Reviewerly/exHarmony huggingface dataset:
Dynamic load balancing upon the replication and deletion of cloud services

In the last decade, the considerable increase of the cloud services use has led to the need to have search and selection techniques that match both the requirements of end users and those of the system. Indeed, to select a cloud service that meet the needs of both system and user is a challenge, due to the several conflicting criteria problem for the user on one hand, and for the system, i.e., the load balancing between Virtual Machines (VMs), on the second hand. Therefore, the main challenge, in this context, is how to ensure the user requirements by maintaining the system performance constraint. To deal with this challenge, we present in this paper an approach based on the cloud service replication on one or more VMs when the number of the user requests will be important at a given moment. This allows better load balancing between VMs by distrusting the users’ requests over them. In addition, it allows to select the best cloud service according to the users need. However, the cloud services replication introduces the problem of the storage space saturation. Thus, our second contribution is to select and delete the cloud service replicas without degradation of the load balancing. The two proposed contributions are based on the MCDM techniques in order to select the VMs that can receive the replica of the cloud service and to select those, which their storage space is overloaded in order to delete the replica cloud service. The experimental results, based on Cloudsim simulator, show that our proposal can effectively achieve good performance (load balancing) and improve the response time.
Near-Optimal Covering Solution for USV Coastal Monitoring using PAES

This paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programming formulation is proposed to model the USV monitoring problem. It consists of a combination of the Covering Salesman Problem (CSP) and Travelling Salesman Problem with Profit (TSPP). Then, we use CPLEX software to provide exact solutions. On the other hand, a customized chromosome-size algorithm is used to find heuristic solution. The latter is a multi-objective evolutionary algorithm known as Pareto Archived Evolution Strategy (PAES). The obtained results showed that the exact solving of the USV monitoring mission problem with mixed-integer programming (MIP) methods needs extensive computational costs. However, the customized PAES was able to provide Near-optimal solutions for large-size graphs in much faster time as compared to the exact one.
Predicting coauthorship using bibliographic network embedding

Abstract Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.
Meta-registration: Learning Test-Time Optimization for Single-Pair Image Registration

Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the “outer” meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.
Investigating the Use of Immersive Technologies for Additive Manufacturing

The demand for Additive Manufacturing (AM) is continuously increasing, so new challenges must be overcome. For instance, in the event of a pandemic, location-independent communication must be ensured so that the production and the training of workers can continue. As a result, digital learning and teaching techniques have gained traction, especially in location-independent interventions. Those techniques enable synchronous and asynchronous, location-independent transmission of information and data, however, they are mostly designed for theoretical contents. In order to ensure physical learning as well, more immersive technologies must be resorted to. Thus, this study investigates the extent to which Extended Reality (XR) technologies can be used in AM. Two workshops were held to gather expert opinions on how Virtual Reality (VR) and Augmented Reality (AR) can be utilized in AM. It turns out that the technologies can be used in a variety of ways, especially in areas such as training, visualization, information, simulation, checking, and communication. The greatest added value with minimal effort could be achieved in the location-independent training and assistance of employees.
Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning

Eye trackers can provide visual guidance to sonographers during ultrasound (US) scanning. Such guidance is potentially valuable for less experienced operators to improve their scanning skills on how to manipulate the probe to achieve the desired plane. In this paper, a multimodal guidance approach (Multimodal-GuideNet) is proposed to capture the stepwise dependency between a real-world US video signal, synchronized gaze, and probe motion within a unified framework. To understand the causal relationship between gaze movement and probe motion, our model exploits multitask learning to jointly learn two related tasks: predicting gaze movements and probe signals that an experienced sonographer would perform in routine obstetric scanning. The two tasks are associated by a modality-aware spatial graph to detect the co-occurrence among the multi-modality inputs and share useful cross-modal information. Instead of a deterministic scanning path, Multimodal-GuideNet allows for scanning diversity by estimating the probability distribution of real scans. Experiments performed with three typical obstetric scanning examinations show that the new approach outperforms single-task learning for both probe motion guidance and gaze movement prediction. The prediction can also provide a visual guidance signal with an error rate of less than 10 pixels for a 224 $$\times $$ 288 US image.
Deep Filter Bank Regression for Super-Resolution of Anisotropic MR Brain Images

In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals. While contemporary super-resolution (SR) methods aim to recover the underlying high-resolution volume, the estimated high-frequency information is implicit via end-to-end data-driven training rather than being explicitly stated and sought. To address this, we reframe the SR problem statement in terms of perfect reconstruction filter banks, enabling us to identify and directly estimate the missing information. In this work, we propose a two-stage approach to approximate the completion of a perfect reconstruction filter bank corresponding to the anisotropic acquisition of a particular scan. In stage 1, we estimate the missing filters using gradient descent and in stage 2, we use deep networks to learn the mapping from coarse coefficients to detail coefficients. In addition, the proposed formulation does not rely on external training data, circumventing the need for domain shift correction. Under our approach, SR performance is improved particularly in "slice gap" scenarios, likely due to the constrained solution space imposed by the framework.
Specifying requirements for collection and analysis of online user feedback

