Reviewerly/exHarmony huggingface dataset:
Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning

This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically convolutional neural networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifera specimen, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an ensemble learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system’s performance compared to other state-of-the-art approaches. The main focus of this paper is to introduce multiple colorization methods that differ from the current cutting-edge techniques; novel strategies like Gaussian or mean-based techniques are suggested. The proposed system was also found to outperform human experts in classification accuracy.
Video Super-Resolution Network with Gated High-Low Resolution Frames

In scenes with large inter-frame motion variations, distant targets, and blurred targets, the lack of inter-frame alignment can greatly affect the effectiveness of subsequent video super-resolution reconstruction. How to perform inter-frame alignment in such scenes is the key to super-resolution reconstruction. In this paper, a new motion compensation method is proposed to design an alignment network based on gated high-low resolution frames. The core idea is to introduce a gating mechanism while using the information of high-low resolution neighboring frames to perform motion compensation adaptively. Meanwhile, within this alignment framework, we further introduce a pre-initial hidden state network and a local scale hierarchical salient feature fusion network. The pre-initial hidden state network is mainly used to reduce the impact of unbalanced quality effects between frames that occur in one-way cyclical networks; the local scale hierarchical salient feature fusion network is used to fuse the features of aligned video frames to extract contextual information and locally salient features to improve the reconstruction quality of the video. Compared with existing video super-resolution methods, this method achieves good performance and clearer edge and texture details.
Tasks, Copilots, and the Future of Search

Tasks are central to information retrieval (IR) and drive interactions with search systems [2, 4, 10]. Understanding and modeling tasks helps these systems better support user needs [8, 9, 11]. This keynote focuses on search tasks, the emergence of generative artificial intelligence (AI), and the implications of recent work at their intersection for the future of search. Recent estimates suggest that half of Web search queries go unanswered, many of them connected to complex search tasks that are ill-defined or multi-step and span several queries[6]. AI copilots, e.g., ChatGPT and Bing Chat, are emerging to address complex search tasks and many other challenges. These copilots are built on large foundation models such as GPT-4 and are being extended with skills and plugins. Copilots broaden the surface of tasks achievable via search, moving toward creation not just finding (e.g., interview preparation, email composition), and can make searchers more efficient and more successful.
Estimation of the Multielement Content in Rocks Based on a Combination of Visible–Near-Infrared Reflectance Spectroscopy and Band Index Analysis

Rock geochemical methods are effective for geological surveys, but typical sampling and laboratory-based analytical methods are time-consuming and costly. However, using visible–near-infrared spectroscopy to estimate the metal element content of rock is an alternative method. This study discussed the potential of hyperspectral estimation of Cu and its significant associated elemental content. Ninety-five rock samples were collected from the Kalatage Yudai copper–nickel deposit in Hami, Xinjiang. The effects of different spectral resolutions, spectral preprocessing, band indices, and characteristic band selection on the estimation of the element contents of Fe, Cu, Co, and Ti were investigated. The results show that when the spectral resolution is 5 nm, good results are obtained for all four metal elements, Fe, Cu, Co, and Ti, with the coefficients of determination R2 reaching 0.54, 0.59, 0.41, and 0.78, respectively. The best results are obtained for all transformed spectra with continuum removal, inverse transformation, continuum removal, and logarithmic transformation, respectively. In addition, the accuracy of the estimation models constructed by combining band indices and feature band selection was superior compared with full-band spectra for Fe (R2 = 0.654, MAE = 1.27%, and RPD = 1.498), Cu (R2 = 0.694, MAE = 20.509, and RPD = 1.711), Co (R2 = 0.805, MAE = 2.573, and RPD = 2.199), and Ti (R2 = 0.501, MAE = 0.04%, and RPD = 1.412). The results indicate that using band indices can provide a more accurate estimation of metal element content, providing a new technical method for the efficient acquisition of regional mineralization indicator element content distribution.
Identification of Lacerations Caused by Cervical Cancer through a Comparative Study among Texture-Extraction Techniques

