Reviewerly/exHarmony huggingface dataset:
Prediction of Fluctuations in a Chaotic Cancer Model Using Machine Learning

Cancer is a group of diseases and the second leading cause of death according to World Health Organization. Mathematical and computational methods have been used to explore the cancer cells spread and the mechanism of their growth. We study a cancer model that exhibits both periodic and chaotic attractors. It describes the interactions among host, effector immune, and cancer cells. It is observed fluctuations in the population of cells. The fluctuation range can be associated with the appearance of tumour cells. In this work, we use machine learning algorithms for the prediction of fluctuations. We show that our machine learning classification is able to identify fluctuations that are associated with the growth rate of cancer cells.
Context Iterative Learning for Aspect-Level Sentiment Classification

Aspect-based sentiment analysis is to predict the sentiment polarity of different aspects of a sentence. Many irrelevant words are mistaken for opinion words in long sentences. According to extensive research, irrelevant words are far removed from the central words. This paper proposes a solution: First, we design the Context Iterative Learning network (CILN). Context attention module (CAM) is proposed, which employs Context Features Dynamic Mask (CDM) to cover words far from the center word and Context Features Dynamic Weighted (CDW) to reduce the weight of words far away. The calculation of CAM is done alternately to reduce the influence of distant irrelevant words. Finally, the obtained feature sequences are linked with the global sentence sequence. The Accuracy and Macro-F1 indicators obtained from the experiments based on benchmark datasets demonstrate the efficacy of the proposed method.
Solar Image Cloud Removal based on Improved Pix2Pix Network

In ground-based observations of the Sun, solar images are often affected by appearance of thin clouds, which contaminate the images and affect the scientific results from data analysis.In this paper, the improved Pixel to Pixel Network (Pix2Pix) network is used to convert polluted images to clear images to remove the cloud shadow in the solar images.By adding attention module to the model, the hidden layer of Pix2Pix model can infer the attention map of the input feature vector according to the input feature vector.And then, the attention map is multiplied by the input feature map to give different weights to the hidden features in the feature map, adaptively refine the input feature map to make the model pay attention to important feature information and achieve better recovery effect.In order to further enhance the model's ability to recover detailed features, perceptual loss is added to the loss function.The model was tested on the full disk H-alpha images datasets provided by Huairou Solar Observing Station, National Astronomical Observatories.The experimental results show that the model can effectively remove the influence of thin clouds on the picture and restore the details of solar activity.The peak signal-to-noise ratio (PSNR) reaches 27.3012 and the learned perceptual image patch similarity (LPIPS) reaches 0.330, which is superior to the existed dehaze algorithms.
Derogations Analysis of European Legislation Through Hybrid AI Approach

This paper presents a use case of hybrid AI approach applied to the European legislation with the aim to detect the derogations in the norms and to extract the main components. The result is modelled in Akoma Ntoso XML standard for supporting further applications, open data sharing and interoperability between different tools. We have conducted this research inside of the project 'Drafting legislation in the era of AI and digitisation' with the support of the EU Commission – Directorate General Informatics Unit B2 – Solutions for Legislation, Policy & HR.
Numerical Simulation of Slotted Screen Plugging Mechanism Based on Cfd-Dem Coupling

As the simplest and commonly used mechanical sand control method in well completion, slotted screen sand control is used for filling sand control, independent sand control in thermal production wells or geothermal wells. In order to explore the plugging mechanism of slotted screen and the factors affecting sand production during sand control, the CFD-DEM coupling method is used in this paper. The discrete element method is used to establish the model considering the physical properties of sand particles in the formation, and the fluid flow model is established combined with the finite volume method, and the full coupling solution of the two is realized and the model verification is completed. The effects of particle size distribution, sand concentration, slotted size and fluid velocity on slotted screen plugging are studied, and the plugging mechanism of slotted screen is revealed. The following research results are obtained. First, the retention form of sand particles at the screen gap is the fundamental factor affecting screen plugging. Sand bridging will have a certain probability of collapse, resulting in “false stability”. Second, the proportion of larger diameter sand determines the formation time and stability of “sand bridge”, and the proportion of smaller diameter sand determines the compactness of solid retention. Third, sand concentration is very important to control the plugging removal frequency of screen. Fourth, the gap size and fluid velocity affect the pressure difference on the screen surface and the stability of the “sand bridge”. This study provides a reference for optimizing the selection and production control of slotted screen during the downhole sand control design.
Text-Mining Using Three Different Machine Learning Algorithms To Segregate Spam Messages From Non-Spam Messages

