Reviewerly/exHarmony huggingface dataset:
Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

The imbalanced datasets and their classification has pulled in as a hot research topic over the years.It is used in different fields, for example, security, finance, health, and many others.The imbalanced datasets are balanced by applying resampling and various solutions are designed to tackle such datasets that mainly focus on class distribution issues.The imbalanced data is rebalanced using these methods.This paper introduces a technique for balancing data through two stages: first, oversampling methods are utilized in the process of rebalancing such imbalanced dataset using the single-point crossover to generate the new data of minority classes, second, it searches for an optimal subset of the imbalanced and balanced datasets by Jellyfish Search (JS) which is an optimization method.Experiments are performed on 18 real imbalanced datasets, and results are compared with famous oversampling methods and the recently published ACOR (Ant Colony Optimization Resampling) method in terms of different appraisal measurements.Higher performance is recorded by the proposed method and comparability with well-known and recent techniques.
‘The Enabling Role of Internal Organizational Communication in Insider Threat Activity – Evidence From a High Security Organization’

This paper explores the role of internal communication in one under-researched form of organizational crisis, insider threat – threat to an organization, its people or resources, from those who have legitimate access. In this case study, we examine a high security organization, drawing from in-depth interviews with management and employees concerning the organizational context and a real-life incident of insider threat. We identify the importance of three communication flows (top-down, bottom-up, and lateral) in explaining, and in this case, enabling, insider threat. Derived from this analysis, we draw implications for communication and security scholars, as well as practitioners, concerning: the impact of unintentional communication, the consequences of selective silence and the divergence in levels of shared understanding of security among different groups within an organization.
FL-PATE: Differentially Private Federated Learning with Knowledge Transfer

Federated learning provides a solution for data privacy protection, while enabling training over the local data samples, without exchanging them. However, it is far from practical and secure because data privacy is still vulnerable due to the well-studied attacks, e.g., membership inference attacks and model inversion attacks. In this paper, to further prevent data leakage against these attacks, we propose FL-PATE, a differentially private federated learning framework with knowledge transfer. Specifically, participants with sensitive data are grouped to train teacher models under federated learning settings, and the knowledge of teacher models is transferred to a publicly accessible student model for prediction via aggregating teacher models&#x0027; outputs of public datasets. A modified client-level differential privacy mechanism is used to guarantee each participant&#x0027;s data privacy during the corresponding teacher model&#x0027;s training process. The proposed framework preserves participant&#x0027;s privacy against membership inference attacks and the differential privacy cost is fixed. The privacy analysis and experiments demonstrate that trained teacher and student models have an excellent performance in accuracy and robustness theoretically and empirically.
Machine learning coupled with mineral geochemistry reveals the origin of ore deposits

As geosciences enter the era of big data, machine learning (ML) that is successful in big data, is now contributing to solving problems in the geosciences, yet there have been few applications in economic geology. This paper highlights the effectiveness of ML-based methods coupled with mineral geochemistry in revealing the origin of the Qingchengzi Pb–Zn ore field in China, which are either metamorphosed sedimentary exhalative (SEDEX) or magmatic–hydrothermal fluid related deposits. Laser ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS) pyrite trace elements coupled with decision tree (DT), K-nearest neighbors (KNN), and support vector machine (SVM) algorithms were applied to train the classification models. Testing of the DT, KNN, and SVM classifiers yielded accuracies of 98.2%, 96.4%, and 93.6%, respectively. The trained classifiers predict that the strata-bound and vein-type ore bodies at Qingchengzi ore field have a magmatic–hydrothermal origin, with DT, KNN, and SVM values of 100%, 97.4%, and 97.4%. In situ δ34S values of pyrite from strata-bound and vein-type ore bodies are 4.04‰ to 9.10‰ and 6.31‰ to 9.29‰, respectively, slightly higher than those of magmatic intrusions. In situ Pb isotopic ratios plot on the upper crust curve and yield two-stage model ages that are younger than metamorphic events in the region. Principal component (PC) analysis was used to determine the formation of the two types of mineralization. Pyrite from vein-type ore bodies (Py1) has lower contents of PC1 elements (Cu, Zn, Ge, Ag, Cd, Sn, Sb, and Pb) and higher contents of PC2 elements (Co, Ni, and Se) compared with pyrite from strata-bound ore bodies (Py2). Combined with previous fluid inclusion data, the vein-type ore bodies are inferred to have formed at higher temperatures than the strata-bound ore bodies. This study presents three visual classifiers to discriminate metamorphosed SEDEX and magmatic–hydrothermal Pb–Zn deposits. The prediction of classifiers and in situ S–Pb isotopic compositions suggest that the Qingchengzi Pb–Zn deposits have a magmatic–hydrothermal origin. The results demonstrate the effective application of ML-based methods to examine the origin of ore deposits.
Multiview Video-Based 3-D Pose Estimation of Patients in Computer-Assisted Rehabilitation Environment (CAREN)

