Reviewerly/exHarmony huggingface dataset:
Geochemical approach to determine the anthropogenic signal at non-intensively settled archaeological sites – The case of an Iron Age enclosure in Bohemia

We present a geochemical analysis of a specific Iron Age type of site known as the Viereckschanze – a square enclosure located in Bělčice, southern Bohemia. We performed soil coring from the topsoil to ca 100 cm depth, with 200 cores (divided into 456 samples). The samples were measured using portable XRF and the final dataset comprised 16 elements (Al, Si, P, K, Ca, Ti, Mn, Fe, Cu, Zn, As, Rb, Sr, Zr, Pb, and LE – 'light elements'). Because of the compositional character of data expressed in ppm, the data were transformed using isometric log-ratio transformation, which enabled them to carry out a multivariate analysis. This made it possible to determine the anthropogenic and natural content of elements. The site was specific when compared to usual archaeological sites for its 'unconventional' chemical signal: 1) the P signal typical for archaeological settlements was found mainly outside of the enclosure; 2) the conventional anthropogenic signal from the inside of the enclosure was only represented by Mn; 3) other elements related to possible anthropogenic activities were revealed only after applying statistical analysis (specifically As, Pb, Zn, Cu); 4) the unusual manifestation of Si and Ti (usually natural signals) strongly connected to anthropogenic contexts was observed. Increased contents of Cu, Zn and especially Pb were recorded in places indicated by previous magnetic measurements which were possibly related to an onsite metalworking activity. Such results could be characterised as a mixture of typical human-related signals (represented mainly by P and Mn) and typical natural signals (like Si, and Ti). These observations corroborate the hypothesis of a specificity of the Viereckschanzen. They did not have to be used exclusively for residential purposes and could have had a different, more complex socio-economic role in the past. Last but not least, their occupation was probably also rather short-time.
Face Image Inpainting Based on Generative Adversarial Network

Face image inpainting is essential in the fields such as protection and preservation of human images with patterns. Using image inpainting to remove facial masks on human faces is one of the challenging tasks. In this paper, we propose a face image inpainting method based on an adversarial neural network. In general, face image inpainting is composed of generators and discriminators in deep nets. The loss function combines the losses from Mean Square Error (MSE) and Generative Adversarial Networks (GANs). In this paper, we have designed and implemented a new model for face image inpainting with up to half of the given image (50% of the area). The average of the evaluation metrics PSNR and SSIM are 31.86dB and 0.89, respectively. We improved image inpainting with a new model that is much suitable for face images.
A faster outsourced medical image retrieval scheme with privacy preservation

With the rapid development of computer technology and medical imaging technology, medical images present an explosive growth. To save storage and computation overhead, hospitals often choose to outsource digital medical images to cloud server. Since medical images are a major auxiliary means for doctors’ diagnosis or medical researchers’ study, the secure retrieval of outsourced medical images is especially important. To address this problem, we propose a Faster outsourced Medical Image Retrieval scheme with privacy preservation (FMIR) in this paper. FMIR first makes a simple classification to outsourced medical images, which narrows the retrieval range and improves the retrieval efficiency compared with the existing unclassified retrieval schemes. Second, FMIR implements a lightweight access control for each class using polynomial-based access control strategy, which provides the fine-grained access control for better privacy protection of medical images. Third, FMIR reduces the interference of random numbers on relevant score to 0, which further improves the accuracy of the retrieval. Finally, the security and performance analysis show that FMIR is secure, accurate and efficient.
A Deep Neural Architecture for Decision-Aware Meta-Review Generation

Automatically generating meta-reviews from peer-reviews is a new and challenging task. Although close, the task is not precisely summarizing the peer-reviews. Usually, a conference chair or a journal editor writes a meta-review after going through the reviews written by the appointed reviewers, rounds of discussions with them, finally arriving at a consensus on the paper's fate. In essence, the meta-review texts are decision-aware, i.e., the meta reviewer already forms the decision before writing the meta-review, and the corresponding text conforms to that decision. We leverage this seed idea and design a deep neural architecture to generate decision-aware meta-reviews in this work. We propose a multi-encoder transformer network for peer-review decision prediction and subsequent meta-review generation. We analyze our output quantitatively and qualitatively and argue that quantitative text summarization metrics are not suitable for evaluating the generated meta-reviews. Our proposed model performs comparably with the recent state-of-the-art text summarization approaches. Qualitative evaluation of our model-generated output is encouraging on an open access peer reviews dataset that we curate from the open review platform. We make our data and codes available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.iitp.ac.in/~ai-nlp-ml/resources.html# decision-aware-meta-review.
Higher-Order Photon Statistics as a New Tool to Reveal Hidden Excited States in a Plasmonic Cavity

