Reviewerly/exHarmony huggingface dataset:
Breast tumor diagnosis via phrase level self-attention mechanism

In recent years there has been a surge in female Breast Cancer patients leading to an increase in the administration of Electronic Health Records (EHR) data during the treatment process. So far, mammogram examinations are the main means of breast cancer detection. The data obtained from such medical reports remains an important source for constructing AI diagnostic models for breast cancer. With a purpose of contributing and assisting doctors in clinical decision-making through provision of high-quality and efficient tumor diagnosis using AI, this paper explores and trains the breast tumor X-ray examination report, and constructs the breast tumor classification neural network (BTDNN) model to understand the auxiliary diagnosis of breast tumor. The proposed model (BTDNN) builds and uses a semantic network to define and structure the standard for breast cancer X-ray report medical entities and entity annotations which act as the standardized input of the model. Based on the semantic network, we also built a breast cancer diagnosis model based on Phrase level self-attention mechanism that uses Phrase level context technology to train the model for analysing medical reports in the same concept as a health care professional with original, local and global method context, in order to improve the prediction accuracy. As result, we compared our proposed model to several other models and concluded that combining the semantic and contextual technology could improve the final prediction accuracy of a medical diagnosis.
Analyzing tracked search and rescue robots

In this paper, we describe the classification, algorithms, and application status of search and rescue robots. By analyzing various types of robots, the robot design can be simple and easy to use, reliable in structure, and low cost. In difficult disaster situations, they can also recognize and track the victim. Furthermore, rather than laboratory technologies, robotics in the search and rescue category should be based on proven and implementable technologies. Traditional tracked robots are now widely used in our daily lives. The robots are also now commercially available. This is motivating and keeps them up to date. In this article, we present an overview of the current state of this cardinality, the prospects for robot search and rescue, relevant research on different mainstream search and rescue algorithms, and the working principles of these algorithms. Our findings will be used to determine which robotic path planning systems are best suited for distinctive and complex environmental programs. In order to look into the future, we also discuss the current situation, limitations, open questions, and challenges.
Survey on user perceived system factors influencing the QoE of audiovisual calls on smartphones

With the widespread use of applications and services supporting audiovisual calls via smartphones, both in business and leisure contexts, a key challenge for service providers is meeting end user Quality of Experience (QoE) expectations and requirements. To successfully meet this challenge, there is a need to identify and analyze the key system-related factors impacting user perceived quality. In this paper, we contribute beyond state-of-the-art by conducting a large scale web-based questionnaire survey to investigate the system-related factors that subjects identify as most influential in contributing to their overall experience and quality perception. We focus in particular on leisure audiovisual calls, established via mobile devices. Our initial survey (Phase 1) was conducted in Feb. 2020, just prior to the outbreak of the COVID-19 pandemic (272 participants). To investigate if the importance of factors has changed due to increased usage of the service caused by the pandemic among the general population, we conducted a second survey (Phase 2) in October 2021 with 249 participants. Based on obtained results, we identify key system-related QoE influence factors belonging to three categories: media quality, functional support, and usability and service design. We observe no significant differences in user opinions and expectations prior to and during the period of increased service usage, despite different participant demographics and study time frames, thus contributing to generalizability of obtained results. Study results contribute to providing insights for designing future user studies investigating QoE, in terms of key factors that should be considered.
Geochemical study on Impact of Tarball on Chendering Beach Kuala Terengganu, Malaysia

Oil pollution is considered one of the main contributors to marine pollution. The threat that oil pollution poses to the marine environment is extremely dangerous to its ecosystem. The South China Sea is an important route for various activities, including fishing, recreational and marine activities, and oil and gas exploration. Tarballs are a common byproduct of oil spills, which arise when the sun, wind, and water break down the oil. Tarballs are clumps or blobs of oil and hydrocarbon that have been weathered and displaced from the main body of oil by ocean currents can be found along the shorelines of the Eastern, South China Sea, and Western, Selat Malacca coasts.. The beaches at Chendering in Kuala Terengganu, which face the South China Sea, are also affected by tarball pollution. The geochemical study of tar balls and surface sediments of Chendering area is located at Chendering beach coastal plain area, 103° 11’ 09” E, 5° 16’ 09” N. In this study, The Inductive Couple Plasma-Optimal Emission Spectroscopy (ICP-OES) was used to analyse 12 soil samples (contaminated with tarballs) for heavy metal components. The ICP-OES result shows that average concentrations of heavy metal of beach sediments display the following ascending pattern Cu &lt; Pb &lt; Ni &lt; Cr &lt; Zn &lt; Mn with the value of range 0.100, 0.331, 0.633, 0.905 and 3.226 ppm respectively.
Mineralogy and Geochemistry of Clay in Sokor and Jeli, Kelantan.

