Reviewerly/exHarmony huggingface dataset:
Prediction of the distribution of soil properties in deep Colluvisols in different pedogeographic regions (Czech Republic) using diffuse reflectance infrared spectroscopy

Colluvisols form a significant part of the erosion–deposition soil catena mainly in undulating, agriculturally managed landscapes. Due to their sedimentary origin, they are soils with highly variable properties. For such diverse and often extremely deep soils, it is essential to capture the vertical distribution of properties in as much detail as possible. This study demonstrates the potential of the diffuse reflectance infrared spectroscopy (DRIFT) to derive accurate information on the stratigraphy and selected soil properties (soil organic carbon and calcium carbonate contents and aluminium and iron concentrations) of deep colluvial profiles in three regions in the Czech Republic with different dominant soil types (Chernozem, Luvisol, and Cambisol). The normalization of the spectrum using the quartz spectral band was found to be suitable for assessing the carbonate content in Chernozems and Luvisols (coefficients of determination (R2) were 0.89 and 0.88, respectively). The amount of organic carbon can be predicted from such normalized spectra with less confidence (R2 = 0.64). Organic matter quality as an indicator of their stability and maturity was computed from aliphatic compounds spectral bands (2930 cm–1) and aromatics and CO groups band around 1640 cm–1. The highest values of this indicator were found in Colluvisols in the Chernozem region (> 6, while, e.g., in the Cambisol region it is < 1), especially in the layers corresponding to the oldest sediments or buried in-situ horizons. In the Cambisol region, the buried B horizon was identified based on normalized (i) clays and Fe oxides bands and (ii) wider band of OH groups, which in this case is related to the content of iron oxyhydroxides. The study proved that the applied set of spectral parameters is an effective tool for the description of diverse soil parameters and for identification of the boundaries of individual soil layers. It is equally suitable for carbonate-free soils as well as for soils with higher carbonate contents, which usually cause difficulties in the interpretation of soil organic matter spectral parameters. It provides a comprehensive view of the soil and its mineral and organic components and can provide information that is difficult to measure by other methods.
Liquid Content Detection In Transparent Containers: A Benchmark

Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.
You Cannot Always Win the Race: Analyzing mitigations for branch target prediction attacks

Many recent attacks such as Branch Target Injection (BTI, aka Spectre v2) take advantage of speculative execution in modern processors, and in particular the inherent race condition between transient execution of code at the predicted target of a branch and the architectural resolution of the branch. This can create a speculation window in which code can be transiently executed at an unintended target, and mitigations for these attacks often focus on minimizing or removing such windows. By investigating the potential sources of latency that may contribute to such a speculation window, such as pipeline contention and simultaneous multithreading (SMT) activity, we show that an attacker can "win the race" despite the adoption of widely-used mitigations, on a variety of different x86 CPUs. We also show that such speculation windows may be present for predictions of direct branches. This enables a new class of BTI-style attacks that do not depend on indirect branches, and bypass the majority of previous mitigations against such attacks.
Post-quantum Cryptography

Abstract Post-quantum Cryptography discusses the need for a new generation of cryptography to protect against future quantum computers. The underlying mathematical concept is a one-way function that makes it easy to encrypt but challenging to decrypt without a secret. However, future quantum computers will likely be able to reverse many of these one-way functions that are widely used and allow the calculation of the secret needed to decrypt the data. Four algorithms believed safe against quantum computing were published in July 2022. Switching to quantum-safe algorithms is critical for data that must remain secret for years. For all other data, ensuring that systems are at least crypto-agile (migration path exists) or already support hybrid algorithms is crucial.
Space-associated domain adaptation for three-dimensional mineral prospectivity modeling

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Variational Autoencoders for Data Augmentation in Clinical Studies

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials.
An Improved YOLO Model for Traffic Signs Small Target Image Detection

