Reviewerly/exHarmony huggingface dataset:
Mitigating Membership Inference in Deep Learning Applications with High Dimensional Genomic Data

The use of deep learning techniques in medical applications holds great promises for advancing health care. However, there are growing privacy concerns regarding what information about individual data contributors (i.e., patients in the training set) these deep models may reveal when shared with external users. In this work, we first investigate the membership privacy risks in sharing deep learning models for cancer genomics tasks, and then study the applicability of privacy-protecting strategies for mitigating these privacy risks.
Minimum relevant features to obtain AI explainable system for predicting breast cancer in WDBC

The potential to explain why a machine learning model produces a certain prediction in incomprehensible terms is becoming increasingly crucial, as it provides accountability and confidence in the algorithm's decision-making process. The interpretation of complex models is difficult. Various approaches to dealing with this issue are being offered. These problems are typically handled in tree ensemble methods by assigning priority levels to input features globally or for a specific prediction. We show that current feature attribution approaches are inconclusive, and develop solutions using SHAP (SHapley Additive Explanation) values, LIME (Local Interpretable Model-Agnostic Explanations), and the Skope Rules package. We employ feature selection methods from SHAP and LIME in this work, which uses the Breast cancer Wisconsin data sets. In the suggested method, features are chosen at the first level of feature selection using Decision tree entropy values. Based on the SHAP and LIME reports, level 2 features are chosen from fewer options. The features are tested on a Decision Tree (DT) model and a DT and Support Vector Machine (SVM) ensemble. Experiments suggest that the ensemble works better as compared to DT. We have also used the Skope Rules package to generate global rules for generalization.
Palaeoclimate, Palaeoecology and Palaeovegetation in and around Nandira Colliery, Talcher Basin, Odisha, India during Early Permian: Inferences from Typical Karharbari Palynofloral and Palynofacies Analysis

Palynofloral study along with palynofacies analysis has been undertaken in Nandira colliery, Talcher basin, Odisha, India. Recovered palynoassemblage is characterized by the dominance of monosaccate pollen grains viz., Parasaccites spp. (25%), Plicatipollenites spp. (5%), Potonieisporites sp. (1%) and Diavarisaccus lelei (1%) along with nonstriate bisaccate Scheuringipollenites maximus (35%). The recovered palynoflora shows its affinity towards the Upper Karharbari palynoflora of the early Artinskian age. The organic matter studied from the palynofacies investigation includes palynomorphs (SP), structured phytoclasts/translucent phytoclasts (ST), opaque phytoclasts/charcoal (CH), degraded organic matter (DOM), and amorphous organic matter (AOM). The carbonaceous shale (Sample Na) is dominated by SP (34.67%) and sub-dominated by ST (22%) along with CH (13.34%), DOM (19%), and AOM (11%). Coal samples (Nb to Ne) are completely devoid of palynomorphs. They are mainly dominated by CH (24.87–62.34%) and ST (20.20–73.38%) with presence of DOM (0.7–8.7%) and AOM (0.4–8.7%). Deposition of the sediments in a lower carbonaceous shale bed (Na) might have occurred in a dysoxic forest swamp in low-energy settings. The coal beds (Nb to Ne) have been deposited in the oxidized swamps in the proximal setting. The predominant occurrence of the opaque phytoclasts associated with the coal sediments may indicate a regular occurrence of palaeofires during the Artinskian.
DisRFC: a dissimilarity-based Random Forest Clustering approach

In this paper we present a novel Random Forest Clustering approach, called Dissimilarity Random Forest Clustering (DisRFC), which requires in input only pairwise dissimilarities. Thanks to this characteristic, the proposed approach is appliable to all those problems which involve non-vectorial representations, such as strings, sequences, graphs or 3D structures. In the proposed approach, we first train an Unsupervised Dissimilarity Random Forest (UD-RF), a novel variant of Random Forest which is completely unsupervised and based on dissimilarities. Then, we exploit the trained UD-RF to project the patterns to be clustered in a binary vectorial space, where the clustering is finally derived using fast and effective K-means procedures. In the paper we introduce different variants of DisRFC, which are thoroughly and positively evaluated on 12 different problems, also in comparison with alternative state-of-the-art approaches.
A Reconfigurable Neural Architecture for Edge–Cloud Collaborative Real-Time Object Detection

