Reviewerly/exHarmony huggingface dataset:
Joint Attention Networks with Inherent and Contextual Preference-Awareness for Successive POI Recommendation

Abstract Nowadays recording and sharing personal lives using mobile devices on the Internet is becoming increasingly popular, and successive POI recommendation is gaining growing attention from academia and industry. In mobile scenarios, multiple influencing factors including the diversity of user preferences, the changeability of user behavior and the dynamic of spatiotemporal context bring great challenges to the POI recommender system. In order to accurately capture both the stable and the contextual preferences of mobile users in dynamic contexts, we propose a fusion framework JANICP (Joint Attention Networks with Inherent and Contextual Preferences) for successive POI recommendation by jointly training an offline/nearline user inherent interest perception model and an online user contextual interest prediction model. The offline model is trained based on the global historical behavior data to achieve stable interest representation, while the online model is trained based on the instantly selected context-sensitive data to achieve dynamic interest perception. An attention aggregation and matching module is used to fully connect the two kinds of preference representations and generate the final POI recommendation. Extensive experiments were conducted on three real datasets and experimental results show that the proposed JANICP outperforms existing state-of-the-art methods.
Resourcing, sensemaking and legitimizing: blockchain technology-enhanced market practices

Purpose This paper aims to focus on how the use of new technologies disrupts markets. To date, marketing literature has lacked studies investigating the link between market practices and new technologies. The study adopts the blockchain technology (BcT) context to elicit novel technology-enhanced market practices. Design/methodology/approach The authors adopt a qualitative multimethod research design to engage in interpretative theorizing. They investigated 77 companies and used the Gioia method for the data coding and analysis. Findings The study of the adoption of blockchain prompts three technology-enhanced market practices. The latter offers new ways of resourcing by removing constraints and expanding actors’ network and knowledge to integrate resources; sensemaking by expressing new language and assigning novel meaning to represent markets; and legitimizing, by structuring new rules and trusting new mechanisms to institutionalize markets. Research limitations/implications The technology-enhanced market practices are distinct from extant market practices as well as related, thus, enriching and complementing them. Therefore, this work expands the understanding of the mechanisms of how markets work. Originality/value This study is the first, to the best of the authors’ knowledge, to focus on how BcT features affect market practices. BcT market practices entail how actors perform, share and interpret symbols and objects and set rules for how markets should work.
A New Framework of Swarm Learning Consolidating Knowledge From Multi-Center Non-IID Data for Medical Image Segmentation

Large training datasets are important for deep learning-based methods. For medical image segmentation, it could be however difficult to obtain large number of labeled training images solely from one center. Distributed learning, such as swarm learning, has the potential to use multi-center data without breaching data privacy. However, data distributions across centers can vary a lot due to the diverse imaging protocols and vendors (known as feature skew). Also, the regions of interest to be segmented could be different, leading to inhomogeneous label distributions (referred to as label skew). With such non-independently and identically distributed (Non-IID) data, the distributed learning could result in degraded models. In this work, we propose a novel swarm learning approach, which assembles local knowledge from each center while at the same time overcomes forgetting of global knowledge during local training. Specifically, the approach first leverages a label skew-awared loss to preserve the global label knowledge, and then aligns local feature distributions to consolidate global knowledge against local feature skew. We validated our method in three Non-IID scenarios using four public datasets, including the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) dataset, the Federated Tumor Segmentation (FeTS) dataset, the Multi-Modality Whole Heart Segmentation (MMWHS) dataset and the Multi-Site Prostate T2-weighted MRI segmentation (MSProsMRI) dataset. Results show that our method could achieve superior performance over existing methods. Code will be released via <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://zmiclab.github.io/projects.html</uri> once the paper gets accepted.
Sharp

We provide optimized range proofs, called Sharp, in discrete logarithm and hidden order groups, based on square decomposition. In the former setting, we build on the paradigm of Couteau et al. (Eurocrypt '21) and optimize their range proof (from now on, CKLR) in several ways: (1) We introduce batching via vector commitments and an adapted ∑;-protocol. (2) We introduce a new group switching strategy to reduce communication. (3) As repetitions are necessary to instantiate CKLR in standard groups, we provide a novel batch shortness test that allows for cheaper repetitions. The analysis of our test is nontrivial and forms a core technical contribution of our work. For example, for λ = 128 bit security and B = 64 bit ranges for N = 1 (resp. N = 8) proof(s), we reduce the proof size by 34% (resp. 75%) in arbitrary groups, and by 66% (resp. 88%) in groups of order 256-bit, compared to CKLR.
23 shades of self-admitted technical debt: an empirical study on machine learning software