Traffic sign detection significantly reduces traffic accidents, but in real-world scenarios, the considerable distance between traffic signs and in-vehicle cameras means only a small proportion of traffic signs are captured in the images. This makes far-off traffic sign detection a small object detection problem, and with fewer details in small sign objects, detection becomes increasingly challenging. In this paper, we specifically address poor localization, low accuracy, and missed detections when using You Only Look Once Version 5 (YOLOv5) for detecting small traffic sign objects. Firstly, we incorporate a decoupled head into YOLOv5’s detection algorithm, which serves to improve detection accuracy and accelerate network convergence. Then, to handle low-resolution targets better, we substitute the network’s original convolution layers with Space-to-Depth Convolution (SPD-Conv) modules. This modification enhances the model’s capacity to extract features from low-resolution traffic sign objects. Lastly, we integrate the Context Augmentation Module (CAM) into YOLOv5 by employing variable rate extended convolution. This module extracts context information from multiple receptive fields, thus providing essential supplementary information and significantly enhancing detection accuracy. Empirical results demonstrate the efficacy of our algorithm, shown by a substantial increase in object detection precision rate to 95.0%, a recall rate of 91.6%, and an average precision of 95.4%. These results represent improvements of 2.1%, 4.8% and 3.7%, respectively, when compared to the original YOLOv5 algorithm. Furthermore, when tested against other state-of-the-art methods, our proposed methodology shows superior performance.
Control of a Novel Parallel Mechanism for the Stabilization of Unmanned Aerial Vehicles

The use of delivery drones is currently hindered by the inability of transported objects to maintain a steady position, which can result from roll-, pitch-, and heave-induced vibrations. This paper proposes a novel parallel manipulator for stabilizing the platform of unmanned aerial vehicles. The proposed mechanism builds upon an existing study of a 3-SRR/SRU parallel stabilizing mechanism by incorporating the dynamical properties of the system into the control model. The resultant control technique is then applied to both the 3-RRS and 3-SRR mechanisms, and a comparative study is conducted to identify the most reliable stabilizer for regulating the platform’s orientation. The results demonstrate that the 3-SRR mechanism exhibits superior robustness and stability characteristics compared to the other two mechanisms. Additionally, the 3-SRR mechanism is controlled using artificial neural networks, which significantly improves the accuracy and stability of the system. Overall, this research presents a novel and effective solution for stabilizing the platform of unmanned aerial vehicles, with significant implications for the development of delivery drone technology.
Enhancing Phishing Email Detection through Ensemble Learning and Undersampling

In real-world scenarios, the number of phishing and benign emails is usually imbalanced, leading to traditional machine learning or deep learning algorithms being biased towards benign emails and misclassifying phishing emails. Few studies take measures to address the imbalance between them, which significantly threatens people’s financial and information security. To mitigate the impact of imbalance on the model and enhance the detection performance of phishing emails, this paper proposes two new algorithms with undersampling: the Fisher–Markov-based phishing ensemble detection (FMPED) method and the Fisher–Markov–Markov-based phishing ensemble detection (FMMPED) method. The algorithms first remove benign emails in overlapping areas, then undersample the remaining benign emails, and finally, combine the retained benign emails with phishing emails into a new training set, using ensemble learning algorithms for training and classification. Experimental results have demonstrated that the proposed algorithms outperform other machine learning and deep learning algorithms, achieving an F1-score of 0.9945, an accuracy of 0.9945, an AUC of 0.9828, and a G-mean of 0.9827.
A perspective on human activity recognition from inertial motion data

