dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, by adding other types of patients to our study subset, such as healthy and NFLE patients, the models will be trained on a larger dataset, which may improve their performances. Moreover, this research will be directly comparable to other works that have used different subsets of patients. Finally, experimentation with Word2Vec instead of Doc2Vec for EEG vectorization using a sequential classification model (such as a long short-term memory) is a compelling proposal. This novel approach will indirectly give more interpretability in terms of the vectorization process since Word2vec is less complex than Doc2Vec and will continue the proposed line of research on the distributional representations of CAP.
This work was possible thanks to the support of the  Mexican government  through the  FORDECYT-PRONACES program Consejo Nacional de Ciencia y Tecnología (CONACYT)  under grant  APN2017-5241 ; the  SIP-IPN  research grants,  SIP-2259 ,  SIP-20231198 , and  SIP-20230140 ;  IPN-COFAA  and IPN-EDI. Institutional Review Board Statement: Not applicable. Not applicable.
3. 1. 1 CAP Sleep DatabaseThe CAP research team led by Terzano et al. (2001)   [4]  released in 2012 the CAP Sleep Database on PhysioNet [33]. It consists of 108 PSG recordings that were registered by the Sleep Disorders Center of the Maggiore Hospital in Parma, Italy. From the 108 PSG, 16 correspond to the recordings of healthy patients, and 92 correspond to the pathological recordings of patients diagnosed with: • Nocturnal Frontal Lobe Epilepsy (NFLE)-40 recordings; • REM Behaviour Disorder (RBD)-22 recordings; • Periodic Leg Movement (PLM)-10 recordings; at least three EEG signals with complementary EOG, chin and tibial EMG, airflow, respiratory effort, pulse oximetry, and ECG signals. In addition, they include annotations of the sleep stage and CAP labels (CAP1, CAP2, and CAP3, corresponding to the three A-phase types).
Experimental Results The experimental results have been carried out using the Deep Fuzzy Logic (DFL) software framework 1 which allows to express a learning task as a set of FOL formulas. The formulas are compiled into a learning task using the theory of generators described in the previous section. The learning task is then cast into an optimization problem like shown in Section 5. 1 and, finally, is solved via tensor propagation within the TensorFlow (TF) environment 2  (Abadi et al. , 2016). In the following of the section, it is assumed that each FOL constant corresponds to a tensor storing its feature representation. DFL functions and predicates correspond to a TF computational graph. If the graph does not contain any parameter, it is said to be given, otherwise the function/predicate is said to be learnable, and the parameters will be optimized to maximize the constraints satisfaction.
The Learning Task The CiteSeer dataset  (Fakhraei et al. , 2015)  consists of 3312 scientific papers, each one assigned to one of 6 classes: Agents, AI, DB, IR, ML and HCI. The papers are not independent as they are connected by a citation network with 4732 links. This dataset defines a relational learning benchmark, where it is assumed that the representation of an input document is not sufficient for its classification without exploiting the citation network. The citation network is typically employed by assuming that two papers connected by a citation belong to the same category. This knowledge can be expressed by providing a general rule (2) are learning processes optimized with standard Gradient Descent, while (3) and (  4 ) are optimized with Adam Gradient Descent. of the form: ∀x ∀y cite(x, y) ⇒ (p(x) ⇐⇒ p(y)) where cite is a binary predicate encoding the fact that x is citing y and p is a task function implementing the membership function of one of the six considered categories.
, 2006)  can be defined by providing, ∀i ∈ {Agents, AI, DB, IR, M L, HCI}, the following two FOL formulas: ∀x ∀y cite(x, y) ⇒ (p i (x) ⇔ p i (y)) (  6 ) ∀x P i (x) ⇒ p i (x) (7) where only positive supervisions have been provided because the trained networks for this task employ a softmax activation function on the output layer, which has the effect of imposing mutually exclusivity among the task functions, reinforcing the positive class and discouraging all the others. While this behaviour could have been trivially expressed using logic, this network architecture provides a principled baseline to compare against and it was therefore used across all the experiments for this dataset. DLF allows the users to specify the weights of formulas, which are treated as hyperparameters. Since we use at most 2 constraints per predicate, the β weight of the constraint expressing the fitting of the supervisions (Equation  7 ) is set to a fixed value equal to 1, while the weight of the manifold regularization rule expressed by Equation 6 is cross-validated from the grid of values {0.