The computer-assisted rehabilitation environment (CAREN) system plays an important role in the training of rehabilitation patients, where the capture of the patient's 3-D pose and gait is critical for assessing the patient's requirements for effective training. Vision-based methods are highly effective for this task due to their low cost, high speed, and noninterference. Although various general vision-based pose estimation methods were developed recently, their performance is limited in the CAREN system due to the specific environment. To address these problems, we propose an improved framework for accurate 2-D and 3-D pose estimation for the CAREN system through using multiview videos. First, for 2-D pose estimation, we propose a coarse-to-fine heatmap shrinking (CFHS) strategy that gradually reduces the kernel size of the heatmap of joints during training to improve the performance. Second, to further obtain 3-D pose estimations, we propose a novel spatial-temporal perception network that fuses the 2-D results from multiple views and multiple moments; multiview early fusion uses complementary spatial information from different views, and multimoment late fusion leverages temporal information from the sequential input for higher accuracy. The experimental results, based on CAREN videos of 225 orthopedic patients, showed that the accuracy of 2-D human pose estimations with the CFHS training strategy reached 99.85% PCKh@0.5. For 3-D results, the mean per joint position error was 25.22 mm, and the 3DPCK reached 98.71%, which outperformed existing general video-based methods. The results showed that the proposed system is capable of estimating human poses with high accuracy for clinical applications.
Sub-pixel dimensional and vision measurement method of eccentricity for annular parts

Eccentricity measurement of annular parts with millimeter scale and micrometer precision requirements is widely used in mechanical engineering applications. To realize accurate eccentricity measurement for large-scaled annular parts, a vision-based and sub-pixel dimensional measurement method is proposed. First, to facilitate the eccentricity measurement, an improved auto focus algorithm is introduced to provide better focused images of the measured parts. Then the traditional Canny operator is modified in gradient direction calculation and a double threshold process to locate the pixel edge more accurately. Next, a model-based sub-pixel edge detection method is studied to extract the sub-pixel edge coordinates. Finally, the eccentricity is calculated according to these sub-pixel edge coordinates. To guarantee measurement accuracy, the pixel equivalent and manual installation error of three degree of freedom (DOF) stages are calibrated, and the verification experiments indicate that the measurement error of the proposed method is better than 1.0 µm.
Weakly Supervised Audio-Visual Violence Detection

Violence detection in videos is very promising in practical applications due to the emergence of massive videos in recent years. Most previous works define violence detection as a simple video classification task and use the single modality of small-scale datasets, e.g., visual signal. However, such solutions are undersupplied. To mitigate this problem, we study weakly supervised violence detection on the large-scale audio-visual violence data, and first introduce two complementary tasks, i.e., coarse-grained violent frame detection and fine-grained violent event detection, to advance the simple violence video classification to frame-level violent event localization, which aims to accurately locate the violent events on untrimmed videos. We then propose a novel network that takes as input audio-visual data and contains three parallel branches to capture different relationships among video snippets and further integrate features, where similarity branch and proximity branch capture long-range dependencies using similarity prior and proximity prior, respectively, and score branch dynamically captures the closeness of predicted score. In both coarse-grained and fine-grained tasks, our approach outperforms other state-of-the-art approaches on two public datasets. Moreover, experiment results also show the positive effect of audio-visual input and relationship modeling.
Interfacing the human/machine

