Reviewerly/exHarmony huggingface dataset:
Eight years of AutoML: categorisation, review and trends

Abstract Knowledge extraction through machine learning techniques has been successfully applied in a large number of application domains. However, apart from the required technical knowledge and background in the application domain, it usually involves a number of time-consuming and repetitive steps. Automated machine learning (AutoML) emerged in 2014 as an attempt to mitigate these issues, making machine learning methods more practicable to both data scientists and domain experts. AutoML is a broad area encompassing a wide range of approaches aimed at addressing a diversity of tasks over the different phases of the knowledge discovery process being automated with specific techniques. To provide a big picture of the whole area, we have conducted a systematic literature review based on a proposed taxonomy that permits categorising 447 primary studies selected from a search of 31,048 papers. This review performs an extensive and rigorous analysis of the AutoML field, scrutinising how the primary studies have addressed the dimensions of the taxonomy, and identifying any gaps that remain unexplored as well as potential future trends. The analysis of these studies has yielded some intriguing findings. For instance, we have observed a significant growth in the number of publications since 2018. Additionally, it is noteworthy that the algorithm selection problem has gradually been superseded by the challenge of workflow composition, which automates more than one phase of the knowledge discovery process simultaneously. Of all the tasks in AutoML, the growth of neural architecture search is particularly noticeable.
An Experimental Analysis of RowHammer in HBM2 DRAM Chips

RowHammer (RH) is a significant and worsening security, safety, and reliability issue of modern DRAM chips that can be exploited to break memory isolation. Therefore, it is important to understand real DRAM chips’ RH characteristics. Unfortunately, no prior work extensively studies the RH vulnerability of modern 3D-stacked high-bandwidth memory (HBM) chips, which are commonly used in modern GPUs.In this work, we experimentally characterize the RH vulnerability of a real HBM2 DRAM chip. We show that 1) different 3D-stacked channels of HBM2 memory exhibit significantly different levels of RH vulnerability (up to 79 % difference in bit error rate), 2) the DRAM rows at the end of a DRAM bank (rows with the highest addresses) exhibit significantly fewer RH bitflips than other rows, and 3) a modern HBM2 DRAM chip implements undisclosed RH defenses that are triggered by periodic refresh operations. We describe the implications of our observations on future RH attacks and defenses and discuss future work for understanding RH in 3D-stacked memories.
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects

Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that this first survey of proactive dialogue systems can provide the community with a quick access and an overall picture to this practical problem, and stimulate more progresses on conversational AI to the next level.
Deep Multi-view Subspace Clustering with Anchor Graph

Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1) they mainly focus on using autoencoders to nonlinearly embed the data, while the embedding may be suboptimal for clustering because the clustering objective is rarely considered in autoencoders, and (2) existing methods typically have a quadratic or even cubic complexity, which makes it challenging to deal with large-scale data. To address these issues, in this paper we propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG). To be specific, DMCAG firstly learns the embedded features for each view independently, which are used to obtain the subspace representations. To significantly reduce the complexity, we construct an anchor graph with small size for each view. Then, spectral clustering is performed on an integrated anchor graph to obtain pseudo-labels. To overcome the negative impact caused by suboptimal embedded features, we use pseudo-labels to refine the embedding process to make it more suitable for the clustering task. Pseudo-labels and embedded features are updated alternately. Furthermore, we design a strategy to keep the consistency of the labels based on contrastive learning to enhance the clustering performance. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.
OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning

Large-scale deep learning models contribute to significant performance improvements on varieties of downstream tasks. Current data and model parallelism approaches utilize model replication and partition techniques to support the distributed training of ultra-large models. However, directly deploying these systems often leads to sub-optimal training efficiency due to the complex model architectures and the strict device memory constraints. In this paper, we propose Optimal Sharded Data Parallel (OSDP), an automated parallel training system that combines the advantages from both data and model parallelism. Given the model description and the device information, OSDP makes trade-offs between the memory consumption and the hardware utilization, thus automatically generates the distributed computation graph and maximizes the overall system throughput. In addition, OSDP introduces operator splitting to further alleviate peak memory footprints during training with negligible overheads, which enables the trainability of larger models as well as the higher throughput. Extensive experimental results of OSDP on multiple different kinds of large-scale models demonstrate that the proposed strategy outperforms the state-of-the-art in multiple regards.
Dual Relation Knowledge Distillation for Object Detection

Knowledge distillation is an effective method for model compression. However, it is still a challenging topic to apply knowledge distillation to detection tasks. There are two key points resulting in poor distillation performance for detection tasks. One is the serious imbalance between foreground and background features, another one is that small object lacks enough feature representation. To solve the above issues, we propose a new distillation method named dual relation knowledge distillation (DRKD), including pixel-wise relation distillation and instance-wise relation distillation. The pixel-wise relation distillation embeds pixel-wise features in the graph space and applies graph convolution to capture the global pixel relation. By distilling the global pixel relation, the student detector can learn the relation between foreground and background features, and avoid the difficulty of distilling features directly for the feature imbalance issue. Besides, we find that instance-wise relation supplements valuable knowledge beyond independent features for small objects. Thus, the instance-wise relation distillation is designed, which calculates the similarity of different instances to obtain a relation matrix. More importantly, a relation filter module is designed to highlight valuable instance relations. The proposed dual relation knowledge distillation is general and can be easily applied for both one-stage and two-stage detectors. Our method achieves state-of-the-art performance, which improves Faster R-CNN based on ResNet50 from 38.4% to 41.6% mAP and improves RetinaNet based on ResNet50 from 37.4% to 40.3% mAP on COCO 2017.
Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators

To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.
An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading

Abstract Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&amp;E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists ( n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971–1.000, 0.875–0.976, and 0.988–1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62–0.80 (0.77 for AI tool) and 0.64–0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.
Distant entanglement generation and controllable information transfer via magnon–waveguide systems

The generation of entanglement and information distribution among macroscopic systems which are spatially separated are of paramount importance in the realm of quantum networks and quantum communication. In this paper, we propose a scheme that enables the preparation of long-distance entanglement between two magnons and controllable information transfer from a giant atom to either magnon, where the two magnons can be positioned symmetrically or asymmetrically in relation to a giant atom, and all of the magnons and the atom are coupled to a microwave cavity-array waveguide. By considering significant frequency detuning, we can virtually excite photons and establish an indirect interaction between the giant atom and the two magnons. Through the modulation of atom-waveguide coupling rates, we can not only evenly transfer atomic excitation to both magnons, thereby generating maximally entangled states, but also selectively transmit the atomic state to a single magnon. It is our hope that our proposed scheme will prove valuable in the field of quantum communications.
Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance

Recently, there are significant advancements in learning-based image compression methods surpassing traditional coding standards. Most of them prioritize achieving the best rate-distortion performance for a particular compression rate, which limits their flexibility and adaptability in various applications with complex and varying constraints. In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression. Specifically, RPN first builds a compression pyramid and generates the resolution fields at different levels in a top-down manner. The key design lies in the cross-resolution context mining module between adjacent levels, which performs feature enriching and distillation to mine meaningful contextualized information and remove unnecessary redundancy, producing informative resolution fields as residual priors. The scalability is achieved by progressive bitstream reusing and resolution field incorporation varying at different levels. Furthermore, between adjacent compression levels, we explicitly quantify the aleatoric uncertainty from the bottom decoded representations and develop an uncertainty-guided loss to update the upper-level compression parameters, forming a reverse pyramid process that enforces the network to focus on the textured pixels with high variance for more reliable and accurate reconstruction. Combining resolution field exploration and uncertainty guidance in a pyramid manner, RPN can effectively achieve spatial and quality scalable image compression. Experiments show the superiority of RPN against existing classical and deep learning-based scalable codecs. Code will be available at https://github.com/JGIroro/RPNSIC.
A Trajectory Privacy-Preserving Scheme Based on Transition Matrix and Caching for IIoT

With the increasing integration of Location-Based Services (LBS) into various societal domains, location privacy preservation has emerged as a pivotal concern. In most continuous LBS privacy protection approaches, the user needs to send a query to an untrusted Location Service Provider (LSP) to request the corresponding query results, and these results are discarded immediately after being used. This leads to similar queries in the future having to be sent to the LSP again, which increases the risk of privacy leakage when facing the LSP. To solve these issues, caching techniques are typically used to provide answers to users’ future queries. However, minimizing the interaction with the LSP is a challenge. Here, we propose a trajectory privacy-preserving scheme based on a transition matrix and caching (TMC) scheme for continuous LBS in the Industrial Internet of Things (IIoT). It employs multi-level caching to reduce the risk of exposing sensitive information to untrusted entities. We designed a transition matrix to predict the user’s next query location and simplify the computation complexity. We designed a cloaking set generation algorithm by considering transition entropy, location prediction, data freshness, and cache contribution degree to enhance user location privacy and improve the cache hit rate. The security analysis demonstrates how the TMC scheme resists attacks from both internal and external entities and ensures robustness in trajectory privacy. The experimental results show that the proposed TMC scheme can provide a higher level of privacy protection and lower system overhead compared to several previous schemes.
An Adaptive TinyML Unsupervised Online Learning Algorithm for Driver Behavior Analysis

The Internet of Things (IoT) has significantly impacted various industries, particularly the automotive sector, due to the growing integration of IoT technologies in vehicles. As a result, the volume of data generated by vehicular sensors has increased, leading to a surge in studies focusing on driver behavior to enhance road safety and optimize transportation networks. However, traditional approaches to analyzing driver behavior have relied on supervised offline learning models, which are unsuitable for handling data streams in online learning environments. This study introduces an unsupervised online k-fix AutoCloud algorithm for detecting driver behavior patterns, leveraging the concepts of typicity and eccentricity while considering the historical-temporal relationships between samples. Furthermore, the algorithm autonomously and adaptively evolves without requiring a supervised training phase, making it compatible with the TinyML concept, encompassing Artificial Intelligence algorithms designed for low-power IoT devices. To validate the proposed method, a real case study was conducted over four days using a vehicle to compare the quantity and quality of clusters generated by the algorithm. The findings demonstrate the potential of the proposed approach for optimizing data processing with minimal computational power.
Digital Competence of Higher Education Teachers at a Distance Learning University in Portugal

