Reviewerly/exHarmony huggingface dataset:
A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

The clustered minimum routing cost tree (CluMRCT) problem is a recent problem with a wide range of real-life applications, especially in designing computer networks with peer-to-peer architecture. Many multifactorial evolutionary algorithms have been proposed to solve multiple CluMRCT problems simultaneously. However, these algorithms only function effectively on complete graphs with small-and-medium sizes. Moreover, the blindness and randomness in the transfer of genetic materials cause a reduction in the exploitation ability and make these algorithms ineffective to solve low-similarity tasks. This paper proposes a hybrid multitasking algorithm named multifactorial firefly algorithm, which integrates the firefly algorithm’s strong exploitation ability to enhance the self-evolution of each task when facing low-similarity tasks while improving inter-task knowledge transfers by delivering higher-quality solutions. Also, the proposed algorithm is equipped with new encoding and decoding to focus more on potential search areas on both complete and sparse graphs. The experiments and Wilcoxon signed-rank tests were conducted on various instances to verify our proposal with several state-of-the-art methods. The results portrayed that the proposed encoding scheme helped multitasking algorithms improve solution quality by 32% on average. Besides, the statistical test values proved the superiority of the proposed hybrid algorithm in terms of solution quality and convergence trend.
CUDA-DClust+: Revisiting Early GPU-Accelerated DBSCAN Clustering Designs

Density-based clustering algorithms are widely used unsupervised data mining techniques to find the clusters of points in dense regions that are separated by low-density regions. This algorithm is inherently sequential and has limitations in its parallel implementation. There have been several parallel algorithms presented in the literature for multi-core CPUs and many-core GPUs. One such algorithm for the GPU is CUDA-DCLUST. In this paper, we propose a new GPU-accelerated DBSCAN algorithm with several optimizations. In comparison to prior work, our algorithm, Cuda-dclust+:(i) computes the indexing structure on the GPU, (ii) uses kernel fusion to combine the index search and cluster expansion kernels, which reduces communication and synchronization overhead with the host, and (iii) seed list management control is primarily given to the GPU rather than the CPU, which further decreases CPU-GPU communication overhead. We compare our algorithm to three state-of-the-art parallel algorithms in the literature on six real-world datasets. We find that our algorithm achieves a speedup of up to ~23x over the fastest GPU algorithm.
Particle Competitive Mechanism Based Multiobjective Rough Clustering Algorithm for Image Segmentation

Rough clustering has attracted increasing attention due to well dealing with the fuzziness and uncertainty of data. It is well known that it needs to manually set the threshold to determine the upper and lower approximations of rough clusters, which may bring a great effect on the clustering performance. When applied to image segmentation, rough clustering is always sensitive to the initialized cluster centers and image noise. Furthermore, only one clustering criterion is considered in rough clustering, which cannot satisfy diverse practical requirements. To handle these issues, a particle competitive mechanism based multiobjective rough clustering algorithm (PCM-MORCA) for image segmentation is proposed. First, a rough intraclass compactness function considering the nonlocal spatial information derived from an image is constructed to overcome the sensitivity to image noise. Next, the constructed rough intraclass compactness function and an interclass separation function are optimized simultaneously to make cluster centers meet diverse segmentation requirements. Then, an adaptive threshold determination mechanism by which the threshold adaptively varies with the clustered data is presented to well determine the upper and lower approximations of rough clusters. After that, to effectively search appropriate cluster centers, a novel pair competition-based particle weight updating strategy is designed for multiobjective particle swarm optimization by improving the elite particle selection and particle update. Finally, a rough clustering index with the nonlocal spatial information is constructed for selecting the optimal solution for PCM-MORCA. Segmentation experiments on Berkeley and magnetic resonance images reveal that PCM-MORCA behaves well on the segmentation accuracy and noise robustness.
Graph Neighborhood Routing and Random Walk for Session-based Recommendation