In particular, the data is split into different datasets, where {10%, 25%, 50%, 75%, 90%} of the available data is used a test set, while the remaining data forms the training data. During training, the fitting of the supervised data defined by Equation 7 can be applied only for the training data, while manifold regularization (Equation  6 ) can be enforced on all the available data. In this experiment, the Adam optimizer and the SS family of parametric t-norms have been employed. Table  4  shows the average test accuracy and its standard deviation over 10 different samples of the train/test splits. As expected, all generator selections improve the final accuracy over what obtained by pure supervised learning, as manifold regularization brings relevant information to the learner. Table  4  also shows test accuracy when the parameter λ of the SS parametric family is selected from the grid {-1.
Conclusions This paper presents a framework to embed prior knowledge expressed as logic statements into a learning task. In particular, it was shown how the choice of the t-norm generator used to convert the logic into a differentiable form defines the resulting loss function used during learning. When restricting the attention to supervised learning, the framework recovers popular loss functions like the cross-entropy loss, and allows to define new loss functions corresponding to the choice of the parameters of t-norm parametric forms. The presented theory has driven to the implementation of a general software simulator, called DFL, which bridges logic reasoning and deep learning using the unifying concept of t-norm generator, as general abstraction to translate any FOL declarative knowledge into an optimization problem solved in TensorFlow.
Acknowledgements The authors thank the  French National Research Agency  for financial support (Project shapeNread, grant numbers  ANR-19-CE29-0015-01  and  ANR-19-CE29-0015-02 ). G. O. thanks the  CSC Graduate School  funded by the  French National Research Agency  ( CSC-IGS ANR-17-EURE-0016 ) for a master fellowship. For the polymer synthesis part, this research work has been started at the  Institut Charles Sadron (ICS)  and finalized at the  Institut de Science et d'Ingénierie Supramoléculaires (ISIS). The authors also thank  Hava Aksoy  and  Cyril Antheaume  ( ISIS ) for preliminary MS characterization.
Clustering results are evaluated using three well-known clustering quality metrics. In addition, we compute an average similarity matrix to assess the quality of each cluster. The results show variation in clustering quality from method to method. The clusters obtained by the hierarchical and Taylor-Butina methods are more computationally expensive to use in cross-validation strategies, and both cluster the molecules poorly. In contrast, the UMAP method provides the best quality, and therefore we recommend it to analyze this highly valuable dataset.
Indeed, many articles fail to account for the nearly inevitable reduction in predictive ability that may occur when something that is a useful predictor in one data set is not as useful in another dataset  [4]. Models that perform well on an independent data set can be achieved using model validation strategies such as bootstrap or k-fold cross-validation (CV)  [4]. More demanding model validation strategies include asymmetric validation embedding (AVE)  [5] , leave-dissimilar-target-out (LDTO) CV  [6] , leave-one-cell-line-out (LOCO) CV, leave-one-tissue-out (LOTO) CV, leave-one-compoundcluster-out (LOCCO) CV  [7] , and those using other similarity metrics between training and test data instances  [8]. These complex model-validation methods pose additional challenges  [9] , particularly the LOCCO CV approach, which inherits the challenges of any clustering method.
