Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing Indian Stock Market Prediction with TRNet: A Graph Neural Network Model

Indian stock market is unique with high volatility and complexity, so special approaches are needed for its computational understanding. This study proposes Temporal Relational Network (TRNet), a new Graph Neural Network model designed for Indian stock market prediction. TRNet considers pre-defined industry-sector links and time to predict future stock prices in NIFTY-50 index, a significant measure for Indian stock market. Using a financial knowledge graph and Graph Convolution Network, TRNet effectively captures complexities and relationships in non-Euclidean financial data. TRNet outperforms existing models in different timeframes, showing its importance for Indian stock market. We also explore how connections between stocks affect predictions and provided an explainable framework to understand the working mechanisms of the proposed approach. Combining finance and intelligent computing, TRNet paves the way for possibilities in the areas of computational finance using deep learning.
A Heuristic Decoding Method for Coded Images of Random Uniformly Redundant Array

This paper presents a novel watermarking scheme, which is designed for cryptographic data such as keys, cipher- texts, and signatures. Such a cryptographic data is defined carefully based on its mathematical structure in order to provide correctness and security. Thus, in general, crypto- graphic data has no redundancy and modified data cannot work for intended use. We first show a general solution to this problem by focusing on cryptographic data of the re- cent major cryptosystems called pairing-based schemes. We then present an example of construction for one of the most known pairing-based schemes. We also prove that both correctness and security remain satisfied even if cryptographic data is watermarked.
A system for measurement of seawater parameters: Statistical postprocessing of collected data

The measurement system for real-time monitoring of water parameters in mariculture farms, along with early warning algorithms for anomalies, has been developed within the research center for INTELligent, innovative, environmentally friendly, and sustainable MARICulture (INTEL-MARIC). This system enables the collection of data on 9 different water parameters. In addition to presenting the main developments achieved within the INTEL-MARIC project, this paper provides an analysis of the data collected from the Krka River in Croatia over a 1-year period. The paper includes a statistical analysis of the measured data, a correlation analysis between different seawater parameters, and descriptions of the measurement and early warning systems. Furthermore, the multiprobe maintenance procedure is described, and the potential use of similar systems in different fields is discussed.
Temporal-Based Action Graph with Sequential Pattern Mining for ChuRN Detection: a Playtika Case Study

Detecting churn is a critical task for any company, especially for online gaming companies, where early user churn rates are high. To improve customer retention rates, recent research has focused on using action graphs and deep neural networks to encode users' in-app transition patterns. However, existing action graphs approaches lack the ability to capture important characterizations of the given events data, such as sequential patterns and sparsity. In this paper, we propose a novel action-graph approach that addresses these limitations by incorporating sequence mining and temporal analysis techniques with graph neural networks. Specifically, we use a graph-based representation that captures the sequence of actions and the distribution of time-intervals between them. We then apply a convolutional neural network to learn the downstream task. Using real-world action data, we demonstrate the superiority of our proposed method compared to state-of-the-art approaches with respect to classification precision and recall. Overall, our approach presents a promising new direction for improving churn prediction in online gaming, as well as in other industries. We believe that our findings will be of interest to researchers and practitioners working on customer retention and churn prediction. The code and data for our approach are readily available on our GitHub repository.
Effect of the Neck Retraction Taping (NRT) on Forward Head Posture and the Upper Trapezius Muscle during Computer Work

[Purpose] The purpose of this study was to investigate the effect of neck retraction taping on forward head posture and the upper trapezius muscle of computer workers during computer work. [Subjects] Twelve males aged 20–30 years were recruited. [Methods] We measured forward head angle and upper trapezius muscle activity during computer work before and after NRT. [Results] The FHP angle significantly decreased during computer work performed with NRT compared to without NRT. The UT muscle activity was also significantly decreased during computer work performed with NRT compared to without NRT. [Conclusion] We think that the taping tension provided by NRT may have provided a mechanical effect that prevented FHP. NRT may also encourage a proper head posture in patients unfamiliar with the neck retraction posture.
A Systematic Review and Future Perspective of Android Malware Detection Based Machine Learning Techniques

After becoming incorporated into normal daily life, smartphones gained access to private data. On most mobile devices running Android OS, malware gathers private user data without the user's awareness. Malware frameworks are becoming more and more complicated, making it challenging to identify them. Traditionally, researchers avoided detection processes that employed static and dynamic analysis by employing strategies such as encryption, packaging, and code obfuscation, but the current study reveals that machine learning-based identification of malicious code for Android is an efficient methodology. The effectiveness of the extracted features obtained as a result of malware detection indicates the efficiency of malware detection analysis. The positive aspects of previous reviews have been augmented in our paper by surveying in a variety of areas. Leading malware analysis tools used on Android systems were thoroughly examined. To fill detection gaps, it is necessary to improve effective measures and detection efficiency with regard to advanced malware. A summary of the difficulties addressed throughout the creation of malware classifiers is also included in the paper. The research work in this paper is concerned with the potential of detecting malware targeting Android devices using machine learning.
Verification & Validation of Agent-Based Simulations using Approximate Model Checking

