Reviewerly/exHarmony huggingface dataset:
BIoMT: A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospital management systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating an attribute-based encryption scheme. Furthermore, an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network. The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner. The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency. Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations. The average latency is 61 ms and 16 ms for read and write operations, respectively.
Requirements Engineering for Automotive Perception Systems: An Interview Study

Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. Challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.
Rigorous non-disjoint discretization for naive Bayes

Naive Bayes is a classical machine learning algorithm for which discretization is commonly used to transform quantitative attributes into qualitative attributes. Of numerous discretization methods, Non-Disjoint Discretization (NDD) proposes a novel perspective by forming overlapping intervals and always locating a value toward the middle of an interval. However, existing approaches to NDD fail to adequately consider the effect of multiple occurrences of a single value — a commonly occurring circumstance in practice. By necessity, all occurrences of a single value fall within the same interval. As a result, it is often not possible to discretize an attribute into intervals containing equal numbers of training instances. Current methods address this issue in an ad hoc manner, reducing the specificity of the resulting atomic intervals. In this study, we propose a non-disjoint discretization method for NB, called Rigorous Non-Disjoint Discretization (RNDD), that handles multiple occurrences of a single value in a systematic manner. Our extensive experimental results suggest that RNDD significantly outperforms NDD along with all other existing state-of-the-art competitors.
Meta-learning biologically plausible plasticity rules with random feedback pathways

Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints.
Human Action Detection using EfficientNetB3 Model

A result of the swift developments in computer vision and machine learning, careers in video analysis have been transitioning from inferring the current state to forecasting the future state. These include the tasks of vision-based action prediction and recognition with social security in public spaces or amongst the population, where action prediction predicts human actions based on partially accomplished activities and action recognition predicts human actions based on fully executed actions with precision. These two challenges have recently gained a lot of attention due to their real-world applications, which are expanding quickly in fields including video retrieval, autonomous driving, entertainment, and visual surveillance. As a result, there have been many efforts over the past few decades to provide a solid and effective framework for action recognition and prediction. In this research, the proposed model shows the accuracy of value 76 percent on the Adam optimizer using EfficientNetB3 model.
Seismic Motion Detection and Classification Methodology for Buildings Using DFT and SVM

Analyzing the acceleration signals of buildings during a seismic event helps us to identify the vibrational intensity of the structure. This research proposes a methodology to recognize the level of the vibrational intensity of a building during an earthquake with a few seconds before its maximum vibration using the P wave response. The proposed methodology is based on the Discrete Fourier Transform (DFT) and Support Vector Machine (SVM). This methodology results in an alert level that is classified according to its vibrational intensity (low, moderate and high). Each building could have different alerts in the event of an earthquake since may have different natural frequencies. A prototype implemented with a Raspberry Pi V4 B embedded system, two acceleration sensors called MPU6050 based on MEMS and a Wi-Fi antenna, mainly, is used. Also, filters were used to attenuate noise. The STA/LTA algorithm is used to compare the detection time. The results show that it is convenient to use the methodology for two main reasons. Firstly, this uses one tenth of the samples needed in the STA/LTA algorithm for detection and with the same efficiency. Secondly, the classification of the alert level in the building has a correlation greater than 0.8 with the PGA of the seismic signal that occurred in its structure.
Machine Learning based Ideal Job Role Fit and Career Recommendation System

With the advancement of technologies and in order to pace with the digital era, robust automated job recommendation systems need to be implemented which overcome the limitations of traditional systems and manual approaches. Identifying field specific jobs with personalized systems by incorporating awareness and information rich which can address many domains. For fresh graduates with a lack of experience and lack of awareness who need to look for a huge number of companies, the proposed system helps them to easily search for their respective job efficiently. In order to save time while searching and applying for jobs and also to avoid misleading job recommendations, it is highly valuable to recommend the most suitable education qualification specific to all the job finders. In the proposed methodology the requirements of the fresh graduates will be considered such as demographic information of the company, organization type, and job preferences using machine learning techniques. The candidate's background information, interests based skills and abilities and profile information will be considered by the automated recommendation system.
3D-Block Partitioning Embedded Coding for Hyperspectral Image Sensors

Computational complexity with the coding efficiency of any hyperspectral image sensor is a challenging issue. 3D-SPIHT has reasonable complexity and generates an embedded bit-stream. Due to the linked lists, the processing of the compression through the 3D-SPIHT gets slow at the high bit rates. This manuscript presents a low-complexity version of 3D-SPIHT which uses the array structure instead of linked lists. These arrays are independent of each other. The coding memory required by the arrays is the same as the linked list used in 3D-SPIHT. Through the use of array, the coding complexity is reduced and coding efficiency is increased with the use of parity of the bit plane. Thus, the proposed compression scheme is an optimum solution for the resource-constraint hyperspectral image sensor.
Probing the Impacts of Visual Context in Multimodal Entity Alignment

