Reviewerly/exHarmony huggingface dataset:
A3SOM, abstained explainable semi-supervised neural network based on self-organizing map

In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are not available in the label set associated with data. In this context, we propose A3SOM, an abstained explainable semi-supervised neural network that associates a self-organizing map to dense layers in order to classify samples. Abstained classification enables the detection of new classes and class overlaps. The use of a self-organizing map in A3SOM allows integrated visualization and makes the model explainable. Along with describing our approach, this paper shows that the method is competitive with other classifiers and demonstrates the benefits of including abstention rules. A use case is presented on breast cancer subtype classification and discovery to show the relevance of our method in real-world medical problems.
“Dancing with Atoms”: A Tribute to Sheila Tinney

Abstract The authors present a short dynamic visualization titled “Dancing with Atoms,” produced in honor of the late Irish mathematical physicist Sheila Tinney (1918–2010), the first Irish-born and -raised woman to receive a doctorate in the mathematical sciences. The visualization is inspired by Tinney’s groundbreaking work on crystal lattice vibrations and consists of an animation showing an atomic lattice structure vibrating based on data derived from a musical piece performed by her son, award-winning pianist Hugh Tinney. The acoustic signal processing and visualization were conducted using the new Science Foundation Ireland–funded “Tinney” high-performance computing cluster in Trinity College Dublin, Ireland.
Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology

Abstract To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.
Quantumness of qubit states interacting with two structured reservoirs in indefinite causal order

Quantumness is studied for single and bipartite states of qubits which interact with the identical structured reservoirs in indefinite causal order. Coherence and temporal steerability are investigated for a single-qubit state. Entanglement, spatial steerability and the Bell-CHSH nonlocality are examined for a bipartite-qubit state. It is shown that the indefinite causal order can reduce the decay of the quantumness of qubit states.
Can't You Tell I am a Human? A Comparison of Common Text and Image CAPTCHAs Using a Low-Fidelity Methodology

Many websites employ CAPTCHAs to limit web robot traffic. Although CAPTCHA challenges may improve security they come at the cost of reduced usability and accessibility. This study set out to explore users’ performance with two common types of CAPTCHAs that rely on image and text challenges. The results show that participants resolved image challenges faster than text challenges. Image challenges were also preferred over text challenges. Overall, each challenge took approximately 10 seconds to solve, and preferences scores were moderate. The error rates were high, but no significant difference was observed. We therefore argue for new methods capable of separating humans from robots that do not rely on user intervention.
Postcards from the Future: Speculating the Future of Built Environments with Citizens

The integration of novel technologies in built environments has increased the complexity of designing cities and spaces with citizens. Unobtrusive and invisible interfaces can lead to new forms of spatial experiences, but also form a black box which is not comprehensible to a wide audience. To address this challenge, we present the results of a study where we employed co-design fiction. By engaging 66 citizens in the critical assessment of futuristic designs of spaces, we reveal their needs and concerns about possible futures. Our findings show that citizens are concerned with the ecological limitations of urban technology, reliability, and data privacy. They also welcome the positive implications of adaptive environments and technological advancement. Participants were also reflective of how urban technology can change human experience, more generally. Our findings point to the need for flexibility, privacy, and adaptability to changing contexts, stimuli, and users in human-building interaction.
A Machine Learning-based Approach for Anomaly Detection in IoT Systems

The increased use of IoT devices has created new hurdles in the detection of anomalies. Anomaly detection is the process of discovering unexpected or abnormal behaviour in a system, and anomalies in IoT systems can be produced by a variety of sources, including hardware and software faults, cyber assaults, and environmental conditions. Machine learning-based approaches for anomaly detection in IoT systems have emerged as a viable option, harnessing the capabilities of machine learning algorithms to detect and categorise anomalies in real-time. However, there are drawbacks to these approaches, such as data quality difficulties, the necessity for real-time analysis, and the possibility of false positives and false negatives. Organizations must carefully analyse the trade-offs associated in their implementation and deployment to overcome these problems. Based on research a review of machine learning-based algorithms for anomaly detection in IoT systems. We explore the problems and potential associated with these approaches, as well as a synopsis of available datasets and models. In addition, the article describes a framework for designing and testing machine learning-based algorithms for anomaly detection in IoT systems. Overall, machine learning-based technologies have the potential to transform the way we detect and respond to abnormalities in IoT systems, but their successful implementation necessitates a cautious and deliberate approach.
IT for Good. How Technology Adoption and Technological Artefacts can support a Local Community: two case studies