Clustering methods are widely explored unsupervised ML-based algorithms whose aim is to discover the underlying structure or patterns existing in a given dataset. The clustering of biological entities, such as small molecules, can be performed using different approaches, including hierarchical clustering (HC), distribution-based clustering, and densitybased clustering  [10]. For example, several clustering algorithms, including hierarchical, Taylor-Butina, and UMAP clustering, have been compared on 29 data sets with between 100 and 5000 small molecules  [11]. In addition, hierarchical clustering has been used to cluster molecules from the PubChem database  [12] , and Taylor-Butina clustering has been used to cluster molecules from the MolPort database  [13]. A clustering analysis in a virtual chemical database can be used to quantify the diversity of compounds, which is relevant in several areas of chemistry such as in highthroughput screening (HTS)  [14]  and in QSAR-based virtual screening predictions  [13].
For instance, they are computationally efficient and can be easily applied to large datasets. Moreover, Morgan fingerprints are robust to small variations in the molecular structure, making them a useful tool in virtual screening, where slight modifications to the molecular structure may occur due to synthetic or computational alterations. In this study, we aim to evaluate and compare three different methods for clustering the NCI-60 molecules to determine the best-performing algorithm that can be used to generate clusters for model validation, ensuring that the resulting clusters are optimal for use in LOCCO-CV strategies or other model validation strategies. For this, we compare the hierarchical, Taylor-Butina, and UMAP clustering algorithms while tuning their essential hyperparameters. Additionally, we provide a definition of outlier molecules that aligns with the clustering problem we are addressing, allowing us to optimize outlier computation.
NCI-60 Dataset The NCI-60 panel utilizes 60 different human tumor cell lines to identify and characterize novel compounds with growth inhibition or killing of tumor cell lines  [3]. These 60 different human tumor cell lines comprise 9 cancer types: leukemia; melanoma; and cancers of the lung, colon, brain, ovary, breast, prostate, and kidney. All of the compounds that are screened have been initially tested in a one-dose assay on the full NCI-60 panel. Compounds showing significant growth inhibition at this assay are then evaluated against the NCI-60 panel in a 5-dose assay  [16]. Each compound submitted to the NCI-60 panel for testing and evaluation is identified with a unique registration number called the National Service Center (NSC) ID. Data quality is crucial in the development of AI/ML models during drug discovery.
Clustering Methods Hierarchical clustering consists in building a binary merge tree, starting from the molecules stored at the leaves and merging them until reaching the root of the tree that contains all the molecules of the dataset  [25]. The linkage criteria determine the metric used for the merge strategy, for example, single linkage, complete linkage, or Ward linkage. In the context of clustering chemical structure databases, the Ward linkage is commonly used  [26] , which minimizes the sum of squared differences within all clusters. The graphical representation of the binary merge tree representing the resulting hierarchical clustering is called a dendrogram. The python implementation of this algorithm uses the RDkit library  [27] , where the first step is to calculate the similarity for each pair of molecules.
• All three clustering methods have been tested on other datasets; however, we are not aware of any previous studies that have clustered all or part of the molecules from the NCI-60 panel nor of studies comparing results when outlier molecules are removed. • High-quality clustering results could improve model generalization on unseen compounds, which is important when the scope and use of the predictive model need to be extended. Therefore, special care must be taken in the clusters to be used in model validation, such as LOCCO-CV strategies.
For the dephasing channel, we exploit the fact that the photon number is invariant under dephasing, and therefore that it is possible to represent the relevant states in a compact form since the Fock states |n 1 , n 2 ⟩ necessarily satisfy n 1 + n 2 ≡ 0 mod 4 and n 1 ≡ 0 mod 2. Throughout this section, we assume that each mode of the 2T -qutrit is affected independently by the same quantum channel. In particular, if the single-mode channel describing pure loss or dephasing is denoted by N , then the overall two-mode quantum channel is given by N ⊗ N. While this independence seems like a reasonable assumption for losses, it may be too pessimistic in the case of dephasing. The python files used to perform the numerical analysis are available on Gitlab.
