Reviewerly/exHarmony huggingface dataset:
HistoTransfer: Understanding Transfer Learning for Histopathology

Advancement in digital pathology and artificial intelligence has enabled deep learning-based computer vision techniques for automated disease diagnosis and prognosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized, making them infeasible to be used directly for training deep neural networks. Hence, for modeling, a two-stage approach is adopted: Patch representations are extracted first, followed by the aggregation for WSI prediction. These approaches require detailed pixel-level annotations for training the patch encoder. However, obtaining these annotations is time-consuming and tedious for medical experts. Transfer learning is used to address this gap and deep learning architectures pre-trained on ImageNet are used for generating patch-level representation. Even though ImageNet differs significantly from histopathology data, pre-trained networks have been shown to perform impressively on histopathology data. Also, progress in self-supervised and multi-task learning coupled with the release of multiple histopathology data has led to the release of histopathology-specific networks. In this work, we compare the performance of features extracted from networks trained on ImageNet and histopathology data. We use an attention pooling network over these extracted features for slide-level aggregation. We investigate if features learned using more complex networks lead to gain in performance. We use a simple top-k sampling approach for fine-tuning framework and study the representation similarity between frozen and fine-tuned networks using Centered Kernel Alignment. Further, to examine if intermediate block representation is better suited for feature extraction and ImageNet architectures are unnecessarily large for histopathology, we truncate the blocks of ResNet18 and DenseNet121 and examine the performance.
Realtime Accident Detection and Alarm Generation System Over IoT

In the recent scenario, there is a drastic improvement in transportation, infrastructure, and communication technology which increases the number of commercial as well as non-commercial vehicles. Therefore, there is also an increase in the number of accident incidence. This ultimately results in a high death rate due to a road accident. More than half of accident incidence results in death due to delayed medical aid to the victim. If medical aid or services received at the proper time, then the victim may survive. With the application of machine learning processes and communication advancements, there is scope for the development of a more accurate system. In this chapter, a model is presented based on IoT devices that can sense and predict the pre-accident/pre-collision state and generates an alarm message about the collision is going to occur. This model is designed to extracts image/video features to determine the possibility of occurrence of a collision. This model is also efficient for post-collision.
Synchronous or asynchronous? Various online learning platforms studied in Indonesia 2015-2020

Abstract Online learning occupies an important position as an indicator of improving the quality of learning in Indonesia. This paper aims to identify the online learning platform studied in Indonesia in 2015-2020. This is to determine the potential for developing the quality of online learning in Indonesia. There are 178 articles studied in this paper as research population using systematic literature review method. Systematic literature review method is used to list, filter, classify, and identify learning approaches that used in each learning platform. This study used secondary data from the IOP database, especially for articles conducted in Indonesia in 2015-2020. It was found that asynchronous online learning is more widely used about 68 articles. Meanwhile, there are 57 research conducted without specific learning approach. Also, the number of research on online learning conducted in Indonesia is increased by 178 articles in total during 2015-2020. The most widely studied platforms in Indonesia were Edmodo, Moodle, MOOC, and Google Classroom. However, there are tendencies that social media which are not learning platform are treated like learning platforms. This has implications for educators’ to apply online learning using various forms of media and platforms, so that it is starting to be widely accepted.
Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. However, the proposed algorithms still have a series of problems; for instance, the algorithms will miss some small-scale aircrafts when applied to the remote sensing image. There are two main reasons for the problem; one reason is that the aircrafts in the remote sensing image are usually small in size, leading to detecting difficulty. The other reason is that the background of the remote sensing image is usually complex, so the algorithms applied to the scenario are easy to be affected by the background. To address the problem of small size, this paper proposes the Multiscale Detection Network (MSDN) which introduces a multiscale detection architecture to detect small-scale aircrafts. With the intention to resist the background noise, this paper proposes the Deeper and Wider Module (DAWM) which increases the perceptual field of the network to alleviate the affection. Besides, to address the two problems simultaneously, this paper introduces the DAWM into the MSDN and names the novel network structure as Multiscale Refined Detection Network (MSRDN). The experimental results show that the MSRDN method has detected the small-scale aircrafts that other algorithms missed and the performance indicators have higher performance than other algorithms.
Review about Online Educational Guidance during the COVID-19 Pandemic

The COVID-19 pandemic has affected almost every area of our lives. It meant a break from our lifestyles, transforming everything from face-to-face to online. Among the places where this situation has become more evident are primary schools, secondary schools, and universities, which have had to adopt online teaching in its entirety. Faced with this situation, the work of the educational counsellors was essential to coordinate the centre with the families and to enable students to follow the classes in the best way possible. The objective of this study is to analyse the scientific literature about online educational guidance, with a focus on the COVID-19 pandemic. To achieve this, a scoping review inspired by the PRISMA model was done. A total of 42 articles were studied. The main results show that many counsellors were at a disadvantage and in a very stressful situation, as they did not have the necessary digital training. On the other hand, this situation has been the first contact for many counsellors with the use of ICT within their profession and in an unexpected situation, without time for training or enough preparation.
Adaptive Streaming of 360 Videos With Perfect, Imperfect, and Unknown FoV Viewing Probabilities in Wireless Networks

