Reviewerly/exHarmony huggingface dataset:
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images

We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant methods that use the bird-eye view (BEV), our proposed detector detects objects from the range view (RV, a.k.a. range image) of the LiDAR points. Due to the range view's compactness and compatibility with the LiDAR sensors' sampling process on self-driving cars, the range view-based object detector can be realized by solely exploiting the vanilla 2D convolutions, departing from the BEV-based methods which often involve complicated voxelization operations and sparse convolutions. For the first time, we show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors while being significantly faster and simpler. More importantly, almost all previous range view-based detectors only focus on single-frame point clouds, since it is challenging to fuse multi-frame point clouds into a single range view. In this work, we tackle this challenging issue with a novel range view projection mechanism, and for the first time demonstrate the benefits of fusing multi-frame point clouds for a range-view based detector. Extensive experiments on nuScenes show the superiority of our proposed method and we believe that our work can be strong evidence that an RV-based 3D detector can compare favourably with the current mainstream BEV-based detectors.
Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification

The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage. This study addresses the overlapping problem in activities performed by the smart home resident and improves the recognition performance of overlapping activities. The overlapping problem occurs due to less interclass variations (i.e., similar sensors used in more than one activity and the same location of performed activities). The proposed approach overlapping activity recognition using cluster-based classification (OAR-CbC) that makes a generic model for this problem is to use a soft partitioning technique to separate the homogeneous activities from nonhomogeneous activities on a coarse-grained level. Then, the activities within each cluster are balanced and the classifier is trained to correctly recognize the activities within each cluster independently on a fine-grained level. We examine four partitioning and classification techniques with the same hierarchy for a fair comparison. The OAR-CbC evaluates on smart home datasets Aruba and Milan using threefold and leave-one-day-out cross-validation. We used evaluation metrics: precision, recall, F score, accuracy, and confusion matrices to ensure the model’s reliability. The OAR-CbC shows promising results on both datasets, notably boosting the recognition rate of all overlapping activities more than the state-of-the-art studies.
AI and the Conditions of Design: Towards A New Set of Design Ideals

The five papers in the DRS 2022 track "AI and the Conditions of Design: Towards A New Set of Design Ideals" offer radical lenses to change the narrative around AI and open pathways towards pluralist digital futures, signaling redirections for experimenting with more inclusive and imaginative design practices.
Coupling algebraic topology theory, formal methods and safety requirements toward a new coverage metric for artificial intelligence models

Safety requirements are among the main barriers to the industrialization of machine learning based on deep learning architectures. In this work, a new metric of data coverage is presented by exploring the algebraic topology theory and the abstract interpretation process. The algebraic topology connects the cloud points of the dataset, and the abstract interpretation evaluates the robustness of the model. Thus, the coverage metric evaluates simultaneously the dataset and the robustness and highlights safe and unsafe areas. We also propose the first complete process to evaluate, in terms of data completeness, the machine learning models by providing a workflow based on the proposed metric and a set of safety requirements applied on autonomous driving. The obtained results provide an interpretable coverage evaluation and a promising line of research in the industrialization of artificial intelligence models. It is important to mention that the proposed metric is not dependent on the specific data. In other terms, it can be applied on 1 to n-dimensional data.
Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

Hand gesture recognition has attracted the attention of many scientists, because of its high applicability in fields such as sign language expression and human machine interaction. Many approaches have been deployed to detect and recognize hand gestures, like wearable devices, image information, and/or a combination of sensors and computer vision. However, the method of using wearable sensors brings much higher accuracy and is less affected by occlusion, lighting conditions, and complex background. Existing solutions separately utilize sensor information and/or only use sensor information processing and decision-making algorithms over conventional threshold comparison algorithms and do not analyze data or utilize machine learning algorithms. In this paper, a multi-modal solution is proposed that combines information for measuring the curvature of the fingers and sensors for measuring angular velocity and acceleration. The provided information from the sensors is normalized and analyzed and various fusion strategies are used. Then, the most suitable algorithm for these sensor-based multiple modalities is proposed. The proposed system also analyzes the differences between gestures and actions that are almost similar but in fact, they are just normal moving gestures.
Rethinking Igor Stravinsky historically and theoretically — III

