Reviewerly/exHarmony huggingface dataset:
MASAL: Bridging Between Two Cultures Through Storytelling with an Interactive E-textile Toy

Stories have an important role in children’s cognitive and emotional development, enhancing their imagination and helping them to make sense of real-life situations. Today, by embedding digital technologies into physical spaces and objects, the storytelling experience can be enhanced by embracing interactive characteristics in order to support children’s imagination and give rise to open-ended play experiences. Masal is an interactive storytelling stuffed toy constructed with e-textiles and an open-source technology for children to co-create stories. It aims at fostering creative thinking and empathy towards a different culture through a collaborative construction of a story. This paper addresses the design and prototyping process of Masal and the storytelling sessions done with three groups of participants, varying from children (3–8 years) to adults. It represents the qualitative results obtained from these sessions reflecting on how an interactive soft toy can enhance children’s experience and imagination in storytelling, and above all can create a bridge between two cultures through co-creation of a story.
Space-Warp with Depth Propagation in XR Applications

In this paper, an image guided depth point extraction and depth map reconstruction approach is presented for generating high quality depth maps used for Space-Warp functions on XR devices. The approach can be divided into two parts. The first part is for adaptive depth point extraction to obtain sparse depth points which can efficiently represent the original depth map which runs on device side and the second part is for depth map recovery with the extracted depth points and Space-Warp with the recovered depth map which runs on HMD side. In adaptive depth point extraction, feature points, edges, object contours are extracted as a candidate point set which represents fast depth changes. A criterion is defined by depth map, color image, and spatial information to compute confidence of the candidate points. They are integrated together with the confidence map to build a depth point set. In depth map recovery, an image guided depth map reconstruction from the sparse depth points with depth propagation is presented. During depth map reconstruction, it keeps the sparse points unchanged and propagates the depths in the neighborhood points to the considered points guided by the color image and original depth map information. The reconstructed depth map is used for Space-Warp to create a new frame based on the current frame and a predicted head pose for the new frame. The algorithm is applied to XR systems and applications. The results demonstrate the advantages of the proposed approach. We compare results created by our algorithm and generally used approaches including bilinear interpolation and multilevel B-Spline interpolation. The comparison shows that our algorithm can create high-quality new frames with clear object boundaries and less artifacts.
Automatic Report Generation based on Multi-modal and Multi-view Model for Fundus Images

Automatic generation of medical reports has gained increasing research interests, as the significant potential to assist physicians in decision-making. Medical reports generated by most existing methods are often inaccurate and inconsistent with clinical practice because these methods ignored multiple views and only focused on a single modal. Therefore, we introduced multi-view and multi-modal images that contain more valuable information into this task to generate more accurate and reliable diagnostic reports, the first attempt in this domain. In this paper, we propose a multi-task method to generate diagnostic reports for multi-view and multi-modal fundus images, which can simultaneously predict retinal-related diseases and diagnostic descriptions. Moreover, a multi-view and multi-modal features fusion module (MVMFF) is designed to fuse visual features extracted from different views and modalities, where channel attention mechanism (CAM) and a 3D convolution module are utilized to improve accuracy. Extensive experiments conducted on an internal dataset demonstrate that our method can generate accurate and reliable diagnostic reports that are clinically relevant.
Hospital Readmission Prediction Using Semantic Relations Between Medical Codes

Unexpected hospital readmissions are problematic to both hospitals and patients. Prediction of patients’ readmission becomes an important task. Recurrent neural networks (RNN) and the attention mechanisms have been proposed to learn temporal relationships between patient’ admissions for readmission prediction. Existing works demonstrate that incorporating medical ontologies can be beneficial to prediction tasks. However, it ignores the importance of semantic information of medical codes which can be found in the codes’ descriptions. Therefore, we propose a model called Code Description Attention Model (CDAM), which adopts codes’ descriptions into readmission prediction model via RNN and the attention mechanisms to explore the semantic information about medical codes. Experimental results show that CDAM improves not only the performance of readmission prediction but also the quality of codes’ embeddings.
Detection of Face Mask During Pandemic of Covid-19