Clay is considered as fine-grained, natural and earthy argillaceous material which are widely distributed in sediments as the products of sedimentation and diagenesis. The sample of rocks and clay soil from Sokor and several areas in Jeli, Kelantan have been analysed for geochemical and mineralogical properties. For geochemical analysis, the concentration of major elements was analysed using X-Ray Diffraction Fluorescene (XRF) and X-Ray Diffraction (XRD) Spectroscopy in order to validate the clay mineral existence. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) also was used for trace element determination while the Scanning Electron Microscopy (SEM) were used to study the mineralogy of clay as well as petrography. From mineralogy and petrography studies using optical microscope and Scanning Electron Microscopy (SEM) with Energy Dispersive Spectroscopy (EDS) analysis show that the mineral distribution in the sample from both Sokor and Jeli area dominantly by quartz, biotite and K-feldspar mineral. It is supported by XRD analysis which showing the prominent peaks of quartz on 27Å and kaolinite on 12Å from the graph is easily identified. Chondrite normalise pattern is generated from ICP-MS result to constrain rare earth element (REE) behaviour and geochemical information. The normalised fractionation trends recorded below than usual REE deposition. This is because there is no mineral association of REE such as apatite and titanite found from the sample.
Salient object contour extraction based on pixel scales and hierarchical convolutional network

Image salient object contours are helpful for many advanced computer vision tasks, such as object segmentation, action recognition, and scene understanding. We propose a new contour extraction method for salient objects based on pixel scale knowledge and hierarchical network structure, which improves the accuracy of object contours. First, a deep hierarchical network is designed to capture rich feature details. Then, a new loss function with adaptive weighted coefficients is developed, which can reduce the uneven distribution influence of contour pixels and non-contour pixels in training datasets. Next, the object contours are classified based on the scale information of contour pixels. By importing the prior knowledge of scale categories into the network structure, the model requires a small number of training samples. Finally, the regression task of contour scale prediction is added to the network, and the precise contour scales of foreground objects are performed as prior knowledge or an auxiliary task. The experimental results demonstrate that compared with related methods, the proposed method achieves satisfactory results from precision/recall curves and F-measure score estimation on three datasets.
Toward a new face recognition approach using OptimLBP and Learning Classifiers

Face recognition has become a popular biometric technology to fight crime, prevent fraud, and provide security of various human-system interactions. For this reason, researchers have developed several face recognition approaches to minimize the impact of the human factor. In this paper, Optimal Local Binary Pattern (Optim-LBP) features extraction was used to improve face description and preserve features of crucial face components. Afterwards, the 2D Linear Discriminant Analysis (2DLDA) subspace method was used to generate reduced and discriminated face features data and the SVMs Classifiers to perform the classification process. Finally, the effectiveness of the proposed approach was evaluated using the ORL, Yale, and AR face's databases.
Impact of Selfish Nodes on Federated Learning Performances

Federated Learning is an artificial intelligence (AI) method that brings a new approach to ensure a high level of confiden-tiality during model creation. In federated Learning, instead of sending the local private data by the nodes to a central server, the server will initiate the process by sending the initial global model to the participants nodes that will perform the training phase based on their data locally and send back the results (Weights of the model) to the server for the aggregation, finally the server updates the global model based on this aggregation to make the model more efficient and send it to the nodes. Sometimes, it can be some selfish nodes injected into the partic-ipants, these selfish nodes send faulty results to the server, if the number of these nodes is minimum, the impact can be negligible, but if there are many of these selfish nodes, it deceives and pushes the whole global model to make false results. Hence, that will impact the other honest node's performances, since the server sends the model updates to them. In this research, we will focus on this problem and we will demonstrate how these selfish nodes can impact the performance of the global model completely, also we will propose a system model that can detect and eliminate them from the participants list.
Verification of the accuracy of three-dimensional coordinates of feature points in a track and field stadium reconstructed by bundle adjustment

