Reviewerly/exHarmony huggingface dataset:
Multi-task learning for aspect level semantic classification combining complex aspect target semantic enhancement and adaptive local focus

Aspect-based sentiment analysis (ABSA) is a fine-grained and diverse task in natural language processing. Existing deep learning models for ABSA face the challenge of balancing the demand for finer granularity in sentiment analysis with the scarcity of training corpora for such granularity. To address this issue, we propose an enhanced BERT-based model for multi-dimensional aspect target semantic learning. Our model leverages BERT's pre-training and fine-tuning mechanisms, enabling it to capture rich semantic feature parameters. In addition, we propose a complex semantic enhancement mechanism for aspect targets to enrich and optimize fine-grained training corpora. Third, we combine the aspect recognition enhancement mechanism with a CRF model to achieve more robust and accurate entity recognition for aspect targets. Furthermore, we propose an adaptive local attention mechanism learning model to focus on sentiment elements around rich aspect target semantics. Finally, to address the varying contributions of each task in the joint training mechanism, we carefully optimize this training approach, allowing for a mutually beneficial training of multiple tasks. Experimental results on four Chinese and five English datasets demonstrate that our proposed mechanisms and methods effectively improve ABSA models, surpassing some of the latest models in multi-task and single-task scenarios.
Customised Concept Weighting: A Neural Network Approach

The aim of concept weighting in ontologies or in other data graphs is to characterise the importance of each concept in a specific domain, and to determine its selective power. This is particularly useful for data analysis tasks. Existing works on concept weighting mainly exploit either the graph’s structure or the frequency of the concept in the data instances. These works provide concept weights independently form the considered analysis task. We argue that these weights should vary according to the targeted task, and we introduce a neural network based approach which computes concept weights using regression on a customised multi-layered structure. The loss function used in the approach is specified according to a given labelling of the elements in the considered dataset. In this paper, we present the principles of our weighting approach and we report on some experiments showing its effectiveness on real data extracted from the national library of France describing the documents’ conservation histories.
Research on Experimental Education for Measuring Metal Resistivity

Education, as an important part of national economic development and the main method for cultivating specialized talents, plays a leading role in promoting the construction of ideological and moral civilization and socialist construction with Chinese characteristics. Based on this, this paper takes the experiment of measuring metal resistivity in high school physics teaching as the main research object, analyzes the method of detecting metal resistivity, further standardizes the detection process of metal resistivity, improves the accuracy of daily detection data, comprehensively enhances students' physics knowledge, and promotes the sustainable development of education.
Exploring the Practical Path of "Industry-University-Research-Use" in the Field of Internet+ for the Joint Construction of Business Administration Programmes by Schools and Enterprises

With the arrival of the Internet+ era, the cooperation between higher education institutions and enterprises to build business administration majors and practice the "industry-academia- research-utilisation" mode has become an important way to promote industrial upgrading and to shape the cultivation of talents adapted to the needs of the times. The purpose of this paper is to discuss the urgency and reasonableness of the joint establishment of business administration majors by universities and enterprises under the perspective of Internet+, to analyse in detail the practical application of the "industry-academia-research-utilization" model in the field of this major, and to put forward a series of practicable paths of practice. This will help cultivate students with more practical application ability and innovative thinking, promote industrial development, and lead the development direction of talent training and industrial integration in the future.
Application of Virtualization Technology in University Data Centers

This paper explores the application of virtualization technology in the construction of university data centers. Firstly, the problems of traditional physical server architecture in data centers are analyzed, including low resource utilization, poor fault tolerance, high construction costs, and complex system maintenance. Then, an overview of virtualization technology is provided, including concepts such as compute virtualization, network virtualization, and storage virtualization. Next, using China University of Geosciences (Beijing) as an example, the specific implementation and effects of data center virtualization based on the VMware vSphere platform are introduced. Finally, the significant benefits of virtualization technology in university data center construction are summarized, including cost savings, improved operational and management efficiency, simplified administration, and enhanced resource utilization. This paper comprehensively and systematically elucidates the advantages of virtualization technology in addressing the challenges faced by university data centers, providing valuable insights for the planning and construction of university data centers.
A Study on the Ways and Methods to Improve the New Question Type "Continuing Writing after Reading" in the Context of the New College Entrance Examination

