Reviewerly/exHarmony huggingface dataset:
Interactive Multi-Scale Fusion of 2D and 3D Features for Multi-Object Vehicle Tracking

Multiple Object Tracking (MOT) is a significant task in autonomous driving. Nonetheless, relying on one single sensor is not robust enough, because one modality tends to fail in some challenging situations. Texture information from RGB cameras and 3D structure information from Light Detection and Ranging (LiDAR) have respective advantages under different circumstances. Therefore, feature fusion from multiple modalities contributes to the learning of discriminative features. However, it is nontrivial to achieve effective feature fusion due to the completely distinct information modality. Previous fusion methods usually fuse the top-level features after the backbones extract the features from different modalities. The feature fusion happens solely once, which limits the information interaction between different modalities. In this paper, we propose multi-scale interactive query and fusion between pixel-wise and point-wise features to obtain more discriminative features. In addition, an attention mechanism is utilized to conduct soft feature fusion between multiple pixels and points to avoid inaccurate match problems of previous single pixel-point fusion methods. We introduce PointNet++ to obtain multi-scale deep representations of point clouds and make it adaptive to our proposed interactive feature fusion between multi-scale features of images and point clouds. Through the interaction module, each modality can integrate more complementary information from the other modality. Besides, we explore the effectiveness of pre-training on each single modality and fine-tuning on the fusion-based model. Our method can achieve 90.32% MOTA and 72.44% HOTA on the KITTI benchmark and outperform other approaches without using multi-scale soft feature fusion.
Multi-Pooling Context Network for Image Semantic Segmentation

With the development of image segmentation technology, image context information plays an increasingly important role in semantic segmentation. However, due to the complexity of context information in different feature maps, simple context capture operations can easily cause context information omission. Rich context information can better classify categories and improve the quality of image segmentation. On the contrary, poor context information will lead to blurred image category segmentation and an incomplete target edge. In order to capture rich context information as completely as possible, we constructed a Multi-Pooling Context Network (MPCNet), which is a multi-pool contextual network for the semantic segmentation of images. Specifically, we first proposed the Pooling Context Aggregation Module to capture the deep context information of the image by processing the information between the space, channel, and pixel of the image. At the same time, the Spatial Context Module was constructed to capture the detailed spatial context of images at different stages of the network. The whole network structure adopted the form of codec to better extract image context. Finally, we performed extensive experiments on three semantic segmentation datasets (Cityscapes, ADE20K, and PASCAL VOC2012 datasets), which fully proved that our proposed network effectively alleviated the lack of context extraction and verified the effectiveness of the network.
Extreme Events Prediction from Nonlocal Partial Information in a Spatiotemporally Chaotic Microcavity Laser

The forecasting of high-dimensional, spatiotemporal nonlinear systems has made tremendous progress with the advent of model-free machine learning techniques. However, in real systems it is not always possible to have all the information needed; only partial information is available for learning and forecasting. This can be due to insufficient temporal or spatial samplings, to inaccessible variables, or to noisy training data. Here, we show that it is nevertheless possible to forecast extreme event occurrences in incomplete experimental recordings from a spatiotemporally chaotic microcavity laser using reservoir computing. Selecting regions of maximum transfer entropy, we show that it is possible to get higher forecasting accuracy using nonlocal data vs local data, thus allowing greater warning times of at least twice the time horizon predicted from the nonlinear local Lyapunov exponent.
ArrayFlex: A Systolic Array Architecture with Configurable Transparent Pipelining

Convolutional Neural Networks (CNNs) are the state-of-the-art solution for many deep learning applications. For maximum scalability, their computation should combine high performance and energy efficiency. In practice, the convolutions of each CNN layer are mapped to a matrix multiplication that includes all input features and kernels of each layer and is computed using a systolic array. In this work, we focus on the design of a systolic array with configurable pipeline with the goal to select an optimal pipeline configuration for each CNN layer. The proposed systolic array, called ArrayFlex, can operate in normal, or in shallow pipeline mode, thus balancing the execution time in cycles and the operating clock frequency. By selecting the appropriate pipeline configuration per CNN layer, ArrayFlex reduces the inference latency of state-of-the-art CNNs by 11 %, on average, as compared to a traditional fixed-pipeline systolic array. Most importantly, this result is achieved while using 13 %-23 % less power, for the same applications, thus offering a combined energy-delay-product efficiency between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.4\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.8\times$</tex> .
Quantification of information processing capacity in living brain as physical reservoir

