Reviewerly/exHarmony huggingface dataset:
Sensemaking, Support, Safety, Retribution, Transformation: A Restorative Justice Approach to Understanding Adolescents’ Needs for Addressing Online Harm

Online harm is a prevalent issue in adolescents' online lives. Restorative justice teaches us to focus on those who have been harmed, ask what their needs are, and engage in the offending party and community members to collectively address the harm. In this research, we conducted interviews and design activities with harmed adolescents to understand their needs to address online harm. They also identified the key stakeholders relevant to their needs, the desired outcomes, and the preferred timing to achieve them. We identified five central needs of harmed adolescents: sensemaking, emotional support and validation, safety, retribution, and transformation. We find that addressing the needs of those who are harmed online usually requires concerted efforts from multiple stakeholders online and offline. We conclude by discussing how platforms can implement design interventions to meet some of these needs.
When is Machine Learning Data Good?: Valuing in Public Health Datafication

Data-driven approaches that form the foundation of advancements in machine learning (ML) are powered in large part by human infrastructures that enable the collection of large datasets. We study the movement of data through multiple stages of data processing in the context of public health in India, examining the data work performed by frontline health workers, data stewards, and ML developers. We conducted interviews with these stakeholders to understand their varied perspectives on valuing data across stages, working with data to attain this value, and challenges arising throughout. We discuss the tensions in valuing and how they might be addressed, as we emphasize the need for improved transparency and accountability when data are transformed from one stage of processing to the next.
Dynamic feature fusion with spatial-temporal context for robust object tracking

Feature fusion has been widely used for improving the tracking performance. However, how to effectively analyze the characteristics of different visual features to realize dynamical feature fusion is still a challenging task. In this paper, we propose a spatial-temporal context-based dynamic feature fusion method (STCDFF) with the correlation filters framework for object tracking. The proposed STCDFF method exploits spatial-temporal context to deeply analyze the characteristics of multiple visual features (e.g., HOG, Color-Names and CNN features) to perform feature fusion. On the one hand, spatial context is employed to evaluate the discriminative ability of different features to distinguish the target object from the background. On the other hand, temporal context is utilized to consider the representative ability of different features to capture significant appearance changes of the target object. The weight of a feature is decided by both its discriminative ability and representative ability. By exploring spatial-temporal context for feature fusion, the STCDFF method can fully utilize the strengths of different features to handle complex appearance changes and background clutters to achieve better performance. Extensive experiments on multiple object tracking datasets prove that our STCDFF method performs competitively against several popular tracking methods.
AutoTransform

Code review is effective, but human-intensive (e.g., developers need to manually modify source code until it is approved). Recently, prior work proposed a Neural Machine Translation (NMT) approach to automatically transform source code to the version that is reviewed and approved (i.e., the after version). Yet, its performance is still suboptimal when the after version has new identifiers or literals (e.g., renamed variables) or has many code tokens. To address these limitations, we propose AutoTransform which leverages a Byte-Pair Encoding (BPE) approach to handle new tokens and a Transformer-based NMT architecture to handle long sequences. We evaluate our approach based on 14,750 changed methods with and without new tokens for both small and medium sizes. The results show that when generating one candidate for the after version (i.e., beam width = 1), our AutoTransform can correctly transform 1,413 changed methods, which is 567% higher than the prior work, highlighting the substantial improvement of our approach for code transformation in the context of code review. This work contributes towards automated code transformation for code reviews, which could help developers reduce their effort in modifying source code during the code review process.
NFTs For 3D Models: Sustaining Ownership In Industry 4.0

Digital manufacturing (DM) is actively adopted to the production lifecycles of a variety of critical industries, and this rapid growth has resulted in exponential increase of 3D computer-aided design (CAD) models. Unfortunately, counterfeiting of intellectual property becomes a prominent threat as many 3D designs are accessible online, combined with the proliferation of cheap consumer 3D printers that enable malicious actors to produce non-authentic parts. State-of-the-art techniques to secure manufacturing processes mostly rely on watermarking, which embeds hidden information inside CAD models to prove ownership and authenticity. Nevertheless, such techniques tamper with the model itself, while existing attacks allow removing such watermarks altogether. To address these shortcomings, we integrate signal processing and cryptographic techniques and describe a tailored solution for CAD model ownership and supply chain management. Our approach generates unique identifiers for 3D designs using frequency-domain transforms and employs non-fungible tokens (NFTs) that persist on public distributed ledgers. Our NFTs are implemented on the Ethereum blockchain using smart contracts and their functionality is twofold: (a) authenticate the owner of a CAD model, and (b) enable ownership transfer. To validate our technique, we deployed our smart contract on Ethereum's proof-of-work Ropsten network and demonstrated the applicability of our methodology.
Antecedents of blockchain technology adoption intentions in the supply chain

