Reviewerly/exHarmony huggingface dataset:
Memeopoly: An AI-Powered Physical Board Game Interface for Tangible Play and Learning Art and Design

Playtime holds great significance in the holistic development of children and young teens, both physically and mentally [16]. Engaging with physical toys and participating in outdoor sports fosters essential skills such as spatial interaction, socialization, and communication in the real world. However, contemporary entertainment that targets young people is predominantly screen-based media, offering simulated worlds, captivating narratives, and interactive experiences. Consequently, children and teens spend substantial amounts of time indoors, preoccupied with online activities and digital content consumption. This paradigm shift towards digital entertainment poses developmental challenges due to the absence of tangibility [19] and face-to-face interaction. This paper introduces Memeopoly (Figure 1), a novel AI-powered physical board game that synergies the strengths of tangible playtime with the dynamism and interactivity of the digital realm. Integrating AI technology into the traditional play framework augments the interactive nature of physical toys [6], enabling a richer and more engaging game-play environment. We designed our AI system in the game to harness and empower human creativity to generate unforeseen story lines personalized to players’ unique preferences, thereby cultivating a thrilling gaming experience that surpasses the static and generic narratives typically found in traditional board games.
CookT: A Fast-Paced Collaborative Cooking Game with Interactive Objects

CookT is a chaotic, tangible interactive multiplayer video game where three players are immersed in the role of chefs. In a race against the clock, the chefs need to finish orders by completing standard tasks. These tasks are completed by using tangible user interfaces, each with their intended purpose as they represent different kitchen utensils, workstations and plates. The chefs need to employ good communication skills and navigate through the chaotic playing area to earn the highest possible profits for their restaurant. This game was designed through the application of guidelines, advice and findings from articles in the field of Human Computer Interaction. By means of play tests the game was evaluated and further improved upon, providing both players and spectators an enjoyable experience.
Technology, Movement, and Play Is Hampering and Boosting Interactive Play

In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount of technology, movement, or play that was needed in a design activity in such a way that it became counterproductive and for each example mention possible adaptations. Finally, we highlight three existing approaches that can balance the overabundance of technology, movement, and play in design processes: activity-centered design, somaesthetic design, and perspective-changing movement-based design.
The Sea Is a Sky: Towards a Poetry of Motion

This paper presents "The Sea is a Sky," an immersive virtual reality (VR) poetry experience that takes inspiration from the Cuban poem "Del Otro Lado Del Mar," which translates to "on the other side of the sea." In this VR game, players are transported into the journey of a Cuban immigrant as they leave their homeland in search of a better future in America. Through physical actions such as crawling, swimming, and rowing within the VR environment, players embody the struggles and challenges faced by the immigrant protagonist. Throughout the experience, they encounter visionary hallucinations and hear messages of hope for the future as they navigate the vast ocean. However, the game also highlights the tragic reality of many Cuban immigrants who have lost their lives while attempting to cross the treacherous sea. The theme of drowning serves as a poignant symbol of their plight. The development of "The Sea is a Sky" involved collaboration among students from diverse disciplines, including English Literature, Computer Science, and Digital Arts. The project aims to achieve two primary objectives: raising awareness about social issues related to refugees and immigrants, and pushing the boundaries of game design by creating an engaging experience for younger generations to connect with poetry through the medium of virtual reality. While poetry has the inherent power to evoke deep reflection, it often struggles to resonate with younger individuals in its traditional forms. By harnessing the potential of virtual reality, our project seeks to expand the horizons of traditional poetry and explore innovative ways of experiencing it in the digital realm. The design and implementation of our VR experience not only offer a unique and engaging approach to poetry but also have the potential to inform future applications of VR in digital poetry and the cultivation of empathy among audiences.
Towards Co-creative Interdisciplinary Exergame Design Processes: A Theory-Based Approach of a VR-Exergame Fall Prevention Training

Proactive actions are vital for promoting healthy aging, emphasizing the necessity to to address the physical, cognitive, and emotional needs through discipline-specific expertise. Exergames, with their inherent characteristics, offer the potential to bridge the gap between the disciplines of exertion, user experience, and games. This paper presents a formal co-creative interdisciplinary design process for VR exergame training to provide effective, enjoyable, and meaningful fall prevention training for healthy aging. Our process involves establishing expert-human relations, incorporating discipline-specific approaches and goals, and assigning an active designer for implementation. The approaches are deconstructed into the language of the active designer using a shared thesaurus and translated into the exergame through construction kits. An early-stage player study and expert assessment indicate positive results through our design process.
Cross-medium imaging model and calibration method based on refractive optical path for underwater morphology measurement