Session-based recommendation (SBR) is to predict the next item for an anonymous item sequence. Although many neural models have proven effectiveness in the SBR task, how to learn better items’ embeddings for neural models still remains a key challenge due to the anonymity of sessions and sparsity of users’ behaviors. This paper proposes a graph-based neural model, called Graph N eighborhood Routing and Random Walk (GNRRW), which learns two kinds of item embeddings for the SBR task. We first construct an item graph based on items’ co-occurrences in all sessions, on which we learn a local embedding and a global embedding for each item. For local embedding learning, we propose a novel neighborhood routing (NR) algorithm to exploit the compositive relations between an item and its neighbors. The NR algorithm has an excellent feature in that no additional parameters are needed in the training process. For global embedding learning, we propose a random walk-based approach to explore a kind of global relations between an item and representative items. Furthermore, we propose a switch-based shared gated recurrent unit (GRU) network to alternatively learn session local representation to make a local prediction, and learn session global representation to make a global prediction. Finally, we design a decision fusion mechanism to adaptively fuse both local and global predictions to output final items’ preference scores. Experiments on the public Yoochoose and Diginetica dataset validate the superiority of our GNRRW model over the state-of-the-art neural models.
Sentiment analysis on product reviews on twitter using Machine Learning Approaches

Social media have been obtained a wide growth within the recent years and have been interfering and affecting human lifestyle as well. This recent development has been resulted a huge collection of product reviews for a huge collection of consumers all round the world. These kinds of data collections are useful for future market predictions, to obtain customers' feedback, to provide product recommendations etc. Most of the above mentioned data collections are unstructured and handling those data bundles is a complex task. The act of examining and classifying the emotion or sentiment represented in any given review or text piece in order to establish whether the reviewer wishes to transmit positive or negative feelings is known as sentiment analysis. Sentiment analysis is one of the major part in natural language processing. In this study, twitter posts based on product reviews have been analyzed by using the support vector machine (SVM), logical regression and k-nearest neighbor machine learning algorithms and count vectorizer and tfidf mechanisms for converting texts into vectors in order to input data to the machine learning model. In this study, there are six main comparisons are done in order to find the most suitable “model vectorising” combinations from above mentioned. The highest accuracy score has been achieved by “Logistic Regression with Count Vectorizer” with an accuracy rate of 88.26%. Finally the true positive rates and true negative rates are going to calculate.
InstaIndoor and multi-modal deep learning for indoor scene recognition

Indoor scene recognition is a growing field with great potential for behaviour understanding, robot localization, and elderly monitoring, among others. In this study, we approach the task of scene recognition from a novel standpoint, using multi-modal learning and video data gathered from social media. The accessibility and variety of social media videos can provide realistic data for modern scene recognition techniques and applications. We propose a model based on fusion of transcribed speech to text and visual features, which is used for classification on a novel dataset of social media videos of indoor scenes named InstaIndoor. Our model achieves up to 70% accuracy and 0.7 F1-Score. Furthermore, we highlight the potential of our approach by benchmarking on a YouTube-8M subset of indoor scenes as well, where it achieves 74% accuracy and 0.74 F1-Score. We hope the contributions of this work pave the way to novel research in the challenging field of indoor scene recognition.
Improving of global solar radiation forecast by comparing other meteorological parameter models with sunshine duration models

The aim of this study is to compare sunshine duration-based models and the other meteorological parameter-based models and to develop new forecasting models. The estimation and comparison of global solar radiation has been made by using twenty-four empirical models including three new models for different location named Arbil, Dohuk, and Sulaimania of Northern Iraq. The reason of using these different locations is to test the accuracy of the other meteorological parameter models by comparing the sunshine duration models for different region. Mostly common statistical error values are used to evaluate the performance of the estimation models and to identify the models that will give the closest results to the actual values. According to the results, it was seen that the models based on other meteorological parameters have better predictions than the models based on the sunshine duration. While the R2 value of the best models depending on the sunshine duration ranged from 0.97 to 0.99, the R2 values of the best models of other meteorological parameters are above 0.99. Furthermore, it is observed that the new proposed models provide better estimates of global solar radiation at different locations than all models used in this study.
Shortened Linear Codes From APN and PN Functions

