Reviewerly/exHarmony huggingface dataset:
LAMP: Leveraging Language Prompts for Multi-Person Pose Estimation

Human-centric visual understanding is an important desideratum for effective human-robot interaction. In order to navigate crowded public places, social robots must be able to interpret the activity of the surrounding humans. This paper addresses one key aspect of human-centric visual understanding, multi-person pose estimation. Achieving good performance on multi-person pose estimation in crowded scenes is difficult due to the challenges of occluded joints and instance separation. In order to tackle these challenges and overcome the limitations of image features in representing invisible body parts, we propose a novel prompt-based pose inference strategy called LAMP (Language Assisted Multi-person Pose estimation). By utilizing the text representations generated by a well-trained language model (CLIP), LAMP can facilitate the understanding of poses on the instance and joint levels, and learn more robust visual representations that are less susceptible to occlusion. This paper demonstrates that language-supervised training boosts the performance of single-stage multi-person pose estimation, and both instance-level and joint-level prompts are valuable for training. The code is available at https://github.com/shengnanh20/LAMP.
Long-Distance Gesture Recognition Using Dynamic Neural Networks

Gestures form an important medium of communication between humans and machines. An overwhelming majority of existing gesture recognition methods are tailored to a scenario where humans and machines are located very close to each other. This short-distance assumption does not hold true for several types of interactions, for example gesture-based interactions with a floor cleaning robot or with a drone. Methods made for short-distance recognition are unable to perform well on long-distance recognition due to gestures occupying only a small portion of the input data. Their performance is especially worse in resource constrained settings where they are not able to effectively focus their limited compute on the gesturing subject. We propose a novel, accurate and efficient method for the recognition of gestures from longer distances. It uses a dynamic neural network to select features from gesture-containing spatial regions of the input sensor data for further processing. This helps the network focus on features important for gesture recognition while discarding background features early on, thus making it more compute efficient compared to other techniques. We demonstrate the performance of our method on the LD-ConGR long-distance dataset where it outperforms previous state-of-the-art methods on recognition accuracy and compute efficiency.
Communicating human intent to a robotic companion by multi-type gesture sentences

Human-Robot collaboration in home and industrial workspaces is on the rise. However, the communication between robots and humans is a bottleneck. Although people use a combination of different types of gestures to complement speech, only a few robotic systems utilize gestures for communication. In this paper, we propose a gesture pseudo-language and show how multiple types of gestures can be combined to express human intent to a robot (i.e., expressing both the desired action and its parameters - e.g., pointing to an object and showing that the object should be emptied into a bowl). The demonstrated gestures and the perceived tabletop scene (object poses detected by CosyPose) are processed in real-time) to extract the human's intent. We utilize behavior trees to generate reactive robot behavior that handles various possible states of the world (e.g., a drawer has to be opened before an object is placed into it) and recovers from errors (e.g., when the scene changes). Furthermore, our system enables switching between direct teleoperation of the end-effector and high-level operation using the proposed gesture sentences. The system is evaluated on increasingly complex tasks using a real 7-DoF Franka Emika Panda manipulator. Controlling the robot via action gestures lowered the execution time by up to 60%, compared to direct teleoperation.
Learning-Augmented Model-Based Planning for Visual Exploration

We consider the problem of time-limited robotic exploration in previously unseen environments where exploration is limited by a predefined amount of time. We propose a novel exploration approach using learning-augmented model-based planning. We generate a set of sub goals associated with frontiers on the current map and derive a Bellman Equation for exploration with these subgoals. Visual sensing and advances in semantic mapping of indoor scenes are exploited for training a deep convolutional neural network to estimate properties associated with each frontier: the expected unobserved area beyond the frontier and the expected time steps (discretized actions) required to explore it. The proposed model-based planner is guaranteed to explore the whole scene if time permits. We thoroughly evaluate our approach on a large-scale pseudo-realistic indoor dataset (Matterport3D) with the Habitat simulator. We compare our approach with classical and more recent RL-based exploration methods. Our approach surpasses the greedy strategies by 2.1% and the RL-based exploration methods by 8.4% in terms of coverage.
Real-world data in legal framework of the Russian Federation

