Reviewerly/exHarmony huggingface dataset:
SSDPT: Self-supervised dual-path transformer for anomalous sound detection

Anomalous sound detection for machine condition monitoring or structural health monitoring is essential in the development of Industry 4.0. However, the anomalous sounds of machines are unpredictable and are hard to collect in real-world factories. Therefore, the anomalous sound detection methods have to learn robust acoustic representations under the situation that only normal sounds are provided, and effectively detect the anomalous sounds while being applied. In this article, we propose a self-supervised dual-path Transformer (SSDPT) network, which is purely based on attention modules, to detect anomalous sounds for predictive maintenance of the machine. The SSDPT network splits the acoustic features into segments and employs several DPT blocks for time and frequency modeling. DPT blocks use self-attention modules to alternately model the interactive information about the frequency and temporal components of the segmented acoustic features. To address the problem of lack of anomalous sound, we adopt a self-supervised learning approach to train the network with normal sound. Specifically, this approach randomly masks and reconstructs the acoustic features, and jointly classifies machine identity information to improve the performance of anomalous sound detection. We evaluated our method on the DCASE2021 task2 dataset. The experimental results show that the SSDPT network increases in the harmonic mean AUC score compared with state-of-the-art methods of anomalous sound detection.
Artificial intelligence‐based digital scores of stromal tumour‐infiltrating lymphocytes and tumour‐associated stroma predict disease‐specific survival in triple‐negative breast cancer

Triple-negative breast cancer (TNBC) is known to have a relatively poor outcome with variable prognoses, raising the need for more informative risk stratification. We investigated a set of digital, artificial intelligence (AI)-based spatial tumour microenvironment (sTME) features and explored their prognostic value in TNBC. After performing tissue classification on digitised haematoxylin and eosin (H&E) slides of TNBC cases, we employed a deep learning-based algorithm to segment tissue regions into tumour, stroma, and lymphocytes in order to compute quantitative features concerning the spatial relationship of tumour with lymphocytes and stroma. The prognostic value of the digital features was explored using survival analysis with Cox proportional hazard models in a cross-validation setting on two independent international multi-centric TNBC cohorts: The Australian Breast Cancer Tissue Bank (AUBC) cohort (n = 318) and The Cancer Genome Atlas Breast Cancer (TCGA) cohort (n = 111). The proposed digital stromal tumour-infiltrating lymphocytes (Digi-sTILs) score and the digital tumour-associated stroma (Digi-TAS) score were found to carry strong prognostic value for disease-specific survival, with the Digi-sTILs and Digi-TAS scores giving C-index values of 0.65 (p = 0.0189) and 0.60 (p = 0.0437), respectively, on the TCGA cohort as a validation set. Combining the Digi-sTILs feature with the patient's positivity status for axillary lymph nodes yielded a C-index of 0.76 on unseen validation cohorts. We surmise that the proposed digital features could potentially be used for better risk stratification and management of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Social robot-delivered customer-facing services: an assessment of the experience

The ability to install social intelligence protocols in robots in order for them to exhibit conversational skills has made them ideal tools for delivering services with a high cognitive and low emotional load. Little is known about how this capability influences the customer experience and the intention to continue receiving these services. Experiences were assessed in a study simulating customer-facing service delivery, and the constructs of the technology readiness index and stated gender were analysed as possible moderators in a quasi-experiment. Hedonic quality was the most relevant factor explaining attitude, and attitude explained intention to use as well as social influence. As for the constructs of technological readiness and gender, optimism and innovativeness seem to be the most likely candidates for moderating the other variables. The most optimistic and the most innovative route would be for the main actors to continue adapting to social robot technology in the future.
Understanding DevOps critical success factors and organizational practices

