Reviewerly/exHarmony huggingface dataset:
Antimony as a Programmable Element in Integrated Nanophotonics

The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of materials known as phase change materials has been extensively used to demonstrate the viability of such computing. However, such materials continue to have relatively slow switching speeds, and issues with cyclability related to phase segregation of phase change alloys. Here, using antimony (Sb) thin films with thicknesses less than 5 nm we demonstrate reversible, ultrafast switching on an integrated photonic platform with retention time of tens of seconds. We use subpicosecond pulses, the shortest used to switch such elements, to program seven distinct memory levels. This portends their use in ultrafast nanophotonic applications ranging from nanophotonic beam steerers to nanoscale integrated elements for photonic computing.
Changes in classroom assessment practices during emergency remote teaching due to COVID-19

This study explores the effects of the shift to emergency remote teaching on assessment practices due to COVID-19 lockdown. A total of 936 Spanish teachers from all educational levels ranging from early childhood to university participated in this nationwide survey. Four aspects were explored: (1) changes in the use of assessment instruments (e.g. exams); (2) changes in assessment criteria, standards and grading; (3) changes in the delivery of feedback and use of rubrics; and (4) changes in students’ involvement in assessment (i.e. self- and peer assessment). In general, results are mixed, with some areas undergoing certain changes with the aim of adapting to the new situation (e.g. primary education teachers lowering their grading standards), whereas many other assessment practices have remained similar, especially among higher education teachers. Unfortunately, some of the assessment practices have worsened, such as students’ involvement in assessment which has decreased.
Knowledge Graph Reasoning with Relational Digraph

Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning ability. However, paths are naturally limited in capturing local evidence in graphs. In this paper, we introduce a novel relational structure, i.e., relational directed graph (r-digraph), which is composed of overlapped relational paths, to capture the KG's local evidence. Since the r- digraphs are more complex than paths, how to efficiently construct and effectively learn from them are challenging. Directly encoding the r-digraphs cannot scale well and capturing query-dependent information is hard in r-digraphs. We propose a variant of graph neural network, i.e., RED-GNN, to address the above challenges. Specifically, RED-GNN makes use of dynamic programming to recursively encodes multiple r-digraphs with shared edges, and utilizes a query-dependent attention mechanism to select the strongly correlated edges. We demonstrate that RED-GNN is not only efficient but also can achieve significant performance gains in both inductive and transductive reasoning tasks over existing methods. Besides, the learned attention weights in RED-GNN can exhibit interpretable evidence for KG reasoning.
The Effect of Artificial Intelligence Using Neural Network in Estimating on An Efficient Accounting Information System: Evidence from Jordanian Commercial Banks

Neural networks is a new topic of great importance, because of the change that it causes in the performance in the accounting profession and the relevance of accounting information. Current study aims to examine the impact neural network on an efficient accounting information system on the Jordanian commercial banks. Collecting date for 13 Jordanian commercial banks listed on the Amman Stock Exchange. The primary was obtained using a questionnaire includes all employees in the accounting departments, internal auditors, and programmers. The study distributed (195) questionnaires to the entire sample members electronically. It is found that the neural networks used by Jordanian commercial banks contribute to increasing the efficiency of its accounting systems, and providing management with basic accounting information. Moreover, the neural networks used by Jordanian commercial banks are distinguished by their ability to analyze contribute to raising the efficiency and ability of workers to develop and progress, and provide management and stakeholders with information that is consistent with the needs of customers.
Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented.
Diffraction-in-action: Designerly Explorations of Agential Realism Through Lived Data

Recent design research has shown an interest in diffraction and agential realism, which promise to offer generative alternatives when designing with data that resist treating data as objective or neutral. We explore engaging diffractively with ‘lived data’ to surface felt and prospective aspects of data as it is entangled in everyday lives of designers. This paper presents five biodata-based case studies demonstrating how design researchers can create knowledge about human bodies and behaviors via strategies that allow them to engage data diffractively. These studies suggest that designers can find insights for designing with data as it is lived by working with it in a slow, open-ended fashion that leaves room for messiness and time for discovering difference. Finally, we discuss the role of ambiguous, open-ended data interpretations to help surface different meanings and entanglements of data in everyday lives.
“It’s common and a part of being a content creator”: Understanding How Creators Experience and Cope with Hate and Harassment Online