Although recent advances in deep neural networks (DNNs) have enabled remarkable performance on various computer vision tasks, it is challenging for edge devices to perform real-time inference of complex DNN models due to their stringent resource constraints. To enhance the inference throughput, recent studies have proposed collaborative intelligence (CI), which splits DNN computation into edge and cloud platforms, mostly for simple tasks, such as image classification. However, for general DNN-based object detectors with a branching architecture, CI is highly restricted because of a significant feature transmission overhead. To resolve this issue, in this study, we propose a reconfigurable DNN architecture for real-time object detection that can configure the optimal split point according to the edge–cloud CI environment. The proposed architecture allows the DNN model to be splittable by a feature reconstruction network and asymmetric scaling. Based on the splittable architecture, we integrate independent splittable models for each split point into a single-weight reconfigurable model that enables multipath inference by switchable quantization and distribution matching. Finally, we introduce an adaptive application procedure of the reconfigurable model for efficient CI, which includes asymmetric scale configuration and split point selection. The performance evaluation using YOLOv5 as the baseline showed that the proposed architecture achieved 30 frames/s ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.6\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> higher than edge-only and cloud-only inference, respectively), on the NVIDIA Jetson TX2 platform in a WiFi environment.
Composed Image Retrieval via Explicit Erasure and Replenishment With Semantic Alignment

Composed image retrieval aims at retrieving the desired images, given a reference image and a text piece. To handle this task, two important subprocesses should be modeled reasonably. One is to erase irrelated details of the reference image against the text piece, and the other is to replenish the desired details in the image against the text piece. Nowadays, the existing methods neglect to distinguish between the two subprocesses and implicitly put them together to solve the composed image retrieval task. To explicitly and orderly model the two subprocesses of the task, we propose a novel composed image retrieval method which contains three key components, i.e., Multi-semantic Dynamic Suppression module (MDS), Text-semantic Complementary Selection module (TCS), and Semantic Space Alignment constraints (SSA). Concretely, MDS is to erase irrelated details of the reference image by suppressing its semantic features. TCS aims to select and enhance the semantic features of the text piece and then replenish them to the reference image. In the end, to facilitate the erasure and replenishment subprocesses, SSA aligns the semantics of the two modality features in the final space. Extensive experiments on three benchmark datasets (Shoes, FashionIQ, and Fashion200K) show the superior performance of our approach against state-of-the-art methods.
Masked Video Modeling with Correlation-Aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound

Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information for cancer diagnosis. However, training models for video analysis is non-trivial as it requires a voluminous dataset which is also expensive to annotate. Furthermore, the diagnosis of breast lesion faces unique challenges such as inter-class similarity and intra-class variation. In this paper, we propose a pioneering approach that directly utilizes US videos in computer-aided breast cancer diagnosis. It leverages masked video modeling as pretraning to reduce reliance on dataset size and detailed annotations. Moreover, a correlation-aware contrastive loss is developed to facilitate the identifying of the internal and external relationship between benign and malignant lesions. Experimental results show that our proposed approach achieved promising classification performance and can outperform other state-of-the-art methods.
A Progressive Hierarchical Alternating Least Squares Method for Symmetric Nonnegative Matrix Factorization

In this article, we study the symmetric nonnegative matrix factorization (SNMF) which is a powerful tool in data mining for dimension reduction and clustering. The main contributions of the present work include: (i) a new descent direction for the rank-one SNMF is derived and a strategy for choosing the step size along this descent direction is established; (ii) a progressive hierarchical alternating least squares (PHALS) method for SNMF is developed, which is parameter-free and updates the variables column by column. Moreover, every column is updated by solving a rank-one SNMF subproblem; and (iii) the convergence to the Karush-Kuhn-Tucker (KKT) point set (or the stationary point set) is proved for PHALS. Several synthetical and real data sets are tested to demonstrate the effectiveness and efficiency of the proposed method. Our PHALS provides better performance in terms of the computational accuracy, the optimality gap, and the CPU time, compared with a number of state-of-the-art SNMF methods.
A Spatial Attention-Based Sensory Network for Fuzzy Controller of Mobile Robot in Dynamic Environments

With the emergence of an ultra-smart society, it is desirable to have a mobility support robot that can act flexibly like a human without prior knowledge or trial-and-error learning. One solution is to appropriately judge the space to be paid attention to and make decisions using only time-series observation information of the surrounding area in public spaces where many people come and go freely. Our goal is to use a spatial-attention-based sensory network for a fuzzy controller to adapt to dynamic environments and safely perform complex navigation tasks using only time-series observation information, without requiring prior knowledge such as the type, speed, and direction of moving obstacles, and more importantly, without trial-and-error learning or control system redesign. The computer simulation results show that the proposed spatial attention-based sensory network and situation-based behavior coordination allow the mobility support robot to adapt online to a complex dynamic environment with multiple moving obstacles. In addition, the proposed method can perform the navigation task more safely and efficiently than the conventional method.
Time Series Processing with Cognitive Maps. The Case of General Forecast Modeling for Time Series of Similar Nature

