Reviewerly/exHarmony huggingface dataset:
STIR-Net: Deep Spatial-Temporal Image Restoration Net for Radiation Reduction in CT Perfusion

Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing the radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, a shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT images effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales. The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.
jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media

In this paper, the team jhan014 presents two methods to identify and categorize the offensive language in Twitter.In the first method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers.In the second method, we establish a probabilistic model, modified sentence offensiveness calculation (MSOC) to evaluate the sentence offensiveness level and target level according to different sub-tasks.Based on task results, We evaluate the performance of each method based on F1 score and analyze the advantages and disadvantages of these two methods with the type I error and type II error.In conclusion, deep neural network behaves well in all subtasks but has more type I error and fails to categorize subclasses with minor data or less character, while MSOC model does better in target categorizing but has more type II error in offensive identifying.
Locally distinguishing unextendible product bases by using entanglement efficiently

Any set of states which cannot be perfectly distinguished by local operations and classical communication (LOCC) alone, can always be locally distinguished using quantum teleportation with enough entanglement resource. However, in quantum information theory, entanglement is a very valuable resource, so it leaves the following open question: how to accomplish this task more efficiently than teleportation, that is, design the local discrimination protocol using less entanglement resource. In this paper, we first present two protocols to locally distinguish a set of unextendible product bases (UPB) in $5\otimes 5$ by using different entanglement resource. Then, we generalize the distinguishing methods for a class of UPB in $d\otimes d$, where $d$ is odd and $d\geqslant 3$. Furthermore, for a class of UPB in $d\otimes d$, where $d$ is even and $d\geqslant 4$, we prove that these states can also be distinguished by LOCC with multiple copies of low-dimensional entanglement resource. These results offer rather general insight into how to use entanglement resource more efficiently, and also reveal the phenomenon of less nonlocality with more entanglement.
A Hyperchaotic Color Image Encryption Algorithm and Security Analysis

The current common color image encryption algorithms applying “scrambling-diffusion” have some problems, such as the small key space, the cumbersome encryption process, and the security vulnerability. Aiming at these problems, this paper proposes a new color image encryption algorithm based on the hyperchaotic system and applying “transforming-scrambling-diffusion” model. Before scrambling, in accordance with the plaintext itself attributes, the number of iterations was calculated, all the pixel values of color image were transformed into gray code iteratively, and then the chaotic sequence was generated from the four-dimensional hyperchaotic system. Pixel matrix after gray code transformation was converted to one-dimensional matrix. The chaotic sequence was sorted and the one-dimensional matrix was changed positions correspondingly to complete the whole domain scrambling. And then, bit-operation was executed for image diffusion. The ciphertext can be obtained by matrix transformation. The key sensitivity, histogram, information entropy, correlation, and other evaluation indexes were calculated and analyzed through the simulation experiment. Compared with other algorithms, it can be proved that the encryption algorithm has the strong antiattack ability.
Server-Aided Fine-Grained Access Control Mechanism with Robust Revocation in Cloud Computing

As an innovative technique for cloud storage services, attribute based encryption (ABE) enables fine-grained access control over encrypted data in many cloud computing applications. However, there exist two main drawbacks that restrict the development of ABE. One is that most of the existing user revocation mechanisms cannot achieve high efficiency, immediacy and robustness simultaneously. Another is that the decryption of ABE contains expensive pairing operations which often grow with the complexity of access policy. In this work, we propose a practical server-aided revocable fine-grained access control mechanism with the help of cloud&#x2019;s storage, computing and management capabilities, which not only achieves efficient fine-grained attribute based access control, but also actualizes immediate and robust user revocation. Moreover, most of the complicated operations in decryption are outsourced to the public cloud server, leaving one exponentiation for the users. At last, we implement our proposed mechanism with <inline-formula><tex-math notation="LaTeX">$\mathsf{Charm}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Charm</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-2925028.gif"/></alternatives></inline-formula> framework. The benchmark results demonstrate the high efficiency and practicality of our proposed mechanism.
Association Rules Mining Among Interests and Applications for Users on Social Networks

Interest is an important concept in psychology and pedagogy and is widely studied in many fields.Especially in recent years, the widespread use of many interest-based recommendation systems has greatly promoted research on interest modeling and mining on social networks.However, the existing studies have rarely tried to explore the relationships among interests and their application value, and most similar studies analyze user behavior data.In this paper, we propose and verify two hypotheses about the interests of social network users.We then use association rules to mine users' interests from LinkedIn users' profiles.Finally, based on the interest association rules and user interest distribution on Twitter, we design an approach to mine interests for Twitter users and conduct two experiments to systematically demonstrate the approach's effectiveness.According to our research, we found that there are a large number of association rules between human interests.These rules play a considerable role in our method of interest mining.Our research work not only provides new ideas for interest mining but also reveals the internal relationship between interest and its application value.The research work has certain theoretical and practical value.
Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