Content creators—social media personalities with large audiences on platforms like Instagram, TikTok, and YouTube—face a heightened risk of online hate and harassment. We surveyed 135 creators to understand their personal experiences with attacks (including toxic comments, impersonation, stalking, and more), the coping practices they employ, and gaps they experience with existing solutions (such as moderation or reporting). We find that while a majority of creators view audience interactions favorably, nearly every creator could recall at least one incident of hate and harassment, and attacks are a regular occurrence for one in three creators. As a result of hate and harassment, creators report self-censoring their content and leaving platforms. Through their personal stories, their attitudes towards platform-provided tools, and their strategies for coping with attacks and harms, we inform the broader design space for how to better protect people online from hate and harassment.
Stakeholders and Value in the NFT Ecosystem

Non-fungible tokens (NFTs) have been a defining trend for design, technology, and business in 2021. The value, legitimacy, and utility of NFTs is disputed: proponents highlight revolutionary economic and cultural potentials of an open, secure, and immutable ownership database, while opponents are displeased by the environmental issues and abundant wrongdoing in the ecosystem. Nevertheless, the phenomenon is relevant to HCI, and signifies important developments for future interactive products. To better understand the NFT phenomenon, and to inform future HCI research and design, we investigated the stakeholders in the NFT ecosystem and relations between them. Based on open data we mined from the social news website Hacker News, we contribute the first data-backed model of stakeholders in the NFT ecosystem. The model reveals a nuanced account of the outlooks of creators, owners, and technologists; identifies investment firms and auction houses as arbiters of knowledge and value; and presents implications for future research.
ReClaym our Compost: Biodegradable Clay for Intimate Making

In this work, we use composted food waste to create ReClaym: a personal biomaterial, made at home, that reflects the makers’ relationship with food. We combine our personal compost with non-toxic binders to create a biomaterial that can completely biodegrade. We propose Intimate Making as an approach for working with ReClaym that leverages familiar, hands-on techniques to enhance maker-material communication, which in turn leads to a deeper material understanding. We explore methods to customize ReClaym via color, texture, sensing, and conductivity. We apply manual fabrication techniques (sculpting, molding, and hand-held extruding) and design explorations to create a collection of applications that include garden paraphernalia, games, and personal items found in the home. We then examine the entire life cycle of ReClaym, which both begins and ends with composting—giving food waste a second life and providing a sustainable end of life for ReClaym artifacts, thus making the process truly circular.
Towards Decomposable Interactive Systems: Design of a Backyard-Degradable Wireless Heating Interface

Sustainability is critical to our planet and thus our designs. Within HCI, there is a tension between the desire to create interactive electronic systems and sustainability. In this paper, we present the design of an interactive system comprising components that are entirely decomposable. We leverage the inherent material properties of natural materials, such as paper, leaf skeletons, and chitosan, along with silver nanowires to create a new system capable of being electrically controlled as a portable heater. This new decomposable system, capable of wirelessly heating to >70°C, is flexible, lightweight, low-cost, and reusable, and it maintains its functionality over long periods of heating and multiple power cycles. We detail its design and present a series of use cases, from enabling a novel resealable packaging system to acting as a catalyst for shape-changing designs and beyond. Finally, we highlight the important decomposable property of the interactive system when it meets end-of-life.
PromptMaker: Prompt-based Prototyping with Large Language Models

Prototyping is notoriously difficult to do with machine learning (ML), but recent advances in large language models may lower the barriers to people prototyping with ML, through the use of natural language prompts. This case study reports on the real-world experiences of industry professionals (e.g. designers, program managers, front-end developers) prototyping new ML-powered feature ideas via prompt-based prototyping. Through interviews with eleven practitioners during a three-week sprint and a workshop, we find that prompt-based prototyping reduced barriers of access by substantially broadening who can prototype with ML, sped up the prototyping process, and grounded communication between collaborators. Yet, it also introduced new challenges, such as the need to reverse-engineer prompt designs, source example data, and debug and evaluate prompt effectiveness. Taken together, this case study provides important implications that lay the groundwork toward a new future of prototyping with ML.
Attention-based deep learning segmentation: Application to brain tumor delineation

