Reviewerly/exHarmony huggingface dataset:
Nighttime Image Stitching Method Based on Guided Filtering Enhancement

Image stitching refers to stitching two or more images with overlapping areas through feature points matching to generate a panoramic image, which plays an important role in geological survey, military reconnaissance, and other fields. At present, the existing image stitching technologies mostly adopt images with good lighting conditions, but the lack of feature points in scenes with weak light such as morning or night will affect the image stitching effect, making it difficult to meet the needs of practical applications. When there exist concentrated areas of brightness such as lights and large dark areas in the nighttime image, it will further cause the loss of image details making the feature point matching unavailable. The obtained perspective transformation matrix cannot reflect the mapping relationship of the entire image, resulting in poor splicing effect, and it is difficult to meet the actual application requirements. Therefore, an adaptive image enhancement algorithm is proposed based on guided filtering to preprocess the nighttime image, and use the enhanced image for feature registration. The experimental results show that the image obtained by preprocessing the nighttime image with the proposed enhancement algorithm has better detail performance and color restoration, and greatly improves the image quality. By performing feature registration on the enhanced image, the number of matching logarithms of the image increases, so as to achieve high accuracy for images stitching.
Precise Two-Dimensional Tilt Measurement Sensor with Double-Cylindrical Mirror Structure and Modified Mean-Shift Algorithm for a Confocal Microscopy System

To improve the accuracy of three-dimensional (3D) surface contour measurements of freeform optics, a two-dimensional (2D) tilt measurement sensor for confocal microscopy (CM) systems is proposed based on a double-cylindrical mirror structure. First, the proposed system is accurately modeled. Second, we introduce a modified mean-shift-based peak-extraction algorithm with a novel kernel function (MSN) because the reflectivity of the measured object and fluctuation of the light source affect the measurement accuracy. Third, a partition fitting (PF) strategy is proposed to reduce the fitting error and improve the measurement accuracy. Simulations and experiments reveal that the robustness, speed, and angular prediction accuracy of the system effectively improved as a function of MSN and PF. The developed sensor can measure the 2D tilt, where each tilt is a composition of two separate dimensions, and the mean prediction errors in the 2D plane from -10°-+10° are 0.0134° (0.067% full scale (F.S)) and 0.0142° (0.071% F.S). The sensor enables the optical probe of a traditional CM to obtain accurate and simultaneous estimates of the 2D inclination angle and spatial position coordinates of the measured surface. The proposed sensor has potential in 3D topographic reconstruction and dynamic sampling rate optimization for 3D contour detection.
Light field image encryption based on steerable cosine number transform

Light field (LF) images can be represented in many different ways but the most convenient, in terms of visualization and processing, seems to be as an array of 2D images, that is, a 4D structure that displays the spatial and angular planes of the image. Previously published works do not consider the entire 4D structure. They apply the encryption technique only in the spatial plane or in both planes separately, which maintains a certain correlation between neighboring pixels. The present paper proposes an encryption technique for LF images considering the encryption of both planes at once. The main motivation for this approach is to achieve a lower correlation coefficient, thus being more robust against statistical attacks. We introduce the steerable cosine number transform (SCNT), which is obtained by rotating the base vectors of the two-dimensional cosine number transform (2D-CNT), using a finite field rotation operator, and we propose an encryption method for light field images based on the SCNT, which uses the angles of the referred rotations as secret parameters. Simulation results show that the proposed scheme is robust against most cryptographic attacks and highly sensitive to changes in the secret key.
Graph-Based Dissimilarity Measurement for Cluster Analysis of Any-Type-Attributed Data

Heterogeneous attribute data composed of attributes with different types of values are quite common in a variety of real-world applications. As data annotation is usually expensive, clustering has provided a promising way for processing unlabeled data, where the adopted similarity measure plays a key role in determining the clustering accuracy. However, it is a very challenging task to appropriately define the similarity between data objects with heterogeneous attributes because the values from heterogeneous attributes are generally with very different characteristics. Specifically, numerical attributes are with quantitative values, while categorical attributes are with qualitative values. Furthermore, categorical attributes can be categorized into nominal and ordinal ones according to the order information of their values. To circumvent the awkward gap among the heterogeneous attributes, this article will propose a new dissimilarity metric for cluster analysis of such data. We first study the connections among the heterogeneous attributes and build graph representations for them. Then, a metric is proposed, which computes the dissimilarities between attribute values under the guidance of the graph structures. Finally, we develop a new k -means-type clustering algorithm associated with this proposed metric. It turns out that the proposed method is competent to perform cluster analysis of datasets composed of an arbitrary combination of numerical, nominal, and ordinal attributes. Experimental results show its efficacy in comparison with its counterparts.
FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning

Representing the structural relations between entities, i.e., knowledge graph embedding, which is a method to learn low-dimensional representations of knowledge, has become an increasingly prevalent research orientation in cognitive and human intelligence. It is significant to study how to interrelate, fuse and embed the knowledge graph data from different domains while considering data not shared. In this paper, we propose a model of cross-domain knowledge graph embedding in federated learning (FedCKE), in which entity/relation embedding between different domains can interact securely in the case that data is not shared. In advance of client model training, we present an inter-domain encrypted entity/relation alignment method using the encrypted sample alignment method in vertical federated learning, which can obtain entity/relation intersections between different domains without revealing any triples structure and additional entities/relations in the respective datasets. On the server, we aggregate the same entity/relation embeddings by the association in conjunction with the parameter-secure aggregation method in horizontal federated learning. Experimental results on three real datasets show that the proposed FedCKE model is able to enhance the embedding of different clients (domains).
A deep matching model for detecting reviews mismatched with products in e-commerce

Reviews of products in e-commerce are great value to both firms and customers. However, these reviews are not always genuine. Fake reviews can stymie firms and customers. Many studies have focused on detecting fake reviews. This paper addresses another kind of fake review on e-commerce websites called mismatched reviews where the content of reviews seems to be genuine, but they actually do not match the reviewed products. These reviews can harm the user experience in online shopping and damage the reputation of e-commerce websites. This paper develops a deep matching network to detect mismatched reviews, called MIRD, to calculate matching scores between the content of reviews and the reviewed products. MIRD encodes both reviews and products to the representation vectors and subsequently calculates a score in a latent space to measure how well the two vectors match. Experiments conducted on Yelp and Amazon datasets show that MIRD can recognize mismatched reviews well. Compared with the baseline models, MIRD obtains an 8% performance improvement on the two datasets. The experimental results demonstrate that our efforts to detect mismatched reviews are feasible and effective.
Blockchain based security protocol for device to device secure communication in internet of things networks

Abstract The internet of things (IoT) is now advancing at a tremendous pace. Devices in the IoT can connect and communicate in a fully autonomous mode. Because of its autonomy and numerous infractions of security policies, the IoT network is subject to various security risks. However, because of this autonomy, dependable security and storage mechanisms for authentication to share data across IoT devices are essential. Furthermore, since it has initially been popularized in Bitcoin, blockchain development has accelerated. Blockchain can be used to solve security challenges in the IoT. Making secure communication between IoT devices is one approach to accomplish this. To address this issue, we present an effective technique for intranetwork and internetwork device‐to‐device secure communication that enables IoT device identification and authentication while also providing secure communication in an open environment. The method also provides data integrity in addition to authentication and secure communication. We have presented informal security analysis, which confirms that there is no important security threats in our protocol. The performance of the protocol is also better in comparison with previously published papers.
Single-Shot Object Detection via Feature Enhancement and Channel Attention

Features play a critical role in computer vision tasks. Deep learning methods have resulted in significant breakthroughs in the field of object detection, but it is still an extremely challenging obstacle when an object is very small. In this work, we propose a feature-enhancement- and channel-attention-guided single-shot detector called the FCSSD with four modules to improve object detection performance. Specifically, inspired by the structure of atrous convolution, we built an efficient feature-extraction module (EFM) in order to explore contextual information along the spatial dimension, and then pyramidal aggregation module (PAM) is presented to explore the semantic features of deep layers, thus reducing the semantic gap between multi-scale features. Furthermore, we construct an effective feature pyramid refinement fusion (FPRF) to refine the multi-scale features and create benefits for richer object knowledge. Finally, an attention-guided module (AGM) is developed to balance the channel weights and optimize the final integrated features on each level; this alleviates the aliasing effects of the FPN with negligible computational costs. The FCSSD exploits richer information of shallow layers and higher layers by using our designed modules, thus accomplishing excellent detection performance for multi-scale object detection and reaching a better tradeoff between accuracy and inference time. Experiments on PASCAL VOC and MS COCO datasets were conducted to evaluate the performance, showing that our FCSSD achieves competitive detection performance compared with existing mainstream object detection methods.
Quality Control in Digital Pathology: Automatic Fragment Detection and Counting

Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.
A Memory-efficient Deep Framework for Multi-Modal MRI-based Brain Tumor Segmentation

Automatic Brain Tumor Segmentation (BraTS) from MRI plays a key role in diagnosing and treating brain tumors. Although 3D U-Nets achieve state-of-the-art results in BraTS, their clinical use is limited due to requiring high-end GPU with high memory. To address the limitation, we utilize several techniques for customizing a memory-efficient yet ac-curate deep framework based on 2D U-nets. In the framework, the simultaneous multi-label tumor segmentation is decomposed into fusion of sequential single-label (binary) segmentation tasks. In addition to reducing the memory consumption, it may also improve the segmentation accuracy since each U-net focuses on a sub-task, simpler than whole BraTS segmentation task. Extensive data augmentations on multi-modal MRI and the batch dice-loss function are also employed to further increase the generalization accuracy. Experiments on BraTS 2020 demonstrate that our framework almost achieves state-of-the-art results. Dice scores of 0.905, 0.903, and 0.822 for whole tumor, tumor core, and enhancing tumor are accomplished on the testing set. Moreover, our customized framework is executable on budget-GPUs with minimum requirement of only 2G RAM. Clinical relevance- We develop a memory-efficient deep Brain tumor segmentation tool that significantly reduces the hardware requirement of tumor segmentation while maintaining comparable accuracy and time. These advantages make our framework suitable for widespread use in clinical applications, especially in low-income regions. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available:https://github.com/Nima-Hs/BraTS.
Feature disentangling and reciprocal learning with label-guided similarity for multi-label image retrieval