The article is a continuation of the author’s reflections on the phenomenon of musical polymorphism (the beginning is in Vol. 124, 2019; the continuation is in Vol. 128, 2020). Stravinsky’s use of the environment, space, motion, dissonance, and Janus morphemes is considered as his inheritance from a tradition dating back to the work of his great predecessors. The musical tableau Sadko by RimskyKorsakov, the introduction Dawn on the Moscow River to Mussorgsky’s Khovanshchina, and Borodin’s symphonic poem In the Steppes of Central Asia are a clear confirmation of this. In Sadko Rimsky-Korsakov reveals himself as the founder of musical polymorphism. The multi-element polymorphism of Mussorgsky’s Dawn on the Moscow River forms the basis of the first tableau in Stravinsky’s Petrushka. In the Steppes of Central Asia is an example of a multi-elemental, polymorphic structure, recreated outside an existing object: a native caravan crossing the desert, guarded by a Russian military detachment. Its stereophonic nature appears in the displacement of the textural elements to the rear and the foreground, their spatial compression or expansion, changes to the acoustic volume, sound coloration. Introductory violins octave unison in In the Steppes of Central Asia displays its hidden timbre-polyphonic nature. In the historical perspective, this compositional discovery by Borodin foreshadows a similar approach in Stravinsky’s musical language. The timbrical layering of the unison can be traced in Dances of the Young Girls from The Rite of Spring, The Lullaby in the Storm from The Fairy’s Kiss. The rhythmic ostinato features of Rimsky-Korsakov’s and Borodin’s scores are developed by Stravinsky to the elaborated part of his musical language. The structures with more or less constant, exact repetitions are used in The Rite of Spring, Three Tales for Children, Three Pieces for String Quartet, The Soldier’s Tale, The Wedding, Symphony of Psalms.
Effect of Employee Commitment on Organizational Performance; Case of Textile Firms in Sweden

Employee commitment helps organizations perform better and achieve their goals because employees feel connected to the organization and are more productive and dedicated to their work. Thus, the study sought to examine the effect of employee commitment on organizational performance in the case of textile firms in Sweden. The study adopted the descriptive research design. The collection of the data was done using the questionnaires. The data was collected from two textile companies in Sweden, namely Meandi Holding AB and Woodline Swedish Outdoor Co. The study conducted a pilot study to examine the validity and reliability of the research instruments. The analysis of the data was done using descriptive and inferential statistics. SPSS was used to generate the output. The study findings indicated that employee commitment is positively and significantly related to organizational performance (β=.1579, p=0.013). Employee commitment is necessary for every organization to have amazing performance on a long-term basis. Committed employees bring added value to the organization, including determination, proactive support, relatively high productivity and an awareness of quality. The study recommended that textile companies should continue ensuring there is employee commitment. Organizations need to increase employee commitment by including them in decision-making, which will ultimately increase their commitment to the organization. Moreover, it is recommended that organizations should encourage transparency. When an organization keeps employees informed, they feel valued and trustworthy. Further, it is recommended that organizations should encourage open and free communication. Open and accessible communication in an organization facilitates an environment of trust. There should be letting the employees be innovative in providing ideas, strategies and ways of communication. The employees should be valued by the organizations and encouraged to develop better ideas and rewards for their achievements and innovation. Keywords: Employee commitment, organizational performance, textile firms, Sweden
Human Capital as a Mediating Factor in the Effects of Green Human Resource Management Practices on Organizational Performance

The goal of this study is to determine the effect of sustainable human resource management (SHRM) on organizational performance (OP) and human capital (HC). 275 employees from various public organizations in Jordan were surveyed. SmartPLS 3 was employed to evaluate the data and test the hypotheses. Herein, it discovered a substantial association between GHRM (green rewards and compensation, as well as green training and development) and OP, as well as a significant relationship between GHRM and HC, as well as a significant relationship between HC and OP. Additionally, HC plays a crucial role in mediating the link between GHRM and OP. The study underlines the importance of introducing GHRM practices into the workplace in order to promote positive green behavior among employees, hence increasing the firm's operating profit.
DEVELOPING AN EDUCATIONAL CULTURAL HERITAGE 3D PUZZLE IN A VIRTUAL REALITY ENVIRONMENT

