Reviewerly/exHarmony huggingface dataset:
Attribute-Aligned Domain-Invariant Feature Learning for Unsupervised Domain Adaptation Person Re-Identification

Domain invariance and discrimination of learned features as two crucial factors affect the performance of unsupervised domain adaptation (UDA) person re-identification (Re-ID). Person attributes (such as “backpack”, “boots”, “handbag”, etc) remaining unchanged across multiple domains have been used as mid-level visual-semantic information in UDA person Re-ID. As two main challenges, both misalignment of attribute-related regions across multiple images and domain shift between source and target domains affect the learning of domain-invariant features