This paper aims to provide an overview of interpretation issues of Fuzzy Cognitive Maps trained to model time series. We analyze the impact of different Fuzzy Cognitive Maps design scenarios on modeling quality. In particular, our goal is to analyze the quality of modeling provided by Fuzzy Cognitive Maps with two aspects: firstly, the varied configuration of objective functions and error functions and, secondly, generalization of modeling from particular elements (time series) to the class of similar nature elements. We compare qualitatively and quantitatively time series models based on good Fuzzy Cognitive Maps designs with the aforementioned design options.
Differences in learning flow and learning outcomes according to the cluster type of college students’ learning presence in online large liberal arts classes

Objectives This study is to verify whether there is any difference in learning flow and learning outcomes according to the type of learning presence cluster in large online liberal arts classes related to psychology, which were conducted after most university education classes were converted to online classes after COVID-19.&#x0D; Methods For this purpose, an online survey was conducted with 268 people who took a real-time online psychology-related liberal arts class operated by University A located in the metropolitan area, and 108 of them responded. For data analysis, cluster analysis, one-way analysis of variance (ANOVA), and multivariate analysis of variance (MANOVA) were performed with cluster type of learning presence as an independent variable and learning flow and learning outcomes as dependent variables.&#x0D; Results As for the clusters according to cognitive presence, emotional presence, and social presence, four clusters were found: ‘high learning presence group’, ‘low learning presence group’, ‘social presence group’, and ‘cognitive and emotional presence group’. As a result of analyzing the differences in learning flow, learning outcomes, and sub-factors according to these clusters, it was found that the groups with the high learning presence group and the cognitive and emotional presence group were higher than the groups with the low learning presence and the group with social presence, showing a statistically significant difference.&#x0D; Conclusions Considering the situation of real-time online liberal arts classes prepared in response to the external factor of COVID-19, this study has significance in verifying the difference in learning flow and learning performance according to the type of learning presence of university students. Based on the results, consideration in online classes were suggested.
Continuous-time Quantum Walks on Cayley Graphs of Extraspecial Groups

We study continuous-time quantum walks on normal Cayley graphs of certain non-abelian groups called extraspecial groups. By applying general results for graphs in association schemes we determine the precise conditions for perfect state transfer and fractional revival. Using partial spreads, we construct Cayley graphs on extraspecial 2-groups that admit these phenomena. We also show that there is no normal Cayley graph of an extraspecial group that admits instantaneous uniform mixing.
Training speaker recognition systems with limited data

This work considers training neural networks for speaker recognition with a much smaller dataset size compared to contemporary work. We artificially restrict the amount of data by proposing three subsets of the popular VoxCeleb2 dataset. These subsets are restricted to 50\,k audio files (versus over 1\,M files available), and vary on the axis of number of speakers and session variability. We train three speaker recognition systems on these subsets; the X-vector, ECAPA-TDNN, and wav2vec2 network architectures. We show that the self-supervised, pre-trained weights of wav2vec2 substantially improve performance when training data is limited. Code and data subsets are available at https://github.com/nikvaessen/w2v2-speaker-few-samples.
Security in Data Sharing for Blockchain-Intersected IoT Using Novel Chaotic-RSA Encryption

With the recent trends in the integration of blockchain with the internet of things (IoT), individuals are more concerned about security issues such as user privacy and confidentiality. Public key cryptography is the commonly used scheme for encryption of blockchain data. Since IoT integrates multiple devices, tracking the origin of any problem is a major issue of these systems. Moreover, IoT market system is highly unregulated. This leads to security as the biggest concern of the blockchain intersected IoT devices. To overcome these issues, in this work we propose a new encryption methodology called Chaotic-Rivest–Shamir–Adleman (C-RSA) algorithm. In this methodology, the chaotic systems are integrated with the RSA algorithm. Chaotic sequences are extremely sensitive to initial conditions and control parameters. They also exhibit extreme random behavior. This system achieved high average PSNR of 52.24 and very low MSE of 0.085. Moreover, the key space attained by this system was as high as (2)600.
Usage of a binary integrated spell check algorithm for an upgraded search engine optimization

Search engines have become an integral part of our lives. To augment the power of such engines - even while offline - was our goal. To accomplish this, a distinct offline search engine was created to retrieve data from archives. An updated UI was achieved to make interacting with the software more appealing and convenient. A spell check technique to recommendations emanated from the assessed query is the most major addition to the interface. The general goal is to make offline search engines more accessible to use. To provide satisfactory results with the spell check mechanism, to use a binary converter so that the query correction and evaluation become even more accurate.
RGB-T tracking by modality difference reduction and feature re-selection

