Reviewerly/exHarmony huggingface dataset:
HNSSL: Hard Negative-Based Self-Supervised Learning

Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracting widespread attention. Self-supervised learning followed by supervised fine-tuning on a few labeled examples can significantly improve label efficiency and outperform standard supervised training using fully annotated data [6]. In this work, we present a novel hard negative-based self-supervised deep learning paradigm, named HNSSL. Specifically, we design a student-teacher network to generate a multi-view of the data for self-supervised learning and integrate an online hard negative pair mining into the training. Then we derive a new triplet-type loss considering both positive sample pairs and online mined hard negative sample pairs. Extensive experiments demonstrate the effectiveness of the proposed method and its components on ILSVRC-2012 based on the same backbone network. Specifically, for the linear evaluation task, the proposed HNSSL with a ResNet-50 encoder achieves the top-1 accuracy of 77.1%, which outperforms its previous counterparts by 2.8%. For the semi-supervised learning task, HNSSL with a ResNet-50 encoder obtains the top-1 accuracy of 73.4%, which outperforms the previous ResNet-50 encoder-based semi-supervised learning results by 4.6% using only 10% labels. For the task of transfer learning with linear evaluation, HNSSL with a ResNet-50 encoder achieves the best accuracy on six of seven widely used transfer learning datasets, which averagely outperforms previous ResNet-50 encoder-based transfer learning results by 2.5%.
Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation

To overcome the domain gap between synthetic and real-world datasets, unsupervised domain adaptation methods have been proposed for semantic segmentation. Majority of the previous approaches have attempted to reduce the gap either at the pixel or feature level, disregarding the fact that the two components interact positively. To address this, we present CONtrastive FEaTure and pIxel alignment (CON-FETI) for bridging the domain gap at both the pixel and feature levels using a unique contrastive formulation. We introduce well-estimated prototypes by including category-wise cross-domain information to link the two alignments: the pixel-level alignment is achieved using the jointly trained style transfer module with the prototypical semantic consistency, while the feature-level alignment is enforced to cross-domain features with the pixel-to-prototype contrast. Our extensive experiments demonstrate that our method outperforms existing state-of-the-art methods using DeepLabV2. Our code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> has been made publicly available.
Hand-Guiding Gesture-Based Telemanipulation with the Gesture Mode Classification and State Estimation Using Wearable IMU Sensors

This study proposes a telemanipulation framework with two wearable IMU sensors without human skeletal kinematics. First, the states (intensity and direction) of spatial hand-guiding gestures are separately estimated through the proposed state estimator, and the states are also combined with the gesture’s mode (linear, angular, and via) obtained with the bi-directional LSTM-based mode classifier. The spatial pose of the 6-DOF manipulator’s end-effector (EEF) can be controlled by combining the spatial linear and angular motions based on integrating the gesture’s mode and state. To validate the significance of the proposed method, the teleoperation of the EEF to the designated target poses was conducted in the motion-capture space. As a result, it was confirmed that the mode could be classified with 84.5% accuracy in real time, even during the operator’s dynamic movement; the direction could be estimated with an error of less than 1 degree; and the intensity could be successfully estimated with the gesture speed estimator and finely tuned with the scaling factor. Finally, it was confirmed that a subject could place the EEF within the average range of 83 mm and 2.56 degrees in the target pose with only less than ten consecutive hand-guiding gestures and visual inspection in the first trial.
Asynchronous Federated Continual Learning

The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and pre-defined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at https://github.com/LTTM/FedSpace.
Continual Deep Learning for Time Series Modeling

The multi-layer structures of Deep Learning facilitate the processing of higher-level abstractions from data, thus leading to improved generalization and widespread applications in diverse domains with various types of data. Each domain and data type presents its own set of challenges. Real-world time series data may have a non-stationary data distribution that may lead to Deep Learning models facing the problem of catastrophic forgetting, with the abrupt loss of previously learned knowledge. Continual learning is a paradigm of machine learning to handle situations when the stationarity of the datasets may no longer be true or required. This paper presents a systematic review of the recent Deep Learning applications of sensor time series, the need for advanced preprocessing techniques for some sensor environments, as well as the summaries of how to deploy Deep Learning in time series modeling while alleviating catastrophic forgetting with continual learning methods. The selected case studies cover a wide collection of various sensor time series applications and can illustrate how to deploy tailor-made Deep Learning, advanced preprocessing techniques, and continual learning algorithms from practical, real-world application aspects.
Automatic Digitization of JMA Strong-Motion Seismograms Recorded on Smoked Paper: An Attempt Using Deep Learning