This paper focusses on the usefulness of approximate probabilistic model checking for the internal and external validation of large-scale agent-based simulations. We describe the translation of typical validation criteria into a variant of linear time logic. We further present a prototypical version of a highly customisable approximate model checker which we used in a range of experiments to verify properties of large scale models whose complexity prevents them from being amenable to conventional explicit or symbolic model checking.
Machine Learning Qualification Process and Impact to System Assurance

To address the technical challenges associated with the verification, validation, assurance, and trust of Artificial Intelligence and Machine Learning (AI/ML) in safety critical applications, ARiA in partnership with Arizona State University (ASU) proposed the framework of a Machine Learning Qualification Process (MLQP) in response to a Small Business Technology Transfer (STTR) solicitation. The MLQP incorporates measures and metrics to qualify data sets and models and considerations for the use of data cards, feature cards, and model cards. The US Army Combat Capabilities Development Command Armaments Center (DEVCOM AC) has been developing a roadmap [1] to mitigate the risks associated with the development and deployment of AI/ML enabled systems. The proposed MLQP addresses many of the key challenges and considerations from that roadmap to enable the development of assured and trusted AI/ML enabled systems. This paper will examine how the proposed approach can be leveraged as a tool to build assurance into the cycle of AI/ML development and deployment to ensure safe and reliable systems and the alignment to Army assurance practices as well as DoD guidance.
A Concept-Oriented Approach to Support Software Maintenance and Reuse Activities

A major activity in software development is to obtain knowledge and insights about the application domain. Even though this is supported by a wide range of tools and techniques, a lot of knowledge remains implicit in the resulting artefacts (e.g. class diagrams). Examples of such implicit knowledge are amongst others the links between the different artefacts, the knowledge that is lost as a result of iterative refinements, and the knowledge that is regarded common sense by the involved parties. Most of this knowledge resides in the heads of the stakeholder, the domain experts, and the developers. As it is likely that they can no longer remember it or that they are no longer available when software reuse and maintenance activities are initiated, this poses a problem. In this paper we present ongoing research in which we focus on our use of an ontology as a medium to tackle this problem. Making the different kinds of knowledge explicit will be accomplished by representing them as concepts in the ontology. Subsequently we will link these concepts to the artefacts created during analysis, design and implementation. This enables a bi-directional navigation between the concepts and the artefacts which consequently will serve as a vehicle to start reuse and maintenance activities.
Valvular Heart Disease Prediction Using CNN-based Residual Network

For decades, the cardiovascular disease which is also known as coronary artery disease, acts on the heart and has been recognized as the preeminent invoke of disease. Heart diseases is among the most severe diseases, and it impacts a lot of people all around the world. Early detection of heart illness may result in its prevention or amelioration, which may lower mortality rates. The proposed study used different heart disease datasets and deep learning techniques for classification. Deep learning (DL) can deliver a powerful answer for decision-making and correct predictions of valvular heart diseases. Early prediction of coronary heart disorder may also store many people’s lives; detecting cardiovascular illnesses like coronary heart attacks, coronary artery illness, etc., is a reproving task using ordinary scientific analysis. Feature extraction and classification techniques of heart sound provide a better way of study of phonocardiography (PCG) signal analysis which eventually reduces the cost, makes the system compact, and at the same time, can work with big data. This paper mainly has two segments feature extraction and classification. In feature extraction section, time domain features, statistical features, and acoustic features are used. For the classification of heart sound, the proposed CNN-based Residual Network is used. The CNN-based network is very effective in heart sound analysis, requiring much less computation time. The proposed CNN-based model scored an accuracy of 97.04 ±0.05 % on the test dataset with a sensitivity of 96.8 ± 0.03 % and specificity of 96.2 ± 0.04%.
Targeting Cancer Gene Therapy with Magnetic Nanoparticles

Recent advances in cancer genomics have opened up unlimited potential for treating cancer by directly targeting culprit genes. However, novel delivery methods are needed in order for this potential to be translated into clinically viable treatments for patients. Magnetic nanoparticle technology offers the potential to achieve selective and efficient delivery of therapeutic genes by using external magnetic fields, and also allows simultaneous imaging to monitor the delivery in vivo. Compared to conventional gene delivery strategies, this technique has been shown to significantly increase gene delivery to human xenograft tumors models, as well as various internal organs (e.g. liver, kidney) and the central nervous system. Magnetic nanoparticle technology, therefore, has the potential to turn the challenge of gene therapy in vivo into a new frontier for cancer treatment.
Hybrid Content-Collaborative Filtering: Improving Collaborative Filtering and Neural Network-based Recommendation