There is tremendous exploration going on in field of text mining nowadays. Various Machine Learning and NLP techniques are being used to segregate spam message from non spam messages. Short Message Service spam (sometimes called cell phone spam) is any junk message delivered to a mobile phone as text messaging through the SMS. In email, there is a Spam folder where a spam email automatically takes place, but in mobile phones there is no such scope to isolate a spam SMS in a separate folder. Hence, we need a predictive model to separate the spam SMS from legitimate ones. The task of automatic filtering SMS spam still is a real challenge. There are mainly three main problems that hinder the development of algorithms in this specific field of research. These include: the lack of public and real datasets, the low number of features that can be extracted per message, and the fact that the text is rife with idioms and abbreviations. To fill some of those gaps, in this paper a mobile phone spam is worked upon. The classification algorithm like Na√Øve Bayes classifier, Random Forest and logistic regression are implemented to build the best predictive model to detect a new SMS whether it is spam or not
Aging Analysis Framework of Windows-Based Systems through Differential-Analysis of System Snapshots

When a Windows-based system is used for an exceedingly long time, its performance degrades, and the error occurrence rate tends to increase.This is generally called system aging.To investigate the reasons for system aging, various studies have been conducted within the range of the operating system kernel to the user application.However, finding an accurate reason for system performance degradation remains challenging research topic.In this study, system monitoring was conducted by dividing a system into 'before software installation,' 'after software installation,' and 'after software removal.'We confirmed that when a software installed in a system is removed, various system elements, such as storage and memory, are not restored to the level prior to the software installation.Consequently, we established a hypothesis regarding the performance degradation of a computer system owing to repeated software installation/removal operations, investigated the correlation between system aging and repeated software installation/removal operations, and proposed a system aging analysis framework for analyzing the reason behind system aging.In the proposed system aging analysis framework, we aim to forcibly age a Windows-based system by repeating the software installation/removal operation by utilizing the system forced aging module.The framework identifies the elements affecting system performance through a differential data analysis of the system time-series data extracted by the system performance extraction and system component snapshot modules.Consequently, the aging analysis framework presented in this study is expected to be effectively utilized as an index for studying system aging.
ReferEmo: A Referential Quasi-multimodal Model for Multilabel Emotion Classification

Textual emotion classification is a task in affective AI that branches from sentiment analysis and focuses on identifying emotions expressed in a given text excerpt. It has a wide variety of applications that improve human-computer interactions, particularly to empower computers to understand subjective human language better. Significant research has been done on this task, but very little of that research leverages one of the most emotion-bearing symbols we have used in modern communication: Emojis. In this research, we propose ReferEmo, a model that processes Emojis as textual inputs and leverages DeepMoji to generate affective feature vectors used as reference when aggregating different modalities of text encoding. To evaluate ReferEmo, we experimented on two benchmark datasets: SemEval'18 and GoEmotions for emotion classification, and achieved competitive performance compared to state-of-the-art models tested on these datasets. Notably, our model performs better on the underrepresented classes of each dataset. The source code of ReferEmo is available on Github ( https://github.com/alvarosness/ReferEmo ).
The Most Human Right: Why Free Speech is Everything

Throughout history countless systems of justice have promised to providde human goods. So what makes human rights different? What must human rights have that other systems have lacked? International human rights have collapsed through our ongoing failures to distinguish between goods and rights. Human goods become objects of human rights only when citizens can claim them publicly. Free speech is not just another right on the human rights checklist, but rather is the singular element that distinguishes a human rights regime from other models of justice. In failing to draw these basic distinctions, international organizations may well steward state-dominated, managerial regimes of human goods, but have foregone the possibility of creating citizen-directed regimes of human rights.
Learning to Automatically Spectate Games for Esports Using Object Detection Mechanism

