preprocessing module includes the three-stage process for identifying the buying behavior. The buyer pattern modeling contains the sequence of the process of website navigation. Here, the classification process has improved IncSpan algorithm; it classifies the recurrent, semi-recurrent, and in-recurrent sequence. The improved IncSpan algorithm utilized previous navigation results; from that, user behavior is analyzed.
Lossless Remote Video Transmission Delay Measurement

With the popularity of 5G networks, network transmission delay is no longer an obstacle to real-time video transmission. Existing video transmission focuses on how to improve the compression ratio of data, while ignoring the delay of video in the transmission process. Long-distance video transmission delay measurement problem due to long transmission distance, high measurement cost, large measurement error, etc., there has been no practical measurement plan. This paper proposes to synchronize time information through GPS, uses perceptual hash matching to find the correspondence between video sequences, and uses the synchronization time information between the receiver itself and the source video sequence to calculate the video transmission delay. The measurement results illustrate the method effectiveness.
Early-Warning of Peri-operative Critical Event Based on Multimodal Information Fusion

The occurrence of perioperative critical adverse events will affect the quality of medical services and threaten the safety of patients. Using scientific methods to assess the risk of critical illness in perioperative period is of great significance to improve the quality of medical service and ensure the safety of patients. However, the diagnosis and treatment data of perioperative patients are multi-source and irregular, and only one physiological information can not accurately reflect the patient's condition. In previous studies, it is found that a variety of physiological information can transmit the information of human health or not, which can be used to evaluate critical illness and physical condition. Therefore, this paper integrates the preoperative clinical structure data, intraoperative vital signs monitoring time series data and intraoperative anesthesia event time series data. Based on deep learning technology, the multi-modal data of patients are embedded and mapped into the same recessive semantic space to realize the real-time tracking and early warning of severe events, reduce postoperative complications and improve the early diagnosis efficiency of critical adverse events. The results showed that the performance of the model based on the multi-modal data was better than that based on the real military data.
Research on the application framework of blockchain technology in digital creative industry

Blockchain which changes productivity and production relations, affects many industries. Using ASMI analysis method, this paper explores the business problems in the digital creative industry and the application of blockchain in solving the corresponding problems. The study found that before the advent of the blockchain, there were three main types of pain points in the digital creative industry: the lack of proof of existence function, the lack of coordination mechanisms among stakeholders, and the inability of the Internet to realize value transferring fully. Blockchain ensures that the information on the chain is immutable and traceable and can realize the confirmation of virtual assets and copyright and the circulation, distribution, and measurement of value. In the future, the expansion from the end of the industrial chain to the upstream and midstream, the formation of various applications with the consortium chain as the underlying protocol, cross-chain interoperability, and the application of non-fungible tokens will become the trend of the integration of blockchain and digital creative industry.
Multi-UAV Reconnaissance Task Assignment based on Wide-Area Search Scenario

As one of the research hotspots of unmanned control systems, unmanned aerial vehicle (UAV) mission allocation technology has received extensive attention from various military powers. The level of this technology directly affects the efficiency of combat missions. In order to improve the efficiency of multi-UAV reconnaissance task allocation and fit the difficult-to-obtain characteristics of modern warfare battlefield intelligence, this paper proposes a multi-UAV reconnaissance task allocation model based on wide-area search scenarios. This model converts the reconnaissance problem into a transport problem with capacity limitations. Based on the discovery of new targets, the optimal solution is obtained through continuous iteration of the model. The simulation results show that the method can handle the reconnaissance missions of multi-UAV in a certain area under the condition of no intelligence or weak intelligence, and has real-time characteristics, which is more suitable for the actual situation of the battlefield.
Cloud Computing Technology in the Construction of Intelligent Campus

Education cloud technology manages a large number of computing and storage resources in a unified way and quickly realizes the development and deployment of campus services. Education cloud platform mainly consists of IAAs cloud resource pool, cloud storage, desktop cloud, etc. The smart campus application of cloud computing is reflected in providing rapid deployment, resource sharing, elastic expansion, and promoting innovation for campus applications, creating a comprehensive teaching cloud information system composed of content, platform, network, and terminal, realizing cloud end collaboration, and providing rich cloud services for students, teachers, and parents.
What Affects Income in Sub-Saharan Africa?