In software development, the term "technical debt" (TD) is used to characterize short-term solutions and workarounds implemented in source code which may incur a long-term cost. Technical debt has a variety of forms and can thus affect multiple qualities of software including but not limited to its legibility, performance, and structure. In this paper, we have conducted a comprehensive study on the technical debts in machine learning (ML) based software. TD can appear differently in ML software by infecting the data that ML models are trained on, thus affecting the functional behavior of ML systems. The growing inclusion of ML components in modern software systems have introduced a new set of TDs. Does ML software have similar TDs to traditional software? If not, what are the new types of ML specific TDs? Which ML pipeline stages do these debts appear? Do these debts differ in ML tools and applications and when they get removed? Currently, we do not know the state of the ML TDs in the wild. To address these questions, we mined 68,820 self-admitted technical debts (SATD) from all the revisions of a curated dataset consisting of 2,641 popular ML repositories from GitHub, along with their introduction and removal. By applying an open-coding scheme and following upon prior works, we provide a comprehensive taxonomy of ML SATDs. Our study analyzes ML SATD type organizations, their frequencies within stages of ML software, the differences between ML SATDs in applications and tools, and quantifies the removal of ML SATDs. The findings discovered suggest implications for ML developers and researchers to create maintainable ML systems.
3D reconstruction method based on the optimal projection intensity of a polarization system

The measurement of shiny objects is a major challenge for structured light 3D measurement. Polarization systems can be used to measure shiny objects. However, the polarizer may attenuate the light intensity of the captured image, reducing the SNR of the image, and the measurement accuracy will be affected. To improve the measurement accuracy and efficiency, this paper proposes a 3D measurement method based on polarization optimal projection intensity. By establishing the camera response function under the polarization system, the desired optimal projection intensity can be directly estimated and used to compensate for the image light intensity attenuated by the additional polarizer. This method does not require rotating polarizers and multiple exposure times. In addition, the optimal fringe images are obtained by applying image fusion algorithms. The experimental results show that the method improves pixels in the image that are too dark, while significantly reducing the saturated pixels on the shiny surface. This method can effectively improve the image contrast, get better fringe images, extract more effective information, and reconstruct a more complete 3D point cloud.
Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.
Multiqudit quantum hashing and its implementation based on orbital angular momentum encoding

Abstract A new version of quantum hashing technique is developed wherein a quantum hash is constructed as a sequence of single-photon high-dimensional states (qudits). A proof-of-principle implementation of the high-dimensional quantum hashing protocol using orbital-angular momentum encoding of single photons is implemented. It is shown that the number of qudits decreases with increase of their dimension for an optimal ratio between collision probability and decoding probability of the hash. Thus, increasing dimension of information carriers makes quantum hashing with single photons more efficient.
Optimization Algorithm of Power Marketing AI Response System in the Era of Intelligent Technology

The intelligent question and answer platform can improve customer satisfaction, strengthen the self-service function of customers, and obtain business information conveniently and quickly. It is an effective means to reduce the company's overall operating costs and an important measure to promote the capacity building of remote service channels. The purpose of this paper is to study the optimization algorithm of the power marketing AI response system based on the era of intelligent technology. Combined with knowledge graph visualization and other technical means, the basic knowledge graph of the power industry is constructed, and an intelligent dialogue system is designed in combination with the constructed knowledge graph to help the power industry's customer service capabilities to improve, knowledge storage, knowledge management and other work to be carried out well. From the perspective of model evaluation indicators, this paper chooses the accuracy of the model as an important indicator to measure the efficiency of the model. In the relational classification model of information extraction, the comprehensive indicators of precision, recall, and F1 value of the BERT-BiLSTM-CRF knowledge extraction model are better than word embedding + BiLSTM + CRF.
Optical neural ordinary differential equations

Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance. However, the successive cascading of network hidden layers results in larger integrated photonic chip areas. To address this issue, we propose the optical neural ordinary differential equations (ON-ODEs) architecture that parameterizes the continuous dynamics of hidden layers with optical ODE solvers. The ON-ODE comprises the PNNs followed by the photonic integrator and optical feedback loop, which can be configured to represent residual neural networks (ResNets) and implement the function of recurrent neural networks with effectively reduced chip area occupancy. For the interference-based optoelectronic nonlinear hidden layer, the numerical experiments demonstrate that the single hidden layer ON-ODE can achieve approximately the same accuracy as the two-layer optical ResNets in image classification tasks. In addition, the ON-ODE improves the model classification accuracy for the diffraction-based all-optical linear hidden layer. The time-dependent dynamics property of ON-ODE is further applied for trajectory prediction with high accuracy.
A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks

Many IoT-blockchain systems in which blockchain connections run on an infrastructure-based network, such as Wi-Fi or LTE, face a severe problem: the single point of failure (SPoF) (i.e., depending on the availability, an access point of an LTE base station). Using infrastructure-less networks (i.e., ad hoc networks) is an efficient approach to prevent such highly disruptive events. An ad hoc network can automatically restore blockchain communication using an ad hoc routing protocol, even if a node fails. Moreover, an ad hoc routing protocol is more efficient when considering the IoT nodes’ mobility. In this paper, we first construct IoT-blockchain systems on emulated and real ad hoc networks with Ethereum and three ad hoc routing protocols (i.e., OLSR, BATMAN, and BABEL). We then evaluate the blockchain recovery time in static and mobile scenarios. The results show that BATMAN achieves the best blockchain recovery performance in all investigated scenarios because BATMAN only determines whether to switch a route by comparing the number of OGM packets received from a different next-hop. More specifically, in the small-scale real IoT-blockchain, BATMAN recovers at least 73.9% and 59.8% better than OLSR and BABEL, respectively. In the medium-scale emulated IoT-blockchain, the recovery time of BATMAN is at least 69% and 60% shorter than OLSR or BABEL, respectively.
All-in-One YOLO Architecture for safety Hazard Detection of Environment along High-Speed Railway

The safety hazards of environment along high-speed railway are paramount importance for railway transportation safety. The (UAV image)-based intelligent inspection method for environmental hazard is an effective alternative to manual inspection. This paper proposed an All-in-One YOLO architecture (AOYnet), which assembles semantic segmentation and object detection in two parallel tasks, aiming at eliminating the low- efficiency issue caused by single-task detector. Finally, the experiments evaluated on the Railway Surrounding Environment Dataset acquire 0.733 of mAP on detected objects and 0.972 of IoU on segmented objects, which outperform state-of-the-art methods. Thus, the proposed method is efficient and feasible for the detection of environmental hazards along high-speed railway using UAV Images.
Multi-robot Coordination for a Heterogeneous Fleet of Robots

There is an increasing need for autonomous mobile robots (AMRs) in industrial environments. The capability of autonomous movement and transportation of items in industrial environments provides a significant increase in productivity and efficiency. This need, coupled with the possibility of controlling groups of heterogeneous robots, simultaneously addresses a wide range of tasks with different characteristics in the same environment, further increasing productivity and efficiency. This paper will present an implementation of a system capable of coordinating a fleet of heterogeneous robots with robustness. The implemented system must be able to plan a safe and efficient path for these different robots. To achieve this task, the TEA* (Time Enhanced A*) graph search algorithm will be used to coordinate the paths of the robots, along with a graph decomposition module that will be used to improve the efficiency and safety of this system. The project was implemented using the ROS framework and the Stage simulator. Results validate the proposed approach since the system was able to coordinate a fleet of robots in various different tests efficiently and safely, given the heterogeneity of the robots.
Broadband polarization-entangled source for C+L-band flex-grid quantum networks

The rising demand for transmission capacity in optical networks has motivated steady interest in expansion beyond the standard C-band (1530–1565 nm) into the adjacent L-band (1565–1625 nm) for an approximate doubling of capacity in a single stroke. However, in the context of quantum networking, the L-band has yet to be fully leveraged with the suite of advanced tools for characterization and management available from classical lightwave communications. In this work, we demonstrate an ultrabroadband two-photon source integrating both C- and L-band wavelength-selective switches for complete control of spectral routing and allocation across 7.5 THz in a single setup. Polarization state tomography of all 150 pairs of 25-GHz-wide channels reveals an average fidelity of 0.98 and total distillable entanglement greater than 181 kebits/s. This source is explicitly designed for flex-grid optical networks and can facilitate optimal utilization of entanglement resources across the full C+L-band.
Pixel-Guided Association for Multi-Object Tracking

