Reviewerly/exHarmony huggingface dataset:
Ox-Ethnio: Blockchain E-Voting System

Abstract: In comparison to conventional paper-based systems, electronic voting, or e-voting, offers many benefits, including increased efficiency and fewer mistakes. It has developed into many different forms. However, gaining public approval of these systems remains a constant problem, especially when it comes to making them more resilient to possible weaknesses. With its potential to increase the overall resilience of electronic voting systems, blockchain technology is emerging as a market disruptor. With the use of blockchain technology, this project aims to create an efficient e-voting infrastructure by using its transparency and cryptographic underpinnings. The proposed technique satisfies the essential requirements for electronic voting systems and offers end-to-end variabilities essay delves into the planned e-voting mechanism and its actual implementation on the Multichain platform in great depth. Furthermore, Electronic voting systems, when applied in elections, need to uphold legality, accuracy, security, and convenience. Nevertheless, the potential challenges associated with digital voting methods can impede their widespread adoption. Blockchain technology has emerged as a promising solution to address these concerns, offering decentralized networks for electronic voting. Its main appeal lies in the ability to provide end-to-end verification, making it a preferred choice for developing electronic voting systems.
Boosting HPC data analysis performance with the ParSoDA-Py library

Abstract Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.
Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review

Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.
The Effects of Mobile Learning and Group Discussion for Psychotropic Drug Education on Nursing Students’ Learning, Satisfaction, and Attitude: An Educational Intervention

Background: Familiarity with and management of drug side effects is among nurses' main educational needs with respect to pharmacological care in psychiatry departments. Objectives: This study aimed to determine the effects of psychotropic drug education through mobile learning and group discussion on nursing students’ learning, satisfaction, and attitude. Methods: This educational intervention was done at EbneSina Psychiatric Hospital, affiliated with Shiraz University of Medical Sciences, in 2018. A total of 28 students were randomly divided into 2 groups, each containing 16 and 12 subjects. The first group underwent the educational intervention through mobile learning. The second group received the educational intervention through group discussion. The students’ learning and satisfaction were assessed and compared before and 2 weeks after the intervention. Attitudes were also assessed within the mobile learning group. Data were analyzed using paired and independent sample t-tests. Results: The results revealed a significant difference in satisfaction between the 2 groups after the intervention (38.69 ± 4.78 in the mobile learning group vs 33.13 ± 5.51 in the discussion group; P = 0.02). However, no significant difference was found after adjustment for baseline, age, and sex. The results also indicated that the students in the mobile learning group developed a significantly more positive attitude after the intervention compared to the baseline (P = 0.038). Conclusions: The study findings indicated that the utilization of smartphones for learning about psychotropic drug management in psychiatric departments might be effective in enhancing student's learning outcomes, satisfaction, and attitude. Thus, further studies with larger sample sizes are recommended to be conducted on other medical and nursing apprenticeships.
HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement

Abstract Underwater image enhancement presents a significant challenge due to the complex and diverse underwater environments that result in severe degradation phenomena such as light absorption, scattering, and color distortion. More importantly, obtaining paired training data for these scenarios is a challenging task, which further hinders the generalization performance of enhancement models. To address these issues, we propose a novel approach, the Hybrid Contrastive Learning Regularization (HCLR-Net). Our method is built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples. This approach enables the network to develop a more robust sample distribution. Notably, we utilize non-paired data for both positive and negative samples, with negative samples are innovatively reconstructed using local patch perturbations. This strategy overcomes the constraints of relying solely on paired data, boosting the model’s potential for generalization. The HCLR-Net also incorporates an Adaptive Hybrid Attention module and a Detail Repair Branch for effective feature extraction and texture detail restoration, respectively. Comprehensive experiments demonstrate the superiority of our method, which shows substantial improvements over several state-of-the-art methods in terms of quantitative metrics, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability. Our code is available at: https://github.com/zhoujingchun03/HCLR-Net .
Multi‐Hop anonymous payment channel network based on onion routing

Abstract Payment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.
MVIndEmo: a dataset for micro video public-induced emotion prediction on social media

