Reviewerly/exHarmony huggingface dataset:
The role of machine learning in clinical research: transforming the future of evidence generation

Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum.Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas.ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.
A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

Vehicle re-identification (ReID) aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario. It has gradually become a core technology of intelligent transportation system. Most existing vehicle re-identification models adopt the joint learning of global and local features. However, they directly use the extracted global features, resulting in insufficient feature expression. Moreover, local features are primarily obtained through advanced annotation and complex attention mechanisms, which require additional costs. To solve this issue, a multi-feature learning model with enhanced local attention for vehicle re-identification (MFELA) is proposed in this paper. The model consists of global and local branches. The global branch utilizes both middle and high-level semantic features of ResNet50 to enhance the global representation capability. In addition, multi-scale pooling operations are used to obtain multi-scale information. While the local branch utilizes the proposed Region Batch Dropblock (RBD), which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions. Then features from both branches are combined to provide a more comprehensive and distinctive feature representation. Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.
Efficient large-scale language model training on GPU clusters using megatron-LM

Large language models have led to state-of-the-art accuracies across several tasks. However, training these models efficiently is challenging because: a) GPU memory capacity is limited, making it impossible to fit large models on even a multi-GPU server, and b) the number of compute operations required can result in unrealistically long training times. Consequently, new methods of model parallelism such as tensor and pipeline parallelism have been proposed. Unfortunately, naive usage of these methods leads to scaling issues at thousands of GPUs. In this paper, we show how tensor, pipeline, and data parallelism can be composed to scale to thousands of GPUs. We propose a novel interleaved pipelining schedule that can improve throughput by 10+% with memory footprint comparable to existing approaches. Our approach allows us to perform training iterations on a model with 1 trillion parameters at 502 petaFLOP/s on 3072 GPUs (per-GPU throughput of 52% of theoretical peak).
Knowledge graphs as tools for explainable machine learning: A survey

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions. An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations. Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research.
Potential assessment of photovoltaic power generation in China

Accurate assessment of the photovoltaic (PV) power generation potential in China is important for the reduction of carbon emission intensity and the achievement of the goal of Carbon Neutral. This study used a PV power generation potential assessment system based on Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods to investigate the PV power generation potential in China. Firstly, the high spatial-temporal resolution and high-quality ERA5 data and related technical, geographic, and social factors were used to assess the theoretical power generation and land suitability of PV power generation. Then, the theoretical power generation and land suitability were comprehensively considered to evaluate the PV power generation potential of China in 2015. The results showed that the average suitability score of land in China is 0.1058 and the suitable land for PV power generation is about 993,000 km2 in 2015. The PV power generation potential of China is 131.942 PWh, which is approximately 23 times the electricity demand of China in 2015. The spatial distribution characteristics of PV power generation potential mainly showed a downward trend from northwest to southeast. Meanwhile, there were clear spatial dislocations between the PV power generation potential and the population distribution and electricity demand in China. In areas that accounting for about 75% of the PV potential, population and electricity demand only accounted for about 16% of the total population and total electricity demand in China. Besides, the degree of tapping PV potential in China is not high, and the installed capacity of most provinces in China accounted for no more than 1% of the capacity potential, especially in the PV potential-rich areas.
SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles

As an application of Internet of Things (IoT) technology, the Internet of Vehicles (IoV) faces two main security issues: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To address these issues, in this paper, we propose SG-PBFT (Score Grouping-PBFT), a secure and efficient distributed consensus algorithm for blockchain applications in the IoV. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm by optimizing the PBFT consensus process and using a score grouping mechanism to achieve a higher consensus efficiency. The experimental results show that the method can greatly improve the consistency efficiency and effectively prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art PBFT consensus algorithm. Our proposed SG-PBFT algorithm is versatile and can be used in other application scenarios which require high consensus efficiency.
A comprehensive survey on robust image watermarking

