Reviewerly/exHarmony huggingface dataset:
Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study

The pathologic diagnosis and Gleason grading of prostate cancer are time-consuming, error-prone, and subject to interobserver variability. Machine learning offers opportunities to improve the diagnosis, risk stratification, and prognostication of prostate cancer.To develop a state-of-the-art deep learning algorithm for the histopathologic diagnosis and Gleason grading of prostate biopsy specimens.A total of 85 prostate core biopsy specimens from 25 patients were digitized at 20× magnification and annotated for Gleason 3, 4, and 5 prostate adenocarcinoma by a urologic pathologist. From these virtual slides, we sampled 14803 image patches of 256×256 pixels, approximately balanced for malignancy.We trained and tested a deep residual convolutional neural network to classify each patch at two levels: (1) coarse (benign vs malignant) and (2) fine (benign vs Gleason 3 vs 4 vs 5). Model performance was evaluated using fivefold cross-validation. Randomization tests were used for hypothesis testing of model performance versus chance.The model demonstrated 91.5% accuracy (p<0.001) at coarse-level classification of image patches as benign versus malignant (0.93 sensitivity, 0.90 specificity, and 0.95 average precision). The model demonstrated 85.4% accuracy (p<0.001) at fine-level classification of image patches as benign versus Gleason 3 versus Gleason 4 versus Gleason 5 (0.83 sensitivity, 0.94 specificity, and 0.83 average precision), with the greatest number of confusions in distinguishing between Gleason 3 and 4, and between Gleason 4 and 5. Limitations include the small sample size and the need for external validation.In this study, a deep learning-based computer vision algorithm demonstrated excellent performance for the histopathologic diagnosis and Gleason grading of prostate cancer.We developed a deep learning algorithm that demonstrated excellent performance for the diagnosis and grading of prostate cancer.
Formulasi Model Perkuliahan Daring Sebagai Upaya Menekan Disparitas Kualitas Perguruan Tinggi

&lt;p&gt;A one-step using network technology and information technology for the development of learning system in universities is online lecturing system. This learning system is assessed as highly efficient, because the same learning resources can be used by hundred people at the same time. So, college student that lives in isolated area can access the subjects of reputable universities in Indonesia. Hence, the quality disparity of universities will be decreased.&lt;/p&gt;&lt;p&gt;This study is qualitative descriptive research. The object of this study is on website pditt.belajar.kemdikbud.go.id. This website is developed by the Ministry of Education and Culture Indonesia. The selection of that locus is because the website is an official website of government which collaborate with some reputable universities in Indonesia, such as University of Indonesia, Bandung Institute of Technology, Gajahmada University, Sepuluh November Institut of Technology, Aptikom, and Bina Nusantara University.&lt;/p&gt;&lt;p&gt;The result showed that online lecturing system has positive contribution for pushing the quality disparity of universities in Indonesia. The indications such as 1) Minimize the limitation of access to higher education that have a certain quality. 2) Cut-off the limitation of facilities that had been cosidered as one of obstacles of the lower quality of higher education. 3) Eliminate the limitation of understanding to certain material. 4) Online lecturing system gives wide access to educational resources, especially in reputable universities.&lt;/p&gt;
Deep Match to Rank Model for Personalized Click-Through Rate Prediction

Click-through rate (CTR) prediction is a core task in the field of recommender system and many other applications. For CTR prediction model, personalization is the key to improve the performance and enhance the user experience. Recently, several models are proposed to extract user interest from user behavior data which reflects user's personalized preference implicitly. However, existing works in the field of CTR prediction mainly focus on user representation and pay less attention on representing the relevance between user and item, which directly measures the intensity of user's preference on target item. Motivated by this, we propose a novel model named Deep Match to Rank (DMR) which combines the thought of collaborative filtering in matching methods for the ranking task in CTR prediction. In DMR, we design User-to-Item Network and Item-to-Item Network to represent the relevance in two forms. In User-to-Item Network, we represent the relevance between user and item by inner product of the corresponding representation in the embedding space. Meanwhile, an auxiliary match network is presented to supervise the training and push larger inner product to represent higher relevance. In Item-to-Item Network, we first calculate the item-to-item similarities between user interacted items and target item by attention mechanism, and then sum up the similarities to obtain another form of user-to-item relevance. We conduct extensive experiments on both public and industrial datasets to validate the effectiveness of our model, which outperforms the state-of-art models significantly.
Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification

Graph classification is critically important to many real-world applications that are associated with graph data such as chemical drug analysis and social network mining. Traditional methods usually require feature engineering to extract the graph features that can help discriminate the graphs of different classes. Although recently deep learning based graph embedding approaches are proposed to automatically learn graph features, they mostly use a few vertex arrangements extracted from the graph for feature learning, which may lose some structural information. In this work, we present a novel motif-based attentional graph convolution neural network for graph classification, which can learn more discriminative and richer graph features. Specifically, a motif-matching guided subgraph normalization method is developed to better preserve the spatial information. A novel subgraph-level self-attention network is also proposed to capture the different impacts or weights of different subgraphs. Experimental results on both bioinformatics and social network datasets show that the proposed models significantly improve graph classification performance over both traditional graph kernel methods and recent deep learning approaches.
A Multi-Scale Approach for Graph Link Prediction

Deep models can be made scale-invariant when trained with multi-scale information. Images can be easily made multi-scale, given their grid-like structures. Extending this to generic graphs poses major challenges. For example, in link prediction tasks, inputs are represented as graphs consisting of nodes and edges. Currently, the state-of-the-art model for link prediction uses supervised heuristic learning, which learns graph structure features centered on two target nodes. It then learns graph neural networks to predict the existence of links based on graph structure features. Thus, the performance of link prediction models highly depends on graph structure features. In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. A theory for analyzing the information loss during the re-scaling procedure is also provided. Graphs in different scales can provide scale-invariant information, which enables graph neural networks to learn invariant features and improve link prediction performance. Our experimental results on 14 datasets from different areas demonstrate that our proposed method outperforms the state-of-the-art methods by employing multi-scale graphs without additional parameters.
Relational Prototypical Network for Weakly Supervised Temporal Action Localization

In this paper, we propose a weakly supervised temporal action localization method on untrimmed videos based on prototypical networks. We observe two challenges posed by weakly supervision, namely action-background separation and action relation construction. Unlike the previous method, we propose to achieve action-background separation only by the original videos. To achieve this, a clustering loss is adopted to separate actions from backgrounds and learn intra-compact features, which helps in detecting complete action instances. Besides, a similarity weighting module is devised to further separate actions from backgrounds. To effectively identify actions, we propose to construct relations among actions for prototype learning. A GCN-based prototype embedding module is introduced to generate relational prototypes. Experiments on THUMOS14 and ActivityNet1.2 datasets show that our method outperforms the state-of-the-art methods.
A multicenter random forest model for effective prognosis prediction in collaborative clinical research network

The accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. However, sharing sensitive biomedical data for research involves complicated issues. Machine learning models such as random forests (RF), though they are commonly used and achieve good performances for prognostic prediction, usually suffer worse performance under multicenter privacy-preserving data mining scenarios compared to a centrally trained version. In this study, a multicenter random forest prognosis prediction model is proposed that enables federated clinical data mining from horizontally partitioned datasets. By using a novel data enhancement approach based on a differentially private generative adversarial network customized to clinical prognosis data, the proposed model is able to provide a multicenter RF model with performances on par with—or even better than—centrally trained RF but without the need to aggregate the raw data. Moreover, our model also incorporates an importance ranking step designed for feature selection without sharing patient-level information. The proposed model was evaluated on colorectal cancer datasets from the US and China. Two groups of datasets with different levels of heterogeneity within the collaborative research network were selected. First, we compare the performance of the distributed random forest model under different privacy parameters with different percentages of enhancement datasets and validate the effectiveness and plausibility of our approach. Then, we compare the discrimination and calibration ability of the proposed multicenter random forest with a centrally trained random forest model and other tree-based classifiers as well as some commonly used machine learning methods. The results show that the proposed model can provide better prediction performance in terms of discrimination and calibration ability than the centrally trained RF model or the other candidate models while following the privacy-preserving rules in both groups. Additionally, good discrimination and calibration ability are shown on the simplified model based on the feature importance ranking in the proposed approach. The proposed random forest model exhibits ideal prediction capability using multicenter clinical data and overcomes the performance limitation arising from privacy guarantees. It can also provide feature importance ranking across institutions without pooling the data at a central site. This study offers a practical solution for building a prognosis prediction model in the collaborative clinical research network and solves practical issues in real-world applications of medical artificial intelligence.
Joint Watermarking-Encryption-JPEG-LS for Medical Image Reliability Control in Encrypted and Compressed Domains

In this paper, we propose the first joint watermarking-encryption-compression scheme for the purpose of protecting of medical images. The main originality of this scheme stands on its ability to give access to watermarking-based security services from both encrypted and compressed image bitstreams without having to decrypt or to decompress them, even partially. More clearly, there is no need neither to decrypt the encrypted image bitstream nor to decode the compressed image bitstream in order to extract watermarks. A second contribution is that it combines in a single algorithm the bit substitution watermarking modulation with JPEG-LS and the AES block cipher algorithm in its CBC mode. On their side, decompression, decryption and message extraction are conducted separately. Doing so makes our scheme compliant to the medical image standard DICOM. This scheme allows tracing images and controlling their reliability (i.e. based on proofs of image integrity and authenticity) either from the encrypted domain or from the compressed one. Experiments conducted on broad sets of Retina and ultrasound medical images demonstrate the capability of our system to securely make available a message in both encrypted and compressed domains while minimizing image distortion. Achieved watermarking capacities are large enough to support several watermarking-based security services at the same time.
Estimating the deep replicability of scientific findings using human and artificial intelligence

Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and demonstrates the need for new approaches for estimating a study's replicability. Here, we trained an artificial intelligence model to estimate a paper's replicability using ground truth data on studies that had passed or failed manual replication tests, and then tested the model's generalizability on an extensive set of out-of-sample studies. The model predicts replicability better than the base rate of reviewers and comparably as well as prediction markets, the best present-day method for predicting replicability. In out-of-sample tests on manually replicated papers from diverse disciplines and methods, the model had strong accuracy levels of 0.65 to 0.78. Exploring the reasons behind the model's predictions, we found no evidence for bias based on topics, journals, disciplines, base rates of failure, persuasion words, or novelty words like "remarkable" or "unexpected." We did find that the model's accuracy is higher when trained on a paper's text rather than its reported statistics and that n-grams, higher order word combinations that humans have difficulty processing, correlate with replication. We discuss how combining human and machine intelligence can raise confidence in research, provide research self-assessment techniques, and create methods that are scalable and efficient enough to review the ever-growing numbers of publications-a task that entails extensive human resources to accomplish with prediction markets and manual replication alone.
High-speed Instruction-set Coprocessor for Lattice-based Key Encapsulation Mechanism: Saber in Hardware