Abstract We study the problem of multimodal embedding-based entity alignment (EA) between different knowledge graphs. Recent works have attempted to incorporate images (visual context) to address EA in a multimodal view. While the benefits of multimodal information have been observed, its negative impacts are non-negligible as injecting images without constraints brings much noise. It also remains unknown under what circumstances or to what extent visual context is truly helpful to the task. In this work, we propose to learn entity representations from graph structures and visual context, and combine feature similarities to find alignments at the output level. On top of this, we explore a mechanism which utilizes classification techniques and entity types to remove potentially un-helpful images (visual noises) during alignment learning and inference. We conduct extensive experiments to examine this mechanism and provide thorough analysis about impacts of the visual modality on EA.
Experimental simulation of loop quantum gravity on a photonic chip

The unification of general relativity and quantum theory is one of the fascinating problems of modern physics. One leading solution is Loop Quantum Gravity (LQG). Simulating LQG may be important for providing predictions which can then be tested experimentally. However, such complex quantum simulations cannot run efficiently on classical computers, and quantum computers or simulators are needed. Here, we experimentally demonstrate quantum simulations of spinfoam amplitudes of LQG on an integrated photonics quantum processor. We simulate a basic transition of LQG and show that the derived spinfoam vertex amplitude falls within 4% error with respect to the theoretical prediction, despite experimental imperfections. We also discuss how to generalize the simulation for more complex transitions, in realistic experimental conditions, which will eventually lead to a quantum advantage demonstration as well as expand the toolbox to investigate LQG.
Q-Learning-Aided Offloading Strategy in Edge-Assisted Federated Learning over Industrial IoT

Federated learning (FL) is a key solution to realizing a cost-efficient and intelligent Industrial Internet of Things (IIoT). To improve training efficiency and mitigate the straggler effect of FL, this paper investigates an edge-assisted FL framework over an IIoT system by combining it with a mobile edge computing (MEC) technique. In the proposed edge-assisted FL framework, each IIoT device with weak computation capacity can offload partial local data to an edge server with strong computing power for edge training. In order to obtain the optimal offloading strategy, we formulate an FL loss function minimization problem under the latency constraint in the proposed edge-assisted FL framework by optimizing the offloading data size of each device. An optimal offloading strategy is first derived in a perfect channel state information (CSI) scenario. Then, we extend the strategy into an imperfect CSI scenario and accordingly propose a Q-learning-aided offloading strategy. Finally, our simulation results show that our proposed Q-learning-based offloading strategy can improve FL test accuracy by about 4.7% compared to the conventional FL scheme. Furthermore, the proposed Q-learning-based offloading strategy can achieve similar performance to the optimal offloading strategy and always outperforms the conventional FL scheme in different system parameters, which validates the effectiveness of the proposed edge-assisted framework and Q-learning-based offloading strategy.
Rare-Metal Mineralization in Salt Lakes and the Linkage with Composition of Granites: Evidence from Burabay Rock Mass (Eastern Kazakhstan)

This paper represents the results of a study of the chemical composition of salt lakes within the Burabay rock mass in eastern Kazakhstan. We sampled water and bottom deposits, geochemically analyzed the composition of the salt lakes, and performed aerial photography of the lakes to geometrize them. We studied the linkage between the rare-metal mineralization of granites of the Burabay rock mass and the salt lakes within the territory. Based on the obtained data, we identified the prospects of the survey area for rare metals. The surveys included 15 water samples and 15 bottom-deposit samples. To identify the source of salt in the lake water and deposits, the chemical composition of Burabay rocks was analyzed and seven samples of coarse-grained and porphyritic granites were selected. It was established that the water and bottom deposits accumulated elevated concentrations of valuable components (i.e., Sr, Rb, and U); this fact may be considered as an argument for conducting special surveys of small lakes in the region. The results obtained can be used for conducting further explorations and mining operations in the survey area in order to revive the rare-metal industry in the region, which will allow the development of new high-tech production and the creation of new jobs in this sphere.
Cross-Modality Pyramid Alignment for Visual Intention Understanding

Visual intention understanding is the task of exploring the potential and underlying meaning expressed in images. Simply modeling the objects or backgrounds within the image content leads to unavoidable comprehension bias. To alleviate this problem, this paper proposes a Cross-modality Pyramid Alignment with Dynamic optimization (CPAD) to enhance the global understanding of visual intention with hierarchical modeling. The core idea is to exploit the hierarchical relationship between visual content and textual intention labels. For visual hierarchy, we formulate the visual intention understanding task as a hierarchical classification problem, capturing multiple granular features in different layers, which corresponds to hierarchical intention labels. For textual hierarchy, we directly extract the semantic representation from intention labels at different levels, which supplements the visual content modeling without extra manual annotations. Moreover, to further narrow the domain gap between different modalities, a cross-modality pyramid alignment module is designed to dynamically optimize the performance of visual intention understanding in a joint learning manner. Comprehensive experiments intuitively demonstrate the superiority of our proposed method, outperforming existing visual intention understanding methods.
Self-supervised Dual Hypergraph learning with Intent Disentanglement for session-based recommendation