In the continuous promotion of the new college entrance examination reform, English test paper propositions pay more attention to the examination of students' English comprehension ability, knowledge application ability, and knowledge system construction ability, and tend to cultivate students' comprehensive abilities in the thematic context. The design of question types that combine reading and writing requires high school English teachers to accurately control the content of the new textbook, pave the way for "reading and writing", find the right direction for "writing", help students through English learning activities such as learning comprehension, application practice, and transfer innovation, guide students to deeply think and explore the text, cultivate students' creative thinking, and improve their comprehensive English application ability. This article adopts action research methods, including pilot research and two rounds of action research, and collects data through field research, experiments, and questionnaire surveys. Through two rounds of action research, focusing on the content and structure of continuation, as well as the influencing factors of continuation language, in order to improve students' reading and writing abilities.
Genome-Scale Algorithms

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Towards metropolitan free-space quantum networks

Abstract Quantum communication has seen rapid progress towards practical large-scale networks, with quantum key distribution (QKD) spearheading this development. While fibre-based systems have been shown to be well suited for metropolitan scales, suitable fibre infrastructure may not always be in place. Here, we make the case for an entanglement-based free-space quantum network as a practical and efficient alternative for metropolitan applications. We developed a deployable free-space QKD system and demonstrated its use in realistic scenarios. For a representative 1.7-km free-space link, we showcase its ad hoc deployability and achieve secure key rates of up to 5.7 kbps, with 2.5 kbps in direct noon sunlight. By extrapolating experimental data, we show that kbps key rates are achievable even for 10-km distances and multi-user scenarios. We anticipate that our work will establish free-space networks as a viable solution for metropolitan applications and an indispensable complementary building block in the future global quantum internet.
TAO

Translating fine-grained activity detection (e.g., phone ring, talking interspersed with silence and walking) into semantically meaningful and richer contextual information (e.g., on a phone call for 20 minutes while exercising) is essential towards enabling a range of healthcare and human-computer interaction applications. Prior work has proposed building ontologies or temporal analysis of activity patterns with limited success in capturing complex real-world context patterns. We present TAO, a hybrid system that leverages OWL-based ontologies and temporal clustering approaches to detect high-level contexts from human activities. TAO can characterize sequential activities that happen one after the other and activities that are interleaved or occur in parallel to detect a richer set of contexts more accurately than prior work. We evaluate TAO on real-world activity datasets (Casas and Extrasensory) and show that our system achieves, on average, 87% and 80% accuracy for context detection, respectively. We deploy and evaluate TAO in a real-world setting with eight participants using our system for three hours each, demonstrating TAO's ability to capture semantically meaningful contexts in the real world. Finally, to showcase the usefulness of contexts, we prototype wellness applications that assess productivity and stress and show that the wellness metrics calculated using contexts provided by TAO are much closer to the ground truth (on average within 1.1%), as compared to the baseline approach (on average within 30%).
E3D

The increase of distributed embedded systems has enabled pervasive sensing, actuation, and information displays across buildings and surrounding environments, yet also entreats huge cost expenditure for energy and human labor for maintenance. Our daily interactions, from opening a window to closing a drawer to twisting a doorknob, are great potential sources of energy but are often neglected. Existing commercial devices to harvest energy from these ambient sources are unaffordable, and DIY solutions are left with inaccessibility for non-experts preventing fully imbuing daily innovations in end-users. We present E3D, an end-to-end fabrication toolkit to customize self-powered smart devices at low cost. We contribute to a taxonomy of everyday kinetic activities that are potential sources of energy, a library of parametric mechanisms to harvest energy from manual operations of kinetic objects, and a holistic design system for end-user developers to capture design requirements by demonstrations then customize augmentation devices to harvest energy that meets unique lifestyle.
VibPath

Technical advances in the smart device market have fixated smartphones at the heart of our lives, warranting an ever more secure means of authentication. Although most smartphones have adopted biometrics-based authentication, after a couple of failed attempts, most users are given the option to quickly bypass the system with passcodes. To add a layer of security, two-factor authentication (2FA) has been implemented but has proven to be vulnerable to various attacks. In this paper, we introduce VibPath, a simultaneous 2FA scheme that can understand the user's hand neuromuscular system through touch behavior. VibPath captures the individual's vibration path responses between the hand and the wrist with the attention-based encoder-decoder network, authenticating the genuine users from the imposters unobtrusively. In a user study with 30 participants, VibPath achieved an average performance of 0.98 accuracy, 0.99 precision, 0.98 recall, 0.98 f1-score for user verification, and 94.3% accuracy for user identification across five passcodes. Furthermore, we also conducted several extensive studies, including in-the-wile, permanence, vulnerability, usability, and system overhead studies, to assess the practicability and viability of the VibPath from multiple aspects.
PoseSonic