According to data-driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.
Extracting keywords of educational texts using a novel mechanism based on linguistic approaches and evolutive graphs

Keyword extraction is an important topic applicable to a wide range of areas such as span detection, information classification, sentiment analysis, and so on. There are hundreds of algorithms which can extract keywords from text documents. Many of these algorithms also use the functionality of the keywords, which is important, especially if we need to limit to a specific area of knowledge. This research work focuses on extracting keywords from educational texts. In an educational context, the keywords are the most important parts of the lesson and may answer the professor's questions. Classic keyword extraction algorithms have a very low success rate extracting keywords from educational texts, as the words extracted by these algorithms are very different from those selected by the teachers. Normally, the most important words from an educational point of view would not match with the most repeated words in that text. This research work attempts to improve automatic keyword extraction in educative texts, avoiding professors from having to do this tedious task. The possibility of detecting keywords automatically could be a starting point for the creation of applications capable of generating questions and exercises automatically. We tested whether the most popular word extraction algorithms were able to extract the keywords selected by professors efficiently. The result obtained by current algorithms were no good at all, as they showed a low true positive rate or very high rates of false positive. Due to these reasons, we designed a novel algorithm based on linguistic approaches and evolutive graphs. The research method to obtain the new algorithm was the design of a complex graph which operates with numerous characteristics related to the relationships between words and their linguistic properties. The graph was trained with a set of texts and keywords to establish the optimal weights for each of the characteristics. The proposal achieves a rate of true positives (TP) and F1 score significantly better than other algorithms.
Certificateless encryption scheme with provable security in the standard model suitable for mobile devices

Public key encryption is an important method to ensure data security. In recent years, researchers have designed multiple certificateless encryption (CLE) schemes, and the security proofs of most known schemes are done in random oracle model (ROM). It is generally known that the standard model (SM) is more reliable than the ROM. Currently, there are only three CLE schemes with provable security in SM. Unfortunately, all three schemes require pairing operations, enjoy high computation costs, and are not suitable for mobile devices. In this paper, we constructed a new CLE scheme. Compared with other schemes, the new scheme is with two advantages: First, the security proofs of the new scheme are done in SM. Second, the new scheme does not use pairing operation and enjoys higher computation efficiency. Efficiency analysis shows that the computation cost of the new scheme is only 12.4% of that of the most efficient scheme known, which is suitable for mobile devices.
Bounded complete domains and their logical form

We establish a framework of approximable disjunctive sequent calculus, which is sound and complete with respect to approximable FD-algebras. We show that the category of approximable FD-algebras with approximable mappings is equivalent to that of bounded complete domains with Scott continuous functions. This extends Abramsky's logical representation of Scott domains as domain prelocales to a continuous setting. We also consider some domain constructions applied to approximable FD-algebras and show how to construct the approximable FD-algebras we need for defining the semantics of programming languages. According to a substructure relation, we define a pointed ω-chain complete class of approximable FD-algebras, on which the domain constructions are made continuous and then the initial solutions to recursive domain equations are transformed into the fixed-points of such continuous functions.
Self-Supervised Cross-Modal Distillation for Thermal Infrared Tracking

Target representations play an important role in performance improvement for Thermal Infrared tracking. To tackle this problem, we propose a Cross-Modal Distillation method to distill representations of the TIR modality from the RGB modality, which conducts on a large amount of unlabeled paired RGB-TIR data in a self-supervised way. Benefiting from the powerful model in the RGB modality, the cross-modal distillation can learn the TIR-specific representation for promoting TIR tracking. The proposed approach can be incorporated into different baseline trackers conveniently as a generic and independent component. In practice, three different approaches are explored to generate paired RGB-TIR patches with the same semantics for training in a self-supervised way. It is easy to extend to an even larger scale of unlabeled training data. Our tracker outperforms the baseline tracker by achieving an absolute gain of 2.3% Success Rate, 2.7% Precision, and 2.5% Norm Precision on published datasets, respectively.
Artificial intelligence in retail – a systematic literature review

Purpose The purpose of this study is to present a systematic literature review of academic peer-reviewed articles in English published between 2005 and 2021. The articles were reviewed based on the following features: research topic, conceptual and theoretical characterization, artificial intelligence (AI) methods and techniques. Design/methodology/approach This study examines the extent to which AI features within academic research in retail industry and aims to consolidate existing knowledge, analyse the development on this topic, clarify key trends and highlight gaps in the scientific literature concerning the role of AI in retail. Findings The findings of this study indicate an increase in AI literature within the field of retailing in the past five years. However, this research field is fairly fragmented in scope and limited in methodologies, and it has several gaps. On the basis of a structured topic allocation, a total of eight priority topics were identified and highlighted that (1) optimizing the retail value chain and (2) improving customer expectations with the help of AI are key topics in published research in this field. Research limitations/implications This study is based on academic peer-reviewed articles published before July 2021; hence, scientific outputs published after the moment of writing have not been included. Originality/value This study contributes to the in-depth and systematic exploration of the extent to which retail scholars are aware of and working on AI. To the best of the author’s knowledge, this study is the first systematic literature review within retailing research dealing with AI technology.
Context‐aware Siamese network for object tracking