Human game observers, who control in-game cameras and provide viewers with an engaging experience, are a vital part of Electronic sports (Esports) which has emerged as a rapidly growing industry in recent years. However, such a professional human observer poses several problems. For example, they are prone to missing events occurring concurrently across the map. Furthermore, human observers are hard to afford when only a small number of spectators are spectating the game. As a result, the need for automatic observers has grown in recent years. To create automatic observers, various rule-based and learning-based methods are explored. So far, these methods are both based on defining in-game events. However, such event-based methods necessitate detailed predefined events, demanding high domain knowledge when developing. Furthermore, they cannot observe events not specifically defined by humans, making them unsuitable for complex games with numerous event cases. In this paper, we propose the method to overcome these problems by utilizing multiple human observational data and an object detection method, Mask R-CNN, in the real-time strategy game (e.g., StarCraft). Our method can observe events that were not predefined by learning from human observing data rather than defining and focusing on specific events as previous automatic observers did. As a result, we show that our automatic observer outperforms both current rule-based methods and human observers. The game observation video that compares our method and the rule-based method is available at https://www.youtube.com/watch?v=61JIfSrLHVk .
Sequence Recommendation Model with Double-Layer Attention Net

Personalized prediction for users based on their historical behavior sequences is a challenging problem in the field of recommender systems. The development of recurrent neural networks enables systems to better process sequence information to capture users' long-term preferences. While it cannot effectively utilize both long-term and short-term preferences. In this paper, we propose a novel double-layer attention mechanism mode, which not only increases the weight of short-term preferences in the model, but also assigns separate weights to users' recent behaviors, and then effectively preventing the prediction bias caused by mis-click. The proposed model is experimented on MovieLens, Amazon video game and Amazon digital music datasets, and the results show that our model achieves the best performance in all datasets.
Jointly Learning Propagating Features on the Knowledge Graph for Movie Recommendation

Knowledge graphs are widely used as auxiliary information to improve the performance in recommender systems. This enables items to be aligned with knowledge entities and provides additional item attributes to facilitate learning interactions between users and items. However, the lack of user connections in the knowledge graph may degrade the profiling of user preferences, especially for explicit user behaviors. Furthermore, learning knowledge graph embeddings is not entirely consistent with recommendation tasks due to different objectives. To solve the aforementioned problems, we extract knowledge entities from users' explicit reviews and propose a multi-task framework to jointly learn propagating features on the knowledge graph for movie recommendations. The review-based heterogeneous graph can provide substantial information for learning user preferences. In the proposed framework, we use an attention-based multi-hop propagation mechanism to take users and movies as center nodes and extend their attributes along with the connections of the knowledge graph by recursively calculating the different contributions of their neighbors. We use two real-world datasets to show the effectiveness of our proposed model in comparison with state-of-the-art baselines. Additionally, we investigate two aspects of the proposed model in extended ablation studies.
Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral.However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications.So, there is a need exists to develop a proper technique for both identification and classification of sentiments in social media.To get rid of these problems, Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability.The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification (SOADL-SAC) for social media.The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media.In order to attain this, SOADL-SAC model carries out data preprocessing to clean the input data.In addition, Glove technique is applied to generate the feature vectors.Moreover, Self-Head Multi-Attention based Gated Recurrent Unit (SHMA-GRU) model is exploited to recognize and classify the sentiments.Finally, Seeker Optimization Algorithm (SOA) is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results.In order to validate the enhanced outcomes of the proposed SOADL-SAC model, various experiments were conducted on benchmark datasets.The experimental results inferred the better performance of SOADL-SAC model over recent state-of-the-art approaches.5986
Trustworthy Explainable Recommendation Framework for Relevancy