Brain tumor segmentation is an important step in brain cancer diagnosis and treatment. Manual segmentation is highly time consuming and tedious. To address these issues deep learning-base methods have been employed and demonstrated tremendous improvement in terms of performance accuracy and time-efficiency compared to the conventional segmentation methods. Due to varying size, location and shape of tumors, tumor segmentation task is highly challenging in comparison to the anatomical organ segmentation, thus it seems essential to develop robust, accurate, and efficient deep learning-based solutions for this task. In this work, we proposed a method that utilized UN et architecture as backbone (referred to as Attention-based UNet (AbUNet)) to establish an efficient tumor segmentation framework. AbUNet consist of two basic components, attention and segmentation modules. The purpose of attention module is to estimate approximate location of tumor through a rough/coarse segmentation with high true positive rate to guarantee the inclusion of the entire tumor. To this end, a new cost function was introduced and the dilated masks of ground-truths were employed for training of the attention module. The outcome and some feature maps from decoder part of attention module were fed to the segmentation module to guide the segmentation network focusing on the confined regions of the image which contain tumor. In the last step, the segmentation module predicts final mask of the tumor. To evaluate our proposed method, we used the BraTS dataset which contains brain MRI scans with manually defined tumor masks delineated by an expert. Our baseline UNet without attention module achieved a Dice score of 0.68 compared to AbUNet with Dice score of 0.79 that indicates significant improvement in segmentation accuracy. It was demonstrated that incorporation of the proposed attention module in the deep learning-based segmentation network would significantly enhance the robustness and accuracy of the segmentation.
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition

Self-supervised learning (SSL) is a powerful tool that allows learning of underlying representations from unlabeled data. Transformer based models such as wav2vec 2.0 and HuBERT are leading the field in the speech domain. Generally these models are fine-tuned on a small amount of labeled data for a downstream task such as Automatic Speech Recognition (ASR). This involves re-training the majority of the model for each task. Adapters are small lightweight modules which are commonly used in Natural Language Processing (NLP) to adapt pre-trained models to new tasks. In this paper we propose applying adapters to wav2vec 2.0 to reduce the number of parameters required for downstream ASR tasks, and increase scalability of the model to multiple tasks or languages. Using adapters we can perform ASR while training fewer than 10% of parameters per task compared to full fine-tuning with little degradation of performance. Ablations show that applying adapters into just the top few layers of the pre-trained network gives similar performance to full transfer, supporting the theory that higher pre-trained layers encode more phonemic information, and further optimizing efficiency.
Deep Reinforcement Learning Based Trajectory Planning Under Uncertain Constraints

With the advance in algorithms, deep reinforcement learning (DRL) offers solutions to trajectory planning under uncertain environments. Different from traditional trajectory planning which requires lots of effort to tackle complicated high-dimensional problems, the recently proposed DRL enables the robot manipulator to autonomously learn and discover optimal trajectory planning by interacting with the environment. In this article, we present state-of-the-art DRL-based collision-avoidance trajectory planning for uncertain environments such as a safe human coexistent environment. Since the robot manipulator operates in high dimensional continuous state-action spaces, model-free, policy gradient-based soft actor-critic (SAC), and deep deterministic policy gradient (DDPG) framework are adapted to our scenario for comparison. In order to assess our proposal, we simulate a 7-DOF Panda (Franka Emika) robot manipulator in the PyBullet physics engine and then evaluate its trajectory planning with reward, loss, safe rate, and accuracy. Finally, our final report shows the effectiveness of state-of-the-art DRL algorithms for trajectory planning under uncertain environments with zero collision after 5,000 episodes of training.
Metrological Characterization of Non-Gaussian Entangled States of Superconducting Qubits

Multipartite entangled states are significant resources for both quantum information processing and quantum metrology. In particular, non-Gaussian entangled states are predicted to achieve a higher sensitivity of precision measurements than Gaussian states. On the basis of metrological sensitivity, the conventional linear Ramsey squeezing parameter (RSP) efficiently characterizes the Gaussian entangled atomic states but fails for much wider classes of highly sensitive non-Gaussian states. These complex non-Gaussian entangled states can be classified by the nonlinear squeezing parameter (NLSP), as a generalization of the RSP with respect to nonlinear observables and identified via the Fisher information. However, the NLSP has never been measured experimentally. Using a 19-qubit programmable superconducting processor, we report the characterization of multiparticle entangled states generated during its nonlinear dynamics. First, selecting ten qubits, we measure the RSP and the NLSP by single-shot readouts of collective spin operators in several different directions. Then, by extracting the Fisher information of the time-evolved state of all 19 qubits, we observe a large metrological gain of 9.89_{-0.29}^{+0.28} dB over the standard quantum limit, indicating a high level of multiparticle entanglement for quantum-enhanced phase sensitivity. Benefiting from high-fidelity full controls and addressable single-shot readouts, the superconducting processor with interconnected qubits provides an ideal platform for engineering and benchmarking non-Gaussian entangled states that are useful for quantum-enhanced metrology.
Federated Learning Challenges and Opportunities: An Outlook

Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers’ privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development as part of the ICASSP 2022 special session entitled "Frontiers of Federated Learning: Applications, Challenges, and Opportunities." The outlook is categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices.
SSR-TVD: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization

We present SSR-TVD, a novel deep learning framework that produces coherent spatial super-resolution (SSR) of time-varying data (TVD) using adversarial learning. In scientific visualization, SSR-TVD is the first work that applies the generative adversarial network (GAN) to generate high-resolution volumes for three-dimensional time-varying data sets. The design of SSR-TVD includes a generator and two discriminators (spatial and temporal discriminators). The generator takes a low-resolution volume as input and outputs a synthesized high-resolution volume. To capture spatial and temporal coherence in the volume sequence, the two discriminators take the synthesized high-resolution volume(s) as input and produce a score indicating the realness of the volume(s). Our method can work in the in situ visualization setting by downscaling volumetric data from selected time steps as the simulation runs and upscaling downsampled volumes to their original resolution during postprocessing. To demonstrate the effectiveness of SSR-TVD, we show quantitative and qualitative results with several time-varying data sets of different characteristics and compare our method against volume upscaling using bicubic interpolation and a solution solely based on CNN.
Instance weighted SMOTE by indirectly exploring the data distribution

The synthetic minority oversampling technique (SMOTE) algorithm is considered a benchmark algorithm for addressing the class imbalance learning (CIL) problem. However, SMOTE fails to observe the distribution of the training data and to explore its internal structure, resulting in an unstable and non-robust classification result. Recently, more than 100 SMOTE variants have been developed to solve this problem. Most of them attempt to directly explore the prior distribution information of the training data, which may provide extremely inaccurate guidance in some classification scenarios. In this study, we present the instance weighted SMOTE (IW-SMOTE) algorithm, a more robust and universal solution for improving SMOTE by exploit distribution data indirectly. In particular, an UnderBagging-alike undersampling ensemble algorithm that uses classification and regression tree (CART) as the base classifier is first adopted to classify each training instance and acquire the corresponding confusing information. We can accurately estimate location information for each instance, including noise, borders and safety, based on the confusing information. Then, the noisy instances can be removed, and the borderline instances can be given more chances than the safe instances to be seed instances in the SMOTE procedure. Finally, the balanced instance set was used to train the CART, K nearest neighbors (KNN) and support vector machine (SVM) classifiers to verify whether the proposed algorithm is irrelevant to the specific classification model. We compare IW-SMOTE with several state-of-the-art SMOTE-based algorithms on many class imbalance data sets, and IW-SMOTE shows promising results.
LPViT: A Transformer Based Model for PCB Image Classification and Defect Detection

PCB (printed circuit board) is an extremely important component of all electronic products, which has greatly facilitated human life.Meanwhile, tons of PCBs in the waste streams become a waste of resources, which puts the recycling and reuse of PCBs in urgent need.In the manufacturing and recycling of electronic products, the classification of PCBs, recognition of sub-components, and defect detection have been the key technology.Traditional manual detection and classification are subjective and rely on individuals' experience.With the development of artificial intelligence, lots of research efforts have been dedicated to the automated detection and recognition of PCBs.In this paper, we propose a transformerbased model, LPViT, for defect detection and classification of PCBs.We conduct the defect detection task on the dataset DeepPCB, which consists of six different types of PCB defects.Defect detection benefits both manufacturing and recycling of PCBs.Among many electronic products, a group of affordable, generalpurpose, and small-size PCBs is very popular, which are referred to as micro-PCBs.The classification and recognition of those PCBs will greatly facilitate the recycling and reuse process.We conduct the classification task on a dataset called micro-PCB, which includes 12 types of popular, general-purpose, affordable, and small PCBs.Through comparative experiments, our system demonstrates its advantage in both classification and defect detection tasks.