Reviewerly/exHarmony huggingface dataset:
Fast One-Stage Human Pose Estimation by Polar Coordinate Representation

The typical bottom-up human pose estimation methods leverage two-stage approaches to detect individual instances and predict keypoints, which leads to high computation costs and low efficiency. In this paper, we present a novel single- stage model via the Polar Coordinate Representation (PCR). In PCR, one human pose is integrated by a root joint, the angle of joint displacement, and the length of joint displacement. To locate the position of a person instance, previous one-stage methods required the creation of an additional root joint confidence map. To further reduce the computation cost of the one-stage method, we directly exploit the output map of the angle in the polar coordinate to replace the root joint confidence map. In our model, the redundancy prediction head of this root joint confidence map is dropped, which decreases the parameters of the network. For inference, our proposed network only needs one simple decoupling process to generate final poses. Our method achieves comparable performance to previous single-stage bottom-up methods, with lower computation cost and higher speed.
Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Consistently the web and online media producing terabytes of images. Furthermore, the picture stockpiling and important recovery is a difficult task. Content Based Image Retrieval (CBIR) is the most common way of recovering pertinent photos regarding query images from the assortment of images. CBIR profoundly relies upon three elements: determination, extraction, and portrayal of elements/features. The proposed review work discusses about these factors in detail. It begins with examining the need for CBIR and its applications. Subsequent sections discuss selecting features such as Color, Texture, Shape, and Descriptors. Feature extraction and their representation are analysed straightaway. Further, the investigation of the most recent papers and their techniques for CBIR is presented. A CBIR has also been applied using Deep Learning Techniques in recent years. An overview of these techniques is presented in this work. The proposed article is an outline of 214 papers covering everything conceivable analysis performed in the field of CBIR.
Mean Maximum Raw Thresholding Algorithm for Detecting Suspected Regions of Interest of Tumors in Breast Magnetic Resonance Imaging

In this study, the regions of interest of the suspected tumor in the breast magnetic resonance imaging (MRI) are detected using a new automatic global thresholding approach. As a key pre-processing stage alongside the tumor segmentation process, image thresholding is one of the most significant image processing techniques. In order to prepare the image for more precise tumor detection in the following stage of segmentation, it is crucial to identify the areas of any suspected tumor in the breast MRI. The proposed Mean Maximum Raw Thresholding approach (MMRT), which uses the sub-window frames concept to automatically seek the best threshold value that separates the image into two classes-suspected tumor locations and the background-considers the tumor intensity features. On 40 test images from the RIDER breast MRI dataset, the approach is implemented and tested. The results are then analyzed and recorded in relation to the dataset's Ground Truths using two assessment strategies: pixel-based assessment (Jaccard = 0.643 and Dice = 0.683 measures), and quality assessment (PSNR = 69.97 and MSE = 0.011 measures). The evaluation findings demonstrate that the suggested approach outperforms five common thresholding methods that were tried on the same dataset.
Artificial intelligence in health care

Artificial intelligence (AI) has evolved from a theoretical concept to techniques that could address real-world health care challenges. As the complexity and amount of health care data continue to increase, AI is expected to have a significant role in medical practice and research in decades to come. In this chapter, we first discuss the history of AI and introduce key concepts in AI studies, including unsupervised learning, supervised learning, reinforcement learning, neural networks, and explainability. In addition, we summarize developments in AI for medical image assessment, mining of electronic health record data, mobile health, public health surveillance, and genomic data interpretation. We further discuss ongoing challenges in the development of medical AI applications, including gaining the trust of users, ensuring that AI does not cause unreasonable harm, and the potential to aggravate health disparities using models derived from historical data. We conclude the chapter by outlining future opportunities for developing reliable AI systems for augmenting health care.
Artificial intelligence in pathology and application to liver disease

Pathology has a critical role in managing patients with liver tumors and disease. It allows for a definitive diagnosis, provides prognostic information, and enables several histologic scores to be routinely performed to guide therapeutic strategies. Pathology is undergoing a major revolution because it is possible to transform conventional glass slides into digital files that can be viewed with a computer or screen. Artificial intelligence automatically processes these files and standardizes the analysis of histologic images. Information that is inaccessible to the human eye can also be extracted. The challenge is to transform research into clinically validated products.
Fast Yolo V7 Based CNN for Video Streaming Sea Ship Recognition and Sea Surveillance