Effective recommendation systems have become increasingly important in the current period, which is characterised by an exponential increase in the amount of content available. These systems are essential for guiding users through a sea of possibilities by providing recommendations that are tailored to their tastes. Given the vast volume of content on streaming services and internet databases, movie suggestions are especially important. A new era of precise and customised movie recommendations has arrived thanks to sophisticated algorithms that have arisen as a result of technological advancements. By contrasting them with conventional collaborative filtering techniques, this study examines the potential of deep learning techniques in the field of movie recommendation. While deep learning approaches are adept at recommendation jobs, collaborative filtering uses user-item interactions to make predictions from unstructured data. The purpose of the study is to determine whether collaborative filtering's limitations, particularly for new users and objects, can be overcome using deep learning. In order to determine whether deep learning may improve accuracy and circumvent problems associated with conventional approaches, the research probes the complex dynamics of deep learning in movie recommendations. It looks at how deep learning's capacity to find latent patterns can produce more precise and nuanced recommendations. The broad objectives of the research include a comparative performance analysis, an investigation of strengths and shortcomings, and useful insights for model construction.
Loss of miR-200b promotes invasion via activating the Kindlin-2/integrin β1/AKT pathway in esophageal squamous cell carcinoma: An E-cadherin-independent mechanism

Our previous studies have shown that loss of miR-200b enhances the invasiveness of esophageal squamous cell carcinoma (ESCC) cells. However, whether the miR-200-ZEB1/2-E-cadherin regulatory cascade, a master regulator of epithelial-to-mesenchymal transition (EMT), is involved in the regulation of ESCC invasion remains elusive. Here, we show that miR-200b represses ESCC cell invasion in vivo without altering the expression of E-cadherin and vimentin, two surrogate markers of EMT. However, an inverse correlation was observed between the expression levels of miR-200b and ZEB1/2 in both ESCC cell lines (n = 7, P < 0.05) and ESCC tumor samples (n = 88, P < 0.05). Methylation of E-cadherin gene was found to block the regulation of E-cadherin by the miR-200b-ZEB1/2 axis, indicating that an E-cadherin-independent mechanism can mediate the biological function of miR-200b in ESCC. We revealed that miR-200b suppresses the integrin β1-AKT pathway via targeting Kindlin-2 to mitigate ESCC cell invasiveness. In two independent cohorts of ESCC samples (n = 20 and n = 53, respectively), Kindlin-2 expression positively correlated with the activation status of both the integrin signaling pathway and the PI3K-AKT signaling pathway (both P < 0.01). These data highlight that suppression of the Kindlin-2-integrin β1-AKT regulatory axis is an alternative mechanism underlying the tumor suppressor function of miR-200b in ESCC.
Dimensionless contact stiffness model to unveil the strain rate effect of nickel-based single crystal superalloy by nanoindentation

The strain rate effect of nickel-based single crystal superalloy DD6 is studied by nanoindentation experiments. Nanoindentation tests are conducted under load control to investigate the indentation response of nickel-based single crystal superalloy DD6 at loading rates varied from 10 to 1000 mN/min. It is found that when the loading rate is 110mN/min, the contact stiffness mechanism changes as the loading rate changes from low to high. The indentation residual morphology also shows that, when the loading rate is higher than 110mN/min, the residual indentation morphology begins to show obvious slip trace, which is consistent with the transition mechanism of contact stiffness. The relationship between the contact stiffness and the strain rate measured by nanoindentation is revealed, the theoretical framework between them is established and the dimensional analysis is conducted. The dimensionless contact stiffness model proposed can effectively calculate the indentation response of nickel-based single crystal superalloy DD6 in the loading rate range of 10–1000mN/min. Once the loading rate is determined, the model can extract the contact stiffness of the material.
Nerve growth factor (NGF) differentially regulates the chemosensitivity of adult rat cultured sensory neurons

We have studied the effects of NGF on the chemosensitivity of adult rat DRG neurons over a 1–2 week period in vitro, using voltage-clamp and radioactive ion flux methods. A sustained proton evoked current was reversibly lost in NGF-free medium after 1 week. Proton-evoked efflux of radioactive 86Rb+ ions was also depressed in NGF deprived cultures, although depolarization with 40 mM potassium still evoked a large 86Rb+ efflux. A similar reversible loss of capsaicin sensitivity was noted. The response to GABA and a second, transient proton evoked current were also regulated by NGF, but over a longer time course. In contrast, the sensitivity to ATP was not influenced by the presence or absence of NGF. These data show that NGF regulates some, but not all, chemosensitivities of DRG neurons and that loss of sensitivity occurs at different rates for different agonists. The precise co-regulation of the response to capsaicin and the sustained response to protons provides further evidence that protons activate capsaicin-operated ion channels.
Detecting Dialogue Hallucination Using Graph Neural Networks