Abstract Human activity recognition (HAR) using inertial motion data has gained a lot of momentum in recent years both in research and industrial applications. From the abstract perspective, this has been driven by the rapid dynamics for building intelligent, smart environments, and ubiquitous systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc., which necessitate and subsume activity recognition aiming at recognizing the actions, characteristics, and goals of one or more agent(s) from a temporal series of observations streamed from one or more sensors. From a more concrete and seemingly orthogonal perspective, such momentum has been driven by the ubiquity of inertial motion sensors on-board mobile and wearable devices including smartphones, smartwatches, etc. In this paper we give an introductory and a comprehensive survey to the subject from a given perspective. We focus on a subset of topics, that we think are major, that will have significant and influential impacts on the future research and industrial-scale deployment of HAR systems. These include: (1) a comprehensive and detailed description of the inertial motion benchmark datasets that are publicly available and/or accessible, (2) feature selection and extraction techniques and the corresponding learning methods used to build workable HAR systems; we survey classical handcrafted datasets as well as data-oriented automatic representation learning approach to the subject, (3) transfer learning as a way to overcome many hurdles in actual deployments of HAR systems on a large scale, (4) embedded implementations of HAR systems on mobile and/or wearable devices, and finally (5) we touch on adversarial attacks, a topic that is essentially related to the security and privacy of HAR systems. As the field is very huge and diverse, this article is by no means comprehensive; it is though meant to provide a logically and conceptually rather complete picture to advanced practitioners, as well as to present a readable guided introduction to newcomers. Our logical and conceptual perspectives mimic the typical data science pipeline for state-of-the-art AI-based systems.
Enabling federated learning of explainable AI models within beyond-5G/6G networks

The quest for trustworthiness in Artificial Intelligence (AI) is increasingly urgent, especially in the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge amount of devices and will offer innovative services empowered with AI and Machine Learning tools. Nevertheless, private user data, which are essential for training such services, are not an asset that can be unrestrictedly shared over the network, mainly because of privacy concerns. To overcome this issue, Federated Learning (FL) has recently been proposed as a paradigm to enable collaborative model training among multiple parties, without any disclosure of private raw data. However, the initiative to natively integrate FL services into mobile networks is still far from being accomplished. In this paper we propose a novel FL-as-a-Service framework that provides the B5G/6G network with flexible mechanisms to allow end users to exploit FL services, and we describe its applicability to a Quality of Experience (QoE) forecasting service based on a vehicular networking use case. Specifically, we show how FL of eXplainable AI (XAI) models can be leveraged for the QoE forecasting task, and induces a benefit in terms of both accuracy, compared to local learning, and trustworthiness, thanks to the adoption of inherently interpretable models. Such considerations are supported by an extensive experimental analysis on a publicly available simulated dataset. Finally, we assessed how the learning process is affected by the system deployment and the performance of the underlying communication and computation infrastructure, through system-level simulations, which show the benefits of deploying the proposed framework in edge-based environments.
Examining the impact of service robot communication styles on customer intimacy following service failure

With the growing use of robots in the service industry, service failures have increased. This study applies the Communication Accommodation Theory to examine how robot employees' communication style can foster perceived intimacy, reducing customer anger and negative word-of-mouth. The study also tests if this relationship varies based on failure severity. Results show that an informal communication style significantly reduces negative word-of-mouth through intimacy, but only for low severity failures. These findings offer valuable insights for service providers using robots, emphasizing the need to adapt communication styles based on failure severity to effectively manage negative customer experiences.
Efficient federated item similarity model for privacy-preserving recommendation

Previous federated recommender systems are based on traditional matrix factorization, which can improve personalized service but are vulnerable to gradient inference attacks. Most of them adopt model averaging to fit the data heterogeneity of federated recommender systems, requiring more training costs. To address privacy and efficiency, we propose an efficient federated item similarity model for the heterogeneous recommendation, called FedIS, which can train a global item-based collaborative filtering model to eliminate user feature dependencies. Specifically, we extend the neural item similarity model to the federated model, where each client only locally optimizes the shared item feature matrix. We then propose a fast-convergent federated aggregation method inspired by meta-learning to address heterogeneous user updates and accelerate the convergence of global training. Furthermore, we propose a two-stage perturbation method to protect both local training and transmission while reducing communication costs. Finally, extensive experiments on four real-world datasets validate that FedIS can provide more competitive performance on federated recommendations. Our proposed method also shows significant training efficiency with less performance degradation.
Flexible thermal camera solution for Smart city people detection and counting