Cervical cancer is a disease affecting a worrisomely large number of women worldwide. If not treated in a timely fashion, this disease can lead to death. Due to this problematic, this research employed the LBP, OC_LBP, CS-LTP, ICS-TS, and CCR texture descriptors for the characteristic extractions of 60 selected carcinogenic images classified as Types 1, 2, and 3 according to a database; afterward, a statistical multi-class classifier and an NN were used for image classification. The resulting characteristic vectors of all five descriptors were implemented in four tests to identify the images by type. The statistical multi-class combination and classification of all images achieved a classification efficiency of 83–100%. On the other hand, with the NN, the LBP, OC_LBP, and CCR descriptors presented a classification efficiency of between 81.6 and 98.3%, differing from that of ICS_TS and CS_LTP, which ranged from 36.6 to 55%. Based on the tests performed with regard to ablation, ROC curves, and confusion matrix, we consider that an efficient expert system can be developed with the objective of detecting cervical cancer at early stages.
Sufficient conditions for quantum advantage in random access code protocols with two-qubit states

A random access code (RAC) is an important communication protocol to obtain information about a randomly specified substring of an $n$-bit string, while only having limited information about the $n$-bit string. Quantum RACs usually utilize either communication of quantum bits or a shared-in-advance quantum state used in conjunction with classical communication. Here we consider the latter version of the quantum protocols under the constraint of single-bit communication and with a shared arbitrary state of two qubits. Taking the worst-case success probability as the figure of merit, we demonstrate that any state with an invertible correlation matrix can be used to outperform the best classical RAC for $n=3$. We derive an additional condition sufficient to beat the best classical performance in the case of $n=2$. In particular, separable states turn out to be a useful resource behind the quantum advantage for $n=2,3$. For $n\ensuremath{\ge}4$, RACs assisted with a single copy of a quantum state do not outperform the classical RACs.
No time to read? How precarity is shaping learning and teaching in the humanities

Humanities educators are frequently frustrated by students’ poor engagement in reading. The contemporary student experience is characterised by disruption and precarity. Similarly, is that of teachers who work in casual employment. This discussion is located within broader conversations around the neoliberal university, but aims to make more visible ways that teaching and learning are increasingly shaped by precarity, and consequences for the humanities. It describes what precarity in higher education looks like and considers the kinds of strategies that students and their teachers are positioned to develop by virtue of engaging in education under such conditions, amid chaos, making these meaningful through the learning theory of connectivism. This discussion points to some examples of humanities-based pedagogical innovations that seek to strengthen reading skills, while also acknowledging the changing circumstances of students to point towards avenues for ongoing consideration, reflection, and innovation in the humanities.
Multimicrogrid collaborative economic scheduling method based on the FISCO BCOS blockchain

Abstract With the advancement of carbon peaking and carbon neutrality goals, microgrid clusters have attracted widespread attention due to their high stability and energy efficiency. This article proposes a scheduling architecture for microgrid clusters that integrates blockchain smart contracts, consensus mechanisms, and encryption technologies to address the issues of data trust, privacy protection, and security reliability in microgrid clusters. An economic adjustment model for microgrid clusters is constructed that combines economic and environmental objectives. Finally, based on the RPBFT (Rotating Proof of Believability) algorithm and group architecture of FISCO BCOS blockchain technology, an economic scheduling method for microgrid clusters is designed. The authors built a microgrid cluster model on the FISCO BCOS blockchain platform and conducted experiments on the effectiveness, economy, fault tolerance, and scalability of economic scheduling methods. The experimental results showed that the economic scheduling method proposed in this paper can effectively reduce the risk of nodes tampering with data in a microgrid cluster system, ensure the privacy of submicrogrids, and support the deployment of large‐scale microgrid clusters, the realization of multiparty collaborative autonomy and the stable, low‐carbon, economic operation of microgrid clusters.
FLIRT: Federated Learning for Information Retrieval

A wide range of core information retrieval (IR) tasks, such as searching, ranking, and filtering, to name a few, have seen tremendous improvements thanks to machine learning (ML) and artificial intelligence (AI). The traditional centralized approach to training AI/ML models is still predominant: large volumes of data generated by end users must be transferred from their origins and shared with remote locations for processing. However, this centralized paradigm suffers from significant privacy issues and does not take full advantage of the computing power of client devices like modern smartphones. A possible answer to this need is provided by federated learning (FL), which enables collaborative training of predictive models among a set of cooperating edge devices without disclosing any private local data. Unfortunately, FL is still far from being fully exploited in the IR ecosystem.
A Novel DFA on AES: Based on Two–Byte Fault Model with Discontiguous Rows

