Reviewerly/exHarmony huggingface dataset:
A Grey Wolf-Based Method for Mammographic Mass Classification

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871.
Pyramid Global Context Network for Image Dehazing

Haze caused by atmospheric scattering and absorption would severely affect scene visibility of an image. Thus, image dehazing for haze removal has been widely studied in the literature. Within a hazy image, haze is not confined in a small local patch/position, while widely diffusing in a whole image. Under this circumstance, global context is a crucial factor in the success of dehazing, which was seldom investigated in existing dehazing algorithms. In the literature, the global context (GC) block has been designed to learn point-wise long-range dependencies of an image for global context modeling; however, patch-wise long-range dependencies were ignored. To image dehazing, patch-wise long-range dependencies should be highlighted to cooperate with patch-wise operations of image dehazing. In this paper, we first extend the point-wise GC into a Pyramid Global Context (PGC), which is a multi-scale GC, after undergoing the pyramid pooling. Thus, patch-wise long-range dependencies can be explored by the PGC. Then, the proposed PGC is plugged into a U-Net, getting an attentive U-Net. Further, the attentive U-Net is optimized by importing ResNet's shortcut connection and dilated convolution. Thus, the finalized dehazing model can explore both long-range and patch-wise context dependencies for global context modeling, which is crucial for image dehazing. The extensive experiments on synthetic databases and real-world hazy images demonstrate the superiority of our model over other representative state-of-the-art models from both quantitative and qualitative comparisons.
COVID-19 Outbreak in Finland: Case Study on the Management of Pandemics

COVID-19 has created an unprecedented situation for Finland like never before. These are desperate times for Finland. And desperate times need desperate measures. The Government of Finland is pulling out all the stops and doing everything possible in its continued fight against COVID-19 virus. The crisis primarily erupted due to the initial delay in action and lack of preparedness required to tackle this kind of crisis. Communication channels were put to best use by the Finnish Government in an effort to reach out to all the people in Finland. The people living in Finland should strictly follow the guidelines and support the measures by the Government in full tandem to ensure that the COVID-19 virus is defeated and stops further transmission by breaking the chain. This paper portrays different possible trajectories and outcomes associated with the impacts of the pandemic in Finland.
Hiding sensitive information in eHealth datasets

Privacy in the realm of data mining known as PPDM has become a hot topic in both academic research and industry due to the fact it can discover implicit rules as well as hide sensitive information for data sanitization. Many different algorithms and heuristics have been investigated to hide sensitive information using the act of transaction deletion based on evolutionary computation techniques, but to date, these algorithms only consider a uniform threshold value for sanitization progress. This technique is not applicable in real-world situations, especially for eHealth based medical datasets. For example, a patient can still be identified if he/she has more confidential information (i.e., symptoms) that cause privacy threats and security leakage in medical applications. In this work, we investigate a unique novel methodology to set varied threshold values that lead to varied lengths of sensitive patterns within a Genetic Algorithm (GA)-based framework. As the pattern length increases, a tighter threshold manifests to provide better protection of sensitive information that can avoid individual patients to be identified in eHealth datasets. Two GA-based models are developed for data sanitization using record deletion techniques. The experimental results are conducted and compared with the traditional Evolutionary Computation (EC)-based PPDM approaches and the results showed that the designed methods offer greater protection than previous methods in terms of side effects. Therefore, the designed models are effective to hide sensitive information in medical situations that can be used in real-world scenarios.
Image Captioning Through Image Transformer

Automatic captioning of images is a task that combines the challenges of image analysis and text generation. One important aspect of captioning is the notion of attention: how to decide what to describe and in which order. Inspired by the successes in text analysis and translation, previous works have proposed the transformer architecture for image captioning. However, the structure between the semantic units in images (usually the detected regions from object detection model) and sentences (each single word) is different. Limited work has been done to adapt the transformer’s internal architecture to images. In this work, we introduce the image transformer, which consists of a modified encoding transformer and an implicit decoding transformer, motivated by the relative spatial relationship between image regions. Our design widens the original transformer layer’s inner architecture to adapt to the structure of images. With only regions feature as inputs, our model achieves new state-of-the-art performance on both MSCOCO offline and online testing benchmarks. The code is available at https://github.com/wtliao/ImageTransformer.
Deep Relation Embedding for Cross-Modal Retrieval

Cross-modal retrieval aims to identify relevant data across different modalities. In this work, we are dedicated to cross-modal retrieval between images and text sentences, which is formulated into similarity measurement for each image-text pair. To this end, we propose a Cross-modal Relation Guided Network (CRGN) to embed image and text into a latent feature space. The CRGN model uses GRU to extract text feature and ResNet model to learn the globally guided image feature. Based on the global feature guiding and sentence generation learning, the relation between image regions can be modeled. The final image embedding is generated by a relation embedding module with an attention mechanism. With the image embeddings and text embeddings, we conduct cross-modal retrieval based on the cosine similarity. The learned embedding space well captures the inherent relevance between image and text. We evaluate our approach with extensive experiments on two public benchmark datasets, i.e., MS-COCO and Flickr30K. Experimental results demonstrate that our approach achieves better or comparable performance with the state-of-the-art methods with notable efficiency.
The effects of an augmented reality based magnetic experimental tool on students' knowledge improvement and cognitive load

