Reviewerly/exHarmony huggingface dataset:
Measuring Embodiment: Movement Complexity and the Impact of Personal Characteristics

A user's personal experiences and characteristics may impact the strength of an embodiment illusion and affect resulting behavioral changes in unknown ways. This paper presents a novel re-analysis of two fully-immersive embodiment user-studies (n=189 and n=99) using structural equation modeling, to test the effects of personal characteristics on subjective embodiment. Results demonstrate that individual characteristics (gender, participation in science, technology, engineering or math - Experiment 1, age, video gaming experience - Experiment 2) predicted differing self-reported experiences of embodiment Results also indicate that increased self-reported embodiment predicts environmental response, in this case faster and more accurate responses within the virtual environment. Importantly, head-tracking data is shown to be an effective objective measure for predicting embodiment, without requiring researchers to utilize additional equipment.
Identity discovery: Small learning interventions as catalysts for change in design education

As design has taken a social turn toward the co-designing of complex situations, design students are increasingly adopting multiple identities including that of a thinker, facilitator, activist and observer through engagement and collaboration with others. This expansion of identity from being solely a creative maker has presented a challenge for design students, which has been exacerbated by the external shock of the COVID-19 pandemic. In this article, we introduce small learning interventions that can be flexibly incorporated into the current design curriculum to promote identity discovery and self-awareness among students. Through four case studies, these interventions are organized at three levels of scale in understanding identity: learning from self, learning from each other and learning from self with others. By providing students with opportunities for reflection and introspection, we aim to help them develop their social mindset and identity as designers, as well as prepare for more comprehensive curricular changes in the future to catalyse the shift towards the social turn of design.
A link prediction-based recommendation system using transactional data

Abstract Recommending relevant items to users has become an important task in many systems due to the increased amount of data produced. For this purpose, transaction datasets such as credit card transactions and e-commerce purchase histories can be used in recommendation systems to understand underlying user interests by exploiting user-item interactions, which can be a powerful signal to perform this task. This study proposes a link prediction-based recommendation system combining graph representation learning algorithms and gradient boosting classifiers for transaction datasets. The proposed system generates a network where nodes correspond to users and items, and links represent their interactions. A use case scenario is examined on a credit card transaction dataset as a merchant prediction task that predicts the merchants where users can make purchases in the next month. Performances of common network embedding extraction techniques and classifier models are evaluated via various experiments conducted and based on these evaluations, a novel system is proposed, and a matrix factorization-based alternative recommendation method is compared with the proposed model. The proposed method has shown superior performance to the alternative method in terms of receiver operating characteristic curves, area under the curve, and mean average precision metrics. The use of transactional data for a recommendation system is found to be a powerful approach to making relevant recommendations.
Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images

Abstract Semantic segmentation of breast and surrounding tissues in supine and prone breast magnetic resonance imaging (MRI) is required for various kinds of computer-assisted diagnoses for surgical applications. Variability of breast shape in supine and prone poses along with various MRI artifacts makes it difficult to determine robust breast and surrounding tissue segmentation. Therefore, we evaluated semantic segmentation with transfer learning of convolutional neural networks to create robust breast segmentation in supine breast MRI without considering supine or prone positions. Total 29 patients with T1-weighted contrast-enhanced images were collected at Asan Medical Center and two types of breast MRI were performed in the prone position and the supine position. The four classes, including lungs and heart, muscles and bones, parenchyma with cancer, and skin and fat, were manually drawn by an expert. Semantic segmentation on breast MRI scans with supine, prone, transferred from prone to supine, and pooled supine and prone MRI were trained and compared using 2D U-Net, 3D U-Net, 2D nnU-Net and 3D nnU-Net. The best performance was 2D models with transfer learning. Our results showed excellent performance and could be used for clinical purposes such as breast registration and computer-aided diagnosis.
Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in explainable AI, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.
FactGen: Faithful Text Generation by Factuality-aware Pre-training and Contrastive Ranking Fine-tuning