In this paper we discuss how ecology of artefacts and their design are connected with motivations and values within a community. We consider artefacts both as social constructs and as tools and means by which communities can pursue their goals. The complex intertwining of artefacts creates ecologies made of interactions between communities, humans and technologies. Our focus is on grassroots communities oriented towards sustainability-related goals, where we explore how the nexus between motivations and community goals is embodied (or not) by the technologies chosen and domesticated by communities. Thus, motivational aspects are all but immaterial or fortuitous. Indeed, they proved to be tangible, context-dependent and they work as infrastructures whose shape continuously change according to the necessities of the community members.
High Performance on a Pragmatic Task May Not Be the Result of Successful Reasoning: On the Importance of Eliciting Participants’ Reasoning Strategies

Formal probabilistic models, such as the Rational Speech Act model, are widely used for formalizing the reasoning involved in various pragmatic phenomena, and when a model achieves good fit to experimental data, that is interpreted as evidence that the model successfully captures some of the underlying processes. Yet how can we be sure that participants' performance on the task is the result of successful reasoning and not of some feature of experimental setup? In this study, we carefully manipulate the properties of the stimuli that have been used in several pragmatics studies and elicit participants' reasoning strategies. We show that certain biases in experimental design inflate participants' performance on the task. We then repeat the experiment with a new version of stimuli which is less susceptible to the identified biases, obtaining a somewhat smaller effect size and more reliable estimates of individual-level performance.
Inception Network for Anthropometric Body Measurements Estimation from Structured Point Clouds

The need for an automatization in anthropometric body measurements estimation mainly arises from the time-consuming and inefficient process of manual tape measuring of a human body. An automated method for computing the body measurements makes the applications more easily accessible to the end-users, without a need to be familiar with the measuring standards. The lack of standardization is also a big drawback of existing methods in the field. To address the task of body measurements estimation from visual data, we present an end-to-end deep learning-based model built from inception modules to accurately extract values for a pre-defined set of body dimensions from a single grid-structured point cloud. Such data structure jointly combines the benefits of a grid structure as present in 2D images, and the 3D real world coordinates contained in 3D data. We evaluate our method on a large-scale dataset of virtually scanned synthesized human bodies, while reaching the mean absolute test error of approximately 4.5 mm.
Enhancing Accessibility with Informed VR and AR Authoring for Hybrid Geometry Learning

We use educational VR and AR solutions in hybrid learning and authoring and present several approaches for teachers enabling them to better engage their students in the educational process. We propose ideas for working with students with attention difficulties and multiple means to intentionally hold their attention. Methodological suggestions are provided for teachers helping them to improve their lectures and thinking in terms of rhetoric and semiotics.
Phenomenology of Culture and Issues of Culturology in L.N. Kogan’s Academic Work

The article examines the creative and theoretical aspects of the scientific activity of Lev Naumovich Kogan (1923—1997), an outstanding Russian scholar, Doctor of Philosophy, Professor of the Ural State University. The uniqueness of L.N. Kogan’s creative activity was rooted not only in his manifold scholarly interests, which included philosophy and culturology, political science and sociology, ethics and aesthetics, but also in his special spiritual potential, manifested both in research and in pedagogical areas. The methodological basis of the scholar’s theoretical heritage was culture — creative and political, economic and ecological, everyday and industrial. The article shows that L.N. Kogan’s interest in phenomenology of culture coincided with the period of emergence and development of Russian culturology (1960s—1980s), when the field of humanities engaged in exploration and development of its specific culturological methodology and when the first conceptual studies in the theory and history of culture were published. The author points out that in his works, L.N. Kogan raised a wide range of humanistic ideas — the purpose and meaning of human life, the issues of an individual’s socialization and self-realization, the moral responsibility of a person before History, before Eternity. L.N. Kogan’s academic legacy, which includes about 450 works published in the USSR, in Russia and abroad, is still relevant in the theoretical and practical space of culturology and other humanities.
The Specifics of the Director’s Work with a Central Figure in Film and TV Documentaries