The real-world data (RWD) constitute a modern trend in the healthcare system and in the structure of taking decisions. The trend is being actively developed in the Russian Federation. Ethical aspects of these trials and legal regulation of RWD are, however, still unresolved. In this paper, development of the legal field and national regulation of RWD in Russia is discussed during registration, assessment of healthcare technologies, management of patient registries, and development of clinical recommendations. It is shown that the acting regulatory field of the Russian Federation contains no bans on the use of RWD to support regulatory decisions. Nevertheless, the existing legal laws set limited opportunities for practical application and accounting of RWD by the competent authority. In conclusion, recommendations on further development of RWD legal field in Russia are provided.
Validity of Guided Inquiry Based Spermatophyta Sub Materials E-Book as an Additional Learning Resource

This study aims to determine the validity of e-books guided inquiry-based spermatophyta sub-material as an additional learning resource. Subjects according to material experts, design experts layout, an expert on instructional design and student feedback. This type of research is development rese
Validity E-Book Based Biology of Materials of the Nervous System Mind Map as an Additional Learning Resource

The purpose of this study is to determine validity e-book nervous system based material mind map as an additional learning resource for class XI MIPA students. The subjects of this study were a team of material experts, learning design experts and design experts layout. The type of research used
Embracing the virtual: a comprehensive strategic foresight analysis of virtual reality’s impact on tourism marketing

Virtual Reality (VR) is a technology that immerses users in a virtual world, giving them an entirely new experience. Based on past research, experts discovered that VR has more advantages than disadvantages. However, because VR is still in its early phases, little research has been conducted on it as a tourism marketing tool. The aim of this research is to uncover difficulties and drivers in order to forecast the future of this technology. This study employs both qualitative and quantitative methods, as well as future wheel implications. STEEPV analysis will be utilised to identify key drivers of technology, and SPSS was used to examine the data. The online questionnaire was distributed via Chinese social media, and had a 75.94% response rate, with 60 of 79 feedback recorded. The most important drivers of the technology are the rising number of VR users, increased brand recognition, and the ability to provide emotional value. The environment is one of the least discussed drivers. To establish the future implications of Virtual Reality as a Tourism Marketing tool, future forecasts were produced using Future Wheel analysis. According to the impact-uncertainty analysis, the top two drivers were "Tourism marketing in VR will increase brand recognition due to the unique experience" and "Virtual Reality enables users to explore places and contents without physically being there." The forecasts produced utilising future wheel analysis were market leaders for marketing purposes, increases brand recognition, fosters innovation, and greater immersion based on the two leading drivers. These forecasts have a favourable impact on the future of technology. An increase in the period of data collection was suggested as a way to improve this study. This allows for more accurate responses from customers. VR has a potential impact on tourism marketing in the future, which will boost the current tourism sector.
Exploring Cybercrime: Unveiling its influence on Today's Youth

Cybercrime it’s not be a stranger word for the today’s world. Cybercrime is a type of crime that committed on internet and by a computer lot his papers are write for describe the cyber crime and study the how Today’s youth are trapped in this web. Cybercrime is not only committed by the young; it is done by people of all ages. Sometimes, a person may not even know that they are engaging in cybercrime.
Introducing Spring Security

In this chapter, you learn what Spring Security is and how to use it to address security concerns about your application.
Privacy Protection Optimization for Federated Software Defect Prediction via Benchmark Analysis

&lt;p&gt;Federated learning is a privacy-preserving machine learning technique that coordinates multi-participant co-modeling. It can alleviate the privacy issues of software defect prediction, which is an important technical way to ensure software quality. In this work, we implement Federated Software Defect Prediction (FedSDP) and optimize its privacy issues while guaranteeing performance. We first construct a new benchmark to study the performance and privacy of Federated Software defect prediction. The benchmark consists of (1) 12 NASA software defect datasets, which are all real software defect datasets from different projects in different domains, (2) Horizontal federated learning scenarios, and (3) the Federated Software Defect Prediction algorithm (FedSDP). Benchmark analysis shows that FedSDP provides additional privacy protection and security with guaranteed model performance compared to local training. It also reveals that FedSDP introduces a large amount of model parameter computation and exchange during the training process. There are model user threats and attack challenges from unreliable participants. To provide more reliable privacy protection without losing prediction performance we proposed optimization methods that use homomorphic encryption model parameters to resist honest but curious participants. Experimental results show that our approach achieves more reliable privacy protection with excellent performance on all datasets.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Enforcement Against Ponzi Scheme, The Fraudulent Method to Generate Easy Money in Malaysia