Existing works on session-based recommendation have shown the advantage in enhancing the prediction ability of recommendation with various deep learning techniques. However, the following challenges need to be addressed: (1) the hierarchy of item transition patterns is overlooked; (2) existing works fail to distinguish various factors of item transition within a single session for disentangling user intents. To cope with the above challenges, we propose a novel session based recommendation model called Self-supervised Dual Hypergraph learning with Intent Disentanglement model (SDHID). Specifically, we first propose a disentangled capsule hypergraph convolutional channel for ne-grained intent learning to capture the intra-session pattern. Accordingly, we introduce the hypergraph and capsule networks in disentangling to learn the item embedding for different factors, and then the representation of the intra-session pattern is obtained by aggregating item embedding with attention weights. Moreover, we build a novel dual–primal hypergraph convolutional channel by mapping the hypergraph to a dual–primal graph for learning the item transition pattern of inter-session. In addition, the above two channels are combined into a self-supervised contrastive learning framework by maximizing mutual information between the learned session representations. We unify the recommendation and the self-supervised tasks under a primary and auxiliary learning framework. The combined optimization of two tasks leads to a hierarchical joint learning item transition for intra- and inter-session. Extensive experiments on real datasets show that the proposed model outperforms several state-of-the-art models.
A gradient boosting classifier for purchase intention prediction of online shoppers

Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers for product suggestions, offer discount and for many other interventions. Several work has already been done using session log for analyzing customer behavior whether he performs a purchase on the product or not. In most cases, it is difficult to find out and make a list of customers and offer them discount when their session ends. In this paper, we propose a customer's purchase intention prediction model where e-shoppers can detect customer's purpose earlier. First, we apply feature selection technique to select best features. Then the extracted features are fed to train supervised learning models. Several classifiers like support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and XGBoost classifiers have been applied along with oversampling method for balancing the dataset. The experiments were performed on a standard benchmark dataset. Experimental results show that XGBoost classifier with feature selection techniques and oversampling method has the significantly higher area under ROC curve (auROC) score and are under precision-recall curve (auPR) score which are 0.937 and 0.754 respectively. On the other hand accuracy achieved by XGBoost and Decision tree are significantly improved and they are 90.65% and 90.54% respectively. Overall performance of the gradient boosting method is significantly improved compared to other classifiers and state-of-the-art methods. In addition to this, a method for explainable analysis on the problem was outlined.
Transformer-Based User Alignment Model across Social Networks

Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from neighboring nodes through graph convolution, and embed social networks into the low-dimensional vector space. However, as the network depth increases, the effect of the model will decrease. Therefore, in order to better obtain the network embedding representation, a Transformer-based user alignment model (TUAM) across social networks is proposed. This model converts the node information and network structure information from the graph data form into sequence data through a specific encoding method. Then, it inputs the data to the proposed model to learn the low-dimensional vector representation of the user. Finally, it maps the two social networks to the same feature space for alignment. Experiments on real datasets show that compared with GAT, TUAM improved ACC@10 indicators by 11.61% and 16.53% on Facebook–Twitter and Weibo–Douban datasets, respectively. This illustrates that the proposed model has a better performance compared to other user alignment models.
High dynamic range 3D measurement technique based on adaptive fringe projection and curve fitting

This paper proposes a technique combining an adaptive fringe projection algorithm and a curve fitting algorithm for the three-dimensional (3D) reconstruction of highly reflective objects. First, an adaptive projection algorithm is proposed to avoid image saturation. The phase information obtained by projecting vertical and horizontal fringes is used to establish the pixel coordinate mapping between the camera image and the projected image, and the highlight area in the camera image is found and linearly interpolated. By modifying the mapping coordinates of the highlight area, the optimal light intensity coefficient template of the projection image is calculated, the coefficient template is mapped to the projector image, and it is multiplied by the standard projection fringes to obtain the adaptive projection fringes we need. Secondly, after obtaining the absolute phase map, the phase at the hole is calculated by fitting the correct phase values at both ends of the data hole, and the phase value closest to the actual surface of the object is obtained by performing a fitting in the horizontal and vertical directions. Multiple experimental results show that the algorithm can reconstruct high-quality 3D shapes for highly reflective objects, with high adaptability and reliability in high dynamic range measurements.
GPU acceleration of Levenshtein distance computation between long strings

Computing edit distance for very long strings has been hampered by quadratic time complexity with respect to string length. The WFA algorithm reduces the time complexity to a quadratic factor with respect to the edit distance between the strings. This work presents a GPU implementation of the WFA algorithm and a new optimization that can halve the elements to be computed, providing additional performance gains. The implementation allows to address the computation of the edit distance between strings having hundreds of millions of characters. The performance of the algorithm depends on the similarity between the strings. For strings longer than million characters, the performance is the best ever reported, which is above TCUPS for strings with similarities greater than 70% and above one hundred TCUPS for 99.9% similarity.