Manual Generation Research Articles

Surveying More Than Two Decades of Music Information Retrieval Research on Playlists

In this article, we present an extensive survey of music information retrieval (MIR) research into music playlists. Our survey spans more than 20 years, and includes around 300 papers about playlists, with over 70 supporting sources. It is the first survey that is self-contained in the sense that it combines all the different MIR research into playlists. It embraces topics such as algorithms for automatic generation, for automatic continuation, for assisting with manual generation, for tagging and for captioning. It looks at manually constructed playlists, both those that are constructed for and by individuals and those constructed in collaboration with others. It covers ground-breaking research into enhancing playlists by cross-fading consecutive songs and by interleaving consecutive songs with speech, similar to what happens on a radio show. Most significantly, it is the first survey that can fully incorporate the paradigm shift that has taken place in the way people consume recorded music: the shift from physical media to music streaming. This has wrought profound changes in the size of music collections available to listeners and thus the algorithms that support the construction, curation and presentation of playlists and the methods adopted by users when they also construct, curate and listen to playlists.

Structural Synthesis of Platform Type Manipulators via Connection Based Adjacency Matrix: Topology Generation

Abstract This paper focuses on the design procedures of Alizade's direct and Gezgin's inverse structural synthesis methodologies for platform type manipulators. These task based synthesis procedures includes versatile methodologies for any designer to generate various platform type manipulators with respect to given constraints via simple universal mobility formulation. However they rely on manual generation of mobility independent coarse topological structures, where it is nearly impossible to generate all possible results. In light of this, current study introduces a unique connection based adjacency matrix (CAM) for topology representation, in which legs connected to the fixed ground and platform connections (hinges and branches) are treated separately. The main objective of CAM is to ensure an efficient transition from manual topology generation to automated methods by providing compatibility with software algorithms to describe topologies. Throughout the paper CAM based automated topology generation algorithm that reveals all possible unique coarse topologies with respect to given design constraints is also presented along with examples. Introduced algorithm both allows investigation of isomorphism and eliminates necessity of inversion.

Demonstration of Self-Assembled Cell Sheet Culture and Manual Generation of a 3D Tendon/Ligament-Like Organoid by using Human Dermal Fibroblasts.

Tendons and ligaments (T/L) are strong hierarchically organized structures unitingthemusculoskeletal system. These tissues have a strictly arranged collagen typeI-rich extracellular matrix (ECM) and T/L-lineage cells mainly positioned in parallel rows. After injury, T/L require a long time for rehabilitation with high failure risk and often unsatisfactory repair outcomes. Despite recent advancements in T/L biology research, one of the remaining challenges is that the T/L field still lacks a standardized differentiation protocol that is able to recapitulate T/L formation process in vitro. For example, bone and fat differentiation of mesenchymal precursor cells require just standard two-dimensional (2D) cell culture and the addition of specific stimulation media. For differentiation to cartilage, three-dimensional (3D) pellet culture and supplementation of TGFß is necessary. However, cell differentiation to tendon needs a very orderly 3D culture model, which ideally should also be subjectable to dynamic mechanical stimulation. We have established a 3-step (expansion, stimulation, and maturation) organoid model to form a 3D rod-like structure out of a self-assembled cell sheet, which delivers a natural microenvironment with its own ECM, autocrine, and paracrine factors. These rod-like organoids have a multi-layered cellular architecture within rich ECM and can be handled quite easily for exposure to static mechanical strain. Here, we demonstrated the 3-step protocol by using commercially available dermal fibroblasts. We could show that this cell type forms robust and ECM-abundant organoids. The described procedure can be further optimized in terms of culture media and optimized toward dynamic axial mechanical stimulation. In the same way, alternative cell sources can be tested for their potential to form T/L organoids and thus undergo T/L differentiation. In sum, the established 3D T/L organoid approach can be used as a model for tendon basic research and even for scaffold-free T/L engineering.