The object detection in the maritime field is becoming more important thanks to the different offers it provides, such as: monitoring, traffic management, coastal control, etc. However, the maritime environment is known for its complexity, which poses difficulties in detecting targets, especially with the classical method. Hence the importance of introducing an intelligence layer to the acquisition data that will support the decision support part and facilitate the detection of an undesirable event. This paper aims to make video surveillance in the maritime domain intelligent through the use of the advantages of machine learning, and more specifically the implementation of the YOLOv7 model that will allow us to provide real-time detection, rigorous precision of the different types of vessels, plus the speed of processing of frames in the configuration used. The experimental results prove that the model YOLOV7 as the latest version of yolo is the model that gives the most efficient results when compared with the other existing models.
The Use of Augmented Reality in the Food Industry

In this chapter, augmented reality (AR) and its use in the food industry are evaluated under important headings. AR applications that aim to improve the customer or user experience and the contents of these applications are explained. The use and effects of these applications have been examined separately in important steps of the food industry, such as food safety, food cooking stage, food education, food promotion, and user/consumer experience. The aim is to deeply understand the use of AR and to show that the power of the technological opportunities provided by AR can be used at every step of the food industry. In addition, the perspectives of the applications developed and considered to be developed in the literature are examined to present the aspects that should be considered when planting seeds for new applications.
Enhancing Learnability with Micro Teachings

Around 20% of the Western world’s population has low digital literacy, which hinders them from participating in today’s digital society. Despite the increasing complexity of systems, the learnability is often neglected. In this pilot study, we investigate the potential of embedding digital literacy training into the user journey to improve first-use learnability and encourage user independence. To achieve this, we created Micro Teachings (MT), which are small just-in-time learning sequences paired with reflection-in-action exercises for reinforcement. In addition, the MT are designed as Micro Worlds representing a subset of the reality to learn in a controlled and secure environment through exploration. For conveying the content we implemented the MT with two different learning styles for comparison: agent-based and observational learning. The pilot study is conducted on the Interactive Tabletop Projection (ITP) called live paper, as part of a larger research project. Results from 11 participants show that users performed better with the observational learning method than with the agent-based approach. Their confidence increased while their fear of the system decreased. In addition, the error rate and the number of help interventions were drastically reduced. These results show that by utilizing an appropriate approach and environment, learnability can be considerably enhanced and also simplify the entry into the digital world for people with low digital literacy, embracing inclusion and equality.
The Real-World Applications

The research relies on several real-world applications since 25 years in a very large spectrum of domains: power systems, subway exploitation, enology with wine-making, computer security, collaborative understanding, car driver support, web site analysis, self-training of car drivers, medical image access, contextualization of platforms of interoperable open-source tools for enterprises, database administration, strategic infrastructure project in transportation, cognition-driven explorer for histopathology for breast cancer grading, process and sharing of medical images and contextualized support on a tablet for a Control and Command system for French military.
What's cooking beyond 2028

In this chapter, we focus on four currently used technologies that are expected to be developed toward maturity in 2028 and will change healthcare paradigms in that process. Specifically, precision medicine, the opportunities afforded by 3D printing of medicines, the availability of social robots, and blockchain technology. Having these technologies in every home are innovations that will significantly affect how we provide pharmaceutical care. The overview provided in this chapter is not meant to be all-conclusive but is meant as an inspiring view of what lies ahead.
FPGA-Based Network-Attached Accelerators – An Environmental Life Cycle Perspective

Abstract Homogeneous computing systems are reaching their limits with the growing demands of current applications. Accelerating compute-intensive applications ensures manageable computing times and boosts energy efficiency, which is an important lever as part of ongoing efforts to tackle global climate change. Field Programmable Gate Array (FPGA) accelerators are well-known for increasing throughput and, in particular, energy efficiency for many applications. FPGA accelerators connected directly to the data center high-speed network are ideal for integration into a heterogeneous data center, avoiding the energy and resource overhead of a carrier system. The standalone Network-attached Accelerators (NAAs) further benefits from low latency and predictable line-rate network throughput, as well as an interoperable communications interface. For selected use cases, we compare a heterogeneous computing cluster extended by NAAs with a homogeneous CPU-based cluster not only in terms of computing performance and energy efficiency, but also considering resource efficiency. For this purpose, we perform a Life Cycle Assessment (LCA) for both systems based on the Key Performance Indicators for Data Center Efficiency (KPI4DCE) indicator set, which takes into account the manufacturing phase in addition to the usage phase. The KPI4DCE tool has been extended to include modeling of NAAs. This allows us to show that NAAs are not only more energy-efficient, but also more resource-efficient for the selected applications, leading to a strong improvement of the environmental impact of the manufacturing phase.
Action. Charge each team member to guard one number