In this paper, we introduce PoseSonic, an intelligent acoustic sensing solution for smartglasses that estimates upper body poses. Our system only requires two pairs of microphones and speakers on the hinges of the eyeglasses to emit FMCW-encoded inaudible acoustic signals and receive reflected signals for body pose estimation. Using a customized deep learning model, PoseSonic estimates the 3D positions of 9 body joints including the shoulders, elbows, wrists, hips, and nose. We adopt a cross-modal supervision strategy to train our model using synchronized RGB video frames as ground truth. We conducted in-lab and semi-in-the-wild user studies with 22 participants to evaluate PoseSonic, and our user-independent model achieved a mean per joint position error of 6.17 cm in the lab setting and 14.12 cm in semi-in-the-wild setting when predicting the 9 body joint positions in 3D. Our further studies show that the performance was not significantly impacted by different surroundings or when the devices were remounted or by real-world environmental noise. Finally, we discuss the opportunities, challenges, and limitations of deploying PoseSonic in real-world applications.
A Meta-Synthesis of the Barriers and Facilitators for Personal Informatics Systems

Personal informatics (PI) systems are designed for diverse users in the real world. Even when these systems are usable, people encounter barriers while engaging with them in ways designers cannot anticipate, which impacts the system's effectiveness. Although PI literature extensively reports such barriers, the volume of this information can be overwhelming. Researchers and practitioners often find themselves repeatedly addressing the same challenges since sifting through this enormous volume of knowledge looking for relevant insights is often infeasible. We contribute to alleviating this issue by conducting a meta-synthesis of the PI literature and categorizing people's barriers and facilitators to engagement with PI systems into eight themes. Based on the synthesized knowledge, we discuss specific generalizable barriers and paths for further investigations. This synthesis can serve as an index to identify barriers pertinent to each application domain and possibly to identify barriers from one domain that might apply to a different domain. Finally, to ensure the sustainability of the syntheses, we propose a Design Statements (DS) block for research articles.
MI-Poser

Inside-out tracking of human body poses using wearable sensors holds significant potential for AR/VR applications, such as remote communication through 3D avatars with expressive body language. Current inside-out systems often rely on vision-based methods utilizing handheld controllers or incorporating densely distributed body-worn IMU sensors. The former limits hands-free and occlusion-robust interactions, while the latter is plagued by inadequate accuracy and jittering. We introduce a novel body tracking system, MI-Poser, which employs AR glasses and two wrist-worn electromagnetic field (EMF) sensors to achieve high-fidelity upper-body pose estimation while mitigating metal interference. Our lightweight system demonstrates a minimal error (6.6 cm mean joint position error) with real-world data collected from 10 participants. It remains robust against various upper-body movements and operates efficiently at 60 Hz. Furthermore, by incorporating an IMU sensor co-located with the EMF sensor, MI-Poser presents solutions to counteract the effects of metal interference, which inherently disrupts the EMF signal during tracking. Our evaluation effectively showcases the successful detection and correction of interference using our EMF-IMU fusion approach across environments with diverse metal profiles. Ultimately, MI-Poser offers a practical pose tracking system, particularly suited for body-centric AR applications.
Society's Attitudes Towards Human Augmentation and Performance Enhancement Technologies (SHAPE) Scale

Human augmentation technologies (ATs) are a subset of ubiquitous on-body devices designed to improve cognitive, sensory, and motor capacities. Although there is a large corpus of knowledge concerning ATs, less is known about societal attitudes towards them and how they shift over time. To that end, we developed The Society's Attitudes Towards Human Augmentation and Performance Enhancement Technologies (SHAPE) Scale, which measures how users of ATs are perceived. To develop the scale, we first created a list of possible scale items based on past work on how people respond to new technologies. The items were then reviewed by experts. Next, we performed exploratory factor analysis to reduce the scale to its final length of thirteen items. Subsequently, we confirmed test-retest validity of our instrument, as well as its construct validity. The SHAPE scale enables researchers and practitioners to understand elements contributing to attitudes toward augmentation technology users. The SHAPE scale assists designers of ATs in designing artifacts that will be more universally accepted.
Iris