In this paper, we study the problem of conflict detection and resolution for unmanned aerial vehicle (UAV) swarms.Specially, we propose a distributed conflict detection and resolution method for multi-UAVs in formation based on consensus algorithm and strategy coordination.When encountering threat swarms, the UAVs in one swarm act as one unit and are together treated as one control object.Each swarm in conflict selects three candidate collision avoidance maneuvers from the preset strategy pool, generates the corresponding planned trajectories with an uncertainty trajectory modeling, and then broadcasts and shares them.All of the swarms in conflict coordinate and determine an optimal combination of strategies.When a collision is imminent, the primary strategy is activated.Each swarm adopts a ''leader-follower'' strategy, where the leader UAV is regarded as the controller and flies independently, and the others follow the leader UAV.During motion, a decentralized consensus algorithm is adopted for agents to converge to their positions for the desired formation and to maintain a stable geometric configuration.A temporal and spatially integrated conflict-detection model is improved, especially for UAV swarms.A token-allocation strategy is especially improved for distributed coordination to resolve the partial knowledge of the airspace condition.Damping of the coordination is proposed to address data dropouts and transmission delays.Two typical scenarios are conducted to test the methodology proposed in this paper.The simulation result demonstrates the effectiveness and rationality of the proposed methodology.
Efficient Privacy-Preserving Anomaly Detection and Localization in Bitstream Video

In cloud computing, videos may be in an encrypted format to protect privacy. Therefore, encrypted video processing is an important application in secure cloud computing. In this paper, we focus on parameter estimation and anomaly detection in an encrypted video bitstream. By analyzing the common properties of video encoding frameworks and the format-compliant encryption schemes, we propose an anomaly detection scheme for encrypted video bitstream with format-compliant encryption. From the encrypted bitstream, we extract three types of complementary features, i.e., the macroblock sizes, the macroblock partitions, and the motion vector difference magnitude, and then propose a method to combine these three features. The proposed detection and localization scheme does not involve video decryption, full decompression, or an interactive protocol, which makes it efficient. Our scheme is also compatible with different video encryption methods. To accelerate the running time, we develop a parallel implementation for our scheme. The experimental results show that our method achieves good running time and detection rate performance.
Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration

For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs–particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.
Cost-sensitive Fuzzy Multiple Kernel Learning for imbalanced problem

Abstract Multiple Kernel Learning (MKL) improves the classification accuracy by exploring different formulations of data. However, its classification performance is still unsatisfactory in imbalanced problems. To address this issue, we take the characteristics of imbalanced data into account by introducing the fuzzy memberships. In our work, the fuzzy memberships are determined by both the entropies of samples and the cost for each class so as to make different samples have different contributions to the decision boundary. Thus the newly proposed method can result in more favorable classification performances on imbalanced datasets. Further, we introduce the fuzzy memberships into existing MKL to form a new algorithm Cost-sensitive Fuzzy Multiple Kernel Learning named CFMKL in short. Experimental results validate the great effectiveness of the proposed CFMKL on synthetic, real-world binary and multi-class imbalanced datasets. The major contributions of this paper are as follows. Firstly, extending the MKL to handle the imbalanced problems for the first time. Secondly, generating a new fuzzy memberships function by both the entropy and the cost-sensitive. Thirdly, proposing a new algorithm named CFMKL for imbalanced problems and validating its effectiveness.
An Improved Weighted Nuclear Norm Minimization Method for Image Denoising

Patch-based low rank matrix approximation has shown great potential in image denoising.Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising performance.For each local patch in an image, the WNNM method groups nonlocal similar patches by block matching to formulate a low-rank matrix.However, the WNNM often chooses irrelevant patches such that it may lose fine details of the image, resulting in undesirable artifacts in the final reconstruction.In this regards, this paper aims to provide a denoising algorithm which further improves the performance of the WNNM method.For this purpose, we develop a new nonlocal similarity measure by exploiting both pixel intensities and gradients and present a filter that enhances edge information in a patch to improve the performance of low rank approximation.The experimental results on widely used test images demonstrate that the proposed denoising algorithm performs better than other state-of-the-art denoising algorithms in terms of PSNR and SSIM indices as well as visual quality.
A Framework for Public Health Monitoring, Analytics and Research

This paper presents a framework for public healthcare data acquisition and management model based on standard protocol for its easy adoption by any country or international health organizations. The model assumes basic digitization of electronic health record (EHR) at basic health facilities. Thus far, the models in the literature have utilized EHR in multiple secondary contexts; however, there is a gap in developing an integrated and comprehensive framework that addresses the use of EHR in a standardized way for public health, privacy issue by anonymizing patient specific information, fusing multiple records with slight changes in the same information, augmenting a broad spectrum of contextual data, and so on. We present a framework that can be used in the context for acquisition and transmission of EHR from multiple sources as an evidence base for addressing public health-related activities, including surveillance, registries, and immunization record keeping while addressing all the gaps we have identified in the literature that are critical for developing countries. In addition, EHR data are also effectively processed to serve as a knowledge base for building artificial intelligence-based research models. We, in our model, utilize Health Level Seven (HL7) as an interoperability health standard and recommend creation of specialized data marts to support public health and research-related knowledge bases. The proposed framework in its adoption provides a very effective platform for generating alerts and alarms along with providing statistics for better planning of healthcare-related issues at national, district, or at any level of administrative hierarchy. It is applicable to any country even when there is no standard EHR and has hospitals working in silos with limited digitalization. We have validated this framework for its mapping to a national level public health hierarchy in Pakistan.
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context

