Reviewerly/exHarmony huggingface dataset:
A Comprehensive Empirical Study on Personalized Dialogue Generation

Current studies on the generation of personalized dialogue primarily contribute to an agent presenting a consistent personality and driving a more informative response. However, we found that the responses generated from most previous models were self-centered, with little consideration for the user in the dialogue. Moreover, we consider human-like conversations to be essentially based on inferring information about the persona of the other party. Therefore, we propose a novel personalized dialogue generator that detects implicit user personas. Because it is difficult to collect a large amount of detailed personal facts for each user, we attempted to model the potential persona of a user and its representation from the dialogue history with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people becoming aware of each other’s personas and producing a corresponding expression in conversations. Subsequently, posterior-discriminated regularization was performed to enhance the training procedure. Finally, a selector was designed to help our model provide long-sighted responses. Comprehensive experiments demonstrate that compared to the state-of-the-art methods, our approach is more concerned with the user’s persona and achieves a notable boost across both automatic metrics and human evaluations.
Poster - Noiseboard Project: Using Sensors and Long-Range Wireless Communication For Musical Augmentation of Skateboarding

The article discusses the Noiseboard project, a sensor-equipped skateboard that allows for creating music while skateboarding. Unlike previous projects, the musical augmentation of the board does not limit the movement possibilities of the skater who can do all the usual tricks. The project is designed to be a skateboard augmented with musical feedback primarily but can also be intended as a tangible interface for music performance based on the use of the skateboard. The system was created by embedding sensors and wireless technology in a novel way into a skateboard, and the technical choices and possible scenarios are discussed in detail. The system’s feasibility and functionality were tested during live music performances, and current limitations and possible future improvements are presented.
Effects of Continuous Security Monitoring on Security Controls of Electronic Health Records in Public Hospitals, Tanzania

This paper examines the effects of continuous security monitoring on the security of electronic health records in Tanzanian public hospitals. The study adopted a cross-sectional research design and quantitative research approach using a sample of 300 respondents from the six public hospitals in Tanzania. A questionnaire was used to collect data from the main users of EHRs such as medical doctors, IT officers, nurses, pharmacists, laboratory technologists, record officers and administrative officers. A multiple linear regression model was used to evaluate the effects of continuous security monitoring on the security of electronic health records in Tanzanian public hospitals. The findings revealed that continuous security monitoring is a significant predictor of the security of electronic health records in Tanzanian public hospitals, (B= .509, p&lt; 0.001). This implies that, continuous security monitoring explains 50.9% of the variance in security of electronic health records in Tanzanian public hospitals. Based on this finding, is recommended that, to enhance effective security controls in electronic health records, public hospitals in Tanzanian should consider the adoption of continuous security monitoring by making security controls more automated
Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches

Background Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance. Methods Adults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data. Results 2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features. Conclusion Postoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management.
Deep nonlocal low-rank regularization for complex-domain pixel super-resolution

Pixel super-resolution (PSR) has emerged as a promising technique to break the sampling limit for phase imaging systems. However, due to the inherent nonconvexity of phase retrieval problem and super-resolution process, PSR algorithms are sensitive to noise, leading to reconstruction quality inevitably deteriorating. Following the plug-and-play framework, we introduce the nonlocal low-rank (NLR) regularization for accurate and robust PSR, achieving a state-of-the-art performance. Inspired by the NLR prior, we further develop the complex-domain nonlocal low-rank network (CNLNet) regularization to perform nonlocal similarity matching and low-rank approximation in the deep feature domain rather than the spatial domain of conventional NLR. Through visual and quantitative comparisons, CNLNet-based reconstruction shows an average 1.4 dB PSNR improvement over conventional NLR, outperforming existing algorithms under various scenarios.
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment

Deep neural networks (DNN) have demonstrated unprecedented success for various applications. However, due to the issue of limited dataset availability and the strict legal and ethical requirements for data privacy protection, the broad applications of DNN (e.g., medical imaging classification) with large-scale training data have been largely hindered, greatly constraining the model generalization capability. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method, aiming to improve the generalization capability under the privacy-preserving condition. In particular, we propose to improve the information aggregation process on the centralized server side with a novel gradient alignment loss, expecting that the trained model can be better generalized to the “unseen” but related data. The rationale and effectiveness of our proposed method can be explained by connecting our proposed method with the Maximum Mean Discrepancy (MMD) which has been widely adopted as the distribution distance measure. Experimental results on three domain generalization benchmark datasets indicate that our method can achieve better cross-domain generalization capability compared to the state-of-the-art federated learning methods.
CTopPRM: Clustering Topological PRM for Planning Multiple Distinct Paths in 3D Environments

In this letter, we propose a new method called Clustering Topological PRM (CTopPRM) for finding multiple topologically distinct paths in 3D cluttered environments. Finding such distinct paths, e.g., going around an obstacle from a different side, is useful in many applications. Among others, it is necessary for optimization-based trajectory planners where found trajectories are restricted to only a single topological class of a given path. Distinct paths can also be used to guide sampling-based motion planning and thus increase the effectiveness of planning in environments with narrow passages. Graph-based representation called roadmap is a common representation for path planning and also for finding multiple distinct paths. However, challenging environments with multiple narrow passages require a densely sampled roadmap to capture the connectivity of the environment. Searching such a dense roadmap for multiple paths is computationally too expensive. Therefore, the majority of existing methods construct only a sparse roadmap which, however, struggles to find all distinct paths in challenging environments. To this end, we propose the CTopPRM which creates a sparse graph by clustering an initially sampled dense roadmap. Such a reduced roadmap allows fast identification of topologically distinct paths captured in the dense roadmap. We show, that compared to the existing methods the CTopPRM improves the probability of finding all distinct paths by almost 20% in tested environments, during same run-time. The source code of our method is released as an open-source package.
How to Evaluate If Collaborative Augmented Reality Speaks to Its Users

Augmented reality (AR) is increasingly considered to support scenarios of co-located and remote collaboration. Thus far, the core goal has been advancing the supporting technologies and assessing how they perform to inform design and development, thus providing support toward their maturity. Nevertheless, while understanding the performance and impact of supporting technology is indisputable groundwork, we argue that the field needs to adopt a framework that moves from answering questions about the proposed methods and technologies to a more holistic view, also encompassing collaboration. However, moving toward this goal challenges how evaluations are designed, adding complexity and raising several questions about what needs to be considered. In this article, we briefly examine the different dimensions entailed in collaborative AR and argue in favor of a distinctive evaluation framework that goes beyond current practice and sets its eyes on the elements that allow judging how collaboration unfolds while informing the role of the supporting technology.
The Intersection of Economic Inequality and Political Conflict in Africa: A Comprehensive Analysis

This study explores the interaction between economic inequality and political conflict in Africa. It investigates the relationship between economic inequality and political conflict in Africa and assesses how political conflicts affect economic inequality. The study also examines the role of political leaders in addressing conflicts in Africa. The study employs a qualitative research method, conducting a systematic literature review to gather and evaluate relevant studies on economic inequality and political conflict in Africa. The study applies the Human Needs theory, developed by John Burton, as a theoretical framework to comprehend the underlying causes of conflicts. This theory highlights unmet fundamental human needs like security, recognition, identity, and livelihood. The findings of the research indicate that economic inequality significantly contributes to political conflict in Africa, and conflicts have adverse effects on the African economy. The study recommends that policymakers in Africa should implement policies promoting economic equality to mitigate conflicts, and political leaders should take proactive measures to resolve conflicts on the continent.
Empowering Preventive Care with GECA Chatbot