Linear codes generated by component functions of perfect nonlinear (PN for short) and almost perfect nonlinear (APN for short) functions and the first-order Reed-Muller codes have been an object of intensive study in coding theory. The objective of this paper is to investigate some binary shortened codes of two families of linear codes from APN functions and some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -ary shortened codes associated with PN functions. The weight distributions of these shortened codes and the parameters of their duals are determined. The parameters of these binary codes and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -ary codes are flexible. Many of the codes presented in this paper are optimal or almost optimal. The results of this paper show that the shortening technique is very promising for constructing good codes.
Aggregation of Rankings Using Metaheuristics in Recommendation Systems

Recommendation systems are a powerful tool that is an integral part of a great many websites. Most often, recommendations are presented in the form of a list that is generated by using various recommendation methods. Typically, however, these methods do not generate identical recommendations, and their effectiveness varies between users. In order to solve this problem, the application of aggregation techniques was suggested, the aim of which is to combine several lists into one, which, in theory, should improve the overall quality of the generated recommendations. For this reason, we suggest using the Differential Evolution algorithm, the aim of which will be to aggregate individual lists generated by the recommendation algorithms and to create a single list that will be fine-tuned to the user’s preferences. Additionally, based on our previous research, we present suggestions to speed up this process.
StrategyAtlas: Strategy Analysis for Machine Learning Interpretability

Businesses in high-risk environments have been reluctant to adopt modern machine learning approaches due to their complex and uninterpretable nature. Most current solutions provide local, instance-level explanations, but this is insufficient for understanding the model as a whole. In this work, we show that strategy clusters (i.e., groups of data instances that are treated distinctly by the model) can be used to understand the global behavior of a complex ML model. To support effective exploration and understanding of these clusters, we introduce StrategyAtlas, a system designed to analyze and explain model strategies. Furthermore, it supports multiple ways to utilize these strategies for simplifying and improving the reference model. In collaboration with a large insurance company, we present a use case in automatic insurance acceptance, and show how professional data scientists were enabled to understand a complex model and improve the production model based on these insights.
Underwater image enhancement with latent consistency learning‐based color transfer

Due to the inevitable wavelength-dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learning-based approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation-enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of color-transfer techniques, it is found that clear template image-assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color-transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete color-correction models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy.
PhiNets: A Scalable Backbone for Low-power AI at the Edge

In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity. Due to limited computational and communication capabilities, low memory and limited energy budget, bringing artificial intelligence algorithms to peripheral devices, such as end-nodes of a sensor network, is a challenging task and requires the design of innovative solutions. In this work, we present PhiNets , a new scalable backbone optimized for deep-learning-based image processing on resource-constrained platforms. PhiNets are based on inverted residual blocks specifically designed to decouple the computational cost, working memory, and parameter memory, thus exploiting all available resources for a given platform. With a YoloV2 detection head and Simple Online and Realtime Tracking (SORT), the proposed architecture achieves state-of-the-art results in (i) detection on the COCO and VOC2012 benchmarks, and (ii) tracking on the MOT15 benchmark. PhiNets obtain a reduction in parameter count of around 90% with respect to previous state-of-the-art models (EfficientNetv1, MobileNetv2) and achieve better performance with lower computational cost. Moreover, we demonstrate our approach on a prototype node based on an STM32H743 microcontroller (MCU) with 2 MB of internal Flash and 1MB of RAM and achieve power requirements in the order of 10 mW. The code for the PhiNets is publicly available on GitHub. 1
VR-Technologies in Foreign Language Learning for Engineering Students