At present, temporal and spatial contexts are widely used to improve the adaptability of a tracker. However, most existing methods usually focus on one aspect of the temporal or spatial context and rarely exploit them simultaneously. In this paper, a context-aware Siamese Network (CSNet) is proposed, which skilfully integrates the modelling of temporal and spatial context into the Siamese tracking framework. Specifically, CSNet consists of a context-based channel attention module and a context-based cross-attention module. The former aggregates spatial context information from different channels and dynamically emphasizes target features, which makes it easier for the tracker to distinguish the target from the background. The latter propagates the temporal context from the previous frames to the current frame to establish the part-level relationship between the search region and the historical target state, which enables the tracker better adapt to the target deformation. In addition, to further mine context information, the CSNet is equipped with a state-aware strategy to control the contribution of different context information in tracking. Extensive experiments on OTB2015, UAV123, GOT-10k, LaSOT, and TrackingNet show that the proposed tracking method achieves comparable performance to the advanced trackers.
Spectrum Sensing Method Based on Wavelet Transform and Residual Network

Spectrum sensing is the premise and foundation of cognitive radio technology to solve the scarcity of spectrum resources. In this letter, a spectrum sensing method (WT-ResNet) based on wavelet transform (WT) and residual network (ResNet) is proposed for the problem of spectrum sensing of non-stationary signals with a low signal-to-noise ratio (SNR). The method uses the wavelet transform to process the signal for the characteristics of non-stationary signals, obtains the time-frequency matrix of the signal as the test statistic, and combines the residual network. The method is data-driven and does not require any a priori information about the signal. In addition, we not only test the commonly used wavelet functions but also analyze the SNR robustness and generalization ability of the algorithm. Finally, the simulation results show that the method outperforms other spectrum sensing methods. In particular, the method achieves a detection probability of 85% at SNR = −16dB when the false alarm probability is 10%.
Fusion sampling networks for skeleton-based human action recognition

After a few seconds of an action, the human eye only needs a few photos to judge, but the action recognition network needs hundreds of frames of input pictures for each action. This results in a large number of floating point operations (ranging from 16 to 100 G FLOPs) to process a single sample, which hampers the implementation of graph convolutional networks (GCN)-based action recognition methods when the computation capabilities are restricted. A common strategy is to retain only the portions of the frames, but this results in the loss of important information in the discarded frames. Furthermore, the selection progress of key frames is too independent and lacks connections with other frames. To solve these two problems, we propose a fusion sampling network to generate fused frames to extract key frames. Temporal aggregation is used to fuse adjacent similar frames, thereby reducing information loss and redundancy. The concept of self-attention is introduced to strengthen the long-term association of key frames. The experimental results on three benchmark datasets show that the proposed method achieves performance levels that are competitive with state-of-the-art methods while using only 16.7% of the number of frames (∼50 and 300 frames in total). On the NTU 60 dataset, the number of FLOPs and Params with a single-channel input are 3.776 G and 3.53 M, respectively. This would greatly reduce the excessive computational power cost in practical applications due to the large amount of data processed by action recognition.
Adaptive window-based filter for high-density impulse noise suppression

One of the primary factors that lower image quality is noise. In the world of image processing, image denoising is a crucial image-enhancing operation. This paper introduces and presents an improved denoising technique based on an adaptive ROAD-TGM-HT (rank order absolute difference-trimmed global mean Hough transform) filter that effectively eliminates high-density salt and pepper noise. The proposed approaches are evaluated using the MATLAB program. In qualitative and quantitative statistical analyses, performance metrics such as the Peak Signal to Noise Ratio, Image Enhancement Factor, and Structural Similarity Index have been utilized. Performing image denoising using these performance criteria. Grayscale and color pictures are utilized to test the Proposed Algorithm (PA), which offers an exceptional PSNR and IEF, especially at rising noise densities. This method considerably enhances image quality in comparison to prior techniques by efficiently reducing high-density noise produced during image acquisition or resulting from data transfer. To overcome this issue, we strengthened the ROAD-TGM by making it adaptable, and for the remaining pixels, we added the Hough Transform method, allowing it to perform better in scenarios with a high noise density.
Verifiable autonomy: From theory to applications

The Autonomy and Verification group11 Part of a wider, international, Autonomy and Verification Network of activity: https://autonomy-and-verification.github.io sits within the Department of Computer Science22 https://www.cs.manchester.ac.uk at the University of Manchester. The group has a long history of research into agents and multi-agent systems (both at Manchester and, previously, at the University of Liverpool) particularly in the areas of formal specification and verification, multi-agent programming, ethical agent reasoning, and swarms, teams and organisations.