Even though large language models (LLMs) accumulate tremendous knowledge, dialogue systems built with LLMs induce hallucinations, leading to the generation of non-factual responses. How to provide proper references to achieve interpretable hallucination detection is a key issue that needs to be addressed. In this paper, we propose a graph neural network (GNN)-based method to achieve high-performance and interpretable hallucination detection for domain-specific dialogue systems. The method involves performing graph matching between a reference knowledge graph obtained from a knowledge database and a response knowledge graph extracted from the response to detect non-factual responses. By comparing with strong baselines, our method achieves a recall improvement of up to 11% and infers the cause of hallucinations with a probability of over 79%.
An Enhanced Short Video Caching Strategy Based on User Behaviour and Time Factors

Caching plays a crucial role in network systems, and an appropriate caching strategy can effectively reduce network load and enhance user experience during video browsing. The proliferation and development of smartphones have led to a surge in global mobile data traffic. This paper targets the viewing characteristics of short video platform users, and proposes two adaptive video cache update mechanisms, TelLRU and TelLRU(sigmoid), aiming to achieve optimal performance by dynamically adjusting the cache video block size according to the video playback duration. This strategy is an improvement on the LRU cache replacement algorithm, adding a section of the video file of a new request to the cache. During video playback, the length of the video block changes dynamically according to a specific algorithm, and the LRU replacement strategy replaces the least recently used video block. Experimental results show that this strategy can significantly improve the cache hit rate of mobile video-on-demand platforms, reducing delay time and delay proportion.
Target Circumnavigation Control with an Unmanned Surface Vehicle based on Relative Range Measurements

This paper considers the problem of uncooperative target circumnavigation with an unmanned surface vehicle (USV) in the marine environment. The vehicle is driven to circumnavigate the target with the desired radius. A coordinate-free controller with a designed saturation function is proposed using only the local relative range information. The stability of the proposed algorithm is theoretically guaranteed by Lyapunov control theory. Moreover, a second-order sliding mode (SOSM) observer combined with linear and nonlinear terms is designed to estimate the range rate with respect to the target. The global stability and convergence are also proved using the Lyapunov theory. Finally, numerical simulations and real experiments with a USV are carried out to show the effectiveness of proposed approach. The results show that the steady-state circumnavigation error converges to zero and the SOSM observer can recover the real range rate within finite time.
Multi-USV Formation Control and Obstacle Avoidance Under Virtual Leader

In recent years, Unmanned Surface Vehicles (USVs) have been widely used in ocean transportation, search and rescue operations and other fields. This article focuses on the formation navigation problem of multi-USV clusters. We used deep reinforcement learning methods to establish a model for formation navigation and designed a reward function which guides each USV to its expected position while flexibly avoiding obstacles. By introducing a virtual leader, the multi-USV cluster successfully follow the expected path while maintaining formation and avoiding obstacles. The simulation results demonstrate the effectiveness of our algorithm in achieving both formation completion and formation navigation.
Proteasome inhibitor bortezomib enhances the effect of standard chemotherapy in small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive cancer showing a very poor prognosis because of metastasis formation at an early stage and acquisition of chemoresistance. One key driver of chemoresistance is the transcription factor Forkhead box protein M1 (FOXM1) that regulates cell cycle proliferation, maintenance of genomic stability, DNA damage response, and cell differentiation in numerous tumor entities. In this study we investigated the role of FOXM1 in SCLC progression and analyzed the effect of FOXM1 inhibition using two proteasome inhibitors, bortezomib and siomycin A. FOXM1 was strongly expressed in patient-derived SCLC samples (n=123) and its nuclear localization was associated with the proliferation marker Ki-67. Both proteasome inhibitors successfully inhibited FOXM1 expression leading to a significantly reduced proliferation and a decreased mitotic rate along with cell cycle arrest and apoptosis induction. These effects were further enhanced by addition of bortezomib to standard chemotherapy. Treatment of mice bearing chemoresistant SCLC xenografts with bortezomib reduced the mean bioluminescence signal of tumors by 54%. Similarly, treatment with cisplatin as a standard chemotherapy reduced the mean bioluminescence signal of tumors by 58%. However, in combination with standard chemotherapy bortezomib further reduced the mean bioluminescence signal by 93% (p=0.0258). In conclusion, we demonstrate the effect of bortezomib in inhibiting FOXM1 expression and thus in sensitizing resistant SCLC cells to standard chemotherapy. Thus, addition of bortezomib to standard chemotherapy might potently improve SCLC therapy, particularly in an extensive cancer stage.