DevOps is a combination of practices and a company culture which aims minimize the barriers between the operation and development teams in the organization. As its adoption and use in the industry has been growing, different kinds of research are trying to explore DevOps practices, processes and implementations in the organizations. Most of the extant research conducted in the past was to investigate how DevOps worked, what impacts it made into the organizations and how the adoption of DevOps played a role for the overall success for companies. This paper presents a qualitative analysis of a dataset collected via an open ended survey from 15 software professionals who are experienced in DevOps. The focus of the study is on reporting DevOps practices in organizations, and how they impact on the success of DevOps. We discuss the DevOps professionals point-of-view on DevOps practices and align their thoughts through a DevOps model of the critical success factors.
Untrained deep learning-based phase retrieval for fringe projection profilometry

Fringe projection profilometry (FPP) based on deep learning shows potential for challenging 3-D sensing tasks, e.g., bio-medicine, reverse engineering, and face recognition, etc. Supervised deep learning has been introduced to retrieve the desired phase for the 3-D reconstruction, which relies on lots of advanced training to construct the fringe-to-phase transformation. The traditional deep learning-based method becomes unreliable for scenes that are different from the training ones, which restricts it to be applied for actual applications. In this paper, an untrained deep learning-based phase retrieval method is proposed. By adding a camera to the traditional FPP system, scene-independent physical constraints such as phase, structure and 3-D consistency are constructed to optimize the fringe-to-phase transformation. The proposed deep learning-based method can retrieve the desired phase from a single fringe pattern without advance training. Both theoretical analyses and experimental results demonstrate its accurateness and robustness. The proposed method also shows potential for single-shot 3-D sensing applications such as high-speed 3-D measurement.
Eye Tracking in Virtual Reality

This chapter explores the current state of the art in eye tracking within 3D virtual environments. It begins with the motivation for eye tracking in Virtual Reality (VR) in psychological research, followed by descriptions of the hardware and software used for presenting virtual environments as well as for tracking eye and head movements in VR. This is followed by a detailed description of an example project on eye and head tracking while observers look at 360° panoramic scenes. The example is illustrated with descriptions of the user interface and program excerpts to show the measurement of eye and head movements in VR. The chapter continues with fundamentals of data analysis, in particular methods for the determination of fixations and saccades when viewing spherical displays. We then extend these methodological considerations to determining the spatial and temporal coordination of the eyes and head in VR perception. The chapter concludes with a discussion of outstanding problems and future directions for conducting eye- and head-tracking research in VR. We hope that this chapter will serve as a primer for those intending to implement VR eye tracking in their own research.
Plain language summaries: Enabling increased diversity, equity, inclusion and accessibility in scholarly publishing

Key points Plain language summaries (PLS) are accessible, short, peer‐reviewed summaries of scholarly journal articles written in non‐technical language. The aim of PLS is to enable a broader audience of experts and non‐experts to understand the original article. Here, we outline the evidence base for the value and impact of PLS and how they can enable diversity, equity, inclusion and accessibility in scholarly publishing. PLS can diversify readership and authorship, address information inequity, include typically under‐represented stakeholders and provide an accessible route into scholarly literature.
A Lightweight YOLOv5 Optimization of Coordinate Attention

As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by factors such as cost and the tight supply of relevant chips, making it impossible to effectively deploy complex network models to small processors with limited resources and to perform efficient real-time detection. In this paper, YOLOv5 is studied to achieve the goal of lightweight devices by reducing the number of original network channels. Then detection accuracy is guaranteed by adding a detection head and CA attention mechanism. The YOLOv5-RC model proposed in this paper is 30% smaller and lighter than YOLOv5s, but still maintains good detection accuracy. YOLOv5-RC network models can achieve a good balance between detection accuracy and detection speed, with potential for its widespread use in industry.
Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Visual inspection of peripheral blood samples is a critical step in the leukemia diagnostic process. Automated solutions based on artificial vision approaches can accelerate this procedure, while also improving accuracy and uniformity of response in telemedicine applications. In this study, we propose a novel GBHSV-Leuk method to segment and classify Acute Lymphoblastic Leukemia (ALL) cancer cells. GBHSV-Leuk is a two staged process. The first stage involves pre-processing, which uses the Gaussian Blurring (GB) technique to blur the noise and reflections in the image. The second stage involves segmentation using the Hue Saturation Value (HSV) technique and morphological operations to differentiate between the foreground and background colors, which improve the accuracy of prediction. The proposed method attains 96.30% accuracy when applied on the private dataset, and 95.41% accuracy when applied on the ALL-IDB1 public dataset. This work would facilitate early detection of ALL cancer.
Application of remote sensing data integration in detecting mineralized granitic zones: A case study of the Gabal Al-Ijlah Al-Hamra, Central Eastern Desert, Egypt