Abstract Full-field optical morphology measurement methods are widely used in the shape measurement of underwater targets. The existing models and calibration methods in air could be invalid due to the refraction of light. To deal with these problems, this paper develops a cross-medium imaging model and a relative calibration method. In the modeling, the actual refraction optical path is considered when using arbitrary transparent structures. In the calibration, the accurate parameters of the model are first determined in the air environment and then updated by the refraction index replacement strategy. By using these methods, the accurate restoration of the morphology of objects in any medium is realized, and the inconvenience of underwater calibration process in prior methods can be avoided. The results of conducted experiments show that the accuracy of measurement can achieve 0.42%, which illustrates the validity of the proposed model and the precision of the proposed calibration method.
MidGAN: Mutual information in GAN-based dialogue models

Despite a large amount of research into task-oriented dialogue systems, open-ended dialogue agents have received little attention. Recently, researchers have explored using Generative Adversarial Network (GAN) to build such models, however, these require extensive computation and data. This paper propose MidGAN to address prior limitations of GAN-based dialogue models. It does this by trying to maximize the mutual information of the responses generated by the model. To this end, we propose a new metric, MMI-Like. This is based on Maximizing Mutual Information (MMI), yet unlike MMI, does not rely on an auxiliary generative model. We evaluate MidGAN based on the diversity, informativeness by measuring similarity and relevance of the responses it generates by BLEU metric. Our evaluation results, based on the three benchmark datasets, show that MidGAN outperforms the existing state-of-the-art framework, ADV.
Semi-supervised deep density clustering

Deep clustering generally obtains promising performance by learning deep feature representations. However, there are two limitations: (1) end-to-end deep density clustering needs to be explored; (2) prior information is ignored to guide the learning process. To overcome these limitations, we propose a novel semi-supervised deep density clustering (SDDC). Specifically, a convolutional autoencoder is applied to learn embedded features, and semi-supervised density peaks clustering is designed to identify stable cluster centers. Meanwhile, prior information is introduced to instruct the preferable clustering process. By integrating prior information, a joint clustering loss is directly built on embedded features to perform feature representation and cluster assignment simultaneously. Extensive experiments validate the power of SDDC for initializing and the effectiveness on clustering tasks.
Efficient perturbation techniques for preserving privacy of multivariate sensitive data

Cloud data is increasing significantly recently because of the advancement of technology which can contain individuals’ sensitive information like medical diagnostics reports. While deriving knowledge from such sensitive data, different third party can get their hands on this sensitive information. Therefore, privacy preservation of such sensitive data has become a vital issue. Data perturbation is one of the most often used data mining approaches for safeguarding privacy. A significant challenge in data perturbation is balancing the privacy and utility of data. Securing an individual’s privacy often entails the forfeiture of the data utility, and the contrary is true. Though there exist several approaches to deal with the trade-off between privacy and utility, researchers are always looking for new approaches. In order to address this critical issue, this paper proposes two data perturbation approaches namely NOS2R and NOS2R2. The proposed perturbation techniques are experimented with over ten benchmarks UCI data set for analyzing privacy protection, information entropy, attack resistance, data utility, and classification error. The proposed approaches are compared with two existing approaches 3DRT and NRoReM. The thorough experimental analysis exhibits that the best-performing approach NOS2R2 offers 15.48% higher entropy and 15.53% more resistance against ICA attack compared to the best existing approach NRoReM. Furthermore, in terms of utility, the accuracy, f1-score, precision and recall of NOS2R2 perturbed data are 42.32%, 31.22%, 30.77% and 16.15% more close to the original data respectively than the NRoReM perturbed data.
A 10.33 μJ/encryption Homomorphic Encryption Engine in 28nm CMOS with 4096-degree 109-bit Polynomials for Resource-Constrained IoT Clients

Homomorphic Encryption (HE) is used to protect sensitive client data during offloaded compute on a potentially untrusted server. Unfortunately, the computational intensity of HE operations quickly depletes the limited resources on IoT clients. Thus, we present an energy-efficient silicon implementation of encryption/decryption in the Brakerski-Fan Vercauteren HE scheme. To support meaningful applications, including several machine learning workloads, we optimize for fixed parameters N =4096 and lo$g_{2}$q =109 through pipelining, multi-level parallelism, and efficient memory accesses. At an energy-optimal operating point of 60MHz and 0. 64V our chip, fabricated in a 2Snm bulk planar CMOS process with an accelerator core area of 1.69 m$\mathrm{m}^{2}$, consumes 10.33 $\mu$J/encryption. Ultimately, this work enables IoT clients to participate in privacy-preserving offloaded compute using client-aided Homomorphic Encryption.
JNPU: A 1.04TFLOPS Joint-DNN Training Processor with Speculative Cyclic Quantization and Triple Heterogeneity on Microarchitecture / Precision / Dataflow