Abstract Using deep learning, an attempt was made to automatically digitize waveforms in scanned Japan Meteorological Agency (JMA) analog seismograms of strong-motion seismographs recorded on smoked paper. We manually traced the waveforms in the scanned images to be used as supervised data for a convolutional neural network (CNN) model using these images. In the analog recording system, ground shaking was recorded on paper with a needle attached to the end of an arm. Because the other end of the arm is fixed on a pivot, the needle moves in an arc around the pivot for large shaking compared to the arm length. We carefully considered the effects of the finite arm length in the analog system and trained the CNNs. To validate the learning results, the trained CNN model was applied to images that were not used for the training. The automatic digitization by our method works fairly well, except for seismograms rich in high-frequency components and those with long-period large amplitudes. Even these images can be well digitized by resizing the image height by, for example, 1/2. To further improve the accuracy of automatic digitization, it would be effective to prepare many additional input and supervised data, and retrain the model. Other training is also necessary for other types of seismographs that have features and characteristics different from strong-motion seismograms.
Keywords of the manosphere

Abstract This paper examines language used in five of the largest manosphere communities on Reddit (r/TheRedPill, r/braincels, r/MensRights, r/seduction, and r/MGTOW) to identify idiosyncratic language use within these communities. To do so, a novel methodology which combines key-key-word analysis with notions from set theory was used to identify and compare keywords between corpora and to find keywords that are used uniquely within – and thus are distinctive to – these five separate communities. The paper achieves the following: it (i) presents a novel method for identifying what we term ‘complement keywords’ (keywords that are not shared between multiple different corpora when compared against the same reference corpus), and (ii) explores idiosyncratic language use in five separate manosphere communities. The analysis first examines interdiscursive relationships between communities emerging from the complement keywords identified before discussing community-specific preoccupations emergent in the idiosyncratic language use found in these five communities.
Special components of society digital transformation to influence technological and behavioral models of modern libraries (Annual report at the Seventh World Professional Forum “SOCHI–2023”)

In his annual report to the Seventh World Professional Forum “SOCHI–2023”, the author analyzes the status and trends of main technological aspects of modern library services and functions. He characterizes the digital transformation components that dynamically influence technological and behavioral models of modern libraries, e. g. artificial intellect (AI), neural networks, copyright laws, information market transformation, and foreign software import substitution. He gives his opinion on the role of the above-mentioned components in the library information practice. The emphasis is made on the ethics of AI application, neural networks, and changes in the copyright legislation. The author details into the issues of import substitution in the library and information industries, in particular, its principles and methods, and alternative domestic digital solutions. He offers his recommendations to the libraries on the work in new realities. The top-9 library technologies and top-5 technological novelties are specified.
Compositional Signatures of Gold from Different Deposit Types in British Columbia, Canada

A study of both in situ and detrital gold from different deposit types in British Columbia was undertaken to establish deposit-specific compositional characteristics in terms of alloy composition and suites of mineral inclusions. The study is based on 11,840 particles from 160 localities in which nine gold deposit types are represented, although there is a strong bias towards gold of orogenic, low-sulphidation epithermal, and alkalic porphyry origin. In general, Ag values in gold alloys are not a powerful discriminator for deposit type, but minor metals may prove useful where detectable, e.g., Cu in gold from ultramafic associations and Pd and Hg in gold from alkalic porphyry systems. The characterization of inclusion suites is far more illuminating, as they correlate strongly with the mineralogy of auriferous ores from different deposit types. This outcome has confirmed the validity of designing an indicator methodology based on inclusion suites and has permitted the prediction of inclusion suites for gold from other deposit types where data are more scarce. The compositional templates generated in the study were applied to identify the source deposit type(s) of gold from 41 localities (a total of 2916 detrital gold particles) where gold genesis was previously unknown.
Identifying Users and Developers of Mobile Apps in Social Network Crowd