The Gabal Al-Ijlah Al-Hamra granites in the Central Eastern Desert of Egypt are rich in rare metal-bearing minerals. The enhanced Senetinal-2-Planet fused image with 3 m spatial resolution using Gram–Schmidt fused technique was successfully used in detailed discrimination of the rock units in the study area. Various enhanced remote sensing data of Sentinel-2, Landsat-9, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and fused ASTER-Sentinel-2 datasets were used in the mapping process and verified by field investigation and petrographic analysis. The hydrothermal mineralized alteration zones related to rare metal-bearing granites were identified using ASTER and Sentinel-2 fused images. According to petrographic and ASTER mineral indices, various types of alteration zones, including sericitization, chloritization, carbonatization, kaolinitization and silicification were detected. The surface structural features were well detected using Soble directional filtering of Sentinel-1 enhanced image. The major structural directions that have been detected in Al-Ijlah Al-Hamra granitic pluton are NW-SE and NE-SW. These major trends were used to detect potential pathways for migration and concentration of rare-metals. The hyperspectral TerraSpec Halo mineral identifier (ASD) as well as petrographical investigation successfully identified the rock-forming minerals (i.e. quartz, K-feldspar, plagioclase, biotites, muscovite, chlorite, kaolinite, sericite) of syenogranites and alkali feldspar granites in the Gabal Al-Ijlah Al-Hamra area. Rare metal-bearing minerals including zircon, rutile, allanite, parisite and xenotime were identified using Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-Ray Spectroscopy (EDS). The results of remote sensing data, field observations and petrographic investigation are successfully used in exploration for rare metal-bearing minerals in the Central Eastern Desert of Egypt and similar areas worldwide.
Recent advances of Captcha security analysis: a short literature review

CAPTCHA has long been used to keep bots from misusing web services. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. In this paper, we presented a short literature review of the current CAPTCHA schemes, as well as highlighting new trends and open issues, the challenges, and the opportunities as a solid starting point for designing the future secure and usable CAPTCHA schemes.
A structure-enhanced generative adversarial network for knowledge graph zero-shot relational learning

Most knowledge graph completion methods focus on predicting existing relationships in the knowledge graph but cannot predict unseen relationships. To solve this problem, knowledge graph zero-shot relational learning (KGZSL) has gotten more and more attention in recent years. The common method of KGZSL is to leverage Generative Adversarial Networks (GANs) to build the connection between existing relation descriptions and knowledge graph domains. However, the traditional KGZSL method ignores the gap between relation text description and relation structured representation. To bridge this gap, we propose a Structure-Enhanced Generative Adversarial Network (SEGAN). SEGAN adopts a structure encoder to introduce knowledge graph structure information into the generator and guide the generator to generate knowledge graph embeddings more accurately. In addition, in the KGZSL task, the representations of entities are closely tied to the existing relationships, which has a negative impact on the prediction of new instances. Therefore, we design a new plug-and-play feature encoder to decouple entities from existing relationships. Experimental results on the knowledge graph zero-shot relational learning dataset demonstrate that our method has better structure representation ability, and the model performance is improved by 36.3% compared with the current optimal model.
Characteristics and Optimization Strategies of A* Algorithm and Ant Colony Optimization in Global Path Planning Algorithm