The global pandemic caused due to COVID-19 has badly affected the entire world in all the different sectors. With the increasing number of cases of coronavirus, it becomes very necessary for the people to wear a mask, maintain social distancing, and maintain proper sanitization of themselves as well as their surroundings. However, some people are not serious about wearing a mask. Thus, it is necessary to develop a system that can detect the people violating this rule of not wearing a mask. Our system gives provision to detect the people who have not worn the mask appropriately or have not at all worn the mask. Face detection is one of the major problems, to overcome this problem various algorithms are being developed using different architectures. The convolutional architecture has made it possible. Our motive is to design a binary classifier that can detect any face in front of the frame and produce accurate output. Our model has shown very good results in detecting the faces without a mask as well as it is also able to detect the multiple facial mask images in a single frame.
Target-dependent Event Detection: A New Task to Event Extraction from News

Event extraction aims to detect events and extract event arguments. However, various events are not only too nuanced and complex to distinguish, but also involve multiple entities in the real-world scenario, especially in the financial field. This brings a great challenge to the current event extraction. To address these problems, previous event-centric methods detect events first and then extract arguments. Due to the diversity and complexity of events, event detection has a low performance, which is unfit for the huge amount of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from event-centric to target-centric view. In this paper, we propose a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extraction first and dozens of event types detection, that can be applied to massive corpora. Experimental results on a real-world Chinese financial dataset demonstrate that our model outperforms previous methods, especially in complex scenarios.
Voice Controlled Augmented Reality For Real Estate

As technology advances, augmented reality is becoming more prevalent in every business. The most frequent usage of AR is to project real things onto the user, which is usually done via an image target. In the real estate industry, no one can deny AR's capacity to improve the buying and selling experience. AR can help real estate developers expand their marketing methods and give clients a more memorable home experience. It has already been used in apps for house design and land hunting, and the industry's strike proves that Augmented Reality has a lot more to give. Everyone nowadays has a smartphone or tablet with which to access the internet, and the technology utilized in these devices is improving every day. As a result, using AR tools in everyday life and having a comfortable AR experience on mobile devices is becoming more convenient. The amount of time spent touring each site with consumers and not having appropriate resources to impress them is a common challenge that real estate developers confront. Augmented reality software is frequently the seal of approval that realtors receive in order to grow their business and overcome these obstacles. This paper proposes a method for projecting a home onto an image target and allowing the user to explore the interior of the house. Voice controllers incorporated into AR can control the interior.
The number of axioms

We derive results concerning the least possible number of distinct axioms in proofs in classical and intuitionistic sequent calculi and other related systems. In particular, we show that there is no recursive bound on the least possible number of distinct axioms in a proof of a provable sequent in terms of the sequent's length, and that there is no elementary bound on the least possible number of axioms in a cut-free proof of a provable sequent in terms of the least possible length of an arbitrary proof thereof, strengthening a classical result due to Orevkov and Statman.
Understanding Structural, Governance &amp;amp; Regulatory Incentives for Improved Utility Performance: The Case of Uganda and Its Umeme Ltd

The power sectors in most African countries face an enduring problem of poor utility performance - electricity utilities have failed to deliver adequate, reliable and competitively priced electricity to support economic growth and improve the welfare of their populations. Despite more than two decades of power sector reforms, outcomes have been varied and often disappointing. Using a case study design, we explore the five key enduring power challenges: inadequate generation capacity; low access to electricity; unreliable supply; high cost of power, and poor performance of utilities in Uganda. The research utilizes a more powerful analytical approach that combines power sector reform theory and principal-agent theoretical lens to explore the experience of power sector reforms. Empirical findings show that the depth and extent reforms has been deepest in the country. The Umeme concession has achieved strong financial performance - underpinned by embedded contractual and regulatory incentives, strong governance and listing on the securities exchange which introduce a strong discipline around private capital. However, there has been disappointing progress in affordability of tariffs, reliability and access - oversights in the design of the concession. One of our major conclusions is that concessions, if well designed to suit local country conditions and when well managed and consistently supported by all stakeholders can be successful in turning around utility performance. Overall, the Uganda case demonstrates the power of agency in delivering efficient services especially when a clear, well-articulated contractual relationship exist with principals and when proper incentives are allocated to cover existing risks.
Deep face recognition using full and partial face images