Conditional text generation is supposed to generate a fluent and coherent target text that is faithful to the source text. Although pre-trained models have achieved promising results, they still suffer from the crucial factuality problem. To deal with this issue, we propose a factuality-aware pretraining-finetuning framework named FactGen, which fully considers factuality during two training stages. Specifically, at the pre-training stage, we utilize a natural language inference model to construct target texts that are entailed by the source texts, resulting in a more factually consistent pre-training objective. Then, during the fine-tuning stage, we further introduce a contrastive ranking loss to encourage the model to generate factually consistent text with higher probability. Extensive experiments on three conditional text generation tasks demonstrate the effectiveness and generality of our training framework.
Augmented Reality User’s Experience: AI-Based Data Collection, Processing and Analysis

This chapter presents an AI-based approach to the systemic collection of user experience data for further analysis. This is an important task because user feedback is essential in many use cases, such as serious games, tourist and museum applications, food recognition applications, and other software based on Augmented Reality (AR). For AR game-based learning environments user feedback can be provided as Multimodal Learning Analytics (MMLA) which has been emerging in the past years as it exploits the fusion of sensors and data mining techniques. A wide range of sensors have been used by MMLA experiments, ranging from those collecting students’ motoric (relating to muscular movement) and physiological (heart, brain, skin, etc.) behaviour, to those capturing social (proximity), situational, and environmental (location, noise) contexts in which learners are placed. Recent research achievements in this area have resulted in several techniques for gathering user experience data, including eye-movement tracking, mood tracking, facial expression recognition, etc. As a result of user’s activity monitoring during AR-based software use, it is possible to obtain temporal multimodal data that requires rectifying, fusion, and analysis. These procedures can be based on Artificial Intelligence, Fuzzy logic, algebraic systems of aggregates, and other approaches. This chapter covers theoretical and practical aspects of handling AR user’s experience data, in particular, MMLA data. The chapter gives an overview of sensors, tools, and techniques for MMLA data gathering as well as presenting several approaches and methods for user experience data processing and analysis.
Design and value analysis of the blockchain-based port logistics financial platform

Time-consuming ocean transportation for international trades requires enterprises to advance payments for goods, which can be fatal for small and medium enterprises with insufficient self-owned capital. Logistics finance is an effective solution for tackling this problem. However, traditional logistics finance has long been plagued by information asymmetry, tremendous paperwork, and long financing cycles. As a distributed database with characteristics of transparency, traceability, and immutability, blockchain technology shows good potential in addressing pain points on traditional logistics financing platforms. This paper focuses on the operational mechanism and value analysis of the blockchain-based port logistics finance platform. The conceptual architecture of the blockchain-based port logistics finance platform is constructed, and a value analysis model based on the Newsboy Model is established for quantitatively analysing the platform’s value. The results revealed that the introduction of the blockchain into port logistics financing can significantly improve small to medium-sized enterprises’ profitability by providing reliable financing channels. The results also indicate that participants’ profit on the blockchain-based platform may be influenced by the speed of market expansion, initial capital size, and financing cost. This paper also discusses theoretical and managerial implications by providing insights into the participants’ decision-making on the platform under different constraints.
Improving the Capacity of Quantum Dense Coding and the Fidelity of Quantum Teleportation by Weak Measurement and Measurement Reversal

A protective scheme of quantum dense coding and quantum teleportation of the X-type initial state is proposed in amplitude damping noisy channel with memory using weak measurement and measurement reversal. Compared with the noisy channel without memory, the memory factor improves both the capacity of quantum dense coding and the fidelity of the quantum teleportation to a certain extent for the given damping coefficient. Although the memory factor can inhibit decoherence in some degree, it cannot eliminate it completely. In order to further overcome the influence of the damping coefficient, the weak measurement protective scheme is proposed, which found that the capacity and the fidelity can be efficiently improved by adjusting weak measurement parameter. Another practical conclusion is that, among the three initial states, the weak measurement protective scheme has the best protective effect on the Bell-state in terms of the capacity and the fidelity. For the channel with no memory and full memory, the channel capacity of quantum dense coding reaches two and the fidelity of quantum teleportation reaches one for the bit system; the Bell system can recover the initial state completely with a certain probability. It can be seen that the entanglement of the system can be well protected by the weak measurement scheme, which provides a good support for the realization of quantum communication.
Bifurcation behaviors shape how continuous physical dynamics solves discrete Ising optimization