Ponzi schemes are a type of investment fraud that offers investors great rates of return with no risk. Although various measures have been taken by law enforcement agencies as well as financial regulators to prevent this kind of problem, many people are still deceived by this kind of scheme. In Malaysia, there is no specific law mentioning Ponzi scheme. However, relevant laws such as the Penal Code, Financial Services Act 2013 and Direct Sales and Anti–Pyramid Scheme Act 1993 are some applicable laws that can be used to combat the Ponzi scheme. Hence, this article aims to examine the enforcement measures in Malaysia concerning illicit investment schemes and recommend various methods used by the United States and Australia for comparative purposes. Preventive measures and existing regulations used by the United States and Australia are also highlighted to compare the effectiveness of current enforcement done by the authorities in Malaysia. Findings show that despite Malaysia having a few laws in place to tackle this fraudulent scam, they are far from perfect. Therefore, more comprehensive regulations and strategies regarding this scheme are needed since it would assist the relevant authorities in preventing similar fraud. In conclusion, the Malaysian government needs to adopt proper regulation, education, and enforcement to reduce or prevent the number of Ponzi scheme cases in Malaysia. If left unaddressed, the situation will worsen, negatively affecting investors, creditors, and the public.
The moderating effect of the use of virtual reality technologies in the branding of the cultural tourism sector: an analysis from the brand heritage

One of the main concerns of many countries is the maintenance of their cultural expressions over time. Since these expressions allow for defining the most deeply rooted characteristics of the identity of both nations and people in this way, the purpose of this research is to analyze the moderating effect of the use of virtual reality technologies in the relationship between the different categories of cultural branding and the brand heritage in the cultural tourism sector of a country. To achieve this purpose, using the technique of structural equations allows for finding both direct and indirect relationships between the factors that affect the brand heritage of a country. The sample considered the perceptions of 460 individuals who frequently attend cultural events and who have used event advertising with virtual reality. Within the different results, it was found that there is a direct and positive moderation effect in the relationships between the different cultural branding categories and the brand heritage. On the other hand, while the social and personal brand identities have a positive effect on the brand heritage, the branding equity and brand awe not significant in traditional channels, unlike the virtual reality.
Analisis Potensi Energi Angin sebagai PLTB di Pantai Watu Ulo Jember Menggunakan data BMKG

This research aims to analyze the potential of wind energy as a resource for Wind Turbine Power Plants (PLTB) in the coastal area of Watu Ulo Beach, Jember. The research adopts a literature review method, utilizing data provided by the Meteorology, Climatology, and Geophysics Agency (BMKG). BMKG data serves as the primary source for this analysis, providing a robust foundation to evaluate the potential of PLTB. The study also focuses on the availability of state-of-the-art technology to optimize the utilization of wind energy, assessing its suitability for the region's characteristics. The research findings are expected to provide a comprehensive overview of the PLTB potential in Watu Ulo Beach, Jember, serving as a basis for the development of sustainable energy infrastructure in coastal areas. The conclusions drawn from this literature review are anticipated to serve as a reference for further research and the implementation of sustainable PLTB projects in the region.
OP135 Machine Learning And Cancer Registry: Evaluation Of The Effectiveness Of Case Coding