A* algorithm and ant colony optimization (ACO) are more widely used in path planning among global path planning algorithms. The optimization process is analyzed and summarized from the principles and characteristics of the two algorithms, A* algorithm is mainly optimized in terms of point selection and improvement of heuristic function; and ACO is mainly investigated in terms of transfer probability and pheromone positive feedback for improvement and optimization. Taking a single algorithm solving complex optimization problems difficulties into consideration, a splitting strategy can be used. So that local path or intelligent path optimization algorithms are incorporated in global path planning to improve search efficiency and optimization quality.
Efficient Workflow Scheduling algorithm in cloud computing using Whale Optimization

Task Scheduling is a tremendous challenge in cloud computing as tasks are varied in terms of their processing capacity and interdependent on each other i.e. workflows. Therefore, it is difficult to map workflows to suitable virtual resources in cloud environment. Many authors developed various schedulers, which addresses makespan and Quality of service metrics. Authors have not addressed parameters makespan in combination with migration time, which also affects performance of cloud computing environment. In this manuscript, a new workflow-scheduling mechanism is proposed, which takes priorities of tasks and schedules tasks effectively on to corresponding virtual resources. Whale optimization algorithm as the methodology to model this algorithm. Extensive simulations carried out on workflowsim simulator. It was evaluated aganist existing PSO, CS, ACO, GA algorithms. Finally, from simulation results, it was identified that makespan, migration time and energy consumption were minimized to a good extent.
Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.
Improved Hybrid Grey Wolf Optimization Algorithm Based on Dimension Learning-Based Hunting Search Strategy

An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance of exploration and development capabilities, and premature convergence. The algorithm benefits from particle swarm optimization and a dimension learning-based hunting search strategy. In the particle swarm algorithm search strategy, linear variable social learning and self-learning are introduced to improve the population’s ability to communicate information. The individual position, current iteration optimal position, and optimal population position of grey wolves are combined to update the individual position information, thus strengthening the communication between individuals and the population. In the dimension learning-based hunting search strategy, neighborhoods are built for each search member, and neighborhood members can share information, balance global and local searches, and maintain diversity. To validate the algorithm, 23 typical benchmark functions, CEC2022 benchmark functions, and engineering problem sinusoidal low-order-polynomial prediction of positioning error of numerical control machine tools are used to optimize the algorithm’s parameters. Results are compared with those from four other algorithms and analyzed using Friedman’s statistical test. Experimental and statistical tests reveal that the IHGWO algorithm has the best overall benchmark function rating, with an overall effectiveness of 87.23%. In the engineering parameter optimization problem, the mean square error, root mean square error, and goodness of fit of the prediction equation after IHGWO algorithm optimization are 95.3761, 9.7661, and 97.47%, respectively. These numerical values are superior to those of the compared algorithms, effectively demonstrating the comprehensive performance and applicability of the algorithm.
Creating an Explainable Intrusion Detection System Using Self Organizing Maps

Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS) are complex black boxes. This means that a security analyst will have little to no explanation or clarification on why an IDS model made a particular prediction. A potential solution to this problem is to research and develop Explainable Intrusion Detection Systems (X-IDS) based on current capabilities in Explainable Artificial Intelligence (XAI). In this paper, we create a novel X-IDS architecture featuring a Self Organizing Map (SOM) that is capable of producing explanatory visualizations. We leverage SOM's explainability to create both global and local explanations. An analyst can use global explanations to get a general idea of how a particular IDS model computes predictions. Local explanations are generated for individual datapoints to explain why a certain prediction value was computed. Furthermore, our SOM based X-IDS was evaluated on both explanation generation and traditional accuracy tests using the NSL-KDD and the CIC-IDS-2017 datasets. This focus on explainability along with building an accurate IDS sets us apart from other studies.