Simulating physical dynamics to solve hard combinatorial optimization has proven effective for medium- to large-scale problems. The dynamics of such systems is continuous, with no guarantee of finding optimal solutions of the original discrete problem. We investigate the open question of when simulated physical solvers solve discrete optimizations correctly, with a focus on coherent Ising machines (CIMs). Having established the existence of an exact mapping between CIM dynamics and discrete Ising optimization, we report two fundamentally distinct bifurcation behaviors of the Ising dynamics at the first bifurcation point: either all nodal states simultaneously deviate from zero (synchronized bifurcation) or undergo a cascade of such deviations (retarded bifurcation). For synchronized bifurcation, we prove that when the nodal states are uniformly bounded away from the origin, they contain sufficient information for exactly solving the Ising problem. When the exact mapping conditions are violated, subsequent bifurcations become necessary and often cause slow convergence. Inspired by those findings, we devise a trapping-and-correction (TAC) technique to accelerate dynamics-based Ising solvers, including CIMs and simulated bifurcation. TAC takes advantage of early bifurcated "trapped nodes" which maintain their sign throughout the Ising dynamics to reduce computation time effectively. Using problem instances from open benchmark and random Ising models, we validate the superior convergence and accuracy of TAC.
Simulation of Linear Non-Hermitian Boundary-Value Problems with Quantum Singular-Value Transformation

We propose a quantum algorithm for simulating dissipative waves in inhomogeneous linear media as a boundary-value problem. Using the so-called quantum singular value transformation (QSVT), we construct a quantum circuit that models the propagation of electromagnetic waves in a one-dimensional system with outgoing boundary conditions. The corresponding measurement procedure is also discussed. Limitations of the QSVT algorithm are identified in connection with the large condition numbers that the dispersion matrices exhibit at weak dissipation.
Diverse Sample Generation: Pushing the Limit of Generative Data-Free Quantization

Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach generates data utilizing batch normalization (BN) statistics of the full-precision networks to quantize the networks. However, it always faces the serious challenges of accuracy degradation in practice. We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels. This paper presents a generic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> iverse <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ample <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> eneration ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSG</b> ) scheme for the generative data-free quantization, to mitigate detrimental homogenization. We first slack the statistics alignment for features in the BN layer to relax the distribution constraint. Then, we strengthen the loss impact of the specific BN layers for different samples and inhibit the correlation among samples in the generation process, to diversify samples from the statistical and spatial perspectives, respectively. Comprehensive experiments show that for large-scale image classification tasks, our DSG can consistently quantization performance on different neural architectures, especially under ultra-low bit-width. And data diversification caused by our DSG brings a general gain to various quantization-aware training and post-training quantization approaches, demonstrating its generality and effectiveness.
Assessing decision-making skills: preliminary proof-of-concept data for DAssDec - Mod1STY and Mod2STR

From a psychological and neuroscientific perspectives, decision-making can be defined as a skill, a function and/or a process we pervasively implement in our everyday life. Embracing a definition of decision-making that describes it as a multicomponential, pervasive, and instrumental ability, the Digitalized Assessment Tool for Decision-Making (DAssDec) – a novel digitalized assessment tool – has been devised with the specific purpose to capture the multifaceted nature of decision-making and to sketch an articulated profile of its many dispositional and situational manifestations. We here introduce preliminary outcomes from proof-of concept and feasibility study performed on the first two domains of the DAssDec Tool – Mod1STY and Mod2STR – dedicated to decisional styles and strategies. 35 professionals working in managerial departments of a large service company took part in the study. Findings pointed out the feasibility, usability, and practical value of the investigated sections of the DAssDec tool. Also, first quali-quantitative analyses of participants responses and performance at the tasks included in the tested domains have already highlighted the potential of the tool to detect interindividual differences, thus pairing the opportunity to outline a global profile of a work team with specific individual profiles of decision-making skills and their subjective determinants.
DIGITIZATION OF EDUCATION AND ITS SOCIO-ECONOMIC IMPACTS WITH SPECIAL REFERENCE TO TRIVANDRUM

This study is makes a comparative analysis of the rural-urban divide in digital education and examines the benefits and challenges of e-learning. A total of 124 respondents consisting of students, teachers and parents residing in the rural and urban areas of Trivandrum were surveyed using a well-structured questionnaire administered in both English and Malayalam. The sample population was identified through stratified random sampling. The study finds that disruption of internet connectivity due to signal unavailability is a major challenge of e-learning. Also, majority of the stakeholders were unwilling to make a conscious shift to a digitised education mode. Mostly students favoured online education because of its ease of usage, convenience, remote accessibility and system response speed. But most of the parents and teachers preferred offline education.
Toward perturbation theory methods on a quantum computer