Chatbots have become increasingly popular in the healthcare industry. In the area of preventive care, chatbots can provide personalized and timely solutions that aid individuals in maintaining their well-being and forestalling the development of chronic conditions. This paper presents GECA, a chatbot designed specifically for preventive care, that offers information, advice, and monitoring to patients who are undergoing home treatment, providing a cost-effective, personalized, and engaging solution. Moreover, its adaptable architecture enables extension to other diseases and conditions seamlessly. The chatbot's bilingual capabilities enhance accessibility for a wider range of users, including those with reading or writing difficulties, thereby improving the overall user experience. GECA's ability to connect with external resources offers a higher degree of personalization, which is a crucial aspect in engaging users effectively. The integration of standards and security protocols in these connections allows patient privacy, security and smooth adaptation to emerging healthcare information sources. GECA has demonstrated a remarkable level of accuracy and precision in its interactions with the diverse features, boasting an impressive 97% success rate in delivering accurate responses. Presently, preparations are underway for a pilot project at a Portuguese hospital that will conduct exhaustive testing and evaluate GECA, encompassing aspects such as its effectiveness, efficiency, quality, goal achievability, and user satisfaction.
A new look at the Gortdrum copper deposit, Co. Tipperary, Ireland.

The Gortdrum Cu-Ag-(Hg) mine located on the eastern edge of the Limerick Syncline represents an apparent anomaly in the Irish Orefield and has escaped attention over the past few decades. Construction of a three-dimensional geological model from historical drillhole data from the deposit area indicates that the previously recognized Gortdrum “fault” is actually a ramp-relay normal fault system similar to those at known Irish zinc-lead deposits with particularly striking parallels to the Killoran fault system at Lisheen. The Gortdrum area lies between what was a generally shelf limestone environment to the west and a more basinal environment to the east during the Chadian-Arundian. The Gortdrum fault system with downthrow to the northwest, however, cannot be the controlling structure for these regional facies change. The mineralogy and geometry of Gortdrum suggests it could have been a “feeder” zone for a zinc-lead deposit in the now eroded overlying Waulsortian Limestone. The small footprint of the Gortdrum deposit implies that similar, potentially economic copper mineralized zones could be present in other Irish type systems, especially the Pallas Green and Stonepark systems to the west and other prospects in the Limerick Syncline.
A variable structure multi‐model maneuvering target tracking algorithm based on Monte Carlo learning

Abstract A well‐liked maneuvering target tracking algorithm is a variable structure multi‐model (VSMM). One of the crucial elements determining the tracking effect is the successful model set adaptation (MSA). The ability to further enhance tracking accuracy for the conventional VSMM method is constrained by the absence of a mechanism to thoroughly utilise observation and tracking data to optimise MSA. We incorporate the Reinforcement learning (RL) approach into the MSA procedure to address this issue and provide a VSMM algorithm based on Monte Carlo (MC) learning. To formulate the challenge of optimising the number of effective models as a RL problem, we first used the prediction error, the number of effective model sets, and tracking accuracy to build the models of the appropriate state space, decision space, and reward. The number of efficient models was optimised using MC learning, and the entire VSMM algorithm was then created. The proposed approach was compared to the simulated experiment's five maneuvering target tracking algorithms. The outcomes demonstrate that the suggested algorithm has a lower computation scale and accurate tracking accuracy.
Development of Augmented Reality Flashcards as an Innovative Learning Media on Linear Motion