Contemporary discourse on information and communication technology suggests that humans and machines are increasingly converging. However, in this article, we argue that for analysts of digital society it is necessary to understand the simultaneity with which humans and machines are both interconnected and separated from one another. Here, we propose to follow and trace human/machine interfacing, i.e. the manifold practices, by which humans and machines become interconnected, by being kept apart. Drawing on theoretical resources from feminist science and technology studies and the philosophy of technology, we extend common ‘objective’ notions of the interface and propose a performative and ecological framework for human-machine interfacing. We illustrate this framework with two contrasting cases, embodied social robotics and communicative software bots. Social robots denote a rather fragile technology, showing the precariousness of efforts to engender phenomena of human-machine communication. By contrast, communicative software bots can rely on vast digital infrastructures, which create the impression of symmetry between humans and machines by rendering ontological differences invisible.
Real-Time Monocular Joint Perception Network for Autonomous Driving

Comprehensive and accurate perception of the real 3D world is the basis of autonomous driving. However, many perceptual methods focus on a single task or object type, and the accuracy of existing multi-task or multi-object methods is difficult to balance against their real-time performance. This paper presents a unified framework for concurrent dynamic multi-object joint perception, which introduces a real-time monocular joint perception network termed MJPNet. In MJPNet relative weightings are automatically learned by a series of developed network branches. By training an end-to-end deep convolutional neural network on a shared feature encoder and many proposed decoding sub-branches, the information of the 2D category and 3D position/pose/size of an object are reconstructed both simultaneously and accurately. Moreover, the effective information among subtasks is transferred by multi-stream learning, guaranteeing the accuracy of each task. Compared to various state-of-the-arts, comprehensive evaluations on the benchmark of challenging image sequences demonstrate the superior performance of our 2D detection and 3D reconstruction of depth, lateral distance, orientation, and heading angle. Moreover, on the KITTI test set, the real-time runtime (up to 15 fps) of MJPNet significantly outran the public state-of-the-art visual detection methods. Accompanying video: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/Z-goToOlI94</uri> .
Retrieving Forensically Sound Evidence From the ESP Series of IoT Devices

The ESP series of electronics chips and boards are one of the most popular IoT devices and are dominant in the low-end IoT market. Unlike traditional electronic devices, in order to maintain the compact size, ESP devices are not equipped with a hard disk drive. Instead, their microcontrollers are tightly coupled with flash storage chips, which are the high-value targets from a digital forensics (DFs) perspective. To retrieve forensically intact evidences from these devices’ flash storage chips, investigators must be very careful in extracting data, in case the microcontrollers are activated unintentionally, which may allow the microcontroller to write something to the flash storage chips—the result will be unacceptable at court because the data integrity is concerned. This article is trying to solve this challenge, especially for tiny devices, by providing a standard process, utilizing 3D-printed enclosures, spring pins, and cold soldering techniques. The proposals here for the ESP series of IoT devices can be further explored in other IoT devices in the field of DFs.
List-Decodability With Large Radius for Reed-Solomon Codes

List-decodability of Reed–Solomon codes has received a lot of attention, but the best-possible dependence between the parameters is still not well-understood. In this work, we focus on the case where the list-decoding radius is of the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r=1-\varepsilon $ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> tending to zero. Our main result states that there exist Reed–Solomon codes with rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega (\varepsilon)$ </tex-math></inline-formula> which are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-\varepsilon, O(1/\varepsilon))$ </tex-math></inline-formula> -list-decodable, meaning that any Hamming ball of radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1-\varepsilon $ </tex-math></inline-formula> contains at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(1/\varepsilon)$ </tex-math></inline-formula> codewords. This trade-off between rate and list-decoding radius is best-possible for any code with list size less than exponential in the block length. By achieving this trade-off between rate and list-decoding radius we improve a recent result of Guo, Li, Shangguan, Tamo, and Wootters, and resolve the main motivating question of their work. Moreover, while their result requires the field to be exponentially large in the block length, we only need the field size to be polynomially large (and in fact, almost-linear suffices). We deduce our main result from a more general theorem, in which we prove good list-decodability properties of random puncturings of any given code with very large distance.
Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.
Active Learning to Support In-situ Process Monitoring in Additive Manufacturing