This paper investigates adaptive streaming of one or multiple tiled 360 videos from a multi-antenna base station (BS) to one or multiple single-antenna users, respectively, in a multi-carrier wireless system. We aim to maximize the video quality while keeping rebuffering time small via encoding rate adaptation at each group of pictures (GOP) and transmission adaptation at each (transmission) slot. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect, and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility, and worst total utility as the respective performance metrics. In the single-user scenario, we optimize the encoding rates of the tiles, encoding rates of the FoVs, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. In the multi-user scenario, we adopt rate splitting with successive decoding and optimize the encoding rates of the tiles, encoding rates of the FoVs, rates of the common and private messages, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. Then, we separate the challenging optimization problem into multiple tractable problems in each scenario. In the single-user scenario, we obtain a globally optimal solution of each problem using transformation techniques and the Karush-Kuhn-Tucker (KKT) conditions. In the multi-user scenario, we obtain a KKT point of each problem using the concave-convex procedure (CCCP). Finally, numerical results demonstrate that the proposed solutions achieve notable gains over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction on the performance of adaptive streaming of tiled 360 videos.
TCDesc: Learning Topology Consistent Descriptors for Image Matching

The triplet loss is widely used in learning the local descriptors for image matching. However, existing triplet loss-based methods, like HardNet and DSM, employ the point-to-point distance metric, which neglects the neighborhood information of descriptors. Considering the fact that local neighborhood structures of matching descriptors would be similar under the ideal condition, this paper aims to learn the neighborhood topology-consistent descriptors (TCDesc). To this end, we first propose the linear combination weight as the topology weight to depict the neighborhood topology for each descriptor, where the difference between the center descriptor and the linear combination of its neighbors is minimized. For the global comparison, we then define a global topology vector by using the local topology weights. Next, beyond the Euclidean distance, we define a topology distance with the topology vectors to indicate the topological difference between the matching descriptors. Furthermore, we propose an adaptive weighting strategy to jointly minimize the topology distance and Euclidean distance in triplet loss. Experimental results on four widely-used datasets, i.e., UBC PhotoTourism, HPatches, W1BS and Oxford, demonstrate that our method can effectively improve the performance of both HardNet and DSM.
Multi-view clustering by joint spectral embedding and spectral rotation

Discrete Spectral clustering is an effective tool for directly getting discrete labels. However, existing spectral clustering carry out spectral embedding and spectral rotation separately, which may limit their clustering performance. Seamlessly connecting above two processes is challenging. In this paper, we propose a new multi-view clustering framework, namely Multi-view Clustering by Joint Spectral Embedding and Spectral Rotation. In the framework, the differences of Laplacian matrices from different views are learned adaptively. Moreover, the real-valued cluster indicator matrix is approximated by continuous orthogonalization of the discrete clustering index matrix. By doing so, our method has better convergence, which is also strictly mathematically proven. Extensive experiments indicate that our method is superior to several state-of-the-art methods.
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation

In this paper, we propose a generalizable mixed-precision quantization (GMPQ) method for efficient inference. Conventional methods require the consistency of datasets for bitwidth search and model deployment to guarantee the policy optimality, leading to heavy search cost on challenging largescale datasets in realistic applications. On the contrary, our GMPQ searches the mixed-quantization policy that can be generalized to largescale datasets with only a small amount of data, so that the search cost is significantly reduced without performance degradation. Specifically, we observe that locating network attribution correctly is general ability for accurate visual analysis across different data distribution. Therefore, despite of pursuing higher model accuracy and complexity, we preserve attribution rank consistency between the quantized models and their full-precision counterparts via efficient capacity-aware attribution imitation for generalizable mixed-precision quantization strategy search. Extensive experiments show that our method obtains competitive accuracy-complexity trade-off compared with the state-of-the-art mixed-precision networks in significantly reduced search cost. The code is available at https://github.com/ZiweiWangTHU/GMPQ.git.
Joint Multiple Intent Detection and Slot Filling Via Self-Distillation

Intent detection and slot filling are two main tasks in natural language understanding (NLU). These two tasks are highly related and often trained jointly. However, most previous works assume an utterance only corresponds to one intent, ignoring that it can include multiple intents. In this paper, we propose a novel Self-Distillation Joint NLU model (SDJN) for multi-intent NLU. Specifically, we adopt three orderly connected decoders and a self-distillation approach to form an auxiliary loop that establishes interrelated connections between multiple intents and slots. The output of each decoder serves as auxiliary information for the next decoder, and the auxiliary loop completes via the self-distillation. Furthermore, we formulate multiple intent detection as a weakly supervised task and handle it with multiple instance learning (MIL), which exploits token-level intent information to predict multiple intents and guide slot decoder. Experimental results indicate that our model achieves competitive performance compared to others.
Multiview Common Subspace Clustering via Coupled Low Rank Representation