Among the best known quantities obtainable from photon correlation measurements are the g(m) correlation functions. Here, we introduce a new procedure to evaluate these correlation functions based on higher-order factorial cumulants CF,m that integrate over the time dependence of the correlation functions, that is, summarize the available information at different time spans. In a systematic manner, the information content of higher-order correlation functions as well as the distribution of photon waiting times is taken into account. Our procedure greatly enhances the sensitivity for probing correlations and, moreover, is robust against a limited counting efficiency and time resolution in experiment. It can be applied even in case g(m) is not accessible at short time spans. We use the new evaluation scheme to analyze the photon emission of a plasmonic cavity coupled to a single quantum dot. We derive criteria that must hold if the system can be described by a generic Jaynes–Cummings model. A violation of the criteria can be explained by the presence of an additional excited quantum dot state.
Hybrid Quantum Benders’ Decomposition For Mixed-integer Linear Programming

The Benders&#x2019; decomposition algorithm is a technique in mathematical programming for complex mixed-integer linear programming (MILP) problems with a particular block structure. The strategy of Benders&#x2019; decomposition can be described as a strategy of divide and conquer. The Benders&#x2019; decomposition algorithm has been employed in a variety of applications such as communication, networking, and machine learning. However, the master problem in Benders&#x2019; decomposition is still NP-hard, which motivates us to employ quantum computing. In the paper, we propose a hybrid quantum-classical Benders&#x2019; decomposition algorithm. We transfer the Benders&#x2019; decomposition&#x2019;s master problem into the quadratic unconstrained binary optimization (QUBO) model and solve it by the state-of-the-art quantum annealer. Then, we analyze the computational results and discuss the feasibility of the proposed algorithm. Due to our reformulation in the master problem in Benders&#x2019; decomposition, our hybrid algorithm, which takes advantage of both classical and quantum computers, can guarantee the solution quality for solving MILP problems.
Multiagent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

In this article, a novel hybrid multirobot motion planner that can be applied under no explicit communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multirobot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multiagent soft actor-critic (MASAC) algorithm under the centralized training with decentralized execution (CTDE) diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multigroup experimental results verify the effectiveness of the proposed motion planner.
Poisoning Attacks and Data Sanitization Mitigations for Machine Learning Models in Network Intrusion Detection Systems

Among many application domains of machine learning in real-world settings, cyber security can benefit from more automated techniques to combat sophisticated adversaries. Modern network intrusion detection systems leverage machine learning models on network logs to proactively detect cyber attacks. However, the risk of adversarial attacks against machine learning used in these cyber settings is not fully explored. In this paper, we investigate poisoning attacks at training time against machine learning models in constrained cyber environments such as network intrusion detection; we also explore mitigations of such attacks based on training data sanitization. We consider the setting of poisoning availability attacks, in which an attacker can insert a set of poisoned samples at training time with the goal of degrading the accuracy of the deployed model. We design a white-box, realizable poisoning attack that reduced the original model accuracy from 95% to less than 50 % by generating mislabeled samples in close vicinity of a selected subset of training points. We also propose a novel Nested Training method as a defense against these attacks. Our defense includes a diversified ensemble of classifiers, each trained on a different subset of the training set. We use the disagreement of the classifiers' predictions as a data sanitization method, and show that an ensemble of 10 SVM classifiers is resilient to a large fraction of poisoning samples, up to 30% of the training data.
Constructions of new q-cryptomorphisms