Introduction Machine learning (ML) algorithms are computational procedures that use pattern recognition and inference by learning from previously categorized documents to predict the category to which a new document belongs. The role of machine learning within cancer registries remains unclear given the lack of in-depth testing and guidance from health technology assessment (HTA) agencies. We evaluated the effectiveness of coding new cases through machine learning at the Integrated Cancer Registry. Methods The Integrated Cancer Registry covers the eastern area of Sicily in Italy, which has an annual average incidence of about 10,000 cases of malignant neoplasm. Potential new cancer cases were retrieved from pathology services and processed by pathologists who confirmed the neoplastic nature of supposed cases and specified the morphological type and location of the tumors. The current method involves identification by reading the free-text report when International Classification Diseases for Oncology information was not provided. We used the new Microsoft ML.Net Library, a framework developed in response to the challenge of facilitating machine learning pipeline utilization in large software applications. A total of 1,050,952 free-text pathology reports published from 2003 to 2018 were selected separately from all Sicilian pathology services and uploaded to machine learning software that explored eight binary classification algorithms. Results We evaluated each algorithm’s performance by calculating metrics (the number of true positives, true negatives, false positives, and false negatives) from the classification procedure applied to the test dataset. The metrics used were accuracy, F1 score, and area under the receiver operating characteristic curve. With a test set of around 210,000 text diagnoses, each algorithm reached an F1 score of up to 95 percent. Conclusions Machine learning algorithms capture relevant information about tumors from free-text pathology reports, optimizing the process and reducing waste. With the help of machine learning systems, cancer registries can provide more timely data for research and evaluation of all types of new cancer technologies (drugs, devices, radiology and radiotherapy equipment, diagnostic devices, robotic surgery, and vaccines).
The Connotation Evolution and Enhancement Strategies of Digital Leadership in China's Universities in the Context of Digital Transformation

Global digital transformation is affecting all walks of life in a subtle way, and higher education is among them. Digitalization has undoubtedly become one of the core issues in school governance decision-making, and the digital leadership of college leaders is the key to promote the digital transformation of school education to adapt to the digital wave. However, considering the theoretical level, practical level and individual level, it is found that the level of digital leadership of university leaders is weak and urgent changes need to be made. Based on the evolution of the connotation of the dual theories of informatization leadership and digital leadership, the study traces the origins of digital leadership at home and abroad through the literature research method, and combs through the essential differences between educational informatization leadership and digital leadership from four aspects: leadership concept, application scope, data value, and competence requirements, so as to put forward the strategy of improving digital leadership for college leaders: The first is to transform the cognition of digital technology and improve the inductive reflection power of digital application; the second is to strengthen the cultivation of digital planning power and formulate the standard of digital leadership; the third is to cultivate the power of data decision-making and analysis, and to call for the formation of school data team. In order to provide certain reference for the reform and construction of digital team of leaders in China's colleges and universities in the new era.
Applying Scrum in A Game Development Life Cycle For Small Scale Game Project

This research aims to develop a GDLC game development life cycle that focuses on the core stages of software development process. This GDLC proposal consists of 4 stages, namely Pre-Production, Production, Testing, and Release. This proposed GDLC is expected to provide adaptive but consistent guidance for game project design. These conditions are suitable for small-scale game development teams, with small project scales, and short work periods (under 1 year).&#x0D; Pre-Production functions as the initial stage for creating the basis game design for the project linearly. Production, which is carried out using the Scrum approach, is the stage of video game development based on that previously made design. The Testing phase ensures the quality of the game through overall game testing. Lastly, the Release phase includes completing documentation for post-release planning.&#x0D; This GDLC was designed by summarizing the core stages of various existing GDLCs and SDLCs related to multimedia software development. Scrum is implemented per stage, with iteration loops starting and finishing within that stage. However, in general, this GDLC is linear. This GDLC is validated with a case study of an actual video game development to ensire its efficiency in helping game developers.
Long-Term and Short-Term Perception in Knowledge Tracing

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called “Long-term and Short-term perception in knowledge tracing (LSKT)”, which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model.
Conceptual Model of Citizen Science with Machine Learning to Increase The Effectiveness of Land Transportation of Urban Communities

Land transportation systems in big cities play an important role in facilitating the mobility of city residents. Demographic changes demand changes in the land transportation system to suit the needs of urban communities. Urban communities, as users of land transportation, have the potential to contribute knowledge to the creation of a land transportation system that is in accordance with the demographic changes of urban communities. Problems arise when the transportation system cannot respond quickly to changes in the demographics of urban society. On the other hand, citizen science is the concept of involving citizens in a scientific project for a specific purpose, so that citizens can be involved in developing land transportation systems in big cities. Artificial intelligence, especially machine learning, is used in transportation systems with citizen science to process data collected from citizen science projects and other sources that will produce alternative decision-making for land transportation systems in big cities. Thus, the purpose of this research is to create an integration model between citizen science and machine learning to increase the effectiveness of land transportation systems in big cities. The research method uses a qualitative approach through a literature review of previous research on citizen science, transportation systems, and machine learning.