A Study of the Potential Dark Sides of the Metaverse in order to Make Immersive Experiences More Secure-1 Sanjar Mohamed AB The metaverse is the next generation internet-a shift from 2 to 3D ways of consuming information by seamlessly integrating with our physical and virtual lives to create real-time immersive experiences. The development of the metaverse will be bolstered by the advancement and adoption of essential technologies, including 5G communication, extended reality (AR/VR), artificial intelligence, spatial computing, blockchain, and digital twins. In the metaverse, we will live, connect, play, learn, earn, and work in a fundamentally new way. In essence, the metaverse humanizes technology by allowing humans to interact and perceive the digital world. In the holistic and democratic vision, the metaverse is a collection of interoperable, decentralized, spatial and persistent immersive virtual worlds that allow people to take control of their data and digital assets.
, 2022). Metaverse identities include avatars, biometrics, spatial information, conscious and unconscious information generated by interaction with platforms. These data can provide insights into an individual's desires and mannerisms. Data extraction and exploitation for invasive marketing or political purposes pose a problem. What happens if bad actors are able to access a person's geospatial and biometric data? Criminals extract data and steal the identity of the user. By using deep fake technology, criminals can impersonate a particular user without being identified. In the metaverse, identity theft is a major concern.
Security and Governance Issues The growth of gadgets helps people transition into the metaverse world. While Web 2. 0 somewhat restricted people from sharing certain forms of data such as contacts, finances, and engagement, the gadget-driven metaverse world adds a layer of data that firms can collect. Avatars-driven metaverses and the virtual world make users travel via technology. The pseudo-space time makes them act in situations that the human body is not accustomed mainly, generating new forms of data that are often unregulated and unmonitored  (Wang et al. , 2022). Studies in virtual worlds indicate an immediate need to assess the security and governance issues of data, its breach, and its misuse  (Dwivedi et al. , 2022a (Dwivedi et al. , , 2023a;; Wang et al. , 2022). Moreover, the current technology does not restrict users to experiencing metaverse under a particular age bracket.
Having said this, the dark side of the metaverse needs to be studied from a scoping review of linking the following themes: a) How can behavioral and social psychology be used for users inside and outside the metaverse over a long time? b) How do users in the metaverse evolve their psychodynamics and cross-cultural psychology in a fuzzy and unorganized environment? c) How can data breaches and identity theft be understood and controlled in the dark web and misrepresented and delusional presence and activities of users in the metaverse? d) How can new theories be generated for understanding the scope and dimensions of the dark side of the metaverse? e) Will the metaverse lead to new diseases and resurface old ones, and is the healthcare industry ready for it? f) How do firms control the unethical behaviour of employees and users in the metaverse? Can spending time in the metaverse impact their real-world ethics? g) Can knowledge hiding be considered an asset in the metaverse? Does the metaverse change the personality of the being temporarily or permanently?.
Murky Side of Metaverse: Implications and Research Questions-Neeraj Pandey, Mihalis Giannakis and Ramakrishnan Raman The online world is getting transformed with the advent of metaverse-based platforms  (Dwivedi et al. , 2022a). The metaverse, despite its virtues, has several dark side dimensions. The cognition and awareness of these negative facets would aid in building appropriate checks and would go a long way in seamlessly managing metaverse-based platforms. There is a need for organizations to align the conceptualization, implementation, and database usage of their metaverse platforms with the legal provisions of the state. Firms must have clarity that the metaverse is a medium to reach out to its stakeholders in an efficient manner and not an end itself. The focus of the service or product should be providing a superior customer experience where the technology is just an enabler  (Ramasundaram et al.
Digital Marketing and Metaverse The initial experience of firms regarding digital marketing on metaverse platforms like Decentraland and Roblox has been mixed. Many organizations had confusion over applicable attribution models and returns on investment (RoI) in terms of sales conversions. The issue of bots and impersonation on metaverse platforms, besides the growing digital divide, was another issue that needs to be addressed. The increased use of the metaverse for digital marketing, has led to increased energy consumption and thus an increased carbon footprint. Privacy is another major concern on metaverse platforms. The granularity and amount of data collected in the three-dimensional metaverse environment is much more than the traditional two-dimensional social media platforms. The tracking using digital marketing tools to steal metaverse users' data is a real threat.