We propose a novel Visible Light Positioning (VLP) method, called Iris, that leverages light spectral information (LSI) to localize individuals in a completely passive manner. This means that the user does not need to carry any device, and the existing lighting infrastructure remains unchanged. Our method uses a background subtraction approach to accurately detect changes in ambient LSI caused by human movement. Furthermore, we design a Convolutional Neural Network (CNN) capable of learning and predicting user locations from the LSI change data. To validate our approach, we implemented a prototype of Iris using a commercial-off-the-shelf light spectral sensor and conducted experiments in two typical real-world indoor environments: a 25 m2 one-bedroom apartment and a 13.3m × 8.4m office space. Our results demonstrate that Iris performs effectively in both artificial lighting at night and in highly dynamic natural lighting conditions during the day. Moreover, Iris outperforms the state-of-the-art passive VLP techniques significantly in terms of localization accuracy and the required density of light sensors. To reduce the overhead associated with multi-channel spectral sensing, we develop and validate an algorithm that can minimize the required number of spectral channels for a given environment. Finally, we propose a conditional Generative Adversarial Network (cGAN) that can artificially generate LSI and reduce data collection effort by 50% without sacrificing localization accuracy.
Integrating Gaze and Mouse Via Joint Cross-Attention Fusion Net for Students' Activity Recognition in E-learning

E-learning has emerged as an indispensable educational mode in the post-epidemic era. However, this mode makes it difficult for students to stay engaged in learning without appropriate activity monitoring. Our work explores a promising solution that combines gaze and mouse data to recognize students' activities, thereby facilitating activity monitoring and analysis during e-learning. We initially surveyed 200 students from a local university, finding more acceptance for eye trackers and mouse loggers compared to video surveillance. We then designed eight students' routine digital activities to collect a multimodal dataset and analyze the patterns and correlations between gaze and mouse across various activities. Our proposed Joint Cross-Attention Fusion Net, a multimodal activity recognition framework, leverages the gaze-mouse relationship to yield improved classification performance by integrating cross-modal representations through a cross-attention mechanism and integrating the joint features that characterize gaze-mouse coordination. Evaluation results show that our method can achieve up to 94.87% F1-score in predicting 8-classes activities, with an improvement of at least 7.44% over using gaze or mouse data independently. This research illuminates new possibilities for monitoring student engagement in intelligent education systems, also suggesting a promising strategy for melding perception and action modalities in behavioral analysis across a range of ubiquitous computing environments.
Contact Tracing for Healthcare Workers in an Intensive Care Unit

Contact tracing is a powerful tool for mitigating the spread of COVID-19 during the pandemic. Front-line healthcare workers are particularly at high risk of infection in hospital units. This paper presents ContAct TraCing for Hospitals (CATCH), an automated contact tracing system designed specifically for healthcare workers in hospital environments. CATCH employs distributed embedded devices placed throughout a hospital unit to detect close contacts among healthcare workers wearing Bluetooth Low Energy (BLE) beacons. We first identify a set of distinct contact tracing scenarios based on the diverse environmental characteristics of a real-world intensive care unit (ICU) and the different working patterns of healthcare workers in different spaces within the unit. We then develop a suite of novel contact tracing methods tailored for each scenario. CATCH has been deployed and evaluated in the ICU of a major medical center, demonstrating superior accuracy in contact tracing over existing approaches through a wide range of experiments. Furthermore, the real-world case study highlights the effectiveness and efficiency of CATCH compared to standard contact tracing practices.
CASES

The process of reading has attracted decades of scientific research. Work in this field primarily focuses on using eye gaze patterns to reveal cognitive processes while reading. However, eye gaze patterns suffer from limited resolution, jitter noise, and cognitive biases, resulting in limited accuracy in tracking cognitive reading states. Moreover, using sequential eye gaze data alone neglects the linguistic structure of text, undermining attempts to provide semantic explanations for cognitive states during reading. Motivated by the impact of the semantic context of text on the human cognitive reading process, this work uses both the semantic context of text and visual attention during reading to more accurately predict the temporal sequence of cognitive states. To this end, we present a Cognition-Aware Smart Eyewear System (CASES), which fuses semantic context and visual attention patterns during reading. The two feature modalities are time-aligned and fed to a temporal convolutional network based multi-task classification deep model to automatically estimate and further semantically explain the reading state timeseries. CASES is implemented in eyewear and its use does not interrupt the reading process, thus reducing subjective bias. Furthermore, the real-time association between visual and semantic information enables the interactions between visual attention and semantic context to be better interpreted and explained. Ablation studies with 25 subjects demonstrate that CASES improves multi-label reading state estimation accuracy by 20.90% for sentence compared to eye tracking alone. Using CASES, we develop an interactive reading assistance system. Three and a half months of deployment with 13 in-field studies enables several observations relevant to the study of reading. In particular, observed how individual visual history interacts with the semantic context at different text granularities. Furthermore, CASES enables just-in-time intervention when readers encounter processing difficulties, thus promoting self-awareness of the cognitive process involved in reading and helping to develop more effective reading habits.