The information processing capacity (IPC) measure is gaining traction as a means of characterizing reservoir computing. This measure offers a comprehensive assessment of a dynamical system's linear and non-linear memory of past inputs by breaking down the system states into orthogonal polynomial bases of input series. In this study, we demonstrate that IPCs are experimentally measurable in the auditory cortex in response to a random sequence of clicks. In our experiment, each input series had a constant inter-step interval (ISI), and a click was delivered with a 50% probability at each time step. Click-evoked multi-unit activities in the auditory cortex were used as the state variables. We found that the total IPC was dependent on the test ISI and reached a maximum at around 10- and 18-ms ISI. This suggests that the IPC reaches a peak when the stimulus dynamics and intrinsic dynamics in the brain are matched. Moreover, we found that the auditory cortex exhibited non-linear mapping of past inputs up to the 6th degree. This finding indicates that IPCs can predict the performance of a physical reservoir when benchmark tasks are decomposed into orthogonal polynomials. Thus, IPCs can be useful in measuring how the living brain functions as a reservoir. These achievements have opened up future avenues for bridging the gap between theoretical and experimental studies of neural representation. By providing a means of quantifying a dynamical system's memory of past inputs, IPCs offer a powerful tool for understanding the inner workings of the brain.
Cybersecurity in the German Railway Sector

Cybersecurity breaches are on the rise. Due to increased network interconnection, industrial systems are becoming more attractive targets for such attacks. Therefore, various industries must acquire knowledge and experience regarding cybersecurity for their IT and OT systems. One such sector is the safety-critical railway and public transport sector. Technological leaps like digital interlocking, fifth-generation networking, and the move from proprietary field busses to IP networking are driving the sector into an inter-connected age. This work scrutinizes the sector's cybersecurity awareness, knowledge, and preparedness. To this end, we created a two-stage study comprising a quantitative online survey to gain comprehensive insight and qualitative interviews to deepen selected issues in a SWOT analysis. The results show that the sector still has a long way to go regarding cybersecurity, with the average company not meeting basic implementation levels. Furthermore, we identified multiple factors contributing to this result. Key issues are a lack of staff and management sensitivity to cybersecurity and support from authorities.
Towards a semantic approach in GLAM Labs: The case of the Data Foundry at the National Library of Scotland

GLAM organisations have been exploring the benefits of publishing their digital collections in a wide variety of forms since the 2000s. Many institutions, and in particular libraries, have applied the Semantic Web and Linked Data to their main catalogues. Recent advances in technology and innovative approaches concerning the reuse of the digital collections by means of computational access have paved the way for the creation of laboratories within GLAM organisations. In this work, we present a framework that works in several steps to transform into linked open data (LOD), assess and enrich with external repositories the data sets made available by GLAM organisations under open licences. The framework has been applied to three metadata data sets made available by the Data Foundry at the National Library of Scotland obtaining as a result a collection of LOD repositories, a data quality assessment and examples of exploration. The results of this work are publicly available and can be applied to other domains such as digital humanities and data science.
Implementation of augmented reality and mixed reality applications for smart facilities management: a systematic review

Purpose The Operations and Maintenance (O&amp;M) cost of a facility is typically 60–85% of the total life cycle cost of a building whereas its design and construction cost accounts for only 5–10%. Therefore, enhancing and optimizing the O&amp;M of a facility is a crucial issue. In addition, with the increasing complexities in a building's operating systems, more technologically advanced solutions are required for proactively maintaining a facility. Thereby, a tool is needed which can optimize and reduce the cost of facility maintenance. One of the solutions is Augmented or Mixed Reality (AR/MR) technologies which can reduce repair time, training time and streamline inspections. Therefore, the purpose of this study is to establish contextual knowledge of AR/MR application in facilities operation and maintenance and present an implementation framework through the analysis and classification of articles published between 2015 and 2022. Design/methodology/approach To effectively understand all AR/MR applications in facilities management (FM), a systematic literature review is performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed for searching and describing the search strategies. Keywords were identified through the concept mapping technique. The Scopus database and Google Scholar were employed to find relevant articles, books and conference papers. A thorough bibliometric analysis was conducted using VOS Viewer and subsequently, a thematic analysis was performed for the selected publications. Findings The use of AR/MR within facilities O&amp;M could be categorized into five different application areas: (1) visualization; (2) maintenance; (3) indoor localization and positioning; (4) information management and (5) indoor environment. After a thematic analysis of the literature, it was found that maintenance and indoor localization were the most frequently used research application domains. The chronological evolution of AR/MR in FM is also presented along with the origin of publications, which showed that the technology is out of its infancy stage and is ready for implementation. However, literature showed many challenges hindering this goal, that is (1) reluctance of the organizational leadership to bear the cost of hardware and trainings for the employees, (2) Lack of BIM use in FM and (3) system lagging, crashing and unable to register the real environment. A preliminary framework is presented to overcome these challenges. Originality/value This study accommodates a variety of application domains within facilities O&amp;M. The publications were systematically selected from the existing literature and then reviewed to exhibit various AR/MR applications to support FM. There have been no literature reviews that focus on AR and/or MR in the FM and this paper fills the gap by not only presenting its applications but also developing an implementation framework.
QL-ADIFA: Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm

Optimization problems are ubiquitous in engineering and scientific research, with a large number of such problems requiring resolution. Meta-heuristics offer a promising approach to solving optimization problems. The firefly algorithm (FA) is a swarm intelligence meta-heuristic that emulates the flickering patterns and behaviour of fireflies. Although FA has been significantly enhanced to improve its performance, it still exhibits certain deficiencies. To overcome these limitations, this study presents the Q-learning based on the adaptive logarithmic spiral-Levy flight firefly algorithm (QL-ADIFA). The Q-learning technique empowers the improved firefly algorithm to leverage the firefly's environmental awareness and memory while in flight, allowing further refinement of the enhanced firefly. Numerical experiments demonstrate that QL-ADIFA outperforms existing methods on 15 benchmark optimization functions and twelve engineering problems: cantilever arm design, pressure vessel design, three-bar truss design problem, and 9 constrained optimization problems in CEC2020.
IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

The realm of the Internet of Things (IoT), while continually transforming as a novel paradigm in the nexus of technology and education, still contends with numerous obstacles that hinder its incorporation into higher education institutions’ (HEIs) e-learning platforms. Despite substantial strides in IoT utilization from industrialized nations—the United States, the United Kingdom, Japan, and China serving as prime exemplars—the scope of its implementation in developing countries, notably Saudi Arabia, Malaysia, Pakistan, and Bangladesh, lags behind. A significant gap exists in research centered on the trajectory of IoT integration within e-learning systems of economically disadvantaged nations. Specifically, this study centers on Saudi Arabia to illuminate the main factors catalyzing or encumbering IoT uptake within its HEIs’ e-learning sector. As a preliminary step, this research has embarked on an exhaustive dissection of prior studies to unearth critical variables implicated in the IoT adoption process. Subsequently, we employed an inferential methodology, amassing data from 384 respondents in Saudi Arabian HEIs. Our examination divulges that usability, accessibility, technical support, and individual proficiencies considerably contribute to the rate of IoT incorporation. Furthermore, our data infer that financial obstacles, self-efficacy, interactive capability, online surveillance, automated attendance tracking, training programs, network and data safeguarding measures, and relevant tools significantly influence IoT adoption. Contrarily, factors such as accessibility, internet quality, infrastructure preparedness, usability, privacy concerns, and faculty support appeared to have a negligible impact on the adoption rates within HEIs. This research culminates in offering concrete recommendations to bolster IoT integration within Saudi Arabian HEIs, presenting valuable insights for government entities, policy architects, and HEIs to address the hurdles associated with IoT implementation in the higher education sector.
Efficient Token-Guided Image-Text Retrieval With Consistent Multimodal Contrastive Training

Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words. However, the close relations between coarse- and fine-grained representations for each modality are important for image-text retrieval but almost neglected. As a result, such previous works inevitably suffer from low retrieval accuracy or heavy computational cost. In this work, we address image-text retrieval from a novel perspective by combining coarse- and fine-grained representation learning into a unified framework. This framework is consistent with human cognition, as humans simultaneously pay attention to the entire sample and regional elements to understand the semantic content. To this end, a Token-Guided Dual Transformer (TGDT) architecture which consists of two homogeneous branches for image and text modalities, respectively, is proposed for image-text retrieval. The TGDT incorporates both coarse- and fine-grained retrievals into a unified framework and beneficially leverages the advantages of both retrieval approaches. A novel training objective called Consistent Multimodal Contrastive (CMC) loss is proposed accordingly to ensure the intra- and inter-modal semantic consistencies between images and texts in the common embedding space. Equipped with a two-stage inference method based on the mixed global and local cross-modal similarity, the proposed method achieves state-of-the-art retrieval performances with extremely low inference time when compared with representative recent approaches. Code is publicly available: github.com/LCFractal/TGDT.
Using Cloud Functions as Accelerator for Elastic Data Analytics