Abstract While the use of experiments is important for developing students' scientific knowledge and skills, challenges may arise when teachers and students are conducting experiments in class, such as non‐reusable experimental resources, safety issues and difficulties simulating some specific effects. Augmented reality (AR) technology affords an alternative approach to conducting experiments by bringing students a virtual–real mixed learning environment. In this study, taking junior high school physics knowledge on the magnetic field as an example, we designed and developed an AR‐based mobile simulated experiment tool. This study investigates the effect of the AR‐based experiment on students' knowledge improvement and cognitive load compared with 3D and traditional experiments. A sample of 122 ninth‐grade students was randomly chosen and assigned to three groups (AR, 3D and Traditional). The results demonstrate that students in the AR group performed better than those in the 3D and Traditional groups in terms of their knowledge improvement. The AR group students also experienced the lowest cognitive load among the three groups. Moreover, students had positive perceptions about AR and 3D tools. The implications of this study are discussed.
Artistic creation in virtual reality for art therapy: A qualitative study with expert art therapists

Recent advances in technology have enabled the creation of immersive digital environments commonly known as Virtual Reality (VR). The current study explored the potential of artistic creation in VR for art therapy (VRAT) from the perspective of expert art therapists. Seven expert art therapists participated in this study, all of whom experimented with creating visual art in VR and as observers. After the VR experience, a semi-structured interview was conducted to assess central aspects of their experience both as creators and as observers. The interviews were analyzed according to the principles of Thematic Analysis. Four main themes emerged from the findings: (1) The user experience of creation in VR. (2) The qualities of the VR material and medium. (3) The VR environment as a therapeutic setting. (4) The relevance of the VR medium for art therapy. The results indicated that the therapists foresee substantial potential in the novel VR medium for art therapy and highlighted further research directions needed to determine how the virtual medium can be used to treat real world problems.
Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism

Abstract Vehicle counting is an important process in the estimation of road traffic density to evaluate the traffic conditions in intelligent transportation systems. With increased use of cameras in urban centers and transportation systems, surveillance videos have become central sources of data. Vehicle detection is one of the essential uses of object detection in intelligent transport systems. Object detection aims at extracting certain vehicle-related information from videos and pictures containing vehicles. This form of information collection in intelligent systems is faced with low detection accuracy, inaccuracy in vehicle type detection, slow processing speeds. In this research, we propose a vehicle detection system from infrared images using YOLO (You Look Only Once) computational mechanism. The YOLO mechanism can apply different machine or deep learning algorithms for accurate vehicle type detection. In this study we propose an infrared based technique to combine with YOLO for vehicle detection in traffic. This method will be compared with a machine learning technique of K-means++ clustering algorithm, a deep learning mechanism of multitarget detection and infrared imagery using convolutional neutral network
Design of intelligent home pension service platform based on machine learning and wireless sensor network

At present, there is a certain lag in the construction of the service platform of the smart home pension system in my country, which does not reflect the use characteristics of the elderly. In order to improve the reliability of the smart service system for the elderly, this research builds a smart home care service platform based on machine learning and wireless sensor networks around the state of the elderly’s home life, disease stage, physical state, and intellectual state. Moreover, after comparing the advantages and disadvantages of several wireless sensor communication network technologies and in-depth understanding of communication principles and network topology, the overall design of the system is proposed. In addition, this study combines the design requirements of the system to optimize and improve the wearable physiological parameter collection system and focuses on the design and implementation of the hardware and software of the physiological parameter collection module in the construction of the new system platform. Finally, this study analyzes the performance of the model in this study through controlled trials. The results of the study show that the platform constructed in this paper is effective.
Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs

Periapical Radiographs are commonly used to detect several anomalies, like caries, periodontal, and periapical diseases. Even considering that digital imaging systems used nowadays tend to provide high-quality images, external factors, or even system limitations can result in a vast amount of radiographic images with low quality and resolution. Commercial solutions offer tools based on interpolation methods to increase image resolution. However, previous literature shows that these methods may create undesirable effects in the images affecting the diagnosis accuracy. One alternative is using deep learning-based super-resolution methods to achieve better high-resolution images. Nevertheless, the amount of data for training such models is limited, demanding transfer learning approaches. In this work, we propose the use of super-resolution generative adversarial network (SRGAN) models and transfer learning to achieve periapical images with higher quality and resolution. Moreover, we evaluate the influence of using the transfer learning approach and the datasets selected for it in the final generated images. For that, we performed an experiment comparing the performance of the SRGAN models (with and without transfer learning) with other super-resolution methods. Considering Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS), the results of SRGAN models using transfer learning were better on average. This superiority was also verified statistically using the Wilcoxon paired test. In the visual analysis, the high quality achieved by the SRGAN models, in general, is visible, resulting in more defined edges details and fewer blur effects.
Visual Relation Grounding in Videos