In the theory of classical matroids, there are several known equivalent axiomatic systems that define a matroid, which are described as matroid cryptomorphisms. A q-matroid is a q-analogue of a matroid where subspaces play the role of the subsets in the classical theory. In this article we establish cryptomorphisms of q-matroids. In doing so we highlight the difference between classical theory and its q-analogue. We introduce a comprehensive set of q-matroid axiom systems and show cryptomorphisms between them and existing axiom systems of a q-matroid. These axioms are described as the rank, closure, basis, independence, dependence, circuit, hyperplane, flat, open space, spanning space, non-spanning space, and bi-colouring axioms.
MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation

Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Particle swarm optimization still suffers from two primary shortcomings, i.e., premature convergence and easy trapping in local optima. Therefore, this study proposes modified chaotic particle swarm optimization with uniform particle initialization to enhance the comprehensive performance of standard particle swarm optimization by introducing three additional schemes. Firstly, the initialized swarm is generated through a uniform approach. Secondly, replacing the linear inertia weight by introducing the nonlinear chaotic inertia weight map. Thirdly, by applying a personal learning strategy to enhance the global and local search to avoid trap in local optima. The proposed algorithm is examined and compared with standard particle swarm optimization, two recent particle swarm optimization variants, and a nature-inspired algorithm using three software effort estimation methods as benchmark functions: Use case points, COCOMO, and Agile. Detailed investigations prove that the proposed schemes work well to develop the proposed algorithm in an exploitative manner, which is created by a uniform particle initialization and avoids being trapped on the local optimum solution in an explorative manner and is generated by a personal learning strategy and chaotic-based inertia weight.
Behind every great research project is great data management

Research data management (RDM) is the cornerstone of a successful research project, and yet it often remains an underappreciated art that gets overlooked in the hustle and bustle of everyday project management even when required by funding bodies. If researchers are to strive for reproducible science that adheres to the principles of FAIR, then they need to manage the data associated with their research projects effectively. It is imperative to plan your RDM strategies early on, and setup your project organisation before embarking on the work. There are several different factors to consider: data management plans, data organisation and storage, publishing and sharing your data, ensuring reproducibility and adhering to data standards. Additionally it is important to reflect upon the ethical implications that might need to be planned for, and adverse issues that may need a mitigation strategy. This short article discusses these different areas, noting some best practices and detailing how to incorporate these strategies into your work. Finally, the article ends with a set of top ten tips for effective research data management.
Hybrid Federated and Centralized Learning

Many of the machine learning tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole dataset. In this way, FL brings the learning to edge level, wherein powerful computational resources are required on the client side. This requirement may not always be satisfied because of diverse computational capabilities of edge devices. We address this through a novel hybrid federated and centralized learning (HFCL) framework to effectively train a learning model by exploiting the computational capability of the clients. In HFCL, only the clients who have sufficient resources employ FL; the remaining clients resort to CL by transmitting their local dataset to PS. This allows all the clients to collaborate on the learning process regardless of their computational resources. We also propose a sequential data transmission approach with HFCL (HFCL-SDT) to reduce the training duration. The proposed HFCL frameworks outperform previously proposed non-hybrid FL (CL) based schemes in terms of learning accuracy (communication overhead) since all the clients collaborate on the learning process with their datasets regardless of their computational resources.
DBC-Forest: Deep forest with binning confidence screening

As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications. Compared with the traditional deep forest approach, gcForestcs effectively reduces the high time cost by passing some instances in the high-confidence region directly to the final stage. However, there is a group of instances with low accuracy in the high-confidence region, which are called mis-partitioned instances. To find these mis-partitioned instances, this paper proposes a deep binning confidence screening forest (DBC-Forest) model, which packs all instances into bins based on their confidences. In this way, more accurate instances can be passed to the final stage, and the performance is improved. Experimental results show that DBC-Forest achieves highly accurate predictions for the same hyperparameters and is faster than other similar models to achieve the same accuracy.
Adaptive and Progressive Multispectral Image Demosaicking