In this chapter, we discuss state of the art deep learning based methods and techniques for face recognition using both full and partial facial images. We show how deep learning architectures are put together for use in face recognition tasks. We show how such models can be trained and tested. We also show how pretrained deep learning models, through the use of fine tuning, can be utilized for efficient face recognition with full and partial facial data as recognition cues. We also highlight the advances that face recognition has made due to the recent developments in deep learning. Finally, we discuss the present limitations of deep learning based techniques for use in face recognition and highlight some of the challenges in the area.
Uncertainty quantification in store separation analysis using Kestrel, Design of Experiments and surrogate modelling

Kestrel is a multi-physics simulation tool for fixed-wing aircraft, capable of generating store separation trajectories by coupling computational fluid dynamics and 6DOF motion solvers. For generating individual trajectories, Kestrel offers computational cost reduction and workflow simplification compared to traditional methods based on aerodynamic database lookup. However, for analysis of store separation sensitivity to large numbers of uncertain parameters, the computational cost remains prohibitive. Design of Experiments and surrogate modelling methods offer the potential to reduce these computational costs by an order of magnitude, particularly with space-filling designs and Gaussian Process Regression. We applied these methods with Kestrel to model the generic Wing-Pylon-Store benchmark case, analyzing a large flight envelope rapidly with low cost.
Development of motive as an incentive to success at the initial stage of learning music

We present material on the problem of intensification of teaching music to preschool students from the standpoint of the analysis of pedagogical interaction aimed at the development of positive motivation. Due to the many diverse interests, it is difficult for a child at this age to focus on music only and form positive motivation. The motive is understood as the rationale for a sustainable desire for a certain activity. Motives, like other personality traits of a preschool student, are unstable and varied. The child is susceptible to many constructive and destructive factors of development, which often distract him and extinguish the motive for achieving musical success. Strengthening of motivation and appropriate methodological support of the personality-oriented technology of teaching music at the initial stage is required. The development of motive is cross-cutting and accompanies the cognitive and effective stages. For each stage, a selection of creative tasks is presented – methodic support, since reproductive activity quickly reduces interest and the motive is replaced by another. For the majority, a combination of various directions of creative activity is effective. The ideal conditions for keeping motive in the course of learning is a highly qualified teacher who knows personality-oriented technologies, and an interested preschool student, a bearer of musical abilities at a sufficient level. And the main thing is their benevolent interaction. The expected results are presented. That is, the development of the motive for achievements in music at the initial stage will allow the teacher to retain interest and, on the basis of the motivation of the future musician, carry out from small performances to success.
A Database Reconstruction Approach for the Inverse Frequent Itemset Mining Problem

Preservation of sensitive knowledge which is extracted from transactional data of individuals is an increasingly popular field in the data mining research community. In itemset-based mining in particular, a key approach is based on the well-known Apriori algorithm. This paper proposes a novel hiding scheme that produces a sanitized database based on the ideal borders that are computed through the application of a constraint-based frequent itemset mining algorithm. The goal of the proposed approach is to improve at large on computational aspects of the hiding methodology, and in particular to examine how the inverse frequent itemset mining problem can be used in data hiding that ensures privacy protection.
RARE: Rare Action Rule Exploration

Action rule mining seeks to generate rules that indicate what changes can be made to move an object from one class (state) to another class. An action rule is composed of the changes, known as actions, that correlate with the change in the class value. The current work in action rule mining focuses on frequent, or highly occurring, action rules. The working assumption is that the end user is interested in class transitions that move a large number of objects from the initial class to the new (final) class with a high degree of confidence. Currently, very little work focuses on rare action rules; that is, classes which occur infrequently. In this paper, we provide a definition for a rare action rule and then propose a consequent-constraint based algorithm for generating these rare action rules.
Kernel Product Neural Networks

Attention is an important field to explore the importance of each convolutional kernel channel/weight.The existing attention methods mostly use the Squeeze-and-Excitation (SE) technology to extract the global nonlinear feature vectors as the weights of corresponding feature maps.However, the pooling operators and fully-connected layers used in SE technology extract global features at the cost of much valuable information loss and the parameter amount increase.Actually, the feature map containing full information is a ready-made and better attention for other feature maps in the same layer.Simultaneously the products of feature maps will bring powerful non-linearity.Seeing this, Kernel Product (KP) technology is proposed to simply get useful nonlinear attention.To verify the effectiveness of KP, the proposed KP module is employed on Selective Kernel Networks (SKNets) to take the place of the original SE technology.The variety of SKNets is called Kernel Product Networks (KPNets) in this paper.In addition, identity mapping is used to solve the non-convergence problem in very deep neural networks.The KPNets are evaluated on ImageNet-1k, CIFAR-10, and CIFAR-100.The experiment results show that KPNets outperform many state-of-the-art methods and get a similar but more efficient performance than its SKNets with counterpart.
eXplainable and Individualizable for Physiotherapeutic Decision Support for the Elderly