This paper closely examines how selected macroeconomic variables affect income in Sub-Saharan Africa (SSA).The study employs a more recent dataset and uses fixed and random effects models to characterise the nature and direction of impact evidenced from the data.The analysis further incorporates a monetary policy element by introducing money market interest rate and examining its effects.In general, the paper reveals that income in SSA is significantly affected by trade.Additionally, money market interest rate is found to have no major impact on income as may be perceived, suggesting that this aspect of monetary policy has not played a major role in affecting income levels in SSA.The findings of this study can serve as a guide for policy makers within the region when considering policy actions in relation to income.
SSPF: a Simple and Scalable Parameter Free Clustering Method

Centroid-based clustering methods, e.g. K-Means, are widely used due to fast computation and clear interpretation. In this paper, inspired by the convex clustering technique, we propose a simple but very scalable centroid-based approach, named SSPF, that clusters billions of data points within a few minutes. More importantly, it is parameter-free, which means no prior knowledge of K, i.e. the ground-truth number of clusters, is needed. We provide both GPU and CPU versions. Comprehensive experiments are conducted to evaluate both the speed and clustering quality of SSPF. It shows that SSPF is much faster than other parameter-free approaches, and very competitive to the state-of-the-art implementations of parametric methods. We also discuss the limitations and mitigation to provide a full view of this method.
Towards solving the 7-in-a-row game

Our paper explores the game theoretic value of the 7-in-a-row game. We reduce the problem to solving a finite board game, which we target using Proof Number Search. We present a number of heuristic improvements to Proof Number Search and examine their effect within the context of this particular game. Although our paper does not solve the 7-in-a-row game, our experiments indicate that we have made significant progress towards it.
Secure Reversible Data Hiding in Scrambled Pictures by Reserving Room before Encryption

Today reversible information stowing away in encoded pictures by holding room before encryption is a very significant method is use in different utilization of safety. Where information security is essential vital. These instruments are essential use in inelegancy organization. At some point when we manage an information and that information is noticed ob outsider client and that circumstance you need to shroud information specifically component that time we can utilize this reversible information covering up in scrambled pictures by holding room before encryption strategy. In this paper, we propose a novel technique by holding room before encryption with a conventional RDH calculation, and in this manner it is simple for the information hider to reversibly install information in the scrambled picture. The proposed technique can accomplish genuine reversibility, that is, information extraction and picture recuperation are liberated from any blunder. Analyses show that this novel technique can insert more than 10 times as enormous payloads for a similar picture quality as the past techniques, for example, for PSNR dB.
Kidland: An Augmented Reality-based approach for Smart Ordering for Toy Store

Augmented reality (AR) is an iconic topic that can be applied in different domains in modern world technology. With the rapid development of technologies, eCommerce (Online Shopping) has become closer to human life. As a result, AR was started implemented with eCommerce platforms by the developers. With the busy lives and the pandemic situation, people are limited to visiting toy stores while providing a solution. An AR-based virtual toy store is proposed with 3D Toy generation for visualizing selected toys, a Virtual tour for enhancing the remote virtual shopping experience, and an Indoor navigation system visualizing the path within large scale shopping malls are new features of the proposed system. The majority of the existing eCommerce platforms are missing image search features. As a solution, “KidLand” has implemented an image search engine, suggesting add-on-related items and nearest branches using machine learning algorithms. An intelligent chatbot uses a reinforcement learning algorithm and Natural Language Understanding (NLU) to give possible solutions regarding the toy store. As a solution to the language literacy problem, developed a chatbot that can chat both English and Sinhala languages. “Kidland” was developed to provide the users the next level of shopping experience with attractive features of AR technology with marketing and use advanced technologies overcoming the issues of ordinary eCommerce platforms. In Sri Lanka, this system has been identified as a solution for the issues with ordinary shopping platforms.
Fuzzy Cognitive Map Decision Support for Aging in Place for the Elderly

Aging in Place is highly desirable for the elderly, however it is a complex decision for many, due to factors that are related to health, ability, environment, and social support. This paper proposes a soft computing decision support model for aging in place based on Fuzzy Cognitive Maps, taking into consideration 16 factors, some of which are interrelated. Two example cases are presented.
Patient Survival Prediction with Machine Learning Algorithms

In this study, the intensive care unit patient survival is predicted by machine learning algorithms according to the examinations performed in the first 24 hours. The data of intensive care patients collected from approximately two hundred hospitals over a period of one year were used. Algorithms are run in Python environment. Machine learning models were compared with the Cross-Validation method, and the random forest algorithm is used. The model made the prediction with 92,53% accuracy rate.
Search of the Deviation from the Natural Process Using Stepanets Approach for Classification of Functions

Outlier detection is a classic task having many different ways to solve. However, in a number of tasks, it is important to find less pronounced deviations from the natural flow of a certain process. This article proposes to search for such deviations using Stepanets approach for classification of functions describing the corresponding process. It is shown that in some cases this approach allows to reveal better the deviation from standard behavior than a direct comparison of it with current behavior. The proposed approach is illustrated by the example of the analysis of data on the results of football matches in the 2013/14 season in the French Ligue 2.
Document Retrieval Through Cover Density Ranking

Productive and effective content retrieval methods are critical in dealing with the expanding measures of textual information accessible in electronic form. However, information retrieval is an overwhelming job. The paper presents an enhancement over naive information retrieval model i.e., Vector Space Model (VSM), an algebraic model combined with Cover Density Ranking (CDR), leading to a better computational approach to deal with documents with the same rank. Most of the Retrieval techniques depend upon keyword indexing, frequency of keywords or index terms called as term frequency(tf). But unfortunately, term frequency alone can't be enough to catch the contents of the document, resulting in poor retrieval performance since several documents may fall under the same rank. To obtain the most relevant document there is a need in improvising VSM by not just taking the term frequency into consideration but also the positions of the terms in the document.
Focal Triplet Loss for Multi-Object Tracking