This research is a development research that uses the ADDIE Design Research and Development (R&amp;D) model which consists of 5 stages, namely analysis, design, development, implementation, and evaluation. The purpose of this study is to describe the validity and practicality. Augmented reality-assisted flashcards on linear motion material to increase students' learning interest. Product validity was assessed by 3 experts consisting of aspects of material and construct assessment as well as aspects of media and design assessment with an overall average score of 3.64 with a very valid category. The practicality of the product is assessed from the readability aspect and the teacher's perception aspect by obtaining an overall average percentage of 95% in the very practical category. Based on the analysis of the results of the data, it was concluded that the product resulting from the development of augmented reality-assisted flashcards on linear motion material was valid and practical to increase students' interest in learning. Keywords: flashcards, augmented reality, learning interest, linear motion. Abstrak: Penelitian ini merupakan penelitian pengembangan yang menggunakan Desain Research and Development (R&amp;D) model ADDIE yang terdiri dari 5 tahap yaitu analysis, design, development, implementation, dan evaluation. Tujuan penelitian ini yaitu mendeskripsikan kevalidan dan kepraktisan. Flashcards berbantuan augmented reality pada materi gerak lurus untuk meningkatkan minat belajar peserta didik. Kevalidan produk dinilai oleh 3 orang ahli yang terdiri dari aspek penilaian materi dan konstruk serta aspek penilaian media dan desain dengan hasil skor rata-rata keseluruhan 3,64 dengan kategori sangat valid. Kepraktisan produk dinilai dari aspek keterbacaan dan aspek persepsi guru dengan memperoleh persentase rata-rata keseluruhan sebesar 95% dengan kategori sangat praktis. Berdasarkan analisis hasil data disimpulkan bahwa produk hasil pengembangan flashcards berbantuan augmented reality pada materi gerak lurus telah valid dan praktis untuk meningkatkan minat belajar peserta didik. Kata kunci: flashcards, augmented reality, minat belajar, gerak lurus. DOI: http://dx.doi.org/10.23960/jpmipa/v24i1.pp291-297
BLITZCRANK: Factor Graph Accelerator for Motion Planning

Factor graph is a graph representing the factorization of a probability distribution function and serves as a perfect abstraction in many autonomous machine computing stacks, such as planning, localization, tracking and control, which are challenging tasks for autonomous systems with real-time and energy constraints.In this paper, we present BLITZCRANK, an accelerator for motion planning algorithms using the abstraction of a factor graph. By formulating motion planning as a factor graph inference, we successfully reduce the scale of the problem and utilize the inherent matrix sparsity. BLITZCRANK is able to realize the user-defined optimal design by finding the optimal order of the factor graph inference. With a domain specific balancing order, BLITZCRANK achieves up to 7.4× speed up and 29.7× energy reduction compared to the software implementation on Intel CPU.
DriverJar: Lightweight Device Driver Isolation for ARM

Driver-originated vulnerabilities are well-known threats to modern monolithic kernels. However, existing driver isolation solutions either rely on Intel-only or newly-introduced CPU features (e.g., Intel VMFUNC, ARM MTE), or suffer from performance issues, making them unsuitable for existing ARM-based devices. In this work, we leverage a common hardware feature, named hardware watchpoint, to achieve lightweight driver isolation for off-the-shelf ARM devices. Specifically, we utilize watchpoints to prevent the possibly compromised driver from corrupting the rest kernel’s state arbitrarily. We implement a prototype for ARM64 Linux. The security analysis and performance evaluation show the efficiency and practicality of our solution.
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impact. Sparse training (using a fixed number of nonzero weights in each iteration) could significantly mitigate the training costs by reducing the model size. However, existing sparse training methods mainly use either random-based or greedy-based drop-and-grow strategies, resulting in local minimal and low accuracy. In this work, to assist explainable sparse training, we propose important weights Exploitation and coverage Exploration to characterize Dynamic Sparse Training (DST-EE), and provide quantitative analysis of these two metrics. We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property. Experimental results show that sparse models (up to 98% sparsity) obtained by our proposed method outperform the SOTA sparse training methods on a wide variety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10, ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models. On ResNet-50 / ImageNet, the proposed method has up to 8.2% accuracy improvement compared to SOTA sparse training methods.
Path planning of underground mining area transportation scene based on improved A<sup>*</sup> algorithm

The traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm has the problem of being close to obstacles in path planning. For the narrow transportation scene in the underground mining area, it is evident that the algorithm cannot provide a safe path. This paper proposes an improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm that introduces the cost of obstacles. The cost of obstacles in the scene is considered in the heuristic function, and a safer feasible path is obtained by smoothing. The experimental site is built for actual vehicle experiments. The results show that the improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm with obstacle cost can meet the actual driving needs of intelligent mining vehicles in underground mining transportation scenarios.