Reviewerly/exHarmony huggingface dataset:
Thing Constellation Visualizer: Exploring Emergent Relationships of Everyday Objects

Designing future IoT ecosystems requires new approaches and perspectives to understand everyday practices. While researchers recognize the importance of understanding social aspects of everyday objects, limited studies have explored the possibilities of combining data-driven patterns with human interpretations to investigate emergent relationships among objects. This work presents Thing Constellation Visualizer (thingCV), a novel interactive tool for visualizing the social network of objects based on their co-occurrence as computed from a large collection of photos. ThingCV enables perspective-changing design explorations over the network of objects with scalable links. Two exploratory workshops were conducted to investigate how designers navigate and make sense of a network of objects through thingCV. The results of eight participants showed that designers were actively engaged in identifying interesting objects and their associated clusters of related objects. The designers projected social qualities onto the identified objects and their communities. Furthermore, the designers changed their perspectives to revisit familiar contexts and to generate new insights through the exploration process. This work contributes a novel approach to combining data-driven models with designerly interpretations of thing constellation towards More-Than Human-Centred Design of IoT ecosystems.
Few‐shot learning with relation propagation and constraint

Previous deep learning methods usually required large-scale annotated data, which is computationally exhaustive and unrealistic in certain scenarios. Therefore, few-shot learning, where only a few annotated training images are available for training, has attracted increasing attention these days, showing huge potential in practical applications, such as portable equipment or security inspection, and so on. However, current few-shot learning methods usually neglect the valuable semantic correlations between samples, thereby failing in extracting discriminating relations to achieve accurate predictive results. In this work, extending on a recent state-of-the-art few-shot learning method, transductive relation-propagation network (TRPN), which considers the correlations between training samples, a constrained relation-propagation network is proposed to further regularise the distilled correlations and thus achieve favourable few-shot classification performance. The proposed framework contains three main components, namely preprocess module, relational propagation module, and relation constraint module. First, sample features are extracted and a relation graph node is constructed by treating the relation of each support–query pair as a graph node in the preprocess module. After that, in the relation propagation module (RPM), the valuable information of support–query pairs is modelled and propagated to directly generate the relational representations for further prediction. Then, a relation constraint module is introduced to regularise the relational representations and make it consistent with the ground-truth relations as much as possible. With the guidance of the effective RPM and relation constraint module, the relational representations of the support–query pairs are distinguishable and thus can achieve accurate predictive results. Comprehensive experiments conducted on widely used benchmarks validate the effectiveness of our method compared to state-of-the-art few-shot classification approaches.
Implementação de teleatendimento em saúde mental para estudantes de Medicina durante a pandemia da Covid-19

Abstract: Introduction: The COVID-19 pandemic can be considered a severely stressful event and trigger negative repercussions on the mental health of medical students, such as psychological distress and the development or worsening of mental disorders, harming the academic, social and professional life of these students. As a result of the interruption of classes and the social distancing measures advocated by health agencies during the pandemic, the mental health care sector for medical students at the Federal University of Rio de Janeiro (UFRJ) needed to cancel the face-to-face care at the Clementino Fraga Filho University Hospital in early March 2020 and think of other forms of mental health care for these students. Experience report: This is an experience report about the implementation of telemental health care for medical students at UFRJ during the pandemic, for the continuity of mental health care program using remote assistance, started in late March 2020. The service is being offered by a team of five psychiatrists, a psychologist and a social worker, all university employees. Discussion: The teleservice has served as an important space for listening and embracement in face of these students’ psychosocial demands, whose challenge consists in overcoming some barriers that hinder the availability of and access to mental health services on the university campus, including the preservation of the doctor-patient relationship, the guarantee of confidentiality and quality, and the offer of a space for mental health care when the physical presence is not possible. Conclusion: Despite the difficulties inherent in the rapid process of implementing this service, the potential of technology to help the population at this critical moment is perceived, especially regarding the attention to the mental health of specific groups, such as medical students. The telehealth represents a potential for learning and change in the ways how the access to care is offered, with the perspective of bringing benefits to the students’ mental health, even after the current period of the pandemic, with the goal of expanding these services to other courses of the UFRJ.
DERIVED TERMS WITHOUT DERIVATION A SHIFTED PERSPECTIVE ON THE DERIVED-TERM AUTOMATON