Supply chain involves a lot of transactions among the stakeholders. Blockchain technology will make a revolutionary change the way how transactions are being carried out. Blockchain removes all the disadvantages which are related to trust among stakeholders and gives better visibility and transparency. The present study explores factors that affect blockchain technology adoption in the supply chain. The current study is based on the extended unified theory of acceptance and use of technology (UTAUT2) with trust model. Data was collected from 363 supply chain practitioners from India. The results of the present study indicate that behavioural intentions are positively and significantly influenced by performance expectancy, effort expectancy, hedonic motivation, price value, and trust. The study also lists out various aspects for future research regarding the implementation of blockchain in supply chain management.
Blockchain-Enabled supply chains: An application in fresh-cut flowers

Supply chains have often benefited from breakthroughs in information technology. Blockchain, in particular, is a recent technology that is promising to revolutionize the way supply chains are designed and operated. This paper proposes a framework to optimize the adoption of blockchain jointly with the design of the supply chain network. A new form of product differentiation is enabled through blockchain adoption where blockchain-certified products are sold at a premium price to a growing segment of customers. The proposed framework allows supply chain managers to monetize data which has traditionally been used to improve supply chain efficiency. The framework is evaluated using a realistic case study inspired by the global supply chain of fresh-cut flowers. The results show that by strategically using blockchain at certain locations in the supply chain network, significant cost savings are realized compared to fully deploying blockchain throughout the supply chain. These cost savings lead to increased demand, better consumer surplus, and higher supply chain profit. Furthermore, the proposed data-enabled product differentiation leads to fresher products in the market.
Memory Recurrent Elman Neural Network-Based Identification of Time-Delayed Nonlinear Dynamical System

In this article, an attempt has been made to propose an improved version of the classical Elman neural network (ENN) and its application is presented to identify the unknown dynamics of time-delayed nonlinear plants. The model proposed, known as memory recurrent ENN (MRENN), consists of an additional number of weighted self-feedback loops, an extra output context layer, and weighted connections of input signals (through adjustable weights) to the output-layer neuron. The model is proposed to be given only two signals as its inputs: plant unit-delayed value and the present value of the externally applied signal irrespective of the actual order of the plant (which most of the time may not be known). To guarantee stability, the parameters of the MRENN model are updated using the equations that are obtained by applying Lyapunov-stability criteria. Furthermore, the recursive learning rate scheme is constructed for speeding up the learning process. From the simulation results, MRENN model appears to have produced comparably superior outcomes when the performance of the suggested model is compared with that of other well-known models.
Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion

To ensure the safety and stability of the rocket, it is essential to implement accurate anomaly detection on key parts such as the liquid rocket engine (LRE). However, due to the indistinct features of signals under the interference of extreme conditions and the weak distinguishing ability to exist unsupervised methods, it is difficult to distinguish abnormal samples from normal samples, which leads to the failure of reliable anomaly detection. Aiming at this problem, this paper proposed an unsupervised learning algorithm named Memory-augmented skip-connected deep autoencoder (Mem-SkipAE) for anomaly detection of rocket engines with multi-source data fusion. Unlike traditional autoencoders, the input embedding for the decoder is not generated by an encoder but by a combination of memory items that record prototypical patterns of normal samples. Besides, each layer of the encoder and decoder has a skip connection to fully extract the multi-scale features of the normal sample in multi-dimensional space and suppress over-fitting caused by the memory-augmented network. Compared with existing methods and ablation control groups, experiments on four test sets prove the excellent generalization and satisfactory performances of the proposed Mem-SkipAE. Furthermore, the comparison of the single-source model and multi-source model verifies the effectiveness of multi-source fusion.
Cyclic Arbitrage in Decentralized Exchanges

Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users’ financial loss in cyclic arbitrage from the price impact.
KGAN: Knowledge Grouping Aggregation Network for course recommendation in MOOCs

Massive open online courses (MOOCs) are dedicated to providing learners with large-scale and open-access boutique courses. Recently, the course recommendation algorithm in MOOCs has attracted many researchers’ attention. Compared with ordinary recommendation scenarios, course interaction density in MOOCs is more tenuous, since it demands lots of time and gumption of learners. Meanwhile, the connections and semantic information among courses are much more diversiform and plentiful. Therefore, knowledge graph enhanced recommendation algorithms are appropriate for addressing the course recommendation problem in MOOCs scenario. According to relevant research on KG-based algorithms, utilization efficiency of edge information seems to be the key to boosting model effectiveness. In this paper, we propose a high-performance course recommendation model named Knowledge Grouping Aggregation Network (KGAN), which uses the course graph (a heterogeneous graph for describing the relations between courses and facts) to estimate learners’ potential interests automatically and iteratively. More precisely, KGAN constructs an end-to-end recommendation model, which projects the learner’s behavior and course graph into a unified space naturally, which alleviates difficulties in course recommendation such as interaction tenuous, course relevance, and intention diversity. In addition, we proposed intra-group and inter-group attention operator, which packages the propagation set according to the relation-links and obtains the corresponding attention priorities of different entities under different paths for constructing a reasonable and explicit encoding of users. We apply the proposed model on the real-world datasets, and the empirical results demonstrate that KGAN outperforms compelling state-of-the-art baselines. Our implementations are available at https://github.com/StZHY/KGAN.
Neuromorphic photonic technologies and architectures: scaling opportunities and performance frontiers [Invited]

We review different technologies and architectures for neuromorphic photonic accelerators, spanning from bulk optics to photonic-integrated-circuits (PICs), and assess compute efficiency in OPs/Watt through the lens of a comparative study where key technology aspects are analyzed. With an emphasis on PIC neuromorphic accelerators, we shed light onto the latest advances in photonic and plasmonic modulation technologies for the realization of weighting elements in training and inference applications, and present a recently introduced scalable coherent crossbar layout. Finally, we stress that current technologies face challenges endowing photonic accelerators with compute efficiencies in the PetaOPs/W, and discuss future implementation pathways towards improving performance.
Learning spatiotemporal chaos using next-generation reservoir computing

Forecasting the behavior of high-dimensional dynamical systems using machine learning requires efficient methods to learn the underlying physical model. We demonstrate spatiotemporal chaos prediction using a machine learning architecture that, when combined with a next-generation reservoir computer, displays state-of-the-art performance with a computational time 103–104 times faster for training process and training data set ∼102 times smaller than other machine learning algorithms. We also take advantage of the translational symmetry of the model to further reduce the computational cost and training data, each by a factor of ∼10.
Trusted AI with Blockchain to Empower Metaverse

The digital experience emerging in the virtual world is a reality with the advent of the metaverse. Augmented reality(AR), virtual reality(VR), extended reality(XR), and artificial intelligence(AI) algorithms would pave the way for an immersive experience for the users in the virtual space. However, the explosion of these technologies broaches new challenges to threaten the success of metaverse due to security risks. The blockchain technology augmented with AI promises to deliver a trusted metaverse for everyone. Nevertheless, smart contracts fail to produce a cognitive prediction, dissuading users from confiding in the metaverse. We arm smart contracts with intelligence to predict using AI algorithms. Moreover, we deploy the smart contracts on the Ethereum blockchain platform and produce a prediction accuracy of 95% compared to Python scikit-learn-based predictions. Our results show that the prediction delay can obstruct the growth of metaverse applications to accept blockchain technologies. Furthermore, the limitation of blockchain technology can make integration unreasonable. Therefore, we discuss possible scalability solutions that can be part of our future work to help more metaverse applications adopt blockchain solutions.
MSAF: Multimodal Supervise-Attention Enhanced Fusion for Video Anomaly Detection