The topic of the director’s work with a documentary’s central figure on the screen, in the conditions of active distribution of visual content, especially in the genre of interviews, is undoubtedly relevant. The interviewee’s preparation for filming, the methods of their emancipation, the achievement of the ultimate goal and the truth of life on the screen require detailed study, professional knowledge based on the experience of the classics of documentary film directing — D. Lunkov, Yu. Shiller, I. Belyaev, A. Lysenko, etc. Long-forgotten techniques are closely intertwined with innovative ideas of directors. These include what behavior the interviewer or presenter decides to adhere to. Depending on the combination of factors, the tone and course of on-screen conversation in different genres may change. By adopting a tough model of behavior, the presenter can make the conversation’s tone tense and, at the same time, more dynamic and sharp, while a soft behavioral model can make the conversation more sincere and friendly. The article analyzes the specifics of the director’s work with a central figure in the process of creating documentary screen works. The authors attempt to comprehend the experience of previous generations of documentary filmmakers, so that young specialists can take into account their mistakes and achieved results when working with the so-called non-actor — a documentary’s central figure in the audiovisual screen space. The article concludes that, despite the fact that the solutions to complex problems in the work of a director with a documentary’s central figure have already been described by leading practitioners and theorists in the field of audiovisual screen arts, the process of forming a central figure in film and television documentaries is not over. There are controversial points of view on a number of aspects of disclosing the personality of a documentary’s central figure, their emancipation on camera and the truth of life on the screen. Therefore, relying on the previous experience of the professionals and taking into account the emergence of new technologies, the younger generation of directors needs to use already well-proven techniques in their work, as well as try to find and apply new approaches (techniques) when creating documentary screen works having a central figure.
Design and implementation of an electromechanical gear system for optimization of automatic phase-shifting of a Dual Aperture Common-Path Interferometer

In this work, we report experimental results of a proposed gear system for optimization of phase shifts used for extraction of phase between a reference and a test object in a Double Aperture Common Path Interferometer. The electromechanical approach is designed to increase the precision and control of automated transverse translation of a diffraction grating to obtain equidistant displacements, typically carried out manually. Calculations and analytics for the design and operation of the mechanical gear system are presented. The experimental results demonstrate significant error minimizing for equally spaced phase shifts compared to a previously reported implementation of our research group. The proposed system represents a reliable and low-cost implementation to improve the obtaining of suitable interferogram captures for equally spaced phase-shifting interferometry techniques.
Research on multimodal clustering method for E-commerce review

In an increasingly information-based modern society, online shopping has gradually become the first choice for people to buy goods. Therefore, more and more e-commerce platforms have emerged, such as Tmall, JD, etc., and e-commerce economy has gradually become an important part of promoting social progress. Since most e-commerce reviews are short texts, it takes a lot of time for consumers to search for useful information for themselves. Therefore, this paper clusters short texts for the first time, and takes the user's review text as the input of the model to get the clustering results. The review text with similar views will be grouped into the same cluster, At the same time, we can get the topic words in each cluster, that is, where consumers pay most attention in the consumption process. Due to the large number of comment texts, we use Dirichlet and multinomial mixed model as the first clustering model, because this model does not need to set the clusters number in advance, and has good performance on large-scale data sets. For the results of the first clustering, although consumers' concerns can be analysed, it is difficult to judge consumers' attitudes, so we take the subject words in the clustering results as input to form a new short text, use TF-IDF to extract the features of the short text constructed by the subject words, then use K-means method to judge the emotional polarity, and then use homogeneity, integrity NMI effectively evaluates the clustering results. Through the experimental results of two clusters, we can clearly find out where consumers pay most attention after purchasing a certain commodity. For example, after purchasing clothes, most consumers focus on the materials and brands of clothes. After clustering, we can get the clustering results of all consumers and the clustering subject words, that is, consumers in the same cluster have similar concerns about goods. After the second clustering, the emotional polarity of consumers can be analyzed, which is of positive significance to consumers' purchase decisions.
A replication study on the intuitiveness of programming language syntax