Modern multi-object tracking methods tend to focus on similarity metrics for instances. Existing methods adopt the mainstream triplet loss to help deep networks learn instance similarity. However, triplet loss easily ignores samples with abnormal similarity and difficult samples; thus, the networks have limited ability to extract object features for similarity calculation. In this paper, we propose a novel focal triplet loss (FTL) to solve such problems. FTL pays considerable attention to objects of the same ID with low similarity and different IDs with high similarity. In addition, FTL can assign additional reasonable loss weights to difficult samples, making the networks extract more discriminative feature embeddings to calculate instance similarity and improve tracking accuracy. We also propose a new joint detection and embedding model integrating a detector and an embedding model into a single network. Results show that our method achieves state-of-the-art performance, specifically MOTA of 87.28% on the KITTI dataset.
Data-Free Knowledge Distillation with Positive-Unlabeled Learning

In model compression, knowledge distillation is a popular algorithm, which trains a lightweight network (student) by learning the knowledge from a pre-trained complicated network (teacher). It is essential to acquire the training data that the teacher used since the knowledge is obtained by inputting training data to the teacher network. However, the data is often unavailable due to privacy problems or storage costs. Its lead exiting data-driven knowledge distillation methods is unable to apply to the real world. To solve these problems, in this paper, we propose a data-free knowledge distillation method called DFPU, which introduce positive-unlabeled (PU) learning. For training a compact neural network without data, a generator is introduced to generate pseudo data under the supervision of the teacher network. By feeding the generated data into the teacher network and student network, the attention features are extracted for knowledge transfer. The student network is promoted to produce more similar features to the teacher network by PU learning. Without any data, the efficient student network trained by DFPU contains only half parameters and calculations of the teacher network and achieves an accuracy similar to the teacher network.
Software Development Methodologies Evaluation: The Importance of Contextual and Technical Criteria

To meet customers' changing needs, Software Development Methodologies (SDMs) are constantly evolving to match the volatile environment of the recently emerging technologies. SDM selection has always been a challenging task for the organization. Besides, the emergence of complex software requirements, changing requirements, source limitations, and inflexible project planning are often new challenges that the software industry faces. This situation urges the need for more practical and realistic software development methods, paying attention to the allocated budget and resources in projects, and developing high-quality software products. Two essential things that need to be borne in mind in selecting an SDM are technical and contextual criteria. Technical criteria consider SDM's technical suitability and adaptability for the project and organization. Contextual criteria usually focus on the organization and individuals' characteristics. Nevertheless, only a few studies are available to pay attention to these both aspects. This study identifies and prioritizes contextual and technical criteria for SDMs evaluation through a systematic literature review study and a survey. As a result, this study has identified five dimensions for technical criteria comprised of 13 items and four dimensions for contextual criteria, including 16 items. The identified criteria were evaluated and prioritized by 32 SDMs experts via a questionnaire. The results provide valuable insights into the concurrent evaluation of SDMs' contextual and technical aspects, considering their ranks.
Vision-based beatmap extraction in rhythm game toward platform-aware note generation

Recent approaches to deep learning-based music analysis have had significant impact on procedural content generation in music-based games. However, the lack of understanding of the unique features of various platforms and interfaces makes auto-generated content less valuable than manually designed content. Hand-crafted datasets are required, to enhance the quality of content in various platforms, but most rhythm games permit only indirect access to the dataset, as a form of player's experience and its replay video. We develop a vision-based approach to content extraction through video analysis, using a format named beatmap. We cover some common visualized features in well-known rhythm games, and construct a mapping from their content to our beatmap model, using multiple object detection. Our method correctly detects each action button, type, and time, and extracts beatmap representations for our target game.
A Semiotic and Usability Analysis of Diegetic UI: Metro—Last Light

A Game’s narrative is perhaps one of the key components of creating player immersion. As technology advances, game developers increase their toolset for creating increasingly complex game worlds. The UI has a crucial role, providing the player with feedback about the various attributes and mechanics within the game. Some games sought to integrate the traditionally intrusive UI within the game’s narrative and art, by the means of Diegetic UI. The goal of this research paper is to understand how integrating the User Interface into the game’s art and narrative—creating what is called a Diegetic Interface—can increase the feeling of immersion for the player. To identify the processes through which meaning is observed in Diegetic UI, we’ve relied on the Discoursive Semiotics proposed by A. J. Greimas and to assess if these Diegetic Representations affect Usability, we employed Game Usability Heuristics proposed by Desurvire and Wiberg. The methodology proved to yield interesting results regarding the relationships between UI and Narrative as well as the Usability impact derived from such implementation in the game Metro: Last Light.