Perturbation theory, used in a wide range of fields, is a powerful tool for approximate solutions to complex problems, starting from the exact solution of a related, simpler problem. Advances in quantum computing, especially over the past several years, provide opportunities for alternatives to classical methods. Here, we present a general quantum circuit estimating both the energy and eigenstates corrections that is far superior to the classical version when estimating second-order energy corrections. We demonstrate our approach as applied to the two-site extended Hubbard model. In addition to numerical simulations based on qiskit, results on IBM's quantum hardware are also presented. Our work offers a general approach to studying complex systems with quantum devices, with no training or optimization process needed to obtain the perturbative terms, which can be generalized to other Hamiltonian systems both in chemistry and physics.
General framework for genuine multipartite entanglement detection

The design of detection strategies for multipartite entanglement stands as a central importance on our understanding of fundamental quantum mechanics and has had a substantial impact on quantum information applications. However, accurate and robust detection approaches are severely hindered, particularly when the number of nodes grows rapidly like in a quantum network. Here we present a general and operational framework that generates alternative entanglement witness for an arbitrary targeted state. The framework enjoys a systematic and high-efficient character and allows to substantiate genuine multipartite entanglement for a variety of states that arise naturally in practical situations and to dramatically outperform currently standard methods. With excellent noise tolerance, our framework should be broadly applicable to witness genuine multipartite entanglement in various practically scenarios and to facilitate the best use of entangled resources in the emerging area of quantum network.
Hybrid Serverless Computing: Opportunities and Challenges

In recent years, the adoption of serverless computing has surged due to the ease of deployment, attractive pay-per-use pricing, and transparent horizontal auto-scaling. At the same time, infrastructure advancements such as the emergence of 5G networks, the explosion of devices connected to the Internet known as the Internet of Things (IoT), as well as new application requirements that constrain where computation and data can happen, will expand the reach of Cloud computing beyond traditional data centers into the emergent Hybrid Cloud market that is predicted to expand to over a trillion dollars in next few years. In Hybrid Cloud environments, driven by serverless tenants, there is an increased need to focus on enabling productive work for application builders using a distributed platform consisting of public clouds, private clouds, and edge systems. In this chapter we investigate how far serverless computing can be extended to become Hybrid Serverless Computing, outline its definition, describe steps towards achieving it, and identify opportunities and challenges.
MOTUS: How Quantized Parameters Improve Protection of Model and Its Inference Input

Protecting a machine learning model and its inference inputs with secure computation is important for providing services with a valuable model. In this paper, we discuss how a model’s parameter quantization works to protect the model and its inference inputs. To this end, we present an investigational protocol, MOTUS, based on ternary neural networks whose parameters are ternarized. Through extensive experiments with MOTUS, we found three key insights. First, ternary neural networks can avoid accuracy deterioration due to modulo operations of secure computation. Second, the increment of model parameter candidates significantly improves accuracy more than an existing technique for accuracy improvement, i.e., batch normalization. Third, protecting both a model and inference inputs reduces inference throughput four to seven times to provide the same level of accuracy compared with existing protocols protecting only inference inputs. Our source code is publicly available via GitHub.
Blockchain and LDP Based Smart Healthcare IoT Applications: Architecture, Challenges and Future Works

Networks for leading healthcare communication produce vital data and information. Due to their lack of security precautions, medical supply chain communication networks have been the subject of numerous cyberattacks in recent years. Security and data privacy demand intense and more stringent safeguards in the modern day, since cyberattacks are more affordable and straightforward due to the computer power and diverse methods available for nefarious uses. Although many researchers have proposed a number of solutions based on blockchain technology to upgrade the firmware, these methods still have drawbacks, such as enormous storage requirements and centralized firmware storage. We have done a comparison between different technologies in this article. A generic architectural framework based on blockchain, Local Differential Privacy and cloud computing (Icedrive) for IoH Industry applications is also presented in this paper. Besides this, the most conceivable applications and challenges in integrating blockchain and cloud computing into IoTH applications are also indicated. Moreover, we have discussed the future of blockchain in healthcare sector. We hope that this research will be useful in the upcoming growth of blockchain-based IoTH applications.