Common consumer RGB cameras use a single image sensor to capture RGB color images with a color filter array (CFA) placed in front of the image sensor. This system captures particularly one color sample ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> , or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> ) at each pixel location. This captured raw image is called a CFA image. The CFA demosaicking is used then to construct the complete color image. A similar approach of working with a single image sensor and extension of CFA to multispectral filter array (MSFA) enables us to develop a low-cost multispectral camera with the help of MSFA demosaicking. However, due to many spectral bands and their very sparse sampling, extending CFA demosaicking to MSFA demosaicking is not easy. This paper proposes an adaptive and progressive MSFA demosaicking method for various MSFA patterns for 5 to 15 bands multispectral images. Binary tree-based MSFA patterns are used to create MSFA images, and these MSFA patterns can be designed for any N-band MSFA image. The spectral bands are arranged specifically so that the middle band has the highest probability of appearance (PoA) in the MSFA pattern. The middle band is interpolated first using PoA based progressive adaptive interpolation, and the modified bilinear spectral difference method is used to estimate all other spectral bands with the interpolated middle band’s help. Experimental results and comparative analysis on two different datasets reveal that our proposed MSFA demosaicking method outperforms the existing generic MSFA demosaicking methods in terms of computational time and various image quality metrics considered.
Automated Detection of Malaria implemented by Deep Learning in Pytorch

The diagnoses of diseases as widespread as malaria has proven to be tough in rural areas. This is because of the lack of resources and professionals working in these places. In such cases, and for urban areas as well, the automation of diagnoses can play a vital role in detecting, diagnosing, treating, and preventing malaria in such areas of the world. The automation procedure uses a computerised approach for the diagnosis of malaria from the acquired microscopic images of PBC (Peripheral Blood Smear) images. In this paper, we focus on deep learning algorithms implemented in Pytorch through transfer learning to detect malaria in segmented red blood cell images. The process involved using pre-trained ImageNet models (Namely ResNet and DenseNet) and fine-tuning them to the dataset at hand to classify cell images as either parasitized or uninfected. Reported here the results obtained. The ResNet50 model achieved an accuracy of 91.72%. The DenseNet121 model achieved the highest accuracy of 94.43%. The ResNet50 model performance parameters measured, and the specificity and sensitivity are respectively 89.03% and 88.91%. This branch of digital telepathology can enable the healthcare industry to distribute quality services even to unreachable and rural areas of the world.
Notes on the H-measure of classifier performance

Abstract The H-measure is a classifier performance measure which takes into account the context of application without requiring a rigid value of relative misclassification costs to be set. Since its introduction in 2009 it has become widely adopted. This paper answers various queries which users have raised since its introduction, including questions about its interpretation, the choice of a weighting function, whether it is strictly proper, its coherence, and relates the measure to other work.
Semantic Interoperability in Internet of Things

The industry's internet of things (IoT) applications have drawn significant research attention in recent decades. IoT is a technology in which intelligent objects with sensors-enabled RFID tags, actuators, and processors communicate information to cater to a meaningful purpose in the industry. This way, IoT technology aims to simplify the distributed data collection in industrial practice, sharing and processing information and knowledge across many collaborating partners using suitable enterprise information systems. This chapter describes new methods with grounded knowledge representation techniques to address the needs of formal information modeling and reasoning for web-based services. The chapter presents a framework, apparel business decentralized data integration (ABDDI), which uses knowledge representation methods and formal languages (e.g., description logics – DLs) to annotate necessary business activities. This type of web service requires increased interoperability in service management operations.
Transformer-Based Efficient Salient Instance Segmentation Networks With Orientative Query

Salient instance segmentation (SIS) can be considered as the next generation task for the saliency detection community. Most of the existing state-of-the-art methods used for this novel challenging task are built on the mainstream Mask R-CNN architecture. However, this mechanism relies heavily on hand-designed anchors and NMS post-processing. In this paper, we provide a one stage SIS framework with transformers, termed Orientative Query Transformer (OQTR). To leverage the long-range dependencies of transformers, a cross fusion module is designed to efficiently fuse the global features in the encoder and salient query features for salient mask prediction. Furthermore, derived from the center prior in traditional saliency models, we propose an orientative query that is considered as the initial salient object query to accelerate convergence. In addition, to mitigate the issue of the lack of a large-scale dataset with salient instance labels, we collect a new SIS dataset (SIS10 K) containing over 10 K images elaborately annotated with both object- and instance-level labels to promote the community. Without any post-processing, our end-to-end OQTR framework significantly surpasses the top-1 RDPNet by an average of 13.1% AP scores across all three challenging datasets, demonstrating the strong performance of the proposed OQTR. The code and the dataset proposed in this work are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ssecv/OQTR</uri> .