This paper presents JNPU, a 1. 04TFLOPS joint-DNN accelerator that can simultaneously run joint-DNN (MobileNet + GoogLeNet) models with 245FPS (inference) and 1. 26TFLOPS/W (training). It proposes speculative cyclic quantization that enables integer-dominant operations and reduces external memory access by 87.5%. Its tangram dataflow mapper provides optimized sets of heterogeneous stationary types for both forward and backward propagation, enhancing efficiency up to 71.6%. Lastly, its novel processing cluster leverages triple heterogeneity on INT8 arrays and FP16 vector processor, saving 56.3% and 26.9% of computing area and power, respectively.
A 4.27TFLOPS/W FP4/FP8 Hybrid-Precision Neural Network Training Processor Using Shift-Add MAC and Reconfigurable PE Array

This paper presents an energy-efficient FP4/FP8 hybrid-precision training processor. Through hardware-software co-optimization, the design efficiently implements all general matrix multiply (GEMM) operations required for training using only shift-add multiply-accumulate (MAC) units. The reconfigurable processing element (PE) array further improves efficiency by significantly reducing on-chip memory access. The on-chip convolution decomposition technique supports a wide range of kernels using simple homogeneous data routing. Fabricated in 40nm CMOS, the processor achieves 2.61TFLOPS/W real-model efficiency for ResNet-18 training, outperforming prior art by 59%.
PHILOSOPHY OF THE COURSE «SPIRITUALITY AND MORALITY IN THE LIFE OF A PERSON AND SOCIETY»

The article examines the philosophical principles of the new course of moral direction «Spirituality and morality in the life of a person and society» for 5-6 grades of NUS which is identified as an effective resource for spiritual and value development of personality. It is emphasized that the spiritual and moral content of this course is a complex construct that cannot be reduced to a single way of considering it but requires the application of a methodology that focused on integrated vision of problems with the background of the multidimensional ideological space of spiritual and moral education taking into account the complementary achievements of the humanities. Is analyzed the philosophical basis of the system of values, which should guide the process of self-improvement of the individual in the direction of ьachieving a new quality of life, favorable for the spiritual development of both the individual and society. It is evaluated the actualization of the existential potential and interpretative activity of the individual when he or she studying the course for his/her creative self-realization and the acquisition of primary spiritual experience which is especially relevant in the conditions of crisis challenges in today’s society. It has been proven that the content of the course is built on humanistic principles and has a value-integrative character in order to solve the tasks of ethical and aesthetic development, influence on the inner world and actions of the individual, development of the ability for intercultural communication. It is substantiated that the content load of the course is built not on an ideological basis, but on a substantive basis. The authors found a methodological resource for solving the problem of basing spiritual and moral education on values that are not shared by everyone in the use of non-indoctrinal methods that were developed on the principles of phenomenology and constructivism. The phenomenological approach helps students to understand the essence of the spiritual processes that are offered for study. Constructivism ensures the rejection of the desire to inform the student of ideas formulated in a ready-made form. It is noted that the ethical system outlined in the course materials satisfies the intellectual needs of a growing personality regarding the explanation of the world system. It is a system that gives a holistic picture of perception and experience of reality and forms a special view of the world and a person’s place in it.
MATHEMATICAL COMPETENCE MODEL OF A PHYSICS TEACHER

The purpose of this article is to analyze the concept of the physics and astronomy teacher’s mathematical competence structure. Theoretical and experimental methods were used in the research including methods of systemic analysis, pedagogical modeling, testing of statistical hypotheses based on selective studies and descriptive statistics. The research results reveal that the mathematical activity of physics teachers, defined by functional components of subjectmethodological competence, encompasses 20 directions. The importance indices were determined for each of these directions, reflecting the frequency of their application in teachers’ real work. A structural model of the physics teacher’s mathematical competence was constructed. This model includes blocks of subject-methodological and analytical activities, interconnected by feedback loops. Informational connections link these blocks with blocks of analytical, operational, and epistemological functions, as well as with blocks related to the selection of mathematical activities types and directions of mathematical activity. An experimental study of the methodology for measuring the indices of mathematical competence levels was conducted identifying problematic mathematical topics for physics teachers. These topics include expression transformations, elements of differential calculus, geometric problems, and vector-coordinate methods. The practical significance of research lies in the fact that the obtained results facilitate of the postgraduate training system enhancement for teachers in advanced courses, as well as the design and implementation of a methodological system for the development of mathematical competence among physics teachers within the framework of continuous pedagogical education. The analysis conducted in the research also permits the identification of directions for improvement in the training of future physics teachers in classical and pedagogical universities. The main research conclusions, it is noteworthy that an intermediate level of mathematical competence is essential for realizing the functions of subject-methodological competence among physics teachers. Furthermore, it was established that at least 30% of physics teachers require additional mathematical education for their further effective work. Future research in this domain will be focused on the implementation and analysis of the effectiveness of the methodological system for the development of mathematical competence among physics teachers within the framework of continuous pedagogical education.
Developer Productivity for Humans, Part 5: Onboarding and Ramp-Up