With the rapid development and popularity of the Internet, multimedia security has become a general essential concern. Especially, as manipulation of digital images gets much easier, the challenges it brings to authentication certification are increasing. As part of the solution, digital watermarking has made significant contributions to image content security and has attracted increasing attention. In this paper, we present a comprehensive review on digital image watermarking methods that were published in recent years illustrating the conventional schemes in different domains. We provide an overview of geometric invariant techniques and emerging watermarking methods for novel medias, such as depth image based rendering (DIBR), high dynamic range (HDR), screen content images (SCIs), and point cloud model. Particularly, as deep learning has achieved a great success in the field of image processing, and has also successfully been used in the field of digital watermarking, learning-based watermarking methods using various neural networks are summarized according to the utilization of neural networks in the single stage training (SST) and double stage training (DST). Finally, we provide an analysis and summary on those methods, and suggest some future research directions.
AMMU: A survey of transformer-based biomedical pretrained language models

Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power of transformers, transfer learning, and self-supervised learning (SSL). Following the success of these models in the general domain, the biomedical research community has developed various in-domain PLMs starting from BioBERT to the latest BioELECTRA and BioALBERT models. We strongly believe there is a need for a survey paper that can provide a comprehensive survey of various transformer-based biomedical pretrained language models (BPLMs). In this survey, we start with a brief overview of foundational concepts like self-supervised learning, embedding layer and transformer encoder layers. We discuss core concepts of transformer-based PLMs like pretraining methods, pretraining tasks, fine-tuning methods, and various embedding types specific to biomedical domain. We introduce a taxonomy for transformer-based BPLMs and then discuss all the models. We discuss various challenges and present possible solutions. We conclude by highlighting some of the open issues which will drive the research community to further improve transformer-based BPLMs. The list of all the publicly available transformer-based BPLMs along with their links is provided at https://mr-nlp.github.io/posts/2021/05/transformer-based-biomedical-pretrained-language-models-list/.
RETRACTED: Personalized federated learning framework for network traffic anomaly detection

With the widespread use of real-time sensors in various fields, such as IoT systems, it is important to improve the performance of most network traffic anomaly detection methods, which have low accuracy and high false alarm rates. However, there are two key challenges to address. In this study, we proposed a personalized federated anomaly detection framework for network traffic anomaly detection, in which data are aggregated under the premise of privacy protection and relatively personalized models are constructed by fine-tuning. Subsequently, a network traffic anomaly detection method based on the self-coding of long- and short-term memory networks was proposed. Real network traffic was tested to analyze the effects of the model structure and external noise on the detection performance, and the experimental results verified the correctness of the proposed method. Compared with other data-reconstruction-based detection methods, the proposed method has higher detection accuracy and better detection performance.
A framework for GIS-based site selection and technical potential evaluation of PV solar farm using Fuzzy-Boolean logic and AHP multi-criteria decision-making approach

Electricity demand is increasing mainly due to population expansion and the continuous supply of electricity in the residential, industrial, and service sectors. This energy is a critical factor of economic growth, and clean energy resources must be utilized and substituted to reduce the effects of climate change and move toward sustainable development. Site selection and construction of renewable power plants help to diversify the country's energy basket. This study aims to locate and evaluate a photovoltaic solar power plant's potential in Khuzestan province, using Fuzzy-Boolean logic and Analytical Hierarchy Process (AHP) decision analysis based on GIS. The main criteria for selecting the optimal locations include climatic (annual horizontal solar irradiance, sunshine hours, air temperature, relative humidity, wind speed), economic (the proximity to roads, transmission lines, substations, urban areas, and villages), orography (slope, orientation, and altitude) and the environment (land use). According to the results, 0.12% of the province's total area has excellent suitability, 25.66% has suitable suitability, and 59.46% lacks any suitability for installing a photovoltaic system. The annual electric power generation potential using single-crystalline silicon panels and polycrystalline silicon panels is calculated as 3189.54 and 2453.49 TWh, respectively. Izeh, Bandar-e Mahshahr, and Bagh-e Malek cities are the most suitable areas for photovoltaic solar farms in the province.
Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data.
A Unified Framework for Community Detection and Network Representation Learning

Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's intuitive that vertices in the same community tend to connect densely and share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. Inspired by the analogy between network representation learning and text modeling, we propose a unified NRL framework by introducing community information of vertices, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. Moreover, the proposed community enhancement mechanism can be applied to various existing NRL models. In experiments, we evaluate our model on vertex classification, link prediction, and community detection using several real-world datasets. The results demonstrate that CNRL significantly and consistently outperforms other state-of-the-art methods while verifying our assumptions on the correlations between vertices and communities.
A fully convolutional two-stream fusion network for interactive image segmentation

In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactiveimage segmentation. The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution. The TSLFN includes two distinct deep streams followed by a fusion network. The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result. The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution. We conduct comprehensive experiments on four benchmark datasets. The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods. 1.
End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning

We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as input and produces the corresponding camera control signals as output (e.g., move forward, turn left, etc.). Conventional methods tackle tracking and camera control tasks separately, and the resulting system is difficult to tune jointly. These methods also require significant human efforts for image labeling and expensive trial-and-error system tuning in the real world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning. A ConvNet-LSTM function approximator is adopted for the direct frame-to-action prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for successful training. The tracker trained in simulators (ViZDoom and Unreal Engine) demonstrates good generalization behaviors in the case of unseen object moving paths, unseen object appearances, unseen backgrounds, and distracting objects. The system is robust and can restore tracking after occasional lost of the target being tracked. We also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios. We demonstrate successful examples of such transfer, via experiments over the VOT dataset and the deployment of a real-world robot using the proposed active tracker trained in simulation.
Face Detection With Different Scales Based on Faster R-CNN

In recent years, the application of deep learning based on deep convolutional neural networks has gained great success in face detection. However, one of the remaining open challenges is the detection of small-scaled faces. The depth of the convolutional network can cause the projected feature map for small faces to be quickly shrunk, and most detection approaches with scale invariant can hardly handle less than 15×15 pixel faces. To solve this problem, we propose a different scales face detector (DSFD) based on Faster R-CNN. The new network can improve the precision of face detection while performing as real-time a Faster R-CNN. First, an efficient multitask region proposal network (RPN), combined with boosting face detection, is developed to obtain the human face ROI. Setting the ROI as a constraint, an anchor is inhomogeneously produced on the top feature map by the multitask RPN. A human face proposal is extracted through the anchor combined with facial landmarks. Then, a parallel-type Fast R-CNN network is proposed based on the proposal scale. According to the different percentages they cover on the images, the proposals are assigned to three corresponding Fast R-CNN networks. The three networks are separated through the proposal scales and differ from each other in the weight of feature map concatenation. A variety of strategies is introduced in our face detection network, including multitask learning, feature pyramid, and feature concatenation. Compared to state-of-the-art face detection methods such as UnitBox, HyperFace, FastCNN, the proposed DSFD method achieves promising performance on popular benchmarks including FDDB, AFW, PASCAL faces, and WIDER FACE.
Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction

High-dimensional and large-scale time series processing has aroused considerable research interests during decades. It is difficult for traditional methods to reveal the evolution state in dynamical systems and discover the relationship among variables automatically. In this paper, we propose a unified framework for nonuniform embedding, dynamical system revealing, and time series prediction, termed as Structured Manifold Broad Learning System (SM-BLS). The structured manifold learning is introduced for nonuniform embedding and unsupervised manifold learning simultaneously. Graph embedding and feature selection are both considered to depict the intrinsic structure connections between chaotic time series and its low-dimensional manifold. Compared with traditional methods, the proposed framework could discover potential deterministic evolution information of dynamical systems and make the modeling more interpretable. It provides us a homogeneous way to recover the chaotic attractor from multivariate and heterogeneous time series. Simulation analysis and results show that SM-BLS has advantages in dynamic discovery and feature extraction of large-scale chaotic time series prediction.