In this paper, we present an instruction set coprocessor architecture for lattice-based cryptography and implement the module lattice-based post-quantum key encapsulation mechanism (KEM) Saber as a case study. To achieve fast computation time, the architecture is fully implemented in hardware, including CCA transformations. Since polynomial multiplication plays a performance-critical role in the module and ideal lattice-based public-key cryptography, a parallel polynomial multiplier architecture is proposed that overcomes memory access bottlenecks and results in a highly parallel yet simple and easy-to-scale design. Such multipliers can compute a full multiplication in 256 cycles, but are designed to target any area/performance trade-offs. Besides optimizing polynomial multiplication, we make important design decisions and perform architectural optimizations to reduce the overall cycle counts as well as improve resource utilization. For the module dimension 3 (security comparable to AES-192), the coprocessor computes CCA key generation, encapsulation, and decapsulation in only 5,453, 6,618 and 8,034 cycles respectively, making it the fastest hardware implementation of Saber to our knowledge. On a Xilinx UltraScale+ XCZU9EG-2FFVB1156 FPGA, the entire instruction set coprocessor architecture runs at 250 MHz clock frequency and consumes 23,686 LUTs, 9,805 FFs, and 2 BRAM tiles (including 5,113 LUTs and 3,068 FFs for the Keccak core).
Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown breakthrough success in natural language processing (NLP), achieving state-of-the-art results in domains such as language modeling and machine translation. Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting. In this work we demonstrate that the standard transformer architecture is difficult to optimize, which was previously observed in the supervised learning setting but becomes especially pronounced with RL objectives. We propose architectural modifications that substantially improve the stability and learning speed of the original Transformer and XL variant. The proposed architecture, the Gated Transformer-XL (GTrXL), surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding the performance of an external memory architecture. We show that the GTrXL, trained using the same losses, has stability and performance that consistently matches or exceeds a competitive LSTM baseline, including on more reactive tasks where memory is less critical. GTrXL offers an easy-to-train, simple-to-implement but substantially more expressive architectural alternative to the standard multi-layer LSTM ubiquitously used for RL agents in partially observable environments.
Sparse-YOLO: Hardware/Software Co-Design of an FPGA Accelerator for YOLOv2

Convolutional neural network (CNN) based object detection algorithms are becoming dominant in many application fields due to their superior accuracy advantage over traditional schemes.Among them, You Look Only Once (YOLO) is one of the most popular detection frameworks that show best trade-offs between speed and accuracy.However, due to the intrinsic high computational workload of CNN, it is still challenging when targeting high-throughput processing with low cost in energy consumption.In this paper, we propose a hardware/software (HW/SW) co-design methodology targeting CPU+FPGA-based heterogeneous platforms.Firstly, we extend a novel sparse convolution algorithm to the YOLOv2 framework, and then develop a resource-efficient FPGA accelerator architecture based on asynchronously executed parallel convolution cores.Secondly, algorithm-level optimization schemes, including hardware-aware neural network pruning, clustering and quantization are introduced, which successfully save the computational workload of the YOLOv2 algorithm by 7 times.Finally, an end-to-end design space exploration flow for FPGA-based accelerator design is presented and two HW/SW partition strategies are studied and implemented.Experimental results show that our design can achieve a peak throughput of 2.13 TOPS (72.5 fps) on an Intel Arria-10 GX1150 FPGA under the working frequency of 211 MHz, while the detection accuracy is 74.45 on the PASCAL VOC2007 dataset.
Towards Trustable Explainable AI

Explainable artificial intelligence (XAI) represents arguably one of the most crucial challenges being faced by the area of AI these days. Although the majority of approaches to XAI are of heuristic nature, recent work proposed the use of abductive reasoning to computing provably correct explanations for machine learning (ML) predictions. The proposed rigorous approach was shown to be useful not only for computing trustable explanations but also for validating explanations computed heuristically. It was also applied to uncover a close relationship between XAI and verification of ML models. This paper overviews the advances of the rigorous logic-based approach to XAI and argues that it is indispensable if trustable XAI is of concern.
A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen

As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low information embedding efficiency, the use of too many Bitcoin addresses, low communication efficiency, and high costs. The present research improved on this method, designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information. This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used. Under the premise of ensuring the order, the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency. Thus, a more efficient and feasible method for the application of covert communication on the blockchain is proposed. In addition, this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.
Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.