We present here a construction for the derived term automaton (aka partial derivative, or Antimirov, automaton) of a rational (or regular) expression based on a sole induction on the depth of the expression and without making reference to an operation of derivation of the expression. It is particularly well-suited to the case of weighted rational expressions.
Understanding Human-side Impact of Sampling Image Batches in Subjective Attribute Labeling

Capturing human annotators' subjective responses in image annotation has become crucial as vision-based classifiers expand the range of application areas. While there has been significant progress in image annotation interface design in general, relatively little research has been conducted to understand how to elicit reliable and cost-efficient human annotation when the nature of the task includes a certain level of subjectivity. To bridge this gap, we aim to understand how different sampling methods in image batch labeling, a design that allows human annotators to label a batch of images simultaneously, can impact human annotation performances. In particular, we developed three different strategies in forming image batches: (1) uncertainty-based labeling (UL) that prioritizes images that a classifier predicts with the highest uncertainty, (2) certainty-based labeling (CL), a reverse strategy of UL, and (3) random, a baseline approach that randomly selects images. Although UL and CL solely select images to be labeled from a classifier's point of view, we hypothesized that human-side perception and labeling performance may also vary depending on the different sampling strategies. In our study, we observed that participants were able to recognize a different level of perceived cognitive load across three conditions (CL the easiest while UL the most difficult). We also observed a trade-off between annotation task effectiveness (CL and UL more reliable than random) and task efficiency (UL the most efficient while CL the least efficient). Based on the results, we discuss the implications of design and possible future research directions of image batch labeling.
Is Projection Mapping Natural? Towards Physical World Augmentation Consistent with Light Field Context

Projection mapping seamlessly merges real and virtual worlds. Although much effort was made to improve its image qualities so far, projection mapping is still unnatural. We introduce the first steps towards natural projection mapping by making the projection results consistent with the light field context of our daily scene.
The Next Generation Multimodal Conversational Search and Recommendation

The world has become multimodal. In addition to text, we have been sharing a huge amount of multimedia information in the form of images and videos on the Internet. The wide spread use of smart mobile devices has also changed the way we interact with the Internet. It is now natural for us to capture images and videos freely and use as part of a query, in addition to the traditional text and voices. These, along with the rapid advancements in multimedia, natural language processing, information retrieval, and conversation technologies, mean that it is time for us to explore multimodal conversation and its roles in search and recommendation. Multimodal conversation has the potential to help us to uncover and digest the huge amount of multimedia information and knowledge hidden within many systems. It also enables a natural 2-way interactions between humans and machines, with mutual benefits in enriching their respective knowledge. Finally, it opens up the possibilities of disrupting many existing applications and launching new innovative applications. This panel is timely and aims to explore this emerging trend, and discuss its potential benefits and pitfalls to society. The panel will also explore the limitations of current technologies and highlight future research directions towards developing a multimedia conversational system.
Comprehensive assessment of the lifestyle of students studying at the Agrarian University

The article presents the results of a cross-sectional study of the lifestyle of students of an agricultural university. A quantitative assessment (in points) of the components of the way of life is given. In total, 5 blocks were identified that are significant in the formation of students’ health: healthy nutrition, the value of daily physical activity, daily routine, adherence to the rules of personal hygiene and the absence of bad habits. The data obtained indicate that of the total number of factors that have a significant impact on the formation of a healthy lifestyle of students, are nutrition, physical activity, and daily routine. First-year students were found to have: violation of the frequency and regularity of meals, rare use of hot food at the university, deficit of motor activity, insufficient duration of night sleep, especially severe violations in the group of girls. The study made it possible to identify risk groups among students as a whole in terms of lifestyle, as well as in its individual components that require correction. It was found that the number of students leading a healthy lifestyle (option I) is higher among 5th year boys and 3th year girls. A lifestyle with a risk to health (option III) is more typical for 1st year girls. The performed correlation analysis showed the presence of connections between the factors «nutrition», «daily physical activity», «daily routine», as well as their individual components, more pronounced in the group of 3 and 5 year students. The obtained data are presented to the administration of the university for the joint development of a complex of preventive and health-improving measures, taking into account the priority behavioral risks characteristic of students of different age and sex groups.
Automatikus kézírás-felismertetés Kiss József levelezésén