In our column thus far, we’ve focused on understanding and measuring productivity in a human-centered manner.1 Along the way, we have noted that the productivity of less senior and less tenured developers is, at least in some cases, sensitive to different pressures (or differentially sensitive to the same pressures) as that of their more senior and more tenured colleagues.2 This finding is intuitive: developers that are earlier in their career are typically assigned different tasks, they have less variety of experience to draw upon when faced with technical or organizational obstacles, and they may be less familiar with relevant tools, infrastructure, languages, libraries, and processes when compared to their more experienced fellow engineers. But how does a developer go from a rookie to a veteran? What facilitates or hinders developer onboarding and ramp-up? How can one assess interventions aimed at speeding up or otherwise improving developer training and education so that new engineers are enabled to hit their productivity stride quicker and more easily?
My REvelation: Unveiling an Unseen Career in Requirements

My requirements engineering (RE) career started before I knew it. After having various positions in the RE field for the last ten years, I know the importance of terminology. Being aware of how things are actually called opens many doors that could otherwise be hidden in plain sight. Let me share my story—and some useful RE challenges and practices I learned along the way. I gained my bachelor's and master's diplomas in electrical and computing engineering in Serbia at the Faculty of Technical Sciences, University of Novi Sad. My curriculum included subjects like "Software Specification and Modeling" and "Information Systems Analysis and Design," which cover a good part of RE—but we never used that term. During my studies, back in 2010, I started working for Schneider Electric as a software engineer for SCADA systems. But, after three years, I transitioned to the SCADA Subject Matter Expert position, which corresponds to an RE role. I just had no clue at the time.
Resource Exhaustion

Engineers in other disciplines are faced with the problem of finite resources and the corresponding risk of overload in their engineered systems. Software engineering is no exception.
Forecasting Photovoltaic Energy for a Winter House Using a Hybrid Deep Learning Model

As part of the energy transition, controlling energy consumption is a challenge for everyone. To this end, a number of sustainable solutions are being proposed, notably for BIPV (Building Integrated Photovoltaics) buildings. In addition, artificial intelligence (AI) is an effective tool for analyzing photovoltaic (PV) energy production and consumption data. It will then be possible to predict the PV energy production of a BIPV building or any other system integrating PV panels. This paper presents the implementation of artificial learning models for the prediction of the very short-term energy production of PV panels in a Positive Energy Winter House (PEWH). These are methods based on multivariate time series, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and a hybrid model. In the case of a winter house with a long period of snow, accurate prediction of solar power output in the very short term is needed to face the fluctuation that can be caused by climate. In this study, we are working on a winter house located in Poschiavo. The proposed method is applied to data recorded in real time by PEWH's photovoltaic solar panels, and the results are compared and tested over period of time. The results confirm the validity of each proposed model in forecasting PV Energy.
Data Augmentation with ECAPA-TDNN Architecture for Automatic Speaker Recognition

This paper focuses on seven data augmentation methods based on the Emphasized Channel Attention Propagation and Aggregation-Time Delay Neural Network (ECAPA-TDNN) model for increasing the diversity of training data to improve model accuracy and true positive rate (TPR/recall). We propose a method to improve classification performance by replacing and reducing the datasets. We also verified the effect of the number of layers on the classification performance by modifying the number of layers of the SE-Res2Block in the ECAPA-TDNN model. The proposed method is validated with the ZhVoice and VoxCeleb datasets, and the results show that the best model accuracy and classification performance can be obtained by using ZhVoice with seven data augmentations on a 3-layer SE-Res2Block. The accuracy reached 0.9477, the TPR reached 0.8945, and the EER was 0.1278. We also used the diagonal cosine algorithm to determine the similarity between two speakers, validating the classification performance of the model.