The paper discusses using Virtual Reality technologies in foreign language teaching process. VR which is a relatively new technology in EFL teaching is not commonly practiced by language educators and its initial adoption is still slow mostly due to the need of advanced technical knowledge or the high cost of VR devices. The research was conducted at the Department of Foreign Languages for Professional Communication where VR headset is used in foreign language learning classrooms with engineering students. VR technology provides engineering students with immersive learning experience through simulating a virtual environment, in which they can practice language skills, explore English-speaking world using Google Earth VR, visit engineering laboratories and learn to describe and make 360º videos, visit virtual science museums. The study shows that VR-technologies have a great potential in language teaching and increasing engineering student’s motivation in education process. VR environment develops both basic communication skills and communication for specific purposes in the target language. Using VR for language learning fosters skills necessary for 21st century learners such as leadership, teamwork, and cultural awareness. The most valuable aspects of VR in foreign language classroom are immersion, motivation, interaction, and collaboration.
Assessing the Legal Aspects of Information Security Requirements for Health Care in 3 Countries: Scoping Review and Framework Development

The loss of human lives from cyberattacks in health care is no longer a probabilistic quantification but a reality that has begun. In addition, the threat scope is also expanding to involve a threat of national security, among others, resulting in surging data breaches within the health care sector. For that matter, there have been provisions of various legislation, regulations, and information security governance tools such as policies, standards, and directives toward enhancing health care information security-conscious care behavior among users. Meanwhile, in a research scenario, there are no comprehensive required security practices to serve as a yardstick in assessing security practices in health care. Moreover, an analysis of the holistic view of the requirements that need more concentration of management, end users, or both has not been comprehensively developed. Thus, there is a possibility that security practice research will leave out vital requirements.The objective of this study was to systematically identify, assess, and analyze the state-of-the-art information security requirements in health care. These requirements can be used to develop a framework to serve as a yardstick for measuring the future real security practices of health care staff.A scoping review was, as a result, adopted to identify, assess, and analyze the information security requirement sources within health care in Norway, Indonesia, and Ghana.Of 188 security and privacy requirement sources that were initially identified, 130 (69.1%) were fully read by the authors. Subsequently, of these 188 requirement documents, 82 (43.6%) fully met the inclusion criteria and were accessed and analyzed. In total, 253 security and privacy requirements were identified in this work. The findings were then used to develop a framework to serve as a benchmark for modeling and analyzing health care security practices.On the basis of these findings, a framework for modeling, analyzing, and developing effective security countermeasures, including incentivization measures, was developed. Following this framework, research results of health care security practices would be more reliable and effective than relying on incomprehensive security requirements.
Comparison of Neural Language Modeling Pipelines for Outcome Prediction From Unstructured Medical Text Notes

Machine learning techniques and algorithm-based approaches are becoming more and more vital to support clinical decision-making.In the medical area, natural language processing (NLP) techniques have shown the ability to extract useful information from electronic health records.On the one hand, statistic, semantic, and contextualized word embedding-based models and on the other hand preprocessing approaches are the keys to a better representation of a document.Using narratives from the Intensive Care Unit, we elaborated a comparison of the most used methods and preprocessing approaches to tackle an outcome prediction problem and guide researchers into NLP pipelines in the medical area.We used real data from Medical Information Mart for Intensive Care-III (MIMIC-III).We selected all notes related to patients with pneumonia.We conducted a deep analysis on text preprocessing tasks producing three datasets: raw data with minor preprocessing, meticulous preprocessing, and extreme preprocessing filtering only medical-related terminologies using Named Entity Recognition algorithms.We then used these three sets in five models, of which two are based on the traditional noncontextual word embedding techniques and three use contextualized word embedding based on a transformer.We demonstrated that transformer-based models outperform other word embedding models and a profound preprocessing yielded an accuracy of 98.2 F1score.These results show the highly competitive ability of NLP predictive models against other models that use medical data.With an appropriate NLP pipeline, the information contained in medical narratives can be used to draw up a patient profile, and admission notes can help to ascertain a mortality risk of a patient admitted to the Intensive Care Unit.