Plan the work, and then work the plan. Chapter 5 focused on clarifying our “ends,” our goals and strategy to the finish line. Maintaining the discipline to complete the goals can be quite challenging, and this is where we must have a clear cadence of accountability. Here we will discuss centralizing all communications into an Adaptive Execution document (AdEx doc), using that document daily, and achieving and guarding one number for each person on your team.
Towards a Science/Education of Late Neoliberal Bodies

This chapter reflects on the evolution of an analysis and practice of science education that makes the relational and material relations between the scientist and their object the heart of science education. The paper develops a framework for a science education grounded in resistance to the social relations of neoliberalism. The paper first explores my evolving thinking on guinea pig pedagogy, i.e., pedagogies that construes the object of science as agential, historical, and political. It expands upon this by considering the nature of clinical labor (e.g., plasma donation) in the lives of my students and the way that such labor provides insight the nature of contemporary science. I then examine how the politics of radicalized medics reveals dichotomized geographies of precarity within neoliberal governance and also the importance of heteroglossic/culturally entangled sciences to survival of the vulnerable. Finally, I examine STEM/science education policy and how it is disconnected from the lives of students. In its place I imagine a pedagogy that emphasizes skills for those living in precarity, focuses on the hegemonic and destructive nature of STEM/science discourses and practices, and finally reimagines science as contested, global, complex, and multicultural.
Review of Recommendation Systems and their Applications in E-learning

In recent years, we are in front of exponential increasing of the learning resources on the Web. It takes a lot of the learner's time and effort to locate training resources that are appropriate for his or her needs. In this framework, Recommendations Systems are crucial to the field of e-learning. They help the learner to find useful training content that is relevant to their learning needs. In this paper we present the recommendation systems as well as their types, the advantages and drawbacks of each type of recommendation, we will also present some works using recommender systems in e-learning and mention their applied techniques, algorithms, dataset used, and the recommended resources.
Blockchain technology: a novel approach to enhance ecology conservation and management

Blockchain technology has shown promise in various industries, but its application in ecology has been limited. This study explores the potential of blockchain in addressing ecological challenges, improving transparency, and promoting sustainable practices. A comprehensive literature review is conducted, followed by a qualitative analysis of relevant case studies. The results demonstrate the effectiveness of blockchain in enhancing data security, traceability, and stakeholder collaboration. The study concludes that blockchain technology has the potential to revolutionize ecological conservation and management, ultimately leading to more sustainable ecosystems.
Embodied PointCloud: Combining Embodied Avatars with Point Clouds to Represent Users in VR Remote Meetings

Avatar realism is crucial for high-quality virtual reality remote meetings. In this work, we introduce Embodied PointCloud, a user representation technique that combines point clouds’ realism with the increased embodiment of 3D humanoid avatars for improved interaction. After we conducted a user study to compare this approach to full-body tracked humanoid avatars, we found that Embodied PointCloud lowered perceived workload but had no significant effect on presence and social presence. We believe that Embodied PointCloud could help personalize user representation while ensuring high self-embodiment levels.
Asymmetric Communication in Virtual Reality: Designing for Presence, Effectiveness, and Enjoyment

This paper investigates the design of multi-user virtual reality (VR) communication and collaboration, focusing on asymmetric VR where one user is immersed in the virtual environment while the other interacts from the external world. Through an exploratory user study (n = 16), we examine how users experience different asymmetric VR communication methods and how these experiences inform the design of effective communication systems. We identify key factors that influence the effectiveness of different styles of asymmetric VR communication and highlight the benefits and limitations of these styles. Our thematic analysis of the participants’ responses and experiences sheds light on the importance of considering communication methods and their impact on users’ experiences in asymmetric virtual environments. Our study provides insights into how communication methods can be designed to enhance presence, social presence, communication effectiveness, and enjoyment. These findings can inform the design and development of more engaging and effective asymmetric communication methods for multi-user collaboration.
Studying Consensus Mechanisms for Blockchain