In the last fifteen years, an immense expansion has been witnessed in mobile app usage and production. The intense competition in the tech sector and also the rapidly and constantly evolving user requirements have led to increased burden on mobile app creators. Nowadays, fulfilling users’ expectations cannot be readily achieved and new and unconventional approaches are needed to permit an interested crowd of users to contribute in the introduction of creative mobile apps. Indeed, users and developers of mobile apps are the most influential candidates to engage in any of the requirements engineering activities. The place where both can best be found is on Twitter, one of the most widely used social media platforms. More interestingly, Twitter is considered as a fertile ground for textual content generated by the crowd that can assist in building robust predictive classification models using machine learning (ML) and natural language processing (NLP) techniques. Therefore, in this study, we have built two classification models that can identify mobile apps users and developers using tweets. A thorough empirical comparison of different feature extraction techniques and machine learning classification algorithms were experimented with to find the best-performing mobile app user and developer classifiers. The results revealed that for mobile app user classification, the highest accuracy achieved was ≈0.86, produced via logistic regression (LR) using Term Frequency Inverse Document Frequency (TF-IDF) with N-gram (unigram, bigram and trigram), and the highest precision was ≈0.86, produced via LR using Bag-of-Words (BOW) with N-gram (unigram and bigram). On the other hand, for mobile app developer classification, the highest accuracy achieved was ≈0.87, produced by random forest (RF) using BOW with N-gram (unigram and bigram), and the highest precision was ≈0.88, produced by multi-layer perception neural network (MLP NN) using BERTweet for feature extraction. According to the results, we believe that the developed classification models are efficient and can assist in identifying mobile app users and developers from tweets. Moreover, we envision that our models can be harnessed as a crowd selection approach for crowdsourcing requirements engineering activities to enhance and design inventive and satisfying mobile apps.
Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data

This paper employs two data-driven methods, Random Forest (RF) and Support Vector Machines (SVM), to develop mineral prospectivity models for an epithermal Au deposit. Four distinct models are presented for comparison: one employing RF and three using SVM with different kernel functions—namely linear, Radial Basis Function (RBF), and polynomial. The analysis leverages a compact training dataset, encompassing just 20 deposits, with deposit and non-deposit locations chosen from known mineral occurrences. Fourteen predictor maps are constructed based on the available data and the exploration model. The findings indicate that RF is more stable and robust than SVM, regardless of the kernel function implemented. While all SVM models outperformed the RF model in terms of classification capability on the training dataset achieving an accuracy exceeding 89% versus 78% for the RF model, the success rate curves suggest superior predictive abilities of RF over SVM models. This implies that the SVM models may be overfitting the training data due to the limited quantity of training deposits.
Gendered Human–Robot Interactions in Services

Abstract The outbreak of Covid-19 precipitated the use of service robots in customer-facing services as a replacement for employees to avoid human-to-human contact. However, this development has not resolved the debate as to whether robots should be characterized with gender attributes or simply be genderless. This study explores whether endowing a robot with gender attributes makes it more acceptable as a service provider among stated men and women. To this end, an experiment was conducted at a public fair in which a gendered robot simulated the provision of a service to customers, which consisted of offering them advice, hints, and messages of encouragement to help complete a eudaemonic puzzle. A parsimonious version of the Almere model was used to estimate acceptance of the technology. The findings reveal that for both stated men and women, the main drivers for accepting the female-coded robot are perceived usefulness and social influence, although women attach greater importance to social influence. For the male-coded robot, perceived usefulness and social influence are the main arguments for women, while for men they are enjoyment, perceived usefulness and, negatively, ease of use. In addition, different indirect effects between stated sexes are also identified. In summary, men and women consider different factors when accepting robots of each gender.
Identification of the Groundwater Quality and Potential Noncarcinogenic Health Risk Assessment of Nitrate in the Groundwater of El Milia Plain, Kebir Rhumel Basin, Algeria