Abstract Distinct from the realm of perceived emotion research, induced emotion pertains to the emotional responses engendered within content consumers. This facet has garnered considerable attention and finds extensive application in the analysis of public social media. However, the advent of micro videos presents unique challenges when attempting to discern the induced emotional patterns exhibited by content consumers, owing to their free-style representation and other factors. Consequently, we have put forth two novel tasks concerning the recognition of public-induced emotion on micro videos: emotion polarity and emotion classification. Additionally, we have introduced a accessible dataset specifically tailored for the analysis of public-induced emotion on micro videos. The data corpus has been meticulously collected from Tiktok, a burgeoning social media platform renowned for its trendsetting content. To construct the dataset, we have selected eight captivating topics that elicit vibrant social discussions. In devising our label generation strategy, we have employed an automated approach characterized by the fusion of multiple expert models. This strategy incorporates a confidence measure method that relies on three distinct models for effectively aggregating user comments. To accommodate adaptable benchmark configurations, we provide both binary classification labels and probability distribution labels. The dataset encompasses a vast collection of 7,153 labeled micro videos. We have undertaken an extensive statistical analysis of the dataset to provide a comprehensive overview composition. It is our earnest aspiration that this dataset will serve as a catalyst for pioneering research avenues in the analysis of emotional patterns and the understanding of multi-modal information.
Soft Factors and Interaction Vertices from Light‐Cone Actions

Abstract Universal factors associated with the emission of a soft boson in gauge theories and gravity, formulated in the light‐cone gauge, are presented. The inverse‐soft method, for constructing higher‐point amplitudes from lower‐point ones, using these factors is reviewed. These ideas are then examined in (light‐cone) superspace and applied to both the super Yang–Mills and supergravity theories. One highlight is a compact result for the quartic interaction vertex in supergravity, a crucial ingredient for finiteness analyses.
Geochemistry of the North Gondwana Paleozoic Araba and Naqus formation siliciclastics, Sinai: implications for provenance, paleoweathering, and tectonic setting

The Araba and Naqus formations in Ras Milan, Sinai, constitute part of the major continuous Paleozoic clastic belt stretching across northern Gondwana. They are sandstone-dominated successions, resting on Precambrian granitic basement, with lowermost thin basal polymictic conglomerate deposits (1–2 m), marking the unconformity. The Araba Formation section is composed mainly of varicoloured violet to reddish brown, fine to coarse-grained laminated and planner and overturned cross-bedded sandstones, while the overlying Naqus Formation is characterized by chaotically distributed poorly sorted quartz pebbles to cobbles within a moderately-sorted coarse- to medium-grained quartz arenite and by abundance and variety of large-scale tabular and trough cross bedding. Modal QFL classification of Araba and Naqus formation sandstones revealed that they are merely quartzose sandstones (quartz arenites). Geochemistry of the succession shows extremely wide compositional ranges for almost all major elements, but the Araba Formation is relatively enriched in SiO2, TiO2, Fe2O3*, Na2O, P2O5, and depleted in Al2O3, MgO, CaO and K2O relative to Naqus Formation. Only the Araba Formation samples are depleted in the transition elements Ni, Cr and Co and oppositely possess extreme enrichment in the HFSEs (i.e. Zr, Hf, Y, Nb, Ta, Th, U), whereas both formations show variable depletion in the LILE Rb, K and Ba, and excessive REE abundances relative to the PAAS and UCC, and LREE-enriched patterns with marked negative Eu anomalies. The provenance of the Naqus and Araba formations could be an earlier felsic magmatic source, which has undergone extensive sedimentary recycling. They possess high compositional maturity and were subjected to intensive to moderate weathering during humid climatic conditions. Passive margin tectonic setting is implied which denotes the derivation of Araba and Naqus formation siliciclastics from stable continental regions or plate interiors and deposition in intracratonic basins.
“TimeOntoImperfection”: An OWL 2 ontology to representing temporal data imperfection using imperfection theories

Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer’s patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent “TimeOntoImperfection”, a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of “TimeOntoImperfection”, then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step.
Functional data learning using convolutional neural networks

Abstract In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of sine and cosine functions, and the magnitudes and widths of curve peaks. We also use it to classify the monotonicity and curvatures of FD, the algebraic versus exponential growth, and the number of peaks of FD. Second, we apply the same CNNs to Lyapunov exponent estimation in noisy and non-noisy chaotic data, in estimating rates of disease transmission from epidemic curves, and in detecting the similarity of drug dissolution profiles. Finally, we apply the method to real-life data to detect Parkinson’s disease patients in a classification problem. We performed ablation analysis and compared the new method with other commonly used neural networks for FD and showed that it outperforms them in all applications. Although simple, the method shows high accuracy and is promising for future use in engineering and medical applications.
A Survey on Online On-Demand Laundry Services