In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV). The task aims at spatio-temporally localizing the given relations in the form of subject-predicate-object in the videos, so as to provide supportive visual facts for other high-level video-language tasks (e.g., video-language grounding and video question answering). The challenges in this task include but not limited to: (1) both the subject and object are required to be spatio-temporally localized to ground a query relation; (2) the temporal dynamic nature of visual relations in videos is difficult to capture; and (3) the grounding should be achieved without any direct supervision in space and time. To ground the relations, we tackle the challenges by collaboratively optimizing two sequences of regions over a constructed hierarchical spatio-temporal region graph through relation attending and reconstruction, in which we further propose a message passing mechanism by spatial attention shifting between visual entities. Experimental results demonstrate that our model can not only outperform baseline approaches significantly, but also produces visually meaningful facts to support visual grounding. (Code is available at https://github.com/doc-doc/vRGV ).
Digital flashcards vs. wordlists for learning technical vocabulary

Growing research suggests that digital flashcards may facilitate students' technical vocabulary learning efforts. The primary purpose of this quasi-experimental study was to compare the effect of digital flashcards (DFs) and wordlists on learning technical vocabulary as well as to explore students' perceptions regarding the use of DFs. Using convenience sampling method, the study was conducted with a total of 57 undergraduate pharmacy students. Data were collected using a pre-treatment survey, two technical vocabulary tests, and a post-treatment survey. The results indicated that (1) the students learned more technical vocabulary through DFs; (2) their perceptions about the use of DFs to study technical vocabulary were positive; (3) their perceptions of the usefulness of the Quizlet app was a predictor of their success in DF condition; and (4) practicing through digital apps positively predicted their intention to use the Quizlet application. Based on the findings, we concluded that teacher-prepared DFs result in better technical vocabulary learning.
RapidMatch

A subgraph query searches for all embeddings in a data graph that are identical to a query graph. Two kinds of algorithms, either graph exploration based or join based, have been developed for processing subgraph queries. Due to algorithmic and implementational differences, join-based systems can handle query graphs of a few vertices efficiently whereas exploration-based approaches typically process up to several tens of vertices in the query graph. In this paper, we first compare these two kinds of methods and prove that the complexity of result enumeration in state-of-the-art exploration-based methods matches that of the worst-case optimal join. Furthermore, we propose RapidMatch, a holistic subgraph query processing framework integrating the two approaches. Specifically, RapidMatch not only runs relational operators such as selections and joins, but also utilizes graph structural information, as in graph exploration, for filtering and join plan generation. Consequently, it outperforms the state of the art in both approaches on a wide range of query workloads.
Chaos-assisted multi-population salp swarm algorithms: Framework and case studies

Salp swarm algorithm (SSA) is a recently presented algorithm, which is simple in structure and relatively mediocre in its performance. However, the original SSA still has features to be improved because it may face problems in convergence trends or easily being trapped into local optima for more advanced problems. To alleviate this limitation, we propose a new SSA-based method (MCSSA) that performs the chaotic exploitative trends and has a multi-population structure. The new structure can assist SSA in making a more stable tradeoff between global exploration and local exploitation capabilities. First, the exploitation trends and neighborhood searching commands of SSA are enriched using the chaos-assisted exploitation strategy. Next, we arrange a multi-population structure with three sub-strategies to augment the global exploration capabilities of the algorithm. To test the performance of this proposed MCSSA, a set of comprehensive algorithms is used, including 11 other original methods, conventional SSA, and 13 advanced techniques including SCA, SSA, GWO, MFO, WOA, BA, FPA, PSO, ALO, MVO, DE, ABC, CSSA, ESSA, CLSGMFO, LGCMFO, SaDE, jDE, EPSO, ALCPSO, CBA, RCBA, BWOA, CCMWOA, and GA-MPC based on 30 IEEE CEC2017 benchmark functions and 5 IEEE CEC2011 practical test problems. Also, the non-parametric statistics Wilcoxon signed-rank test and Friedman test are also used as an enabling tool to validate the performance of the proposed algorithm. From the result analysis, it can be concluded that the introduced strategy significantly improves the speed of the algorithm converging to the optimal value, and the improvement of the search ability also helps the algorithm to find a better solution than the basic SSA. As a conclusion, it can be said that MCSSA is reliable and efficient in solving complex optimization problems. An online website at https://aliasgharheidari.com supports this research for any guide or info.
3D Human Motion Estimation via Motion Compression and Refinement

We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation using auto-encoder-based motion compression and a residual representation learned through motion refinement. This two-step encoding of human motion captures human motion in two stages: a general human motion estimation step that captures the coarse overall motion, and a residual estimation that adds back person-specific motion details. Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning

Visual similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only generalize from training data to identically distributed test distributions, but in particular also translate to unknown test classes. However, its prevailing learning paradigm is class-discriminative supervised training, which typically results in representations specialized in separating training classes. For effective generalization, however, such an image representation needs to capture a diverse range of data characteristics. To this end, we propose and study multiple complementary learning tasks, targeting conceptually different data relationships by only resorting to the available training samples and labels of a standard DML setting. Through simultaneous optimization of our tasks we learn a single model to aggregate their training signals, resulting in strong generalization and state-of-the-art performance on multiple established DML benchmark datasets.