In this study, we analyzed the quality and the potential noncarcinogenic health risk of nitrate in groundwater in the El Milia plain, Kebir Rhumel Basin, Algeria. Moran’s I and the ordinary kriging (OK) interpolation technique were used to examine the spatial distribution pattern of the hydrochemical parameters in the groundwater. It was found that the hydrochemical parameters Ca, Cl, and HCO3 showed strong spatial autocorrelation in the El Milia plain, indicating a spatial dependence and clustering of these parameters in the groundwater. The groundwater quality was evaluated using the entropy water quality index (EWQI). The results showed that approximately 86% of the total groundwater samples in the study area fall within the moderate groundwater quality category. The spatial map of the EWQI values indicated an increasing trend from the south-west to the northeast, following the direction of groundwater flow. The highest EWQI values were observed near El Milia city in the center of the plain. This spatial pattern suggests variations in groundwater quality across the study area, with potentially higher risks near the city center. The potential noncarcinogenic health risks associated with nitrate contamination in groundwater for adults and children through the drinking water pathway were assessed using the hazard quotient (HQ). The results revealed that approximately 5.7% of the total groundwater samples exceeded the HQ limit for adults, indicating potential health risks. Moreover, a higher percentage, 14.28%, of the total groundwater samples exceeded the HQ limit for children, highlighting their increased vulnerability to noncarcinogenic health hazards associated with nitrate contamination in the study area. Taking timely action and ensuring strict compliance with regulations in groundwater management are crucial for protecting public health, preserving the environment, addressing water scarcity, and achieving sustainable development goals.
On-chip optical matrix-vector multiplier based on mode division multiplexing

We propose and demonstrate a matrix-vector multiplication (MVM) optical signal processor based on mode division multiplexing (MDM), which consists of mode multiplexer, multimode beam splitter, mode demultiplexer, modulator array and combiners. In addition, the characteristics of MDM obviate the need for multiple wavelengths and, consequently, multiple laser light sources, which greatly reduces the complexity and cost. A 4×4 MDM-MVM has been realized on a standard Silicon-On-Insulator (SOI) platform. Combined with the off-chip light source and photodetectors (PDs), 4-level modulation has been demonstrated, and each level of the output signal can represent 2 bits of information.
Image forgery detection by transforming local descriptors into deep-derived features

Image forgery is the intentional alteration of digital images, either manually using image editors or through deep fake techniques, for the purpose of disseminating fake information. We propose a forgery detection approach that efficiently detects copy-move and splicing attacks of varying scales in digital images. Our goal is to identify the homogeneous region(s) inconsistent with the rest of the image. This region property has been typically employed in object detection and classification, while we exploit this property to detect forgery in images. Thus, we generate the deep-derived features from the existing hand-crafted features in forgery detection as input to the VGG16, a deep learning method, trained for object classification. We use a binary class SVM trained on the obtained deep-derived features to determine whether an image is real or fake. We perform extensive experiments on three publicly available image manipulation datasets, DVMM, Casia and Korus to validate the effectiveness of the proposed methodology. The results show a better accuracy compared to the state-of-the-art methods.
Multi-robot task allocation methods: A fuzzy optimization approach

Response-threshold methods stand out among the different developed swarm-like methodologies that address the task allocation problem, which must be faced in multi-robot systems in order to assign to each robot the best task to perform at each instant of time. In many real missions the tasks have associated deadlines. However, the literature only contains a few swarm methodologies, and thus response-threshold methods, tackling tasks with deadlines. Motivated by this fact, in this paper, we propose a new task allocation strategy inspired by response-threshold methods which deals with tasks with time deadlines, models stimuli using fuzzy sets and, in addition, in which each robot makes the decision about the best task to perform through the celebrated Bellman-Zadeh fuzzy optimization technique. An extensive number of simulations have been carried out in order to evaluate the quantitative performance of the swarm system based on the new approach. The results confirm that the proposed mathematical approach is able to model the evolution of the system when tasks with deadlines are under consideration. We have also observed competitive performance on a fleet of real robots, which corroborates the results derived from the simulations.
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact

This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may perform disastrously when the networks are under adversarial attacks. In this work, two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed. These two adversarial attacks are the fast gradient sign method (FGSM) and adversarial patch attack. These attacks are launched on three powerful pre-trained image classifier architectures, ResNet-34, GoogleNet, and DenseNet-161. The classification accuracy of the models in the absence and presence of the two attacks are computed on images from the publicly accessible ImageNet dataset. The results are analyzed to evaluate the impact of the attacks on the image classification task.