This paper aims to address data labelling issues in process data to support in-situ process monitoring of additive manufactured components. For this, we adopted an active learning (AL) approach to minimise the manual effort for data labelling for classification models. In this study, we present an approach that utilises pre-trained models to extract deep features from images, and clustering and query by committee sampling to select the representative samples to build defect classification models. We conduct quantitative experiments to evaluate the proposed method’s performance and compare it with other selected state-of-the-art AL approaches using a dataset of additive manufacturing (AM) and a publicly available dataset. The experimental results show that the proposed approach outperforms AL with committee based sampling, and AL with clustering and random sampling. The results of the statistical significance test show that there is a significant difference between the studied AL approaches. Hence, the proposed AL approach can be considered an alternative method to reduce labelling costs when building defects classification models, whose generalizability is most likely plausible.
An Improved Self Attention Mechanism Based on Optimized BERT-BiLSTM Model for Accurate Polarity Prediction

Abstract Polarity prediction is the field of study that discovers people’s opinions, feelings, assessments, perspectives and feelings about associations and their attributes as communicated in written text. It is one of the most active research areas in the field of text mining. Nowadays online reviews play an important role by giving a helping hand to the customers to know about other customer’s opinions about the product they are going to purchase. This also guides the organizations and government sectors to increase their quality of product and services. Pre-trained BERT (Bidirectional Encoder Representations from Transformers) is used for word embedding in this model. The fine-tuned BERT is used for better word representation which in turn improves the sentimental analysis classification accuracy. Bidirectional Long Short-Term Memory classifier is utilized for polarity prediction. To enhance the performance of Bidirectional Long Short-Term Memory, the weight parameters of Bi-directional LSTM are optimally selected by using APSO algorithm. Improved self-attention mechanism is added with BiLSTM for focusing on significant words in the context. For performance analysis, four bench mark datasets are used for experiments.
Reversible Data Hiding for Color Images Based on Adaptive 3D Prediction-Error Expansion and Double Deep Q-Network

Reversible data hiding (RDH) for color images has attracted increasing attention in recent years. Due to its effective utilization of the correlation between prediction errors, high-dimensional prediction-error expansion (PEE) can achieve much better performance for color image RDH than low-dimensional PEE. However, existing studies only focus on high-dimensional PEE with nonadaptive embedding. To further improve the embedding performance for color images, we propose a novel three-dimensional PEE method that is adaptive to image content. Double deep Q-network (DDQN), introduced to RDH for the first time, is adopted to find the optimal mapping paths for PEE. In addition, an action selection scheme is presented for DDQN to efficiently find the reversible mapping paths. Extensive experiments show that the proposed method outperforms existing color image RDH methods in image quality.
Exploiting context-awareness and multi-criteria decision making to improve items recommendation using a tripartite graph-based model

Integrating useful input information is essential to provide efficient recommendations to users. In this work, we focus on improving items ratings prediction by merging both multiple contexts and multiple criteria based research directions which were addressed separately in most existent literature. Throughout this article, Criteria refer to the items attributes, while Context denotes the circumstances in which the user uses an item. Our goal is to capture more fine grained preferences to improve items recommendation quality using users’ multiple criteria ratings under specific contextual situations. Therefore, we examine the recommenders’ data from the graph theory based perspective by representing three types of entities (users, contextual situations and criteria) as well as their relationships as a tripartite graph. Upon the assumption that contextually similar users tend to have similar interests for similar item criteria, we perform a high-order co-clustering on the tripartite graph for simultaneously partitioning the graph entities representing users in similar contextual situations and their evaluated item criteria. To predict cluster-based multi-criteria ratings, we introduce an improved rating prediction method that considers the dependency between users and their contextual situations, and also takes into account the correlation between criteria in the prediction process. The predicted multi-criteria ratings are finally aggregated into a single representative output corresponding to an overall item rating. To guide our investigation, we create a research hypothesis to provide insights about the tripartite graph partitioning and design clear and justified preliminary experiments including quantitative and qualitative analyzes to validate it. Further thorough experiments on the two available context-aware multi-criteria datasets, TripAdvisor and Educational, demonstrate that our proposal exhibits substantial improvements over alternative recommendations approaches.