The evolution of on-demand laundry services in the digital era as well as the technical and commercial details that affect these services' sustainability. The survey covers a wide range of tactics, from innovative uses of IoT and machine learning, such as IronMan's doorstep laundry, to creative solutions, like "Super-Dry," which sped up services during the pandemic. LaundryMama is a complete laundry management system and on-demand mobile application integration that makes it stand out as a transparent and scalable solution. Notwithstanding hindrances such as geographical limitations and dependence on technology, the "My Door Laundry Application" fosters efficiency and employment prospects. All these technological advancements are intended to modernize the established laundry service sector, addressing persistent problems, and introducing a novel, inventive environment that places a premium on efficiency and user satisfaction
Literature Survey on College Information Chatbot

A chatbot is software that allows users to communicate with computers in natural language, similar to human conversations. Chatbots interact with clients and provide answers based on human input. The user may believe they are conversing with a human, while in reality, they are interacting with a machine. The chat bot program enables students to inquire about college admissions from any location with an internet connection and receive prompt responses. The chatbot technology streamlines the admissions process by delivering necessary information to students and parents, reducing the manpower effort in addressing inquiries.
Goal exploration augmentation via pre-trained skills for sparse-reward long-horizon goal-conditioned reinforcement learning

Abstract Reinforcement learning often struggles to accomplish a sparse-reward long-horizon task in a complex environment. Goal-conditioned reinforcement learning (GCRL) has been employed to tackle this difficult problem via a curriculum of easy-to-reach sub-goals. In GCRL, exploring novel sub-goals is essential for the agent to ultimately find the pathway to the desired goal. How to explore novel sub-goals efficiently is one of the most challenging issues in GCRL. Several goal exploration methods have been proposed to address this issue but still struggle to find the desired goals efficiently. In this paper, we propose a novel learning objective by optimizing the entropy of both achieved and new goals to be explored for more efficient goal exploration in sub-goal selection based GCRL. To optimize this objective, we first explore and exploit the frequently occurring goal-transition patterns mined in the environments similar to the current task to compose skills via skill learning. Then, the pre-trained skills are applied in goal exploration with theoretical justification. Evaluation on a variety of spare-reward long-horizon benchmark tasks suggests that incorporating our method into several state-of-the-art GCRL baselines significantly boosts their exploration efficiency while improving or maintaining their performance.
SR-MACL: Session-Based Recommendation with Multi-layer Aggregation Augmentation in Contrastive Learning

Abstract Session-based recommendation (SBR) aims to predict the next item of interest in chronological order based on a given sequence of short-term behaviour of anonymous users. Due to the limited data available for short-term user interactions, its performance is more susceptible to data sparsity problems than traditional recommendation methods. Contrastive learning is often used to solve the data sparsity problem due to its ability to extract general features from the raw data. Existing session-based recommendation methods based on graph contrastive learning typically build graph contrastive learning by using information from other sessions to generate augmented views. While this avoids the problem that the use of dropout in traditional contrast learning methods can cause damage to the session context, it inevitably introduces irrelevant item information, which interferes with accurately modelling user interests and leads to sub-optimal model performance. To address these issues, we propose a new session recommendation method based on multi-layer aggregation augmentation contrastive learning, namely SR-MACL. In SR-MACL we construct a contrastive view by adding noise to the embedding representation and forming a contrastive embedding representation by multi-layer aggregation, which not only effectively solves the problem that traditional graph enhancement methods can destroy the context of the whole session, but also avoids the interference of irrelevant items. Experimental results on three real datasets have shown that SR-MACL can improve the accuracy of recommendation results and predict the user's next interaction more effectively.
Fast high-fidelity quantum nondemolition readout of a superconducting qubit with tunable transverse couplings

Dispersive readout, the current standard for measuring superconducting qubits, relies on relatively easy to engineer, static transverse coupling between a qubit and a detuned cavity mode. However, this method requires circuit design trade-offs between qubit-cavity coupling, cavity-feedline coupling, detuning, and the resultant decoherence introduced by the cavity mode. Additionally, the criteria for performing quantum nondemolition measurements, including reduced cavity photon numbers, further limits speed and fidelity. In this work, we introduce a circuit with pulse-tunable qubit-cavity and cavity-feedline couplings so that the entire qubit-measurement process can be fully optimized. Here, the core measurement process resembles a quantum gate operation between the qubit and the cavity, with the cavity acting like an ancilla, storing the qubit-state information. To fully explore time-dependent effects of the full qubit-cavity dynamics, we go beyond the rotating-wave, Jaynes-Cummings, or dispersive approximations, to develop a pulsed-measurement protocol. In addition, we propose a realistic circuit architecture that can provide a very fast, high-fidelity, QND measurement with near-unit efficiency.