The digital edition of the József Kiss correspondence is a pilot project of the Centre for Digital Humanities, Petőfi Literary Museum. In addition to the processing of the personal and professional letters of the 19th-century writer, poet, and editor of the literary journal A Hét (The Week), the project is to explore the possibilities offered by the Transkribus software. Handwritten Text Recognition is an emerging field of the digital humanities. The paper will discuss this artificial intelligence-based technology and our experiences in creating a Hungarian model. The best result has a 6,94% character error rate. Apart from the practical experience gained in testing, the paper discusses the possibilities of implementing HTR in public collections.
A learning health system approach to <scp>COVID</scp> ‐19 exposure notification system rollout

Digital exposure notification (EN) approaches may offer considerable advantages over traditional contact tracing in speed, scale, efficacy, and confidentiality in pandemic control. We applied the science of learning health systems to test the effect of framing and digital means, email vs Short Message Service (SMS), on EN adoption among patients of an academic health center.We tested three communication approaches of the Apple and Google EN system in a rapid learning cycle involving 15 000 patients pseudorandomly assigned to three groups. The patients in the first group received a 284-word email that presented EN as a tool that can help slow the spread. The patients in the second group received a 32-word SMS that described EN as a new tool to help slow the spread (SlowTheSpreadSMS). Patients in the third group received a 47-word SMS that depicted the system as a new digital tool that can empower them to protect their family and friends (EmpowerSMS). A brief four-question anonymous survey of adoption was included in a reminder message sent 2 days after the initial outreach.One hundred and sixty people responded to the survey within 1 week: 2.33% from EmpowerSMS, 0.97% from SlowTheSpreadSMS, and 0.53% from emails; 29 (41.43%), 24 (41.38%), and 11 (34.38%) reported having adopted EN from each group, respectively. Patient reported barriers to adoption included iOS version incompatibility, privacy concerns, and low trust of government agencies or companies like Apple and Google. Patients recommended that healthcare systems play an active role in disseminating information about this tool. Patients also recommended advertising on social media and providing reassurance about privacy.The EmpowerSMS resulted in relatively more survey responses. Both SMS groups had slightly higher, but not statistically significant EN adoption rates compared to email. Findings from the pilot not only informed operational decision-making in our health system but also contributed to EN rollout planning in our State.
Collaboration around Multi-touch Spherical Displays: A Field Study at a Science Museum

Multi-touch spherical displays that enable groups of people to collaboratively interact are increasingly being used in informal learning settings such as museums. Prior research on large flatscreen displays has examined group collaboration patterns in museum settings to inform the design of group learning experiences around these displays. However, previous research has shown differences in how users conceptualize interacting with spherical and flatscreen displays, thereby making it important to separately investigate how groups naturally collaborate around spherical displays in a museum setting. The spherical form factor of the display affords new forms of collaboration: unlike flatscreen displays, spherical displays do not have a definite front or center, thus intrinsically creating both shared and private touch interaction areas on the display based on users' viewing angles or physical arrangements. We conducted a 5-day long field study at a local science museum during which 571 visitors (370 adults and 201 children) in 211 groups interacted with a walk-up-and-use collaborative learning application showing global science data visualizations, on a multi-touch spherical display. We qualitatively analyzed groups' natural collaboration patterns including their physical arrangements (F-formations), their collaboration profiles (e.g., turn-taker or independent), and the nature of group discussion around the display. Our results show that groups often engaged in both independent as well as closely collaborative group explorations when interacting around the sphere: physical spacing between group members around the sphere was strongly linked to the way groups collaborated. It was less common for group members to make and accept suggestions or coordinate touch interactions when they did not share the same fields-of-view or touch interaction space with each other around the sphere. We discuss implications for supporting group collaboration in this context which will inform the design of future walk-up-and-use multi-touch spherical display applications for use in public settings.
Error correction of glass mediums in high-temperature digital image correlation deformation measurement