Differential fault attack (DFA) is a distinctive methodology for acquiring the key to block ciphers, which comprises two distinct strategies: DFA on the state and DFA on the key schedule. Given the widespread adoption of the Advanced Encryption Standard (AES), it has emerged as a prominent target for DFA. This paper presents an efficient DFA on the AES, utilizing a two−byte fault model that induces faults at the state with discontiguous rows. The experiment demonstrates that, based on the proposed fault model, the key for AES–128, AES–192, and AES–256 can be successfully recovered by exploiting two, two, and four faults, respectively, without the need for exhaustive research. Notably, in the case of AES–256, when considering exhaustive research, two (or three) faults are needed with 232 (or 216) exhaustive searches. In comparison to the currently available DFA on the AES state, the proposed attack method shows a higher efficiency due to the reduced induced faults.
Nonlinear optics as a source of high-dimensional genuine tripartite entanglement

We lay down a general scheme to quantify the amount of genuine tripartite entanglement present in the spatial and energy-time degrees of freedom using the correlations naturally present in many such sources. To that end, we test our method using the three-photon states generated in three-party extensions of spontaneous parametric down-conversion, and demonstrate that a substantial amount of spatial and energy-time genuine tripartite entanglement can exist for reasonable experimental sources.
Quantum super-resolution imaging and hypothesis testing

Detecting the faint emission of a secondary source in the proximity of the much brighter one has been the most severe obstacle for using direct imaging in searching for exoplanets. Estimating the angular separation between two incoherent thermal sources is a also challenging task for direct imaging. Here, we experimentally demonstrate two tasks for super-resolution imaging based on hypothesis testing, quantum state discrimination and quantum imaging techniques. We show that one can significantly reduce the probability of error for detecting the presence of a weak secondary source (e.g. a planet), especially when the two sources have small angular separations. We reduce the experimental complexity down to a single two-input interferometer: we show that (1) this simple set-up is sufficient for the state discrimination task, and (2) if the two sources are of equal brightness, then this measurement can super-resolve their angular separation, saturating the quantum Cramér-Rao bound. By using a collection baseline of 5.3 mm, we resolve the angular separation of two sources that are placed 15 m apart at a distance of 1.0 <i>&mu;</i>m with an accuracy of 1:7% { this is between 2 to 3 orders of magnitudes more accurate than shot-noise limited direct imaging.
A Real-Time Semantic Segmentation Method Based on STDC-CT for Recognizing UAV Emergency Landing Zones

With the accelerated growth of the UAV industry, researchers are paying close attention to the flight safety of UAVs. When a UAV loses its GPS signal or encounters unusual conditions, it must perform an emergency landing. Therefore, real-time recognition of emergency landing zones on the ground is an important research topic. This paper employs a semantic segmentation approach for recognizing emergency landing zones. First, we created a dataset of UAV aerial images, denoted as UAV-City. A total of 600 UAV aerial images were densely annotated with 12 semantic categories. Given the complex backgrounds, diverse categories, and small UAV aerial image targets, we propose the STDC-CT real-time semantic segmentation network for UAV recognition of emergency landing zones. The STDC-CT network is composed of three branches: detail guidance, small object attention extractor, and multi-scale contextual information. The fusion of detailed and contextual information branches is guided by small object attention. We conducted extensive experiments on the UAV-City, Cityscapes, and UAVid datasets to demonstrate that the STDC-CT method is superior for attaining a balance between segmentation accuracy and inference speed. Our method improves the segmentation accuracy of small objects and achieves 76.5% mIoU on the Cityscapes test set at 122.6 FPS, 68.4% mIoU on the UAVid test set, and 67.3% mIoU on the UAV-City dataset at 196.8 FPS on an NVIDIA RTX 2080Ti GPU. Finally, we deployed the STDC-CT model on Jetson TX2 for testing in a real-world environment, attaining real-time semantic segmentation with an average inference speed of 58.32 ms per image.
ANALYSIS OF THE ECONOMIC EFFICIENCY OF AUTOMATED TESTING IMPLEMENTATION IN SOFTWARE DEVELOPMENT

The article considers the main factors influencing the effectiveness of the implementation of automated testing in software development teams. A method is proposed for assessing the need to automate manual testing (test cases) based on checklists and a decision tree previously presented in other works. Factors related to the availability of specialists necessary for automation, their qualifications and the availability of automation tools in the company were added to the checklist and decision tree. Also, a method for assessing the economic efficiency of introducing test automation based on the analysis of the return of investment ratio is presented.
Simulation and design of quantum key distribution systems