Common grounding is the process of creating, repairing and updating mutual understandings, which is a critical aspect of sophisticated human communication. However, traditional dialogue systems have limited capability of establishing common ground, and we also lack task formulations which introduce natural difficulty in terms of common grounding while enabling easy evaluation and analysis of complex models. In this paper, we propose a minimal dialogue task which requires advanced skills of common grounding under continuous and partially-observable context. Based on this task formulation, we collected a largescale dataset of 6,760 dialogues which fulfills essential requirements of natural language corpora. Our analysis of the dataset revealed important phenomena related to common grounding that need to be considered. Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground. We show that simple baseline models perform decently but leave room for further improvement. Overall, we show that our proposed task will be a fundamental testbed where we can train, evaluate, and analyze dialogue system’s ability for sophisticated common grounding.
An intelligent clustering algorithm for high-dimensional multiview data in big data applications

There are many high-dimensional multiview data in various big data applications. It is very difficult to deal with those high-dimensional multiview data for the classic clustering algorithms, which consider all features of data with equal relevance. To tackle this challenging problem, this paper aims at proposing a novel intelligent weighting k-means clustering (IWKM) algorithm based on swarm intelligence. Firstly, the degree of coupling between clusters is presented in the model of clustering to enlarge the dissimilarity of clusters. Various weights of views and features are used in the weighting distance function to determine the clusters of objects. Secondly, to eliminate the sensitivity of initial cluster centers, swarm intelligence is utilized to find initial cluster centers, weights of views, and weights of features by a global search. Lastly, a precise perturbation is proposed to improve optimization performance of swarm intelligence. To verify the performance of clustering for high-dimensional multiview data, the experiments were performed by the evaluation metrics of Rand Index, Jaccard Coefficient and Folkes Russe in five big data applications on the two different computational platforms of apache spark and single node. The experimental results show that IWKM is effective and efficient in clustering of high-dimensional multiview data, and can obtain better performance than the other 5 kinds of approaches in these complicated data sets with more views and higher dimensions on apache spark and single node.
A neural image captioning model with caption-to-images semantic constructor

The current dominant image captioning models are mostly based on a CNN-LSTM encoder-decoder framework. Although this architecture has achieved remarkable progress, it still has shortcomings for not fully capturing the encoded image information. Specifically, the model only exploits image-to-caption dependency during the process of caption generation. In this paper, we extend the conventional CNN-LSTM image captioning model by introducing a caption-to-images semantic reconstructor, which reconstructs the semantic representations of the input image and its similar images from hidden states of the decoder. Serving as an auxiliary objective that evaluates the fidelity of the generated caption, the reconstruction score of semantic reconstructor is combined with the likelihood to refine model training. In this way, semantics of input image can be more effectively transferred to the decoder and be fully exploited to generate better captions. Besides, during model testing, the reconstruction score can be used along with log likelihood to select better caption via reranking. Experimental results show that the proposed model significantly improves the quality of the generated captions and outperforms a conventional image captioning model, LSTM-A5.
An Adaptive Grid Frequency Support Mechanism for Energy Management in Cloud Data Centers

Grid frequency support is one of the most challenging problems, since minor variations in it may lead to huge financial losses. This problem becomes even more challenging with the horizontal and vertical expansion of modern cloud data centers (DCs). In the past, several efforts have been made to manage frequency deviations using flywheels, commercial buildings, electric vehicles, and renewable energy resources. However, these are not adequate due to their complex operations. To fill these gaps, in this paper, we propose the usage of cloud DCs and uninterruptible power supply (UPS) units for the effective frequency regulation. This is achieved by designing a 2-layer hierarchical control scheme for optimal segregation of the regulation signals amongst the DCs and UPS batteries. The proposed scheme determines the scheduling policy for jobs at DCs; along with the charging and discharging cycles of the UPS batteries. The job scheduling is carried out with respect to MapReduce tasks in accordance with the regulation signals with minimal service level adherence violations. Additionally, the sustainability of DCs is also supported through active participation of UPSes during peak hours. The overall frequency support problem in the considered setup involves multiple objectives under multiconstraint environment. Thus, it has been divided in two subproblems, which are addressed individually using either integer linear programming or mixed integer linear programming. In a nutshell, the proposed scheme serves dual purposes, i.e., manages frequency fluctuations and sustains DCs during peak hours. It has been validated using data traces taken from OpenCloud Hadoop cluster and Pennsylvania New Jersey Maryland. The results obtained prove the effectiveness of the proposed scheme for frequency support and DC's sustainability.