Cloud function (CF) services, such as AWS Lambda, have been applied as the new computing infrastructure in implementing analytical query engines. For bursty and sparse workloads, CF-based query engine is more elastic than the traditional query engines running in servers, i.e., virtual machines (VMs), and might provide a higher performance/price ratio. However, it is still controversial whether CF services are good suites for general analytical workloads, in respect of the limitations of CFs in storage, network, and lifetime, as well as the much higher resource unit prices than VMs. In this paper, we first present micro-benchmark evaluations of the features of CF and VM. We reveal that for query processing, though CF is more elastic than VM, it is less scalable and is more expensive for continuous workloads. Then, to get the best of both worlds, we propose Pixels-Turbo - a hybrid query engine that processes queries in a scalable VM cluster by default and invokes CFs to accelerate the processing of unpredictable workload spikes. In the query engine, we propose several optimizations to improve the performance and scalability of the CF-based operators and a cost-based optimizer to select the appropriate algorithm and parallelism for the physical query plan. Evaluations on TPC-H and real-world workload show that our query engine has a 1-2 orders of magnitude higher performance/price ratio than state-of-the-art serverless query engines for sustained workloads while not compromising the elasticity for workload spikes.
Data Mining Using Multi-Valued Logic Minimization

In a partially defined classification function, each input combination represents features of an example, while the output represents the class of the example. Each variable may have different radix. In this paper, we show a method to minimize the number of variables. Combined with a multiplevalued logic minimizer, data sets of examples are represented by a compact set of rules. Experimental results using University of California Irvine (UCI) benchmark functions show the effectiveness of the approach, especially for imbalanced data sets. The results are compared with J48 and JRIP. This approach produces explainable 100% correct rules, which are promising for bio-medical applications.
CDFSL: Image Registration for Spaceborne Hyperspectral and Multispectral Data Having Large Spatial-Resolution Difference

Image registration aims to eliminate the geometric deviation between multi-source data with the same range, and to promote the collaborative application of data. In recent years, spaceborne hyperspectral (HS) and multispectral (MS) data have been widely used in Earth observation. However, the difference in the number of bands, spatial resolution, and spectral resolution puts forward higher requirements on the registration algorithm. The key to HS and MS image registration is to extract more common key points, weaken and eliminate the difference of radiation and spatial texture information to build superior descriptors, and achieve high-precision matching of key points. This paper introduces a new robust HS and MS registration method based on common deep feature subspaces. We first construct the common deep feature subspaces extraction network to extract consistent edge features and common subspace images of the image pair. Then, Harris algorithm is used to extract key points from consistent edge features between images, which reduces the impact of spatial resolution differences between images. Besides, the SIFT descriptor and subspace images are used to describe key points, which reduces the impact of radiation differences between images. Finally, Euclidean distance is used for the initial matching of key points, and the affine matrix is calculated after the outliers are eliminated, and image registration is performed. We perform experiments on spaceborne HS and MS datasets of different spatial resolutions and comparisons with state-of-the-art methods. Experimental results show that our method can obtain satisfactory registration results and is robust.
Performance Evaluation of Genetic Algorithm and Flower Pollination Algorithm for Scheduling Tasks in Cloud Computing

Resource management during the cloud computing process is one of the prominent issues currently under research. The reason behind this is to procure an optimal utilization of these resources with the least cost and time. Allocation and scheduling of users’ tasks over the servers is an NP-hard problem, which can be solved by applying various heuristic and meta-heuristic approaches. With this motive, the paper has analyzed the performance of two meta-heuristic approaches, i.e., Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) in scheduling the tasks over virtual machines (VM). Tasks to VMs allotment is mainly responsible for the utilization of virtual resources which are actually running over physical machines (PM) or servers. The prime parameter considered in the research is makespan which indirectly measures the consumption of resources for performing a set of tasks. Simulation for the research is performed for a defined set of tasks that need to be allocated over a particular number of servers using the cloudsim tool. Results are measured and analyzed for both the algorithms applied in the scheduling process and also a default approach of first come first serve (FCFS) is compared with the overall results. Analyses prove the GA approach overperforms the FPA in reducing the makespan in various configurations considered.