Reviewerly/exHarmony huggingface dataset:
SELECTOR

Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios. In this paper, we evaluate three heuristics for selecting diverse problem instances which should be involved in the comparison of optimization algorithms in order to ensure robust statistical algorithm performance analysis. The first approach employs clustering to identify similar groups of problem instances and subsequent sampling from each cluster to construct new benchmarks, while the other two approaches use graph algorithms for identifying dominating and maximal independent sets of nodes. We demonstrate the applicability of the proposed heuristics by performing a statistical performance analysis of five portfolios consisting of three optimization algorithms on five of the most commonly used optimization benchmarks. The results indicate that the statistical analyses of the algorithms' performance, conducted on each benchmark separately, produce conflicting outcomes, which can be used to give a false indication of the superiority of one algorithm over another. On the other hand, when the analysis is conducted on the problem instances selected with the proposed heuristics, which uniformly cover the problem landscape, the statistical outcomes are robust and consistent.
In pursuit of data immortality

Data sharing can save important scientific work from extinction, but only if researchers take care to ensure that resources are easy to find and reuse. Data sharing can save important scientific work from extinction, but only if researchers take care to ensure that resources are easy to find and reuse.
Empowering Next POI Recommendation with Multi-Relational Modeling

With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs, aiming to make personalized recommendations of next suitable locations to users by discovering preferences from users' historical activities. Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations). Such relational information holds great potential to facilitate the next POI recommendation. However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. Extensive experiments on real-world LBSN data validate the superiority of our framework over the state-of-the-art next POI recommendation methods.
Güvenli kara sistemi mimari tasarimi motor arizalarini dikkate alarak çok rotorlu mimari tasarimi

There is growing interest to use drones for application like delivery services, air taxi, surveillance, and inspection to reduce operational time and cost and increase the performance and functionalities. However, there are some risks related to using this technology one of them is the hazard to the humans and assets in the case of an emergency scenario like motor failure. The current technologies suggest to land as soon as possible in such these scenarios, however finding and selecting a suitable landing area considering the capabilities of the faulty drone and controlling the drone towards the selected point is a problem which requires provisions during the design process of the drone. The situations get more complex considering that the vehicle should be able to adopt itself by its time varying environment. In this paper the system approach is used to break the safety requirements to functional and physical requirements and based on these requirement analyses, the functional and physical architectures of the drone are designed. The proposed design suggests that the drones aggregate their perception about the environment to maximize the safety of people and assets through a special databank called potential landmark databank.
Testing the Ability of Language Models to Interpret Figurative Language

Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition.However, figurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases.To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired figurative phrases with divergent meanings.We evaluate the performance of several state-of-the-art language models on this task, and find that although language models achieve performance significantly over chance, they still fall short of human performance, particularly in zero-or few-shot settings.This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models. 1
Pessoa idosa com diabetes mellitus tipo 2: Contributos para a compreensão da gestão do regime medicamentoso

Older adults with type 2 diabetes mellitus: Contributions to
Optimization of fuzzy c-means (FCM) clustering in cytology image segmentation using the gray wolf algorithm

Image segmentation is considered an important step in image processing. Fuzzy c-means clustering is one of the common methods of image segmentation. However, this method suffers from drawbacks, such as sensitivity to initial values, entrapment in local optima, and the inability to distinguish objects with similar color intensity. This paper proposes the hybrid Fuzzy c-means clustering and Gray wolf optimization for image segmentation to overcome the shortcomings of Fuzzy c-means clustering. The Gray wolf optimization has a high exploration capability in finding the best solution to the problem, which prevents the entrapment of the algorithm in local optima. In this study, breast cytology images were used to validate the methods, and the results of the proposed method were compared to those of c-means clustering.FCMGWO has performed better than FCM in separating the nucleus from the other dark objects in the cell. The clustering was validated using Vpc, Vpe, Davies-Bouldin, and Calinski Harabasz criteria. The FCM and FCMGWO methods have a significant difference with respect to the Vpc and Vpe indexes. However, there is no significant difference between the performances of the two clustering methods with respect to the Calinski-Harabasz and Davies-Bouldin indices. The results indicate the better efficacy of the proposed method.The hybrid FCMGWO algorithm distinguishes the cells better in images with less detail than in images with high detail. However, FCM exhibits unacceptable performance in both low- and high-detail images.
High-Quality Thermal Gibbs Sampling with Quantum Annealing Hardware

Quantum annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA-hardware platforms have demonstrated that, in the operating regime corresponding to weak interactions, the QA hardware behaves like a noisy Gibbs sampler at a hardware-specific effective temperature. This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs-sampling performance. The experimental results indicate that the proposed protocol results in high-quality Gibbs samples from a hardware-specific effective temperature. Furthermore, we show that this effective temperature can be adjusted by modulating the annealing time and energy scale. The procedure proposed in this work provides an approach to using QA hardware for Ising-model sampling presenting potential opportunities for applications in machine learning and physics simulation.
Birds of a Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation

Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. Present UDA models focus on alleviating the domain shift by minimizing the feature discrepancy between the source domain and the target domain but usually ignore the class confusion problem. In this work, we propose an Inter-class Separation and Intra-class Aggregation (ISIA) mechanism. It encourages the cross-domain representative consistency between the same categories and differentiation among diverse categories. In this way, the features belonging to the same categories are aligned together and the confusable categories are separated. By measuring the align complexity of each category, we design an Adaptive-weighted Instance Matching (AIM) strategy to further optimize the instance-level adaptation. Based on our proposed methods, we also raise a hierarchical unsupervised domain adaptation framework for cross-domain semantic segmentation task. Through performing the image-level, feature-level, category-level and instance-level alignment, our method achieves a stronger generalization performance of the model from the source domain to the target domain. In two typical cross-domain semantic segmentation tasks, i.e., GTA 5→ Cityscapes and SYNTHIA → Cityscapes, our method achieves the state-of-the-art segmentation accuracy. We also build two cross-domain semantic segmentation datasets based on the publicly available data, i.e., remote sensing building segmentation and road segmentation, for domain adaptive segmentation. Our code, models and datasets are available at https://github.com/HibiscusYB/BAFFT.
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference

Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the predictions of unlabeled instances consistency alone to regularize networks. However, treating labeled and unlabeled data separately often leads to the discarding of mass prior knowledge learned from the labeled examples. In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. Specifically, GuidedMix-Net employs three operations: 1) interpolation of similar labeled-unlabeled image pairs; 2) transfer of mutual information; 3) generalization of pseudo masks. It enables segmentation models can learning the higher-quality pseudo masks of unlabeled data by transfer the knowledge from labeled samples to unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, and Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive segmentation accuracy and significantly improves the mIoU over 7$\%$ compared to previous approaches.
Holographic Simulation of Correlated Electrons on a Trapped-Ion Quantum Processor

A quantum tensor network method is introduced to approximately compute the properties of interacting many-electron models, reducing the quantum memory size required to simulate complex materials and molecules and shortening the computation time.
Cross-Language Attacks

Memory corruption attacks against unsafe programming languages like C/C++ have been a major threat to computer systems for multiple decades.Various sanitizers and runtime exploit mitigation techniques have been shown to only provide partial protection at best.Recently developed 'safe' programming languages such as Rust and Go hold the promise to change this paradigm by preventing memory corruption bugs using a strong type system and proper compile-time and runtime checks.Gradual deployment of these languages has been touted as a way of improving the security of existing applications before entire applications can be developed in safe languages.This is notable in popular applications such as Firefox and Tor.In this paper, we systematically analyze the security of multi-language applications.We show that because language safety checks in safe languages and exploit mitigation techniques applied to unsafe languages (e.g., Control-Flow Integrity) break different stages of an exploit to prevent control hijacking attacks, an attacker can carefully maneuver between the languages to mount a successful attack.In essence, we illustrate that the incompatible set of assumptions made in various languages enables attacks that are not possible in each language alone.We study different variants of these attacks and analyze Firefox to illustrate the feasibility and extent of this problem.Our findings show that gradual deployment of safe programming languages, if not done with extreme care, can indeed be detrimental to security.
Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three challenges:(1) word ambiguity, (2) word synonymity, and (3) dynamic contextual dependency. To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification. Specifically, a document-level graph is initially generated by a disjoint union of sentence-level word co-occurrence graphs. Our model collects a set of trainable edges connecting disjoint words between sentences, and employs structure learning to sparsely select edges with dynamic contextual dependencies. Graphs with sparse structure can jointly exploit local and global contextual information in documents through GNNs. For inductive learning, the refined document graph is further fed into a general readout function for graph-level classification and optimization in an end-to-end manner. Extensive experiments on several real-world datasets demonstrate that the proposed model outperforms most state-of-the-art results, and reveal the necessity to learn sparse structures for each document.
Scalable Multicast for Live 360-Degree Video Streaming Over Mobile Networks

Thanks to its ability to provide immersive experience to users, 360-degree video has become one of the key enablers of Virtual Reality. However, the huge data size of 360-degree video poses a challenging problem for live streaming of this special type of video over resource-constrained networks. In this paper, we propose a novel framework for live 360-degree video streaming to multiple users over mobile networks. Our proposed framework jointly utilizes Scalable Video Coding and multicast to deliver 360 video to users in a bandwidth-efficient manner. In particular, 360-degree video is split into small parts called tiles, each is encoded into multiple layers using Scalable Video Encoding. The proposed framework then selects suitable tile layers to maximize the overall Quality of Experience of all users. To support real-time adaptation, we design a Linear Regression-based algorithm to estimate the weights of tiles of individual users. In addition, an efficient algorithm for deciding the suitable tile layers and their transmission modes is proposed. Experimental results show that the proposed method can significantly improve the average viewport quality compared to state-of-the-art methods.
VLF-method of geophysical prospecting: a non-conventional system of processing and interpretation (implementation in the Caucasian ore deposits)