Abstract Tourism management plays an important role in the context of Smart Cities. In this work, we have used thermal cameras for the development of an Object Detection solution in pedestrian areas. The solution can classify people, bikes, strollers, and count people in Real-Time by using telephoto and wide-angle thermal cameras, in hot squares where there is a relevant number of people passing by. This work has improved FASTER-R-CNN and YOLOv5 architectures with new data sets and fine-tuning approaches to enhance mean average precision and flexibility whether compared to state of the art solutions. Both top-down and bottom-up training adaptation approaches have been assessed in order to demonstrate that the proposed bottom-up approach can provide better results. Results have overcome the state-of-the-art in terms of mean Average Precision in counting (i) for relevant number of people in the scene (removing the limitation of previous state-of-the-art solutions that were set to provide good precision up to 10 people) and (ii) in terms of flexibility with respect to different kinds of camera and resolutions. The resulting model can produce results also when executed on thermal camera and in Real-Time on industrial PC of mid-level. The proposed solution has been developed and validated in the framework of the Herit-Data EC project and it has exploited the Snap4City platform for the final collection of data results, monitoring and their publication on real time dashboards.
Organizational and Digital Transformation Projects

The organizational and digital transformation projects are very complex because of many reasons, and they depend on the initial project's phase, which depends on the critical building blocks (BB)-based unbundling process (UP). The organizational UP (OUP), which is a set of UPs whose primary goal is to disassemble the organization's legacy structure, system administration, resources, applications, working models, and components into dynamic reusable BBs which can be (re)used in standardized architectural BBs. The UP faces major difficulties because of the organization's heterogenous human profiles/cultures, system parts, managers' financial ambitions, and the project's limited time/budgets. Another major problem is that transformation and innovation techniques have been monopolized for achieving only immediate tangible goals like business and financial aspects/profits and where the intangible complex technical aspects are simply ignored.
Improvement in K-Means Clustering for Information Retrieval

In the field of data science and machine learning, data clustering is a potent technique that enables academics and businesses to glean valuable insights from massive and complicated datasets. Clustering algorithms can aid in locating patterns, anomalies, and trends that might not be immediately obvious in the raw data by grouping similar data points together. Making data-driven decisions in a variety of industries, from marketing and social media to healthcare and banking, can be really helpful with this. Clustering is the process of grouping data or unlabeled examples based on similarities among them to organize and get insights from the data by using various unsupervised machine-learning algorithms. The similarities are measured as per the features available in the data. More the number of features, the more complex it gets to measure similarity between them. K-means Clustering is an unsupervised machine learning which categorizes the data into K distinct groups of similar examples as it plots the samples in an N-dimensional plane based on the features and categorizes them while conversing from random start points using Euclidean distance measurement. This research work focuses on Information retrieval using K-means Clustering and other data retrieval algorithms as data retrieval. It contains a comparative analysis between the traditional K-means, the Ranking, and Query Redirection Method, and the Term Frequency-Inverse Document Frequency method to suggest a better approach for data mining and information retrieval.
Accelerating Data Delivery of Latency-Sensitive Applications in Container Overlay Network

Container overlay network, though being widely adopted to enable communication between containers on different hosts, is a key downside for latency-sensitive applications. The state-of-the-art solution seeks to shorten the data path in packet processing by replacing overlay connection file descriptors with host namespace ones. While promising, it must block each overlay connection until the relevant host connection is set up, thus heavily influencing the request latency. In this paper, we present ShuntFlow, a systematic data delivery framework that seamlessly integrates the host and overlay networks to reduce the application's request-response latency. ShuntFlow first lets all connections flow in the overlay network directly. Then, it adopts a simple-yet-effective syscall-threshold-based mechanism to pick appropriate connections and switches their data delivery to the host network in a blocking-free way using a multi-threading technique. As such, unnecessary connection switches are prevented; yet, the pre-setup phase dilemma is eliminated. We have implemented a ShuntFlow prototype based on Linux and Docker and evaluated it extensively on a 40 Gbps testbed. The results show that ShuntFlow achieves 13%/72% and 19%/69% reductions, in average/tail request-response latency of a web server and an in-memory key-value store, respectively, while incurring less CPU overhead, compared to Slim.