From Data to Action: A Graph-Based Approach for Decision Support in Civil Protection Operations Planning

In the face of increasing frequency and severity of crises such as natural disasters, pandemics, and geopolitical conflicts, civil protection organizations are crucial for recovery and support for affected populations. However, the efficiency and effectiveness of these organizations in crisis response are often hindered by the manual generation of operation plans, characterized by cognitive overload and limited analytic overview. Existing systems focus on detecting crises or post-crisis analysis, overlooking proactive planning. We present GRETA, a graph-based operational planning approach utilizing semantic historical data for better decision support. GRETA uses Operational Scenario Patterns to model operations, mapping them onto a knowledge graph in JSON-LD format, thus creating a structured representation of past data to improve future crisis response planning. We tested GRETA with Germany's Federal Agency for Technical Relief, analyzing over 157,450 historic operations from 2012-2022. Results show GRETA enhances plan efficiency, accuracy, and comprehensiveness, aiding especially inexperienced planners.

Practitioners' expectations on automated release note generation techniques

AbstractThe software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.

Bukhara from the history of the events of the organization of the air fleet of modern new technology and the introduction of aircraft

In 1920-1924, the activities carried out in Bukhara regarding the establishment of the air fleet and the establishment of airplane traffic were covered through the analysis of primary sources and literature. The discovery of airplanes as a means of transport and the beginning of their use is related to the history of countries in Europe such as France and Germany. In 1924, a total of 1,000 passengers, 200 kg of mail, and 5 tons of cargo were transported by air in our country. The history of the establishment of the air fleet and the arrival of the first airplanes in Turkestan dates back to the first decade of the 20th century. On March 28-29, 1912, the pilot Sedov flew over Tashkent in his plane, and on March 29, 4 women and 3 men flew over Tashkent. The guests who visited Tashkent also flew in an airplane and watched the city view from above. The article serves to improve state programs aimed at creating a new generation of history textbooks and training manuals, writing fundamental works, organizing special courses, and implementing socio-economic and cultural innovations.

Recognition of Hand Activities for Automatic Generation of Assembly Instructions

The manual generation of instructions for assembly processes requires time and effort. This is especially true for assembly processes that are characterized by a high variance of products at low quantities, as they are typically found in small and medium-sized enterprises (SMEs). Consequently, in SMEs, expert knowledge about the correct assembly of products is often only implicitly available among few experienced workers. Due to demographic changes, capturing this implicit knowledge becomes increasingly relevant to prevent expertise loss. A promising approach to reduce the generation effort is the automatic generation of assembly instructions based on process recordings. In prior work, the authors presented a concept of a system for automated instruction generation. The system processes video recordings of assembly processes with several independent modules for object, hand, and activity recognition and converts the results into assembly instructions. Among the individual processing modules, the hand activity recognition plays a special role, since the recognized activities form an important basis for the final assembly instruction. In this work, the utilization of Graph Convolutional Networks (GCNs) for the recognition of basic hand activities, such as rotating, is presented. Since the system for generating assembly instructions is intended to operate on arbitrary manual assembly use cases, the authors investigate the transferability of the proposed approach by training GCN models on the publicly available Assembly101 dataset and own recordings. The trained models are evaluated on respective test sets, mutually, and on a real-life example to assess the applicability of the proposed approach in practice. The identified challenges for reliable activity detection are discussed and offer important insights for future work.

Automatic Generation of Assembly Instructions by Analyzing Process Recordings – A Concept Overview

The manual generation of instructions for complex assembly processes requires time and effort. Therefore, many companies refrain from documenting assembly processes and rely on the tacit expert knowledge of their employees. Due to demographic changes, the capturing of this implicit knowledge becomes increasingly relevant to prevent expertise loss. In this paper, we present a data acquisition and processing concept for the automated generation of assembly instructions. First, the assembly process is recorded using cameras. Then, the recordings are analysed combining multiple machine vision applications for object and activity recognition. Finally, the results are merged and displayed as digital assembly instructions.

A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring

The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments.