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
Optimized Adversarial Example With Classification Score Pattern Vulnerability Removed

Neural networks provide excellent service on recognition tasks such as image recognition and speech recognition as well as for pattern analysis and other tasks in fields related to artificial intelligence. However, neural networks are vulnerable to adversarial examples. An adversarial example is a sample that is designed to be misclassified by a target model, although it poses no problem for recognition by humans, that is created by applying a minimal perturbation to a legitimate sample. Because the perturbation applied to the legitimate sample to create an adversarial example is optimized, the classification score for the target class has the characteristic of being similar to that for the legitimate class. This regularity occurs because minimal perturbations are applied only until the classification score for the target class is slightly higher than that for the legitimate class. Given the existence of this regularity in the classification scores, it is easy to detect an optimized adversarial example by looking for this pattern. However, the existing methods for generating optimized adversarial examples do not consider their weakness of allowing detectability by recognizing the pattern in the classification scores. To address this weakness, we propose an optimized adversarial example generation method in which the weakness due to the classification score pattern is removed. In the proposed method, a minimal perturbation is applied to a legitimate sample such that the classification score for the legitimate class is less than that for some of the other classes, and an optimized adversarial example is created with the pattern vulnerability removed. The results show that using 500 iterations, the proposed method can generate an optimized adversarial example that has a 100&#x0025; attack success rate, with distortions of 2.81 and 2.23 for MNIST and Fashion-MNIST, respectively.
Observer‐based optimal adaptive control for multimotor driving servo systems

Abstract An adaptive control strategy based on the optimal sliding surface is designed for multimotor driving servo systems in this article. By dividing the system into load subsystem and motor subsystem and decomposing the tracking performance index, the optimal control problem of a high‐order systems is simplified into a second‐order system optimal control problem. Then based on the decomposed performance indexes, the optimal sliding surfaces are determined by solving two algebraic Riccati equations. Combined with an adaptive gain, an optimal adaptive tracking controller is designed, which can reduce the settling time and the energy consumption with a small overshoot. In addition, to obtain the unmeasurable angular velocities of motors and the load, an leaky echo state network‐based observer is applied that ensures fast and accurate estimation. Furthermore, to solve the synchronization problem of the multimotor driving servo systems, the mean relative coupling synchronization controller is designed. The simulation and experimental results have demonstrated that the presented control scheme is effective.
Controlled cyclic remote state preparation of single-qutrit equatorial states

The scheme for controlled unidirectional cyclic remote state preparation of single-qutrit equatorial states is put forward. Alice, Bob, Charlie, and David share a seven-qutrit entangled state as the quantum channel. Under the control of David, Alice can remotely prepare a single-qutrit equatorial state at Bob’s site, Bob can remotely prepare a single-qutrit equatorial state at Charlie’s site, Charlie can remotely prepare a single-qutrit equatorial state at Alice’s site simultaneously. The direction of controlled unidirectional cyclic remote state preparation can be reversed by changing measured qutrits of the quantum channel. The scheme for controlled bidirectional cyclic remote state preparation of single-qutrit equatorial states is also proposed. The schemes can be generalized to controlled unidirectional and bidirectional multi-party cyclic remote state preparation of single-qudit equatorial states.
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction Following.

We study the problem of learning a robot policy to follow natural language instructions that can be easily extended to reason about new objects. We introduce a few-shot language-conditioned object grounding method trained from augmented reality data that uses exemplars to identify objects and align them to their mentions in instructions. We present a learned map representation that encodes object locations and their instructed use, and construct it from our few-shot grounding output. We integrate this mapping approach into an instruction-following policy, thereby allowing it to reason about previously unseen objects at test-time by simply adding exemplars. We evaluate on the task of learning to map raw observations and instructions to continuous control of a physical quadcopter. Our approach significantly outperforms the prior state of the art in the presence of new objects, even when the prior approach observes all objects during training.