Reviewerly/exHarmony huggingface dataset:
Federated Reinforcement Learning-Supported IDS for IoT-steered Healthcare Systems

Wireless Networks lack clear boundaries which leads to security concerns and vulnerabilities to numerous kinds of intrusions. With the growth of cyber intruders, the risks on crucial applications monitored by networked systems have also grown. Effective and vigorous Intrusion Detection Systems (IDSs) for protecting shared information continues to be an essential task to keep private data safe especially in the healthcare sphere. Constructing an IDS that detects and returns information efficiently and with the highest accuracy is a challenging task. Machine Learning (ML) techniques have been effectively adopted in IDSs to detect network intruders. Reinforcement learning is considered as one of the main developments in ML. IDS mainly performs a higher accuracy rate, detection rate as well as a higher performance of a classification (ROC curve). According to these and to tackle the security issues, a Federated Reinforcement Learning-based Intrusion Detection System (FRL-IDS) in the Internet of Things (IoT) networks for healthcare infrastructures has been proposed. The proposed model has been evaluated and compared to a similar model (i.e. SVM system). The proposed model shows superiority over the SVM-steered IDS with accuracy and detection rates of ≈ 0.985 and ≈ 96.5%, respectively. This proposed infrastructure will not only aid in intrusion detection of large health care systems but also other wireless decentralized networks found across multiple real-world applications.
A Logarithmic Lower Bound for Oblivious RAM (for All Parameters)

An Oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (J. ACM 1996), is a (probabilistic) RAM that hides its access pattern, i.e., for every input the observed locations accessed are similarly distributed. In recent years there has been great progress both in terms of upper bounds as well as in terms of lower bounds, essentially pinning down the smallest overhead possible in various settings of parameters. We observe that there is a very natural setting of parameters in which no non-trivial lower bound is known, even not ones in restricted models of computation (like the so called balls and bins model). Let N and $${\boldsymbol{w}}$$ be the number of cells and bit-size of cells, respectively, in the RAM that we wish to simulate obliviously. Denote by $${\boldsymbol{b}}$$ the cell bit-size of the ORAM. All previous ORAM lower bounds have a multiplicative $${\boldsymbol{w}}/{\boldsymbol{b}}$$ factor which makes them trivial in many settings of parameters of interest. In this work, we prove a new ORAM lower bound that captures this setting (and in all other settings it is at least as good as previous ones, quantitatively). We show that any ORAM must make (amortized) $$ \varOmega \left( \log \left( \frac{N{\boldsymbol{w}}}{m}\right) /\log \left( \frac{{\boldsymbol{b}}}{{\boldsymbol{w}}}\right) \right) $$ memory probes for every logical operation. Here, m denotes the bit-size of the local storage of the ORAM. Our lower bound implies that logarithmic overhead in accesses is necessary, even if $$ {\boldsymbol{b}}\gg {\boldsymbol{w}}$$ . Our lower bound is tight for all settings of parameters, up to the $$\log ({\boldsymbol{b}}/{\boldsymbol{w}})$$ factor. Our bound also extends to the non-colluding multi-server setting. As an application, we derive the first (unconditional) separation between the overhead needed for ORAMs in the online vs. offline models. Specifically, we show that when $${\boldsymbol{w}}=\log N$$ and , there exists an offline ORAM that makes (on average) o(1) memory probes per logical operation while every online one must make $$\varOmega (\log N/\log \log N)$$ memory probes per logical operation. No such previous separation was known for any setting of parameters, not even in the balls and bins model.
Jointly evolving and compressing fuzzy system for feature reduction and classification

Evolving fuzzy systems (EFSs) are a type of adaptive fuzzy rule-based systems which can self-adapt both their structures and parameters simultaneously. However, the existing EFSs suffer from two drawbacks: 1) classical EFSs usually use all input features to model systems, resulting in lengthy fuzzy rules; 2) some redundant information in fuzzy rules may hinder high generalization. To address these two issues, a promising method is proposed in this paper by combining very sparse random projection (VSRP) with a class of EFSs based-on data clouds, called VSRP-AnYa-EFS. The proposed method introduces: 1) a random sparse-Bernoulli (RSB) matrix based-on VSRP is utilized to compress the lengthy antecedent part into a tighter form, triggering a feature-reduction mechanism. By employing VSRP in RSB matrix, some redundant information in fuzzy rules can be filtered; 2) Local learning is used for consequent parameter optimization to suit decoupled behavior of rules after redundant information between rules is deleted. By adopting VSRP and local learning, the proposed VSRP-AnYa-EFS owns a compact structure and fast learning speed. Numerical examples presented in this paper demonstrate that the proposed method can significantly reduce training time from hours to minutes while the accuracy can be improved up to 5%.
A Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation

The generation-based data augmentation method can overcome the challenge caused by the imbalance of medical image data to a certain extent. However, most of the current research focus on images with unified structure which are easy to learn. What is different is that ultrasound images are structurally inadequate, making it difficult for the structure to be captured by the generative network, resulting in the generated image lacks structural legitimacy. Therefore, a Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation is proposed in this paper, including a network and a strategy. Our Progressive Texture Generative Adversarial Network alleviates the adverse effect of completely truncating the reconstruction of structure and texture during the generation process and enhances the implicit association between structure and texture. The Image Data Augmentation Strategy based on Mask-Reconstruction overcomes data imbalance from a novel perspective, maintains the legitimacy of the structure in the generated data, as well as increases the diversity of disease data interpretably. The experiments prove the effectiveness of our method on data augmentation and image reconstruction on Structurally Inadequate Medical Image both qualitatively and quantitatively. Finally, the weakly supervised segmentation of the lesion is the additional contribution of our method.
Multi-task framework based on feature separation and reconstruction for cross-modal retrieval

Cross-modal retrieval has become a hot research topic in both computer vision and natural language processing areas. Learning intermediate common space for features of different modalities has become one of mainstream methods. In this paper, we propose a novel multi-task framework based on feature separation and reconstruction (mFSR) for cross-modal retrieval based on common space learning methods, which introduces feature separation module to deal with information asymmetry between different modalities, and introduces image and text reconstruction module to improve the quality of feature separation module. Extensive experiments on MS-COCO and Flickr30K datasets demonstrate that feature separation and specific information reconstruction can significantly improve the baseline performance of cross-modal image-caption retrieval.
Automated Short Answer Grading Using Deep Learning: A Survey

Automated Short Answer Grading (ASAG) is the task of assessing short answers authored by students by leveraging computational methods. The task of ASAG is investigated for many years, but this task continues to draw attention because of the associated research challenges. One of the core constraints of ASAG is the limited availability of domain-relevant training data. The task of ASAG can be tackled with several approaches and they can be broadly categorized into the traditional approaches based on handcrafted features and the Deep Learning based approaches. Researchers are applying Deep Learning Approaches for the past five years to address this problem owing to the increasing popularity of this area. The paper aims to summarize various existing deep learning approaches researchers followed to address this problem and to investigate whether Deep Learning based techniques are outperforming traditional approaches from the selected 38 papers. The paper also outlines several state-of-the-art datasets that can be used to do this work and the evaluation metrics to be used for both Regression and Classification settings.
Development of an inside-out augmented reality technique for neurosurgical navigation

OBJECTIVE With the advancement of 3D modeling techniques and visualization devices, augmented reality (AR)–based navigation (AR navigation) is being developed actively. The authors developed a pilot model of their newly developed inside-out tracking AR navigation system. METHODS The inside-out AR navigation technique was developed based on the visual inertial odometry (VIO) algorithm. The Quick Response (QR) marker was created and used for the image feature–detection algorithm. Inside-out AR navigation works through the steps of visualization device recognition, marker recognition, AR implementation, and registration within the running environment. A virtual 3D patient model for AR rendering and a 3D-printed patient model for validating registration accuracy were created. Inside-out tracking was used for the registration. The registration accuracy was validated by using intuitive, visualization, and quantitative methods for identifying coordinates by matching errors. Fine-tuning and opacity-adjustment functions were developed. RESULTS ARKit-based inside-out AR navigation was developed. The fiducial marker of the AR model and those of the 3D-printed patient model were correctly overlapped at all locations without errors. The tumor and anatomical structures of AR navigation and the tumors and structures placed in the intracranial space of the 3D-printed patient model precisely overlapped. The registration accuracy was quantified using coordinates, and the average moving errors of the x-axis and y-axis were 0.52 ± 0.35 and 0.05 ± 0.16 mm, respectively. The gradients from the x-axis and y-axis were 0.35° and 1.02°, respectively. Application of the fine-tuning and opacity-adjustment functions was proven by the videos. CONCLUSIONS The authors developed a novel inside-out tracking–based AR navigation system and validated its registration accuracy. This technical system could be applied in the novel navigation system for patient-specific neurosurgery.
ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities

Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, and so on, which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, and so on, which greatly hinder the performance of the existing deep learning methods. In this work, we propose ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs. This work enables aerial image analytics for various needs in a smart city. In particular, we use dilated convolutions to generate improved spatial context, leading to better discrimination between foreground and background features. The proposed network efficiently reuses the segment-mask features by propagating them from early stages using residual connections. Furthermore, ISDNet makes use of effective anchors to accommodate varying object scales and sizes. The proposed method obtains state-of-the-art results in the aerial context.
CJC-net: A cyclical training method with joint loss and co-teaching strategy net for deep learning under noisy labels

The recent success of deep convolutional neural networks (CNNs) is mostly due to the availability of large-scale datasets with accurate annotations. However, the collection of such large datasets with clean annotations is time-consuming and not always feasible. In this paper, we propose a novel framework for learning with noisy labels, called the Cyclical training method with Joint loss and Co-teaching strategy net (CJC-net), where the net means our method is insensitive to the structures of the CNN. CJC-net pretrains two networks simultaneously and then performs the cyclical training strategy under an improved co-teaching method based on the two pretrained networks. During the training process, we adjust the learning rates of the two networks to make the network states periodically transfer from overfitting to underfitting. The cumulative loss of each sample under two networks is recorded; and the higher the cumulative loss of a sample is, the higher the probability of identifying it as a noisy label or a hard label. Then, we remove those samples with a high loss and fine-tune the two networks using the remaining data. The experimental results on several datasets demonstrate that CJC-net is superior to many state-of-the-art methods.
Insights of strength and weakness of evolving methodologies of sentiment analysis

With every business process and organization being more concerned about adopting the latest technology towards understanding the success rate and risk associated with the product/service launch, they need to understand the intention and review of their prospective customer. Sentiment Analysis is one such advanced technology to analyze and perceive the behavior of a consumer. However, many challenges hinder analyzing precise sense of sentiments and locating the appropriate sentiment divisions. There has been a significant amount of work being carried out in this direction since the last decade. Furthermore, with the evolution of big data technologies, new methodologies have been introduced to improve sentiment analysis with various evolving applications. This paper provides a comprehensive study on sentiment analysis to provide valuable insight into sentiment analysis approaches and related fields. The paper discusses various essential information associated with the dataset, a new arena of application and methodologies, upcoming research methods, study findings, and further contributing to the ultimate study and research gap.
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation

In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs. The conventional feature imitation paradigm introduces imitation masks to focus on informative foreground areas while excluding the background noises. However, we find that those methods fail to fully utilize the semantic information in all feature pyramid levels, which leads to inefficiency for knowledge distillation between FPN-based detectors. To this end, we propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels to provide the optimal guidance to the student. To push the envelop even further, we introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Finally, we propose a generalized detection KD pipeline, which is capable of distilling both homogeneous and heterogeneous detector pairs. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction; (2) for both homogeneous and heterogenous student-teacher pairs and (3) on multiple detection benchmarks. With a powerful X101-FasterRCNN-Instaboost detector as the teacher, R50-FasterRCNN reaches 44.0% AP, R50-RetinaNet reaches 43.3% AP and R50-FCOS reaches 43.1% AP on COCO dataset.
Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction

As one emerging reservoir modeling method, cycle reservoir with regular jumps (CRJ) provides one effective tool for many time series analysis tasks such as ship heave motion prediction. However, the shallow learning structure of single CRJ model limits its memory capacity and leads to unsatisfactory prediction performance. In order to pursue the stronger dynamic characteristic description of time series data, a delayed deep CRJ model is presented in this paper by integrating the deep learning framework with delay links and the evolutionary optimization for mixed-integer problem. Different from the basic CRJ model with only one reservoir, delayed deep CRJ builds multiple serial reservoirs with inserting the delay links between adjacent reservoirs. Due to the design of dynamic deep learning structure, the memory capacity is enlarged to improve ship heave motion prediction. Aiming at the mix-integer optimization problem in delayed deep CRJ model, a heuristic evolutionary optimization scheme based on the stepwise differential evolution algorithm is applied to determine the delayed deep CRJ parameters automatically. The stepwise differential evolution assisted delayed deep CRJ model can avoid the non-optimal solution resulted from the manual parameter setting effectively. Finally, one numerical example and the real experiment data are utilized to validate the methods and the results demonstrate that delayed deep CRJ model has better prediction performance in contrast to the basic CRJ method.
Cambricon-G: A Polyvalent Energy-Efficient Accelerator for Dynamic Graph Neural Networks