Abstract In this article, we present a replication of an empirical experiment that evaluates intuitiveness and comprehensibility of keywords relating to different concepts in programming languages, originally conducted by Stefik and Gellenbeck. Novice programmers face many barriers when learning programming. One of these barriers is syntax, which for many languages is not designed based on empirical evidence. The purpose of the experiment was to provide more empirical evidence on the subject, to find out if the results of the original experiment can be replicated and if conducting the experiment in an environment where English is not the native language affects the results. The results of our experiment replicated most of the findings of the original study and provided further evidence that some syntactic choices in many popular programming languages are unintuitive for novice programmers. Our results suggest that the native language of participants who otherwise had good English skills had little effect when compared to the original study. These results may support programming language designers in making evidence-based design decisions and teachers of introductory programming courses in identifying some of the barriers novice programmers face.
$$\varvec{Brings~It~About~That}$$ Operators Decomposed with Relating Semantics

Abstract In the paper we examine the problem of logical systems that are extensions of Classical Propositional Logic with new, intensional connectives of agency: monadic and dyadic bringing it about that . These systems are usually studied within the neighbourhood semantics. Here we propose a different strategy. We study all of the accepted laws and rules of logic of agency and define a translation of the agency operators into connectives interpreted in relating semantics. After this translation we can make a reduction to more basic semantic properties that are required by the particular groups of laws and axioms. Finally, we define proper semantic structures and prove that they are complete with respect to all possible logical systems determined by the combinations of the axioms and rules.
Implementation and optimization of ORB-SLAM2 algorithm based on ROS on mobile robots

ORB-SLAM2 based on depth camera cannot run directly in ROS operating system, and when the algorithm is implemented through PC, it does not efficiently carry computer resources, and reduces the flexibility of mobile robots. By building a ROS operating system in the embedded system, porting the ORB-SLAM2 algorithm in the system, optimizing the algorithm, adjusting the CPU usage of the algorithm operation, and using a mobile robot based on depth camera for mapping. The results show that the grid map generated by the ROS operating system can meet the needs of robot autonomous navigation, the SLAM effect is more intuitive, the system is more flexible, and the cost and hardware configuration requirements are greatly reduced. It is proved that the optimized algorithm is closer to the actual usage requirements.
Pose+Context: A Model for Recognizing Non-verbal Teaching Behavior of Normal College Student

Normal college students practice their teaching skills to prepare for their teacher career. However, due to the complexity of teaching skills and the overburdening of instructors, their needs for instructional feedback are often not met. To server automatic feedbacks to normal college students, this paper proposes a deep learning model, called Pose+Contex, to recognize three types of non-verbal teaching behaviors (NVTB). The model includes three parts: (1) context detection, (2) pose estimation, and (3) behavior recognition. Our model is featured by the context detection component, and experiments show that it performs better than a similar model without this component.
Efficient Human Activity Recognition Based on Grouped Representations of Multimodal Wearable Data

Human Activity Recognition (HAR) is a vast and complex research domain that has multiple applications, such as healthcare, surveillance or human-computer interaction. Several sensing technologies exist to record data later used to recognize people’s activity. This paper aims to linger over the specific case of HAR based on multimodal wearable sensing devices. Corresponding HAR datasets provide multiple sensors information collected from different body parts. Previous approaches consider each information separately or altogether. Vision HAR methods consider each body segment and their position in space in order to perform activity recognition. This paper proposes a similar approach for Multimodal Wearable HAR (MW-HAR). Datasets are first re-sampled at a higher sampling rate (i.e., lower frequency) in order to both decrease the overall processing time and facilitate interpretability. Then, we propose to group sensing features from all the sensors corresponding to the same body part. For each group, the proposal determines a different representation realm of the group information. This abstracted representation depicts the different states of the corresponding body part. Finally, activity recognition is performed based on these trained abstractions of each considered body part. We tested our proposal on three benchmark datasets. Our evaluations first confirmed that a re-sampled dataset offers similar or even better performance for activity recognition than usual processing. But the primary advantage is to decrease significantly the training time. Finally, results show that a grouped abstraction of the sensors features is improving the activity recognition in most cases, without increasing training time.