Previously, we reported a framework capable of simulating classical transmission systems and QKD based on weak-coherent prepare-and-measure protocols. The framework’s modular architecture provides a rich library of models for various system components (e.g., lasers, modulators, fibers). QKD components include singlephoton detectors, transmitter- and receiver designs, and performance estimators. For realistic system designs, component models can account for various imperfections (e.g., laser model with linewidth, RIN, side modes). Here, we present further developments of the framework and its applications. We applied our simulation tool to investigate a satellite-based BB84 system in the downlink scenario. For fiber-based applications in a coexistence scenario, we studied the usage of multicore fibers to better separate classical and quantum channels, and various PON configurations. The system performance was estimated by analyzing the QBER dependence on the classical channel power due to Raman scattering. Furthermore, we discuss the simulation results of a Gaussian-modulated CV-QKD system with a realistic model of a true local oscillator. We studied how the utilization of DSP techniques improves the system’s performance. We evaluated the secret key rate for different transmission distances.
Upgrading the Fusion of Imprecise Classifiers

Imprecise classification is a relatively new task within Machine Learning. The difference with standard classification is that not only is one state of the variable under study determined, a set of states that do not have enough information against them and cannot be ruled out is determined as well. For imprecise classification, a mode called an Imprecise Credal Decision Tree (ICDT) that uses imprecise probabilities and maximum of entropy as the information measure has been presented. A difficult and interesting task is to show how to combine this type of imprecise classifiers. A procedure based on the minimum level of dominance has been presented; though it represents a very strong method of combining, it has the drawback of an important risk of possible erroneous prediction. In this research, we use the second-best theory to argue that the aforementioned type of combination can be improved through a new procedure built by relaxing the constraints. The new procedure is compared with the original one in an experimental study on a large set of datasets, and shows improvement.
Monte Carlo Based Real-Time Shape Analysis in Volumes

We introduce a Monte Carlo based real-time diffusion process for shape-based analysis in volumetric data. The diffusion process is carried out by using tiny massless particles termed shapetons, which are used to capture the shape information. Initially, these shapetons are randomly distributed inside the voxels of the volume data. The shapetons are then diffused in a Monte Carlo fashion to obtain the shape information. The direction of propagation for the shapetons is monitored by the Volume Gradient Operator (VGO). This operator is known for successfully capturing the shape information and thus the shape information is well captured by the shapeton diffusion method. All the shapetons are diffused simultaneously and all the results can be monitored in real-time. We demonstrate several important applications of our approach including colon cancer detection and design of shape-based transfer functions. We also present supporting results for the applications and show that this method works well for volumes. We show that our approach can robustly extract shape-based features and thus forms the basis for improved classification and exploration of features based on shape.
Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications

More information leads to better decisions and predictions, right? Confirming this hypothesis, several studies concluded that the simultaneous use of optical and thermal images leads to better predictions in crowd counting. However, the way multimodal models extract enriched features from both modalities is not yet fully understood. Since the use of multimodal data usually increases the complexity, inference time, and memory requirements of the models, it is relevant to examine the differences and advantages of multimodal compared to monomodal models. In this work, all available multimodal datasets for crowd counting are used to investigate the differences between monomodal and multimodal models. To do so, we designed a monomodal architecture that considers the current state of research on monomodal crowd counting. In addition, several multimodal architectures have been developed using different multimodal learning strategies. The key components of the monomodal architecture are also used in the multimodal architectures to be able to answer whether multimodal models perform better in crowd counting in general. Surprisingly, no general answer to this question can be derived from the existing datasets. We found that the existing datasets hold a bias toward thermal images. This was determined by analyzing the relationship between the brightness of optical images and crowd count as well as examining the annotations made for each dataset. Since answering this question is important for future real-world applications of crowd counting, this paper establishes criteria for a potential dataset suitable for answering whether multimodal models perform better in crowd counting in general.
A Framework for Art-directed Augmentation of Human Motion in Videos on Mobile Devices

This paper presents a framework and mobile video editing app for interactive artistic augmentation of human motion in videos. While creating motion effects with industry-standard software is time-intensive and requires expertise, and popular video effect apps have limited customization options, our approach enables a multitude of art-directable, highly customizable motion effects. We propose a graph-based video processing framework that uses mobile-optimized machine learning models for human segmentation and pose estimation to augment RGB video data, enabling the rendering and animation of content-adaptive graphical elements that highlight and emphasize motion. Our modular framework architecture enables effect designers to create diverse motion effects that include body pose-based effects such as glow stick or light trail effects, silhouette-based effects such as halos and outlines, and layer-based effects that provide depth perception and enable interaction with virtual objects.