Graph neural networks (GNNs), which extend traditional neural networks for processing graph-structured data, have been widely used in many fields. The GNN computation mainly consists of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge processing</i> to generate messages by combining the edge/vertex features and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vertex processing</i> to update the vertex features with aggregated messages. In addition to nontrivial vector operations in the edge processing, huge random accesses and neural network operations in the vertex processing, the graph topology of GNNs may also vary during the computation (i.e., dynamic GNNs). The above characteristics pose significant challenges on existing architectures. In this article, we propose a novel accelerator named CAMBRICON-G for efficient processing of both dynamic and static GNNs. The key of CAMBRICON-G is to abstract the irregular computation of a broad range of GNN variants to the process of regularly tiled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adjacent cuboid</i> (which extends the traditional adjacent matrix of graph by adding the dimension of vertex features). The intuition is that the adjacent cuboid facilitates exploitation of both data locality and parallelism by offering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multidimensional multilevel tiling</i> (including spatial and temporal tiling) opportunities. To perform the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multidimensional spatial tiling</i> , the CAMBRICON-G architecture mainly consists of the cuboid engine (CE) and hybrid on-chip memory. The CE has multiple vertex processing units (VPUs) working in a coordinated manner to efficiently process the sparse data and dynamically update the graph topology with dedicated instructions. The hybrid on-chip memory contains the topology-aware cache and multiple scratchpad memory to reduce off-chip memory access. To perform the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multidimensional temporal tiling</i> , an easy-to-use programming model is provided to flexibly explore different tiling options for large graphs. Experimental results show that compared against Nvidia P100 GPU, the performance and energy efficiency can be improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.14\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20.18\times $ </tex-math></inline-formula> , respectively, on various GNNs, which validates both the versatility and energy efficiency of CAMBRICON-G.
<scp>3D‐PulCNN</scp>: Pulmonary cancer classification from hyperspectral images using convolution combination unit based <scp>CNN</scp>

Pulmonary cancer is one of the most common malignancies worldwide. Accurate classification of its subtypes is required in differential diagnosis. However, existing algorithms are mostly based on color images, and the improvement of accuracy is quite challenging. In this study, we propose a convolution combination unit (CCU)-based three-dimensional convolutional neural network (3D-PulCNN) for classifying pulmonary cancer presented in microscopic hyperspectral image with both spatial and spectral information. CCU is designed to fuse the features acquired by different convolution scales. Compared with VGGNet, only two fully connected layers are used in this model, reducing the network parameters and model complexity. Experimental results show that 3D-PulCNN achieves overall average (OA) of 0.962 and Precision, Recall, and Kappa of more than 0.920, superior to 2D-VGGNet. Then, 3D-UNet is leveraged to segment cancer cells, and their morphological characteristics are calculated to supply quantitative virtual analysis data for classification results explanation and prognosis assessment.
Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts

Analyze performance of unsupervised embedding algorithms in sentiment analysis of knowledge-rich data sets. We apply state-of-the-art embedding algorithms Word2Vec and Doc2Vec as the learning techniques. The algorithms build word and document embeddings in an unsupervised manner. To assess the algorithms’ performance, we define sentiment metrics and use a semantic lexicon SentiWordNet (SWN) to establish the benchmark measures. Our empirical results are obtained on the Obesity data set from i2b2 clinical discharge summaries and the Reuters Science dataset. We use the Welch’s test to analyze the obtained sentiment evaluation. On the Obesity data, the Welch’s test found significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, the Word2Vec results support the SWN results, whereas the Doc2Vec results partially correspond to the Word2Vec and the SWN results. On the Reuters data, the Welch’s test did not find significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, Word2Vec and Doc2Vec results only in part correspond to the SWN results. In unsupervised sentiment analysis of medical and scientific texts, the Word2Vec sentiment analysis has been more consistent with the SentiWordNet sentiment assessment than the Doc2Vec sentiment analysis. The Welch’s test of the SentiWordNet results has been a strong indicator of future correspondence between Word2Vec and SentiWordNet results.