Accurate measurement of the 3D coordinates of feature points used to estimate camera parameters is critical for highly reliable motion analysis. This study reconstructed the 3D coordinates of feature points in a second corner track derived from photographs captured from multiple locations and verified the accuracy of the reconstructed coordinates. A total of 126 feature points containing information on 3D coordinates (control points) as well as 14 feature points without such information (unknown points) were filmed from 25 locations in the stands of a track and field stadium (169 images). The digitizer coordinates of the feature points in the filmed images were collected. Based on a perspective projection model, camera parameters were estimated using the digitizer coordinates of the feature points and their characteristics in real space. Hybrid particle swarm optimization and a genetic algorithm were used to estimate camera parameters. Three-dimensional coordinates of the known (126) and unknown (14) points were then reconstructed using the estimated camera parameters. The root mean square errors of these points were 6.5 mm and 4.3 mm, respectively. The results revealed that the method used in this study was able to accurately reconstruct the 3D coordinates of feature points used to estimate camera parameters.
Local predictability and coherence versus distributed entanglement in entanglement swapping from partially entangled pure states

Complete complementarity relations, as e.g. $P(\rho_{A})^{2} + C(\rho_{A})^{2} + E(|\Psi\rangle_{AB})^{2}=1$, constrain the local predictability, $P$, and local coherence, $C$, and the entanglement, $E$, of bipartite pure states. For pairs of qubits prepared initially in a particular class of partially entangled pure states with null local coherence, these relations were used in Ref. [Phys. Lett. A, 451, 128414 (2022)] to provide an operational connection between local predictability of the pre-measurement states with the probability of the maximally entangled components of the states after the Bell-basis measurement of the entanglement swapping protocol (ESP). In this article, we extend this result for general pure initial states establishing the relation between $P$, $C$ and the distributed entanglement in the ESP. We use IBM's quantum computers to verify experimentally some instances of these general theoretical results.
Cloud-free mosaic for high-resolution satellite to support natural resources and disaster monitoring

Abstract High-resolution satellite imageries have been widely used for many applications, including monitoring natural resources and disasters. Unfortunately, the existence of the cloud interferes with these activities. The small coverage of high-resolution satellite imageries is also needed to be extended to a larger coverage for monitoring large areas. A method for generating the cloud-free tile-based mosaic for high-resolution satellite images is proposed in this paper to address the issues, namely Mosaic Tile-Based (MTB). The basic idea of this method is to take advantage of the cloud cover information from multi-temporal high-resolution satellite images to select the best image with the minimum cloud cover. SPOT-6 and SPOT-7 pan-sharpened images are used to test the method’s reliability. First, each image is re-shaped becomes tiles and keeps the information of its acquisition date. Each tile image is then calculated and assessed based on its cloud cover. Tile image selection is conducted in the next step to select the best tile with the lowest percentage of the cloud cover. In the last step, a mosaic of each best tile is conducted to produce a cloud-free mosaic. As a result, the mosaic product has a minimum cloud cover and larger coverage. The proposed method has beneficial as it keeps the original acquisition date on each tile and is faster for image processing activities. Thus, this method can be used as an alternative method for generating cloud-free mosaic for high-resolution satellite images to support natural resources and disaster monitoring.
FAST: FPGA Acceleration of Neural Networks Training

Training state-of-the-art ANNs is computationally and memory intensive. Thus, implementing the training on embedded devices with limited resources is challenging. In order to address this challenge, we propose FAST, a low-precision method to implement and optimize ANN training on FPGA. FAST first addresses the challenge of implementing the non-polynomial sigmoid activation function by presenting a solution using PNLA methods. Then, it introduces Hardware optimized PReLU (HOPE) activation function, which is specifically devised to reduce the required resources and increase the accuracy of computations on FPGA. We evaluated FAST against the software implementations of ANNs, using training tasks available in the MNIST benchmark. The results show that FAST improves the training speed by 8.6$\times$ and reduces the required memory size by orders of magnitude. It is worthwhile to mention that the method imposes almost no degradation in training accuracy.
An interactive user groups recommender system based on reinforcement learning

Nowadays, we have access to countless and diverse user data in various fields. Thus, it requires analysis to find a set of users. Several steps are taken to understand and identify users interactively to achieve such a goal. This article introduces a reinforcement learning model of interactive recommendations based on user groups. An agent learns the appropriate policy to discover users among groups based on feedback during a sequential decision-making process and recommends the best action for the next step. There are three datasets available for courses, jobs, and LinkedIn, but these three datasets are not related to each other, which causes errors in learning politics. Furthermore, taking different actions will significantly affect learning from these datasets. To improve learning, semantic similarity and text processing are used to extract relationships between datasets. As a result, a set of groups is constructed based on what the users have in common. Users are chosen from a set of groups represented by an agent. The results and experiments show that the agent can learn the policy without collecting previous sessions and finally provide an acceptable recommendation.
Degarbayan-SC: A Colloquial Paraphrase Farsi Subtitles Dataset