Propagation and association tasks in Multi-Object Tracking (MOT) play a pivotal role in accurately linking the trajectories of moving objects. Recently, modern deep learning models have been addressing these tasks by introducing fragmented solutions for each different problem such as appearance modeling, motion modeling, and object associations. To bring unification in the MOT task, we introduce a pixel-guided approach to efficiently build the joint-detection and tracking framework for multi-object tracking. Specifically, the up-sampled multi-scale features from consecutive frames are queued to detect the object locations by using a transformer–decoder, and per-pixel distributions are utilized to compute the association matrix according to object queries. Additionally, we introduce a long-term appearance association on track features to learn the long-term association of tracks against detections to compute the similarity matrix. Finally, a similarity matrix is jointly integrated with the Byte-Tracker resulting in a state-of-the-art MOT performance. The experiments with the standard MOT15 and MOT17 benchmarks show that our approach achieves significant tracking performance.
Benchmarking the Interactions among Challenges for Blockchain Technology Adoption: A Circular Economy Perspective

The chances of food contamination and spoilage get enhanced as it passes through various stages, and prudent consumers often need transparency on the origin of food products, their production and processing facilities utilized. Blockchain, an emerging digital technology, offers food traceability solutions to consumers and supply chain partners. But presently, blockchain adoption in Indian supply chains is in the nascent stages. The present study identified the challenges of adopting blockchain technology in Indian food supply chains and modelled them using Interpretive Structural Modelling (ISM). As per the ISM, the ‘regulatory structure’ and ‘lack of realised need’ emerged as the most significant driving forces that impact other challenges, viz. ‘privacy breach issues’, ‘high costs’, ‘lack of skills’, ‘lack of technology’, ‘lack of trust’ and ‘lack of infrastructure’. These challenges have an impact on the ‘scalability problem’. The paper underlines the significance of enabling regulatory structure, improved information and communication technologies infrastructure, and convincing the supply chain stakeholders to use blockchain technology to resolve the underlying challenges and achieve its adoption and scalability in the Indian food industry.
A hybrid quantum-classical Hamiltonian learning algorithm

This study develops a concrete near-term quantum algorithm for Hamiltonian learning and demonstrates its effectiveness. In particular, we show that learning the spectrum of Hamiltonians during the learning process could produce high-precision estimates of the target interaction coefficients. Our work may have applications in quantum device certification, quantum simulation, and quantum machine learning.
Advancing Learned Video Compression With In-Loop Frame Prediction

Recent years have witnessed an increasing interest in end-to-end learned video compression. Most previous works explore temporal redundancy by detecting and compressing a motion map to warp the reference frame towards the target frame. Yet, it failed to adequately take advantage of the historical priors in the sequential reference frames. In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate. The predicted frame can serve as a better reference than the previously compressed frame, and therefore it benefits the compression performance. The proposed in-loop prediction module is a part of the end-to-end video compression and is jointly optimized in the whole framework. We propose the recurrent and the bi-directional in-loop prediction modules for compressing P-frames and B-frames, respectively. The experiments show the state-of-the-art performance of our ALVC approach in learned video compression. We also outperform the default hierarchical B mode of x265 in terms of PSNR and beat the slowest mode of the SSIM-tuned x265 on MS-SSIM. The project page: https://github.com/RenYang-home/ALVC.
PlanHelper

Community-based Question Answering (CQA) platforms can provide rich experience and suggestions for people who seek to construct Activity Plans (AP), such as bodybuilding or sightseeing. However, answer posts in CQA platforms could be too unstructured and overwhelming to be easily applied to AP construction, as validated by our formative study for understanding relevant user challenges. We therefore proposed an answer-post processing pipeline, based on which we built PlanHelper, a tool assisting users in processing the CQA information and constructing AP interactively. We conducted a within-subject study (N=24) with a Quora-like interface as the baseline. Results suggested that when creating AP with PlanHelper, users were significantly more satisfied with the informational support and more engaged during the interaction. Moreover, we performed an in-depth analysis on the user behaviors with PlanHelper and summarized the design considerations for such supporting tools.