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k -block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.
Student survey as a tool for quality assurance in higher education: the case of Ukrainian university

Purpose. To determine the level of students interest in internal quality assurance, particularly in passing the survey and changing the teaching quality level and improving teachers pedagogical skills. Methodology. For the data collection, a questionnaire was used which included closed-end questions on the quality of teaching and open questions in terms of comments and recommendations of higher education students on the quality of teaching and several questions concerning the share of classes in the discipline attended by the students, ECTS scores, received by students from the relevant disciplines and the average score for the entire period of study. Findings. Sumy State University (SSU) introduced an online survey of students as the main consumers of educational services regarding the quality of teaching disciplines. Over the last 3 academic years, there has been an increase in the number of teachers whose activities are evaluated by students. There has been an increase in the number of teachers who, according to students, show excellence in teaching, which is a positive trend. If in the 20172018 academic year the number of such teachers in SSU was 57people, then in the 20192020 academic year, it increased by 35% to 77 people. Quality level Above average was determined for 120 teachers in the 20172018 academic year, and in the 20192020 academic year, their number increased by 30% and amounted to 156 people. It is noteworthy that in the 20192020 academic year compared to 20172018, there is a reduction in the number of teachers from 71 to 66 people (7%), who demonstrated the level of teaching Low. Originality. Survey of students on the quality of teaching educational components, on the one hand, allows monitoring students satisfaction with methods used by the teacher in training and communicating with students, and on the other hand, it is a method to control the institutions authority over the educational service quality and the HEIs mission implementation. It also indicates an increase in students interest in participating in higher educations internal quality assurance. An important factor influencing the positive dynamics of the teaching quality level is that each semester teachers receive a detailed analysis of students answers with a visual display for each questionnaire, as well as their comments and suggestions for teaching the relevant discipline through the information service Personal teachers office based on the results of the survey. Practical value. According to the analysis results of the received information, managerial decisions can be developed and implemented to improve the content and practice of educational components implementation, improving the professional skills of research and teaching staff, advancement of best pedagogical practices.
BASCNet: Bilateral adaptive spatial and channel attention network for breast density classification in the mammogram

• An automatic end-to-end convolutional neural network model is designed for breast density classification in mammograms. • By simulating the doctor’s reading mechanism, we combine the information of bilateral breasts to classify breast density. • Adaptive spatial attention module (ASAM) and adaptive channel attention module (ACAM), are employed to explore discriminant information for breast density classification. • Proposed BASCNet has been verified on the DDSM and INbreast datasets, and both have achieved state-of-the-art results. Breast density is a significant element for breast cancer precaution. The existing mammographic density classification methods cannot achieve satisfactory classification accuracy while achieving end-to-end. In this paper, we present a novel bilateral adaptive spatial and channel attention network (BASCNet) which integrates the information of the left and right breasts and adaptively pays attention to the discriminative features in spatial and channel dimensions. The proposed BASCNet has been fully proved on the public Digital Database for Screening Mammography (DDSM) and INbreast dataset, and the classification accuracies of 85.10% and 90.51% were achieved with fivefold cross-validation, respectively. Our method is fully automatic and has achieved the classification performance superior to the existing breast density classification methods. Massive ablation experiments were conducted to demonstrate the effectiveness of the network structure. Moreover, we compared the effects of different views (CC and MLO) on breast density classification and verified the effectiveness of the contralateral breast information integration. Overall, the proposed BASCNet has the potential to be applied to clinical diagnosis.
An unsupervised method for histological image segmentation based on tissue cluster level graph cut

While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.
Preserving Similarity and Staring Decisis for Feature Selection

Feature selection plays an important role in many areas that are relevant to machine learning. Information theory is widely applied to feature selection methods because it can measure linear and nonlinear correlations among variables. However, previous information theory based feature selection methods ignore two critical problems. First, features are collected or annotated manually; it is inevitable to make some incorrect features or outliers. Second, previous information theory based methods pay much attention to the correlations among features while ignoring the correlations between samples. To address these problems, this article proposes an information theory based feature selection method: preserving similarity and staring decisis. The proposed method first employs the similarity between samples to obtain a new transformed data that avoids outliers or incorrect features in original data. Meanwhile, the new transformed data enhances the separability of features, which is beneficial to feature selection. Additionally, different from previous methods that consider all the already-selected features in the feature selection process, the proposed method stares the last already-selected feature only, which reduces the computational cost. Extensive experiments demonstrate that the proposed method outperforms nine state-of-the-art methods in terms of multiple criteria significantly.