The Digital Education Action Plan (2021–2027) launched by the European Commission aims to revolutionize education systems, prioritizing the development of a robust digital education ecosystem and the enhancement of teachers’ digital transformation skills. This study focuses on Universidade Aberta, Portugal, to identify the strengths and weaknesses of teachers’ digital skills within the Digital Competence Framework for Educators (DigCompEdu). Using a quantitative approach, the research utilized the DigCompEdu CheckIn self-assessment questionnaire, validated for the Portuguese population, to evaluate teachers’ perceptions of their digital competences. A total of 118 teachers participated in the assessment. Findings revealed that the teachers exhibited a notably high overall level of digital competence, positioned at the intersection of B2 (Expert) and C1 (Leader) on the DigCompEdu scale. However, specific areas for improvement were identified, particularly in Digital Technologies Resources and Assessment, the core pedagogical components of DigCompEdu, which displayed comparatively lower proficiency levels. To ensure continuous progress and alignment with the Digital Education Action Plan’s strategic priorities, targeted teacher training initiatives should focus on enhancing competences related to Digital Technologies Resources and Assessment.
Accessing continuous-variable entanglement witnesses with multimode spin observables

We present several measurement schemes for accessing separability criteria for continuous-variable bipartite quantum systems. Starting from moments of the bosonic mode operators, criteria suitable to witness entanglement are expressed in terms of multimode spin observables via the Jordan-Schwinger map. These observables are typically defined over a few replicas of the state of interest and can be transformed into simple photon number measurements by passive optical circuits. Our measurement schemes require only a handful of measurements, thereby allowing one to efficiently detect entanglement without the need for costly state tomography as illustrated for a variety of physically relevant states (Gaussian, mixed Schr\"odinger cat, and NOON states). The influence of typical experimental imperfections is shown to be moderate.
Rapid Prototyping of Image Contrast Enhancement Hardware Accelerator on FPGAs Using High-Level Synthesis Tools

Rapid prototyping tools have become essential in the race to market. In this work, we have explored employing rapid prototyping approach to develop an intellectual property core for real-time contrast enhancement which is a commonly employed image processing task. Specifically, the task involves real-time contrast enhancement of video frames, which is used to repair washed out (overexposed) or darkened (underexposed) appearance. Such scenario is frequently encountered in video footage captured underwater. Since the imaging conditions are not known a priori, the lower and upper limits of the dynamic range of acquired luminance values need to be adaptively determined and mapped to the full range permitted by the allocated bitwidth so that the processed image has a high-contrast appearance. This paper describes a hardware implementation of this operation using contrast stretching algorithm with the help of Simulink high-level synthesis tool using rapid prototyping paradigm. The developed model can be directly used as a drop-in module in larger computer vision systems to enhance Simulink computer vision toolbox capabilities, which does not support this operation for direct FPGA implementation yet. The synthesized core consumes less than 1% of total FPGA slice logic resources while dissipating only 7 mW dynamic power. To this end, look-up table has been employed to implement the division operator which otherwise requires exorbitantly large number of logic resources. Moreover, an online algorithm has been proposed which avoids multiple memory accesses. The hardware module has been tested in a real-time video processing scenario at 100 MHz clock rate and depicts functional accuracy at par with the software while consuming lower logic resources than competitive designs. These results demonstrate that the appropriate use of modern rapid prototyping tools can be highly effective in reducing the development time without compromising the functional accuracy and resource utilization.
A Study on the Suggestion of 'Harmony' Among the Aesthetic Thoughts Considering 'Harmony' as Beauty before Confucius in China

The purpose of this study is to analyze and find out how the perception of beauty, which appeared in the process of developing the idea of considering harmony as beauty, affects human life. The so-called
Freshness-Keeping Strategy of Logistics Service Providers: The Role of the Interaction between Blockchain and Overconfidence

As a result of the increasing scrutiny of fresh products, greengrocers are now forced to concern themselves with the deterioration of their products’ freshness and employ blockchain technology as a tracing system. However, in the logistics system, the third-party logistics service provider (LSP) is motivated to be overconfident in order to extract extra profits, thus intensifying the dilemma faced by the fresh agricultural product industry. This paper focuses on the association between blockchain technology and overconfidence, in which the third-party LSP is supposed to overestimate the effect of the retailer’s freshness keeping measures. Differing from the previous literature, we analyze a situation wherein blockchain technology is adopted with explicit execution. Based on the optimal control model, we obtained three main conclusions: First, the overconfidence of a third-party LSP does not damage the logistics system but changes the freshness-keeping strategy of the retailer. Second, interestingly, although blockchain technology performs effectively when it is adopted as an initially established system with a freshness keeping strategy, it is not always a wise decision for managers to adopt a blockchain, especially when adopting it as a countermeasure for overconfidence. Third, we found that blockchain technology has a greater effect on freshness-keeping than overconfidence. Thus, in the fresh agricultural product industry, managers should adopt blockchain technology before overconfidence occurs and pay more attention to exogenous prices and freight to decide whether to adopt blockchain technology.