In the world of computing, Peer-to-Peer and Blockchain systems are gaining ground as they operate without central parties like companies, organizations, or individuals. Despite their popularity, the performance of distributed systems is limited by the consensus mechanism used. There are various algorithms in use, and there is always room for improvement. Some of these protocols are Proof of Work (PoW), Proof of Stake (PoS), and Proof of Reputation (PoR). Hence, it's crucial to compare these consensus algorithms for a better understanding of blockchain execution. Our paper examines various factors that impact blockchain performance, such as algorithms, security, scalability, and energy efficiency. We explore ways for developers to enhance the consensus mechanism by changing the way nodes participate in the blockchain based on factors like work, stake, and reputation. We compare a few selected consensus algorithms based on Energy usage, Scalability, Immutability, Tolerance of adversaries and Throughput.
Risk. Identify risk and frame it as questions

It is time for each function of your Integrated Innovation Team (IIT) to frame the risks that each sees as a valuable question (VQ)—a question that connects to the customer and which can be represented usually as a plot with clear axis titles and units. As mentioned in the first chapter, the process approach to innovation does not rely on a magic breakthrough or quick fixes or cutting corners. Rather, the approach focuses on accumulated wins that allow your entire team to contribute to a better result. In the first chapter, I gave four steps to evaluating risks. Even for experienced teams, these steps are super hard work, and teams often neglect them. Whether it is PhD students or experienced researchers and professionals, I have found this step to be one that is resisted the most, and yet which is so enormously valuable. Frame risks as a set of VQs. Generate multiple hypotheses for each question. Improve your questions by rapid iteration. This will be discussed in the sixth chapter. Estimate paybacks, probabilities, and growth for each hypothesis.
Path Pursuit using Coverage DWA of Two Wheel Drive Robot

This study presents a path pursuit algorithm designed for map coverage. While traditional path planning focuses on generating an optimal path between a robot"s current location and a target point, coverage path planning aims to efficiently cover a designated area without any gaps. Therefore, a path pursuit algorithm is required to minimize path deviation. The coverage path was first calculated based on the map floor plan. Using a dynamic window, we established a new objective function that allowed the robot to minimize path deviation while pursuing the designated coverage path. This was achieved by considering the errors between the paths and the robot"s current location, rather than just focusing on the robot"s current position and the target point. Experimental results showed that our algorithm significantly reduced path deviation by 62% compared to pure pursuit algorithms.
Where Do All Stakeholders Find the Software Product Blueprint?

We present Claritee tool as part of the software development suite relating to the still existing problem of how to integrate the design specifications along the software development lifecycle. The tool aims at answering the stakeholders’ need not just to view UI design artifacts but rather be able to on-going engage with the product blueprint, which reflects the concept that is evolved through whiteboarding. In the case of a change request, it is often crucial to be able to go back to the blueprint and make a collaborative documented conversation and decision over some components. We demonstrate the ability to create and maintain a formal product blueprint deliverable that is highly accessible to all the stakeholders and the whole team.
A New Context-Based Factorization Machines for Context-Aware Recommender Systems

Traditional recommender systems provide recommendations based on two dimensions (item and user), which can impact the quality of recommendation since they only use two entities and neglect other influential factors. Context-aware Recommender systems (CARS) solve this problem by taking in consideration several factors that affect user behavior like contextual factors. In fact, information such as time, location and companion have an influence on users and their choices. Besides item and user information, CARS use extra information (Contextual information) to produce more accurate and personalized recommendations. Factorization Machines (FM) is the most used algorithm in recommendation due to its efficiency, however like every algorithm FM has some drawbacks, one of them is its inability to capture feature interaction strength. In this paper, we proposed a new CARS based on an extension of FM adapted with contextual data and able to capture interaction strength and make the difference between features belonging to different contexts called Context-Based Factorization Machines (CBFM). Experiments show that CBFM improves the accuracy of prediction and realizes competitive performance compared with baselines.