Abstract. The ways our cultural heritage reserve is preserved and disseminated to the public have changed significantly, with the use of immersive technologies, such as virtual reality environments, and serious games. Nowadays, these technologies are also exploited for developing interactive informative applications, to support historical education and enhance museum visits, physical or virtual, especially to younger generations. The field of edutainment, educational entertainment, has been rapidly developing during the last 10 or 15 years. The main goal of this research is to develop an educational 3D puzzle-like serious game which can operate within a virtual reality environment while aiming towards the dissemination of cultural heritage content to the younger public, i.e., students, children etc., through a pleasant gamification process. The cultural heritage objects used are an ancient Greek temple and a statue of the Roman era, whose high-resolution fully textured 3D models were available from previous projects. The game application was developed in Unity game engine with suitable coding to enable the smooth execution of the 3D puzzle solution. The application verified that it is more interesting to learn about cultural assets through a game than in the conventional ways, and even more when it is implemented within a Virtual Reality environment, where the contact with the assets appears to be more direct and realistic. The same application can also be utilized in different educational areas and can be expanded by the inclusion of other digital assets.
Digital Manipulation and Mental Integrity

This chapter discusses the permissible and impermissible use of software agents to influence internet user behaviour through targeted content such as app notifications, product recommendations, and prices for goods and services. It is argued that impermissible behavioural influence typically involves manipulation, coercion, or deception. However, existing analyses of what explains the wrongness of manipulation, coercion, and deception make ineliminable reference to mental state terms such as intention, which software agents cannot obviously satisfy. A novel account is developed of what explains the wrongness of manipulation, coercion, and deception, on the part of software agents. On this view, morally impermissible instances of behavioural influence by software agents are wrong in virtue of undermining the mental integrity of internet users, which is to say, roughly, that these influence strategies target certain preconditions for authentic decision-making on the part of users.
Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control

Homomorphic encryption with the ability to compute over encrypted data without access to the secret key provides benefits for the secure and powerful computation, storage, and communication of resources in the cloud. One of its important applications is fast-growing robot control systems for building lightweight, low-cost, smarter robots with intelligent brains consisting of data centers, knowledge bases, task planners, deep learning, information processing, environment models, communication support, synchronous map construction and positioning, etc. It enables robots to be endowed with secure, powerful capabilities while reducing sizes and costs. Processing encrypted information using homomorphic ciphers uses the sign function polynomial approximation, which is a widely studied research field with many practical results. State-of-the-art works are mainly focused on finding the polynomial of best approximation of the sign function (PBAS) with the improved errors on the union of the intervals [−1,−ϵ]∪[ϵ,1]. However, even though the existence of the single PBAS with the minimum deviation is well known, its construction method on the complete interval [−1,1] is still an open problem. In this paper, we provide the PBAS construction method on the interval [−1,1], using as a norm the area between the sign function and the polynomial and showing that for a polynomial degree n≥1, there is (1) unique PBAS of the odd sign function, (2) no PBAS of the general form sign function if n is odd, and (3) an uncountable set of PBAS, if n is even.
Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network

Effective extraction and representation of action information are critical in action recognition. The majority of existing methods fail to recognize actions accurately because of interference of background changes when the proportion of high-activity action areas is not reinforced and by using RGB flow alone or combined with optical flow. A novel recognition method using action sequences optimization and two-stream fusion network with different modalities is proposed to solve these problems. The method is based on shot segmentation and dynamic weighted sampling, and it reconstructs the video by reinforcing the proportion of high-activity action areas, eliminating redundant intervals, and extracting long-range temporal information. A two-stream 3D dilated neural network that integrates features of RGB and human skeleton information is also proposed. The human skeleton information strengthens the deep representation of humans for robust processing, alleviating the interference of background changes, and the dilated CNN enlarges the receptive field of feature extraction. Compared with existing approaches, the proposed method achieves superior or comparable classification accuracies on benchmark datasets UCF101 and HMDB51.
DEVELOPMENT OF MATHEMATICAL DIGITAL COMICS WITH ETHNOMATEMATICS APPROACH TO CLASS III ELEMENTARY SCHOOL ON WEIGHT UNIT CONVERSION MATERIAL

Technological developments have affected most aspects of human life, including education. The role of technology in education can be found in the development of learning media. One of the difficulties faced by students is connecting the mathematics they get in school with real life. Digital comics with an ethnomathematical approach can be one of the potential learning media to connect school mathematics with the real world. The purpose of this research is to develop and produce a mathematical digital comic with an ethnomathematical approach for converting weight units from g to kg and vice versa for grade III SD which is suitable for use. The method used is ADDIE. The criteria for evaluating digital comics used are valid, practical, and effective. The selection of research subjects was carried out using a purposive sampling technique and the subjects obtained were 20 third grade elementary school students and two media expert validators as well as material experts. The results showed that the digital comics developed met the valid criteria with a validity level of 80.35%, practical with a practicality percentage of 81.87%, and effective because 100% of students passed the KKM. So it can be concluded that the digital comics developed are suitable for use in mathematics learning activities because they meet the criteria of validity, practicality, and effectiveness and are expected to be used by educators as learning media to increase enthusiasm and improve student learning outcomes.
Application of Improved CycleGAN in Laser-Visible Face Image Translation