Image retrieval usually faces scale-variance issues as the amount of image data is rapidly increasing, which calls for more accurate retrieval technology. Besides, existing methods usually treat pair-image similarity as a binary value which indicates whether two images share either at least one common label or none of shared labels. However, such similarity definition cannot truly describe the similarity ranking for different numbers of common labels when handling the multi-label image retrieval problem. In this paper, a Feature Disentangling and Reciprocal Learning (FDRL) method is introduced with label-guided similarity to solve the above multi-label image retrieval problem. Multi-scale features are first extracted by BNInception network and then disentangled to the corresponding high- and low-correlation features under the guidance of estimated global correlations. After that, the disentangled features are combined through a reciprocal learning approach to enhance the feature representation ability. Final hash codes are learned based on the global features derived from BNInception network and the combined features generated by reciprocal learning. The whole network is optimized by the proposed label-guided similarity loss function which aims to simultaneously preserve absolute similarity for hard image pairs and relative similarity for soft image pairs. Experimental results on three public benchmark datasets demonstrate that the proposed method outperforms current state-of-the-art techniques. The code is online here: 'https://github.com/Yong-DAI/FDRL'.
Process-Oriented Stream Classification Pipeline: A Literature Review

Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.
A hierarchical approach for building distributed quantum systems

In this paper, a multi-layer hierarchical architecture is proposed for distributing quantum computation. In a distributed quantum computing (DQC), different units or subsystems communicate by teleportation in order to transfer quantum information. Quantum teleportation requires classical and quantum resources and hence, it is essential to minimize the number of communications among these subsystems. To this end, a two-level hierarchical optimization method is proposed to distribute the qubits among different parts. In Level I, an integer linear programming model is presented to distribute a monolithic quantum system into K balanced partitions which results in the minimum number of non-local gates. When a qubit is teleported to a destination part, it can be used optimally by other gates without being teleported back to the destination part. In Level II, a data structure is proposed for quantum circuit and a recursive function is applied to minimize the number of teleportations. Experimental results show that the proposed approach outperforms the previous ones.
NullSpaceRDAR: Regularized discriminative adaptive nullspace for object tracking

Recently, discriminative-based and Siamese-based trackers have achieved outstanding performance on most tracking benchmarks. However, these trackers use the pre-trained backbone networks that have been mainly designed for classification to extract target-specific features without taking into consideration the visual object tracking task. In this paper, we propose NullSpaceRDAR, a novel tracker that learns a robust target-specific feature representation specifically designed for object tracking. This feature representation is learned by projecting the traditional backbone feature space onto a novel discriminative nullspace that is used to regularize the backbone loss function. We refer to the discriminative nullspace herein as joint-nullspace. The same target features (i.e., target-specific) in the proposed joint-nullspace are collapsed into a single point, and different target-specific features are collapsed into different points. Consequently, the joint-nullspace forces the network to be sensitive to the object’s variations from the same class (i.e., intra-class variations). Moreover, a modified adaptive loss function is developed for bounding box estimation to select the most suitable loss function from a super set family of loss functions based on the training data. This makes NullSpaceRDAR more robust to different challenges such as occlusions and background clutter. Extensive experiments have been conducted on six benchmarks to evaluate NullSpaceRDAR: OTB100, VOT variations (VOT2018, VOT2019, and VOT2020), LaSOT, TrackingNet, UAV123, and GOT10k. The results show that NullSpaceRDAR outperforms the state-of-the-art trackers.
Share the tensor tea

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to supports the TPC-H benchmark, and it provides performance that are comparable to, and often better than, that of specialized CPU and GPU query processors.
Forecasting the Use of Decontamination Robots in the COVID Era

This paper explores the introduction and possible adoption of robots that are designed to clean or decontaminate public spaces, such as airports, lobbies, or medical facilities. Such robots are capable of performing autonomous or semiautonomous cleaning functions. They offer the advantage of cleaning areas that may contain harmful biological agents, like the Sars-Cov-2 virus (also known as COVID-19), without exposing human cleaning crews to the risk of infection. Such robots offer a number of additional advantages, including the ability to work 24 hours a day, 7 days a week, to decontaminate more precisely than human cleaning crews, and a variety of operational characteristics that may make them more efficient and cost-effective than traditional cleaning methods. The initial part of the paper involves a case study of a company called Build with Robots, which is in the process of commercializing robots to perform these types of functions. An overview is provided of the general technology involved with these types of robots, as well as an analysis of the industry, and discussion of commercialization prospects. The paper then conducts a technology forecast to determine overall trends for the development and adoption of this technology. The authors use a bibliometric approach and literature review to gather data sets and perform S-curve analysis. This forecast is then used to provide overall conclusions and recommendations about the current state of this technology and its commercialization prospects.