Investigation of the electromagnetic (EM) fields from distant VLF (very low friquency) military transmitters is one of the fastest and low-expensive geophysical methods.At present, it finds frequent application in prospecting for various deposits, search of subsurface underground water, archaeogeophysical studies and various types of geological mapping.For geophysical investigation can be utilized a few dozens of the VLF transmitters disposed in various countries.The different frequencies and angles of registered EM radiation enable to obtain additional preferences by interpretation.A depth of investigation depends on the radiowave frequency and averaged resistivity of the host medium and usually ranges from several tens to several hundred meters (last valuesunder very favorable conditions).Both the electric and magnetic components of EM field are used in investigation by the VLF method.Generally only the magnetic field (H) is employed.A wide using of the VLF-technique was limited by absence of reliable methods for elimination of the EM field time variations, rugged relief influence and procedures for quantitative interpretation of the VLF-anomalies.These problems are successfully solved and the unified methodological system is developed.For elimination of the temporal variations a special procedure based on the direct continuous filtering is proposed.The correlation technique enables to significantly reduce the rugged relief influence.For quantitative interpretation is proved a possibility to use the modern interpreting methods elaborated in magnetic prospecting for complex geological-geophysical conditions.Finally, for revealing hidden objects against the high-intensive geological noise background, an application of non-conventional statistical, informational and wavelet algorithms is suggested.The main components of the developed system were successfully tested in the Caucasian polymetallic and copper deposits.
Detecting Hate Speech on Twitter Network using Ensemble Machine Learning

Twitter is habitually exploited now-a-days to propagate torrents of hate speeches, misogynistic, and misandry tweets that are written in slang.Machine learning methods have been explored in manifold studies to address the inherent challenges of hate speech detection in online spaces.Nevertheless, language has subtleties that can make it stiff for machines to adequately comprehend and disambiguate the semantics of words that are heavily dependent on the usage context.Deep learning methods have demonstrated promising results for automatic hate speech detection, but they require a significant volume of training data.Classical machine learning methods suffer from the innate problem of high variance that in turn affects the performance of hate speech detection systems.This study presents a voting ensemble machine learning method that harnesses the strengths of logistic regression, decision trees, and support vector machines for the automatic detection of hate speech in tweets.The method was evaluated against ten widely used machine learning methods on two standard tweet data sets using the famous performance evaluation metrics to achieve an improved average F1-score of 94.2%.
Evaluation of discharge coefficient of triangular side orifices by using regularized extreme learning machine

Abstract The present paper attempts to reproduce the discharge coefficient (DC) of triangular side orifices by a new training approach entitled “Regularized Extreme Learning Machine (RELM).” To this end, all parameters influencing the DC of triangular side orifices are initially detected, and then six models are extended by them. For training the RELMs, about 70% of the laboratory measurements are implemented and the remaining (i.e., 30%) are utilized for testing them. In the next steps, the optimal hidden layer neurons number, the best activation function and the most accurate regularization parameter are chosen for the RELM model. As a result of a sensitivity analysis, we figure out that the most important RELM model simulates coefficient values with high exactness. The best RELM model estimates coefficients of discharge using all input factors. The efficiency of the best RELM model is compared with ELM, and it is demonstrated that the former has a lower error and better correlation with the experimental measurements. The error and uncertainty examinations are executed for the RELM and ELM models to indicate that RELM is noticeably stronger. At the final stage, an equation is proposed for computing this coefficient for triangular side orifices and a partial derivative sensitivity analysis is also carried out on it.
A New Hybrid Breast Cancer Diagnosis Model Using Deep Learning Model and ReliefF

Breast cancer is a dangerous type of cancer usually found in women and is a significant research topic in medical science. In patients who are diagnosed and not treated early, cancer spreads to other organs, making treatment difficult. In breast cancer diagnosis, the accuracy of the pathological diagnosis is of great importance to shorten the decision-making process, minimize unnoticed cancer cells and obtain a faster diagnosis. However, the similarity of images in histopathological breast cancer image analysis is a sensitive and difficult process that requires high competence for field experts. In recent years, researchers have been seeking solutions to this process using machine learning and deep learning methods, which have contributed to significant developments in medical diagnosis and image analysis. In this study, a hybrid DCNN + ReliefF is proposed for the classification of breast cancer histopathological images, utilizing the activation properties of pre-trained deep convolutional neural network (DCNN) models, and the dimension-reduction-based ReliefF feature selective algorithm. The model is based on a fine-tuned transfer-learning technique for fully connected layers. In addition, the models were compared to the k-nearest neighbor (kNN), naive Bayes (NB), and support vector machine (SVM) machine learning approaches. The performance of each feature extractor and classifier combination was analyzed using the sensitivity, precision, F1-Score, and ROC curves. The proposed hybrid model was trained separately at different magnifications using the BreakHis dataset. The results show that the model is an efficient classification model with up to 97.8% (AUC) accuracy.
Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech

Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization.We employ pretrained language models (LMs) to alleviate this data bottleneck.We utilize the GPT LM for generating large amounts of synthetic hate speech sequences from available labeled examples, and leverage the generated data in fine-tuning large pretrained LMs on hate detection.An empirical study using the models of BERT, RoBERTa and ALBERT, shows that this approach improves generalization significantly and consistently within and across data distributions.In fact, we find that generating relevant labeled hate speech sequences is preferable to using out-of-domain, and sometimes also within-domain, human-labeled examples.