DRC-SG 2.0: Efficient Design Rule Checking Script Generation via Key Information Extraction

Design Rule Checking (DRC) is a critical step in integrated circuit design. DRC requires formatted scripts as the input to design rule checkers. However, these scripts are manually generated in the foundry, which is tedious and error prone for generation of thousands of rules in advanced technology nodes. To mitigate this issue, we propose the first DRC script generation framework, leveraging a deep learning-based key information extractor to automatically identify essential arguments from rules and a script translator to organize the extracted arguments into executable DRC scripts. We further enhance the performance of the extractor with three specific design rule generation techniques and a multi-task learning-based rule classification module. Experimental results demonstrate that the framework can generate a single rule script in 5.46 ms on average, with the extractor achieving 91.1% precision and 91.8% recall on the key information extraction. Compared with the manual generation, our framework can significantly reduce the turnaround time and speed up process design closure.

Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data

Fast assessment of the composition of amyloid fibril samples from cryo-EM data poses a serious challenge to existing image analysis tools. We develop a method for automated segmentation of single fibrils requiring only little user input during the training process. This is achieved by combining a binary segmentation based on a convolutional neural network with preprocessing steps to allow for easy manual generation of training data. Subsequent skeletonization turns the binary segmentation into a single-object segmentation. Then, we compute properties of shape and texture of each segmented fibril, including an estimation of the fibril width. We discuss the composition of the sample based on the distributions of these computed properties and outline how a classification of fibril morphologies might be performed using these properties.

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, since the sea surface is constantly changing, commonly used photogrammetric methods fail when applied to UAV images captured over water areas. In this work, we evaluate the applicability of a five-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters such as chlorophyll-a (Chl-a) concentration and total suspended solids (TSS), including a new Python workflow for the manual generation of an orthomosaic in aquatic areas exclusively based on the sensor’s metadata. We show water-quality details in two different sites along the Maltese coastline on the centimetre-scale, improving the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m3 and 10 to 20 mg/m3, respectively. Spectral comparisons were also carried out along with some statistics calculations such as RMSE, MAE, or bias in order to validate the obtained results.

Generation of training data for named entity recognition of artworks

As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework.

A Review of Automatic Question Generation in Teaching Programming

Computer programming is a complex field that requires rigorous practice in programming code writing and learning skills, which can be one of the critical challenges in learning and teaching programming. The complicated nature of computer programming requires an instructor to manage its learning resources and diligently generate programming-related questions for students that need conceptual programming and procedural programming rules. In this regard, automatic question generation techniques help teachers carefully align their learning objectives with the question designs in terms of relevancy and complexity. This also helps in reducing the costs linked with the manual generation of questions and fulfills the need of supplying new questions through automatic question techniques. This paper presents a theoretical review of automatic question generation (AQG) techniques, particularly related to computer programming languages from the year 2017 till 2022. A total of 18 papers are included in this study. one of the goals is to analyze and compare the state of the field in question generation before COVID-19 and after the COVID-19 period, and to summarize the challenges and future directions in the field. In congruence to previous studies, there is little focus given in the existing literature on generating questions related to learning programming languages through different techniques. Our findings show that there is a need to further enhance experimental studies in implementing automatic question generation especially in the field of programming. Also, there is a need to implement an authoring tool that can demonstrate designing more practical evaluation metrics for students.

Model-based automatic generation of digital twin models for the simulation of reconfigurable manufacturing systems for timber construction