CycleGAN is widely used in various image translations, such as thermal-infrared-visible-image translation, near-infrared-visible-image translation, and shortwave-infrared-visible-image translation. However, most image translations are used for infrared-to-visible translation, and the wide application of laser imaging has an increasingly strong demand for laser-visible image translation. In addition, the current image translation is mainly aimed at frontal face images, which cannot be effectively utilized to translate faces at a certain angle. In this paper, we construct a laser-visible face mapping dataset; in case of the gradient dispersion of the objective function of the original adversarial loss, the least squares loss function is used to replace the cross-entropy loss function and an identity loss function is added to strengthen the network constraints on the generator. The experimental results indicate that the SSIM value of the improved model increases by 1.25%, 8%, 0, 8%, the PSNR value is not much different, and the FID value decreases by 11.22, 12.85, 43.37 and 72.19, respectively, compared with the CycleGAN, Pix2pix, U-GAN-IT and StarGAN models. In the profile image translation, in view of the poor extraction effect of CycleGAN's original feature extraction module ResNet, the RRDB module is used to replace it based on the first improvement. The experimental results show that, compared with the CycleGAN, Pix2pix, U-GAN-IT, StarGAN and the first improved model, the SSIM value of the improved model increased by 3.75%, 10.67%, 2.47%, 10.67% and 2.47%, respectively; the PSNR value increased by 1.02, 2.74, 0.32, 0.66 and 0.02, respectively; the FID value reduced by 26.32, 27.95, 58.47, 87.29 and 15.1, respectively. Subjectively, the contour features and facial features were better conserved.
A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process

Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised learning models. Data collected by the multiple sensors can be regarded as multivariate time series or multi-sensor stream data. The high dimension of multi-sensor stream data makes building models even more challenging. In this study, a new and easy-to-apply data augmentation approach, namely, imbalanced multi-sensor stream data augmentation (IMSDA), is proposed for imbalanced learning. IMSDA can generate high quality of failure data for all dimensions. The generated data can keep the similar temporal property of the original multivariate time series. Both raw data and generated data are used to train the failure detection models, but the models are tested by the same real dataset. The proposed method is applied to a real-world industry case. Results show that IMSDA can not only obtain good quality failure data to reduce the imbalance level but also significantly improve the performance of supervised failure detection models.
Grammar‐based autonomous discovery of abstractions for evolution of complex multi‐agent behaviours

This paper presents a grammar-based evolutionary approach that facilitates autonomous discovery of abstractions to learn complex collective behaviours through manageable sub-models. We propose modifications to the design of the genome structure of the evolutionary model and the grammar syntax to facilitate representation of abstractions in separate partitions of a genome. Two learning architectures based on parallel and incremental learning are proposed to automatically derive abstractions. The evaluations conducted with three different complex task environments indicate that the proposed approach with both architectures surpass the performance of generic grammar-based evolutionary models by automatically identifying appropriate abstractions and generating more complex rule structures. The evolutionary process shows further performance improvements with the use of scaffolded environments which were used to train the models in increasingly complex environments across several stages. The results infer that the proposed approach incorporating grammatical evolution with techniques to autonomously discover abstractions can facilitate solving complex problems of agent systems in real-world domains.
Designing new money: Creative transactions on Twitch

There is a wealth of contemporary scholarship pointing to ways in which money and payment media are being rapidly reconfigured through data and technology platforms, towards what Swartz terms 'New Money'.In this article, we look at these developments through the lens of design research and ask: how might we approach the design of new money?And how can design research complement and extend critical sociological work on payment technologies, monetization and new cultural economies?To respond to these questions, we analyse a series of 'creative transactions' that take place on live-streaming platform Twitch.Twitch offers a rich example of payments as they are interweaved with social media.Employing Kow et al.'s (2017) framework for 'transactional attributes' we explore how various forms of payment and exchange in Twitch have been designed and adopted to perform relational work across a 'transactional community'.Through this case study, we identify novel qualities and patterns of 'new money', and propose means and opportunities for designers to engage critically with the design of contemporary payment technologies.