Healthcare and Metaverse Besides advertising, entertainment, education and healthcare are other domains where the use of metaverse platforms is expected to grow in the coming years significantly. Lack of human touch and empathy, besides privacy concerns, are leading to adoption challenges of metaverse-based healthcare delivery systems. The legal compliance and safety issues in transferring patient medical records to other departments and hospitals in a metaverse environment are yet to be navigated  (Chengoden et al. , 2022). Metaverse-based healthcare databases would be prone to cyberattacks which may lead to the loss of critical patient medical records and financial details, which in turn can impact the treatment and insurance claim process. There is also reputational risk for the healthcare service provider and regulator in such violations.
More Adverse Effects on Children and Adolescents More adverse effects on children and adolescents has been a common theme of various contributions in this paper. As discussed in Section 2. 13, online virtual world platform VRChat has allegedly allowed children to enter into 'virtual strip clubs' due to the lax rules of the platform. Section 2. 1. 1 and 2. 1. 14 noted that children are more likely to be addicted to the metaverse. As Sections 2. 5. 1. 6, 2. 8. 1, 2. 9. 1 and 2. 11. 5 make clear, cyberbullying (also referred to as metabullying in Section 2. 1. 3) and harassment are likely to be serious DALL•E-AI system that creates realistic images and art from a description in natural language-when prompted with "an oil painting of a young child, of 8 years old, asking her mother "is the metaverse real?" " 3 Discussion and Implications.
As explained in Section 2. 5, the operational technology used in the metaverse lacks any design guidelines for cybersecurity and privacy. It is also important to note here that large language models (LLMs) or generative artificial intelligence (GAI) tools such as ChatGPT (for further details about ChatGPT refer to  Dwivedi et al. , 2023c)  have reduced the entry barriers for cybercriminals, which is further likely to increase cyberthreats facing the metaverse. Cybercriminals are reported to be using ChatGPT to create new Trojans. According to cybersecurity researchers, many cybercriminals with no software development or programming skills had been using OpenAI's technologies for malicious purposes  (Kshetri, 2023a, b). Sections 2. 1. 3 and 2. 2. note that the metaverse makes it possible for marketers to track the movements of eye, pupil and other body parts  (Techtarget, 2022)  as well as the emotional aspects of consumer behavior, which provide more sensitive personal information and hence more severe consequences of privacy violations.
Implications for Practice There are also important implications for practice. Many big metaverse platforms have failed to attract users, leading to significant financial losses. For instance, Meta Platform's business and research unit focusing on VR and AR was reported to lose about $14 billion in 2022, which is expected to lose additional $15 billion in 2023. Consequently, Meta's emphasis has been recently shifted from the metaverse to AI  (Hays, 2023). One possible explanation for the observed underperformance of these platforms is that potentially harmful effects may have outweighed the benefits for users. It is thus important for technology platforms and companies operating in the metaverse to take measures to address potential negative impacts. Businesses are already facing more demanding customers that place a high importance on measures to protect privacy of their data and ensure cybersecurity.
Nevertheless, the IRSC catalog indicates 3 events larger than Mw 5, co-located with the Gulkhari anticline, and in this study considered as the background seismicity. Thanks to high-quality satellite data before and after the mainshock, and co-and post-seismic waveform records (both for mainshock and aftershocks), we focus on understanding whether the growth of the Gulkhari anticline is controlled by a fault directly beneath it or not. We probe links between faulting and folding and investigate the robustness of the vertical separation idea, suggested by  Nissen et al. (2011). Another important question to answer is which fault or faults are responsible for the shaking. Furthermore, the Genaveh earthquake co-located with the major active Gulkhari   (Najafi and Lajmorak, 2020). Red lines show major mapped active faults, including the Mountain Front Flexure (MFF)  (Berberian, 1995) , Kharg-Mish Fault (KMF), Hendijan-Bahregansar Nowrooz Fault (HBNF), Izeh Fault Zone (IFZ), BalaRud Fault Zone (BFZ), and Kazerun Fault (KF).