The complementarity of multimodal signal is essential for video anomaly detection. However, existing methods either lack exploration to multimodal data or ignore the implicit alignment of multimodal features. In our work, we address this problem using a novel fusion method and propose a Multimodal Supervise-Attention enhanced Fusion (MSAF) framework under weak supervision. Our framework can be divided into two parts: 1) the multimodal labels refinement part refines video-level ground truth into pseudo clip-level labels for subsequent training, 2) the multimodal supervise-attention fusion network enhances features via implicitly aligning different information, then fusing them effectively to predict anomaly scores with the help of refined labels. We validate our framework on four challenging datasets: ShanghaiTech, UCF-Crime, LAD, and XD-Violence. Extensive experiments on the benchmarks demonstrate the effectiveness of our framework, which achieves comparable results on several benchmarks and outperforms current state-of-the-art methods on the XD-Violence audiovisual multimodal dataset.
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Learning-based multi-view stereo (MVS) has by far cen-tered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Differentfrom most existing works dedicated to adaptive re-finement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range (depth) finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequen-tial prediction of aID implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We also devise a multi-task learning for better optimization convergence and depth accuracy. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving overall reconstruction score of 0.33mm on DTU andf-score of59.48% on Tanks & Temples.
An improved RRT* algorithm for robot path planning based on path expansion heuristic sampling

Rapidly-exploring Random Tree Star (RRT*) algorithm and its variants based on random sampling can provide a collision-free and asymptotic optimal solution for many path planning problems. However, many RRT* based variants have low sampling efficiency and slow convergence rate in the environment which consists of long corridors, due to a large number of iterations are required in sampling critical nodes. To overcome this problem, the paper proposes the Expanding Path RRT* (EP-RRT*) based on heuristic sampling in path expansion area. By combining the greedy heuristic of Rapidly exploring Random Tree (RRT)-Connect, EP-RRT* quickly explores the environment in order to find a feasible path, and then expands it to obtain the heuristic sampling area. It iteratively searches in the heuristic sampling area which also changes with the continuous optimization of the path, and finally obtains an optimal or suboptimal path connecting starting point and target point. Comparisons of EP-RRT* with RRT* and Informed RRT* in four simulation environments verify that EP-RRT* improves the node utilization, accelerates the convergence rate, and obtains a better path for the same number of iterations.
Lightweight image super-resolution based on deep learning: State-of-the-art and future directions

Recently, super-resolution (SR) techniques based on deep learning have taken more and more attention, aiming to improve the images and videos resolutions. Most of the SR methods are related to other fields of computer vision such as image classification, image segmentation, and object detection. Based on the success of the image SR task, many image SR surveys are introduced to summarize the recent work in the image SR domains. However, there is no survey to summarize the SR models for the lightweight image SR domain. In this paper, we present a comprehensive survey of the state-of-the-art lightweight SR models based on deep learning. The SR techniques are grouped into six major categories: include convolution, residual, dense, distillation, attention, and extremely lightweight based models. Also, we cover some other issues related to the SR task, such as benchmark datasets and metrics for performance evaluation. Finally, we discuss some future directions and open problems, that may help other community researchers in the future.
A digital literacy model to narrow the digital literacy skills gap

The concept of digital literacy is increasingly prevalent in the 21st century. Growing demands from the work sector for individuals to be digitally literate has prompted targeted interventions and innovations from the education sector to instil digital skills into the future workforce. However, despite efforts the digital skills gap remains visible globally. This paper explores the prominent educational frameworks and models, their advantages and limitations in 21st-century learning and teaching. Furthermore, a new innovative digital literacy model has been proposed to be integrated into the existing and future education frameworks and models to assist educationists in narrowing the digital skills gaps and preparing graduates for the work sector. The digital literacy model consists of two components: (1) the digital literacy framework- South Pacific Digital Literacy Framework (SPDLF) and (2) the digital literacy tool. The SPDLF reflects six major literacies identified for the 21st-century while the digital literacy tool--digilitFJ consists of a digital literacy measuring scale and an online intervention program. The exploratory factor analysis showed that the SPDLF was valid. On the other hand, heuristics, student attitude, and satisfaction and effectiveness of the digital literacy tool were also evaluated from the student's perspective to reflect its usefulness. The survey results also showed a positive attitude and perception of the use of the tool. Additionally, Cohen's d value showed that the digital literacy tool was effective. Therefore, if the tool is implemented and adopted, it can narrow the existing digital skills gap in the South Pacific.