Motion and cognitive impairments can make the life of elderly people more difficult. Physiotherapists seek solutions to increase the quality of life of their patients through exercises and monitoring. Analysis of gait and cognitive data, collected during one year, showed patients’ responses during treatment. In this project, we used age as a feature to compare the pacient’s age and the predicted age value from gait data. The aim of this research is to show the most important features that contributed to the result of the age prediction. Support Vector Machine (SVM), Multi Layer Perceptron (MLP) and LightGBM are algorithms that had the lowest estimation errors for age prediction. Next, we carried out experiments to identify the most relevant features for the age difference. SHappley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied for global and local interpretation, respectively. The results showed that groups of features could be identified during the interpretation. These preliminary results encourage our research to apply XAI and reduce the features dimensionality, providing more confidence as well as simplicity in clinical applications that use Artificial Intelligence.
Person Localisation under Fragmented Occlusion

Occlusion is a fundamental challenge in object recognition. Fragmented occlusion is much more challenging than ordinary partial occlusion and occurs in natural environments such as forests. Less is known in computer vision about fragmented occlusion and object recognition. Interestingly, human vision has far more explored this problem as the human visual system evolved to fragmented occlusion at the times when mankind occupied rainforest. A motivating example of fragmented occlusion is object detection through foliage which is an essential requirement in green border surveillance. Instead of detection, this paper studies the simpler problem of localisation with persons. A neural network based method shows a precision larger than 90% on new image sequences capturing the problem. This is possible by two observations: (i) fragmented occlusion is unsolvable in single images without temporal information, and (ii) colour quantisation and colour swapping is essential to force the training of the network to learn from the available temporal information in the spatiotemporal data.
IISD: Integrated Interaction Subgraph Detection for event mining

Event detection on networks is an important research task in data mining. Most previous methods usually detect a particular type of event that satisfies the predefined rules in the model. However, few methods consider human expert’s interests during the detection process to discover unexplored events. In this paper, we regard the interactive event detection as the anomalous subgraph detection on attributed networks, named Integrated Interaction Subgraph Detection (IISD), where events are treated as anomalous connected subgraphs on the network. The core of our method is automatically identifying events by evaluating the abnormality of the subgraph and integrating the human expert’s interaction simultaneously. Specifically, we first define the human expert’s interaction and recommended interaction domain (i.e., the subgraph and neighbor vertices), which are used to conduct interactive operations based on the human expert’s interests. Afterwards, we propose an efficient subgraph detection algorithm that iteratively integrates human expert’s feedback and updates the recommended interaction domain. In this way, our method could retrieve the most anomalous subgraph on the network as the final event, which could contain the potential unexplored information due to the continuously optimized interaction of human experts. We have conducted extensive experiments on two real-world datasets and proved that our algorithm could achieve better performance compared with several competitive baselines. Moreover, the case study shows that our method could detect global abnormal events effectively.
4 Approach and Method

<italic>The Auditory Setting</italic> takes a practice-based approach to its investigation of how ambient sound recorded from particular sites as sonic material contributes to the production of mediated environments in films and media artworks. Places are studied for their specific sonic environment and as sites recorded and (re)presented, firstly, in a number of films and, secondly, in a few sound and media artworks developed from field recordings made at specific sites. Methodologically, five primary tracks are followed that open dialogue with one another: a historical overview; ethnographic research and fieldwork; personal input, including interviews with artists and practitioners; artistic research; and self-reflective analysis....
Technological Advancements in Renewable Energy Sector

Economic development can only be achieved through greater industrialization. But industrial development depends on availability of cheap power. Rising concerns about environmental impact of power generation and sustainability of natural resources has led to greater emphasis on developing renewable energy sources on a commercial scale. In this article we discuss some of the modern technologies such as artificial intelligence and blockchains that are helping the renewable energy sector to become economically viable.