Explainable recommendation systems deal with the problem of 'Why'.Besides providing the user with the recommendation, it is also explained why such an object is being recommended.It helps to improve trustworthiness, effectiveness, efficiency, persuasiveness, and user satisfaction towards the system.To recommend the relevant information with an explanation to the user is required.Existing systems provide the top-k recommendation options to the user based on ratings and reviews about the required object but unable to explain the matched-attribute-based recommendation to the user.A framework is proposed to fetch the most specific information that matches the user requirements based on Formal Concept Analysis (FCA).The ranking quality of the recommendation list for the proposed system is evaluated quantitatively with Normalized Discounted Cumulative Gain (NDCG)@k, which is better than the existing systems.Explanation is provided qualitatively by considering trustworthiness criterion i.e., among the seven explainability evaluation criteria, and its metric satisfies the results of proposed method.This framework can be enhanced to accommodate for more effectiveness and trustworthiness.
CAKE: A Context-Aware Knowledge Embedding Model of Knowledge Graph

Recently, knowledge embedding on knowledge Graph (KG) has drawn increasing attention from both academia and industry for its concise rationale and promising prospects. However, performances of existing knowledge embedding methods are mostly either far from satisfactory, or exhibits weakness for generalization. In this work, a context-aware knowledge embedding model (CAKE) has been proposed for applications like knowledge completion and link prediction. We model the generative process of KG formation based on latent Dirichlet allocation and hierarchical Dirichlet process, where the latent semantic structure of knowledge elements is learned as contexts. Contextual information, i.e. the context-specific probability distribution over elements, is thereafter leveraged in a translation-based embedding model. Essentially, we develop loss function in a probabilistic style to approximately realize the "attention" mechanism in our model. In this work, the learned embeddings of entities and relations are applied to link prediction and triple classification in experiments and our model shows the best performance compared with multiple baselines.
Jellyfish Search Algorithm Based Optimal Thermoelectric Generation Arrays Reconfiguration Under Non-Uniform Temperature Distribution Condition

Thermoelectric generator (TEG) is a device that can convert heat to electricity on the basis of temperature difference between its two ends. However, during practical operation, the low thermoelectric conversion efficiency is usually caused by non-uniform temperature difference (NTD) conditions, thus it is necessary to realize reliable and effective maximum power point tracking (MPPT) at NTD conditions for TEG arrays. Therefore, this work devises a jellyfish search (JS) algorithm to realize TEG arrays reconfiguration under NTD conditions, upon which the overall power losses is effectively declined, energy conversion efficiency is efficiently enhanced, and convergence speed is greatly improved as well. In particular, simulation results demonstrate that JS algorithm based TEG arrays reconfiguration can enhance the output power by 14.43% for 9×9 symmetric TEG arrays, 10.42% for 10×15 unsymmetric TEG arrays. Finally, a hardware-in-the-loop (HIL) test is undertaken on RTLAB platform to verify its feasibility of hardware implementation.
Joint Graph and Reduced Flexible Manifold Embedding for Scalable Semi-Supervised Learning

Recently, graph-based semi-supervised learning (GSSL) has received much attention. On the other hand, less attention has been paid to the problem of large-scale GSSL for multi-class classification.Existing scalable GSSL methods rely on a hard linear constraint. They cannot predict the labelling of test samples, or use predefined graphs, which limits their applications and performance. In this paper, we propose an algorithm that can handle large databases by using anchors. The main contribution compared to existing scalable semi-supervised models is the integration of the anchor graph computation into the learned model. We develop a criterion to jointly estimate the unlabeled sample labels, the mapping of the feature space to the label space, and the affinity matrix of the anchor graph. Furthermore, the fusion of labels and features of anchors is used to construct the graph. Using the projection matrix, it can also predict the labels of the test samples by linear transformation. Experimental results on the large datasets NORB, RCV1 and Covtype show the effectiveness, scalability and superiority of the proposed method.
Identification of Suspicious Persons Using Deep Learning

Video surveillance is used almost everywhere for security. The traditional method of monitoring cameras requires constant human intervention. Using Deep Learning and Image Processing, the proposed work aims to eliminate time and effort wasted on monitoring video surveillance cameras. Deep Learning is used to detect suspicious persons and to warn the user if any suspicious person is detected. The proposed system strives for the identification of abusive persons based on the video clips provided to the user. It uses the CNN algorithms to process the images detected in the videos provided in the database and identify if the person present in the video is identified as suspicious or not. Haar-Cascade Classifiers are used to detect the frontal face features of a person.