• Novel RGB-T tracking algorithm based on modality difference reduction and effective information re-selection. • Selectively promoting cross-modality feature interactions for modality difference reduction. • Adaptively selecting discriminative multi-modal and unimodal features for RGB-T tracking. • State-of-the-art performance on most popular RGB-T tracking benchmarks with real-time tracking speed. RGB-T tracking has attracted increasing attention, since visible and thermal data have strong complementary advantages to improve the robustness of trackers. Most existing models focus on investigating efficient ways of fusing the complementary information from RGB and thermal images for better tracking performance. However, the modality differences caused by different imaging mechanisms may degrade the discriminability of the fused features. Meanwhile, compared with the unimodal features, the fused features may not always improve the tracking performance, especially when one of the input images contain much noisy information. In view of this, we propose a novel RGB-T tracking model by simultaneously reducing modality difference and re-selecting discriminative features from the fused features as well as from the unimodal features. To this end, a Modality Difference Compensation module (MDC) and a Feature Re-selection module (FRS) are presented. The former one reduces the modality differences between RGB and thermal features and obtains the fused features. The latter one adaptively selects such discriminative features from the unimodal features and the fused features for the subsequent classification and regression. Exhausted experiments are conducted on three RGB-T tracking benchmark datasets, which verify that our proposed tracker performs favorably against some state-of-the-art tracking algorithms.
Survey on Autonomous Vehicles using Artificial Intelligence

With large-scale expansion in the field of Artificial Intelligence, there has been an increase in the attempt to study and implement said concept into the development of Autonomous Vehicles (AVs). It has been documented that Autonomous Vehicles have the potential to rapidly decrease the human tendency to cause manual errors that may result in accidents. Therefore, one of the sole purposes of introducing an efficient Autonomous mechanism towards the driving process is to provide a convenient and accident-free vehicle-riding experience. While several surveys led to the discovery of various techniques such as Deep Learning, Q-Learning and so on for the development of AVs, these techniques also come with their own set of pros and cons. However, there is also a need for the investigation into the efficiency of these algorithms, in terms of parameter volume, latency, ability to handle hard tasks and various other factors. It has also been observed that the vast majority of these approaches employed the usage of cameras fitted on the exteriors of vehicles in order to recreate the various scenarios, terrains and riding-circumstances that may be encountered under real-time commutation.
Automated Customization of On-Device Inference for Quality-of-Experience Enhancement

The rapid uptake of intelligent applications is pushing deep learning (DL) capabilities to mobile devices. However, the heterogeneities in device capacity, DNN performances, and user preferences make it challenging to provide satisfactory Quality of Experience (QoE) to mobile users. This paper studies automated customization for DL inference on mobile devices (termed as on-device inference), and our goal is to enhance user QoE by configuring the on-device inference with an appropriate DNN for users under different usage scenarios. The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-device inference with the learned knowledge. It leverages an online learning algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NeuralUCB</i> , that has excellent generalization ability for handling various user QoE patterns. We also embed the knowledge transfer technique in NeuralUCB to expedite the learning process. However, NeuralUCB frequently solicits QoE ratings from users, which incurs non-negligible inconvenience. To address this problem, we design feedback solicitation schemes to reduce the number of QoE solicitations while maintaining the learning efficiency of NeuralUCB. A pragmatic problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregated QoE</i> , is further investigated to improve the practicality of our framework. We conduct experiments on both synthetic and real-world data. The results indicate that our method efficiently learns the user QoE pattern with few solicitations and provides drastic QoE enhancement for mobile devices.
Multi-model Analysis of Language-Agnostic Sentiment Classification on MultiEmo Data

We carried out extensive experiments on the MultiEmo dataset for sentiment analysis with texts in eleven languages. Two adapted versions of the LaBSE deep architecture were confronted against the LASER model. That allowed us to conduct cross-language validation of these language agnostic methods. The achieved results proved that LaBSE embeddings with an additional attention layer within the biLSTM architecture commonly outperformed other methods.
On the well-posedness of a tensor-based second order PDE with bilateral term for image super-resolution

In this paper, we introduce a second-order equation based on a diffusion tensor combined with a bilateral total variation (BTV) term that takes the benefit from the diffusion model of Perona-Malik in the homogeneous regions, the Weickert model near sharp edge and the BTV term in reducing blur. Compared to the other partial differential equations (PDE) used in the SR context, the proposed super-resolution (SR) PDE can efficiently preserve image components such as corners and flat regions with less apparition of blur in homogeneous regions. Moreover, since the SR approaches are always ill-posed, a mathematical study of the existence and uniqueness of the solution is also checked in a Sobolev space. From the numerical experiments we can observe that the proposed PDE can efficiently improve the quality of the high-resolution (HR) image. Surprisingly, the apparition of blur in the restored image is less compared to the other methods. In addition to visual evaluation, two-based metrics are computed to confirm the improvements offered by the proposed PDE.