Paraphrase generation and paraphrase detection are important tasks in Natural Language Processing (NLP), such as information retrieval, text simplification, question answering, and chatbots. The lack of comprehensive datasets in the Persian paraphrase is a major obstacle to progress in this area. In spite of their importance, no large-scale corpus has been made available so far, given the difficulties in its creation and the intensive labor required. In this paper, the construction process of Degarbayan-SC using movie subtitles, together with some of the difficulties we experienced during data extraction and sentence alignment, is addressed. As you know, movie subtitles are in Colloquial language. It is different from formal language. To the best of our knowledge, Degarbayan-SC is the first freely released large-scale (in the order of a million words) Persian paraphrase corpus. Furthermore, this newly introduced dataset will help the growth of Persian paraphrase. We have tested our dataset on neural network models and compared the performances of different attention-based models (transformers) and the GRU model on it. We have also declared the sentences generated by the neural networks and performed human metrics on them. The dataset is available at https://github.com/mut-deep/Degarbayan-SC
Cross-project Defect Prediction with An Enhanced Transfer Boosting Algorithm

A growing number of software projects makes it increasingly crucial to predict software defects. If adequate historical data are accessible, within-project defect prediction models can be reliable. However, during the early phases of software development, sufficient data are not available to train an effective predictor. Cross-project defect prediction (CPDP) utilizes information from previous mature projects (source data) for predicting whether new software modules (target data) will be defective. CPDP models must take into account the fact that data distributions between source and target projects are different. Cross-project defect prediction often reduces distribution differences by either selecting training data or using transfer learning methods. Using transfer learning effectively reduces distribution differences in recent CPDP models, yet none of them have taken into account the possibility that negative transfer may occur as a result of the imbalanced nature of defect data. In this paper, a four-step model is proposed, of which three steps are dedicated to the preparation of training data and their initial weights for use in the fourth step, which involves an enhanced version of the transfer boosting algorithm. In this algorithm, the imbalance nature of data is considered and the weighting of the source data is updated to enhance the prediction performance. Therefore, aside from reducing distribution discrepancy between source and target data, this model also addresses the issues related to defect data class imbalance. As compared to four state-of-the-art CPDP models, this model provided consistent and accurate predictions for fifteen projects from PROMISE, AEEEM, and SOFTLAB. Our proposed model provided the best average results for both AUC and F-measure and in some datasets, the improvements were more than 5%.
2nd special issue on BIOSTATISTICS

This paper develops a new algorithm for learning densely connected sub-Gaussian linear structural equation models (SEMs) in high-dimensional settings, where the number of nodes increases with increasing number of samples. The proposed algorithm consists of two main steps: (i) the component-wise ordering estimation using ℓ2-regularized regression and (ii) the presence of edge estimation using ℓ1-regularized regression. Hence, the proposed algorithm can recover a large degree graph with a small indegree constraint. Also proven is that the sample size n=Ω(p) is sufficient for the proposed algorithm to recover a sub-Gaussian linear SEM provided that d=O(plog⁡p), where p is the number of nodes and d is the maximum indegree. In addition, the computational complexity is polynomial, O(np2max⁡(n,p)). Therefore, the proposed algorithm is statistically consistent and computationally feasible for learning a densely connected sub-Gaussian linear SEM with large maximum degree. Numerical experiments verified that the proposed algorithm is consistent, and performs better than the state-of-the-art high-dimensional linear SEM learning HGSM, LISTEN, and TD algorithms in both sparse and dense graph settings. Also demonstrated through real data is that the proposed algorithm is well-suited to estimating the Seoul public bike usage patterns in 2019.
Dual Memory Structure for Memory Augmented Neural Networks for Question-Answering Tasks

Memory is crucial for machine learning tasks on sequential data. From vanilla RNN to LSTM and memory augmented neural networks, researchers have investigated several types of memory structures. However, they suffer from limitations in the capacity or ability to keep track of long-term dependencies. This paper presents an external memory module composed of two distinct submodules that are inspired by memory in the human brain. Besides, a sleep mechanism is incorporated into this memory, which can mimic sleep’s effects on improving human memory. The proposed method is fully differentiable; thus, backpropagation can be used for its training. Experiments conducted on the bAbI dataset show that the proposed method is successful in 16 out of 20 tasks, and the average error is 2.8%. The performance of the proposed method is far better than the conventional NTM, and it has the lowest prediction error in 7 out of 20 tasks among baseline systems. Besides, the proposed system is the only system that can solve tasks 16 and 17 of the bAbI dataset.