Reconfigurable manufacturing systems adapt to changing requirements and therefore offer a solution to the challenges associated with increasing product variety and shorter product life cycles. This is especially helpful in the prefabrication in timber construction, as components and requirements vary widely between projects. However, reconfigurations must be quick to keep downtimes short. The planning of reconfigurations is a complex problem where digital twins and simulation models of the manufacturing systems are necessary for simulation and validation. In our timber prefabrication use case, building components and requirements change from project to project, and the manufacturing system needs to reconfigure frequently. The building components and the configuration of the manufacturing system are designed simultaneously during a co-design process to ensure the integrity of the manufacturing process in a changing manufacturing environment. Hence, this may require many iterations of the planning process. Each iteration needs validation through a simulation. However, the manual generation of simulation models is time-consuming and error-prone and thus poses an obstacle for the digital planning phase. This paper addresses this issue and presents a model-based approach to generate simulation models for reconfigurable manufacturing systems. Our system and its components are modeled in automation markup language as this domain-specific language allows us to model not only kinematics and geometry but also behavior, skills, and interfaces to check the compatibility of components. We apply the method to our use case with the IntCDC wood prefabrication system. The proposed method builds a component graph for a configuration of the manufacturing system and generates the required packages for the simulation in Gazebo and ROS. To build simulation models automatically, we apply model-to-model and model-to-text transformation techniques. The proposed method is suitable for integration in the digital co-design workflow of IntCDC and allows fast iterations with continuous simulations of machine configurations throughout the planning process.

CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator

For safe autonomous driving, deep neural network (DNN)-based perception systems play essential roles, where a vast amount of driving images should be manually collected and labeled with ground truth (GT) for training and validation purposes. After observing the manual GT generation’s high cost and unavoidable human errors, this study presents an open-source automatic GT generation tool, CarFree, based on the Carla autonomous driving simulator. By that, we aim to democratize the daunting task of (in particular) object detection dataset generation, which was only possible by big companies or institutes due to its high cost. CarFree comprises (i) a data extraction client that automatically collects relevant information from the Carla simulator’s server and (ii) a post-processing software that produces precise 2D bounding boxes of vehicles and pedestrians on the gathered driving images. Our evaluation results show that CarFree can generate a considerable amount of realistic driving images along with their GTs in a reasonable time. Moreover, using the synthesized training images with artificially made unusual weather and lighting conditions, which are difficult to obtain in real-world driving scenarios, CarFree significantly improves the object detection accuracy in the real world, particularly in the case of harsh environments. With CarFree, we expect its users to generate a variety of object detection datasets in hassle-free ways.

Poultry Farm Management Information System (A Case Study of Biyinzika Poultry International Limited)

This report discusses a Poultry Farm Management Information System of Biyinzika Poultry Farm International Limited. The software is sectioned into the sales management section, Purchase management section, Product management section. This system was designed to overcome the problems identified with the current Poultry Farm Management information system of Biyinzika Poultry Farm International Limited. The Problems with the current system include: lack of information sharing in real-time, manual report generation on sales, purchases and the products, slow in information delivery especially in the determination of stock levels after transaction processes, a lot of time consumption, increasing the company costs, and lack of effective data recovery in case of any disaster such as fire outbreak, flood, damages among others. Literature relating to the Poultry Information Management Systems were reviewed. The legacy system used in the company was also studied in more details. With this, more requirements for the Poultry Farm Management Information System were obtained and the system was designed and implemented. The interfaces for the new system were implemented using HTML, Bootstrap and Java Script. MYSQL was also used for implementing the system database while PHP was used to create interactivity with the database. After the implementation, the new system was then tested and validated. When developing the system, the focus was on making the whole process of Information management in the poultry farm faster, more convenient and efficient for the company. This system automates the current poultry farm management information system in the organization.

Towards Automated Choreographing of Web Services Using Planning

For Web service composition, choreography has recently received great attention and demonstrated a few key advantages over orchestration such as distributed control, fairness, data efficiency, and scalability. Automated design of choreography plans, especially distributed plans for multiple roles, is more complex and has not been studied before. Existing work requires manual generation assisted by model checking. In this paper, we propose a novel planning-based approach that can automatically convert a given composition task to a distributed choreography specification. Although planning has been used for orchestration, it is difficult to use planning for choreography, as it involves decentralized control, concurrent workflows, and contingency. We propose a few novel techniques, including compilation of contingencies, dependency graph analysis, and communication control, to handle these characteristics using planning. We theoretically show the correctness of this approach and empirically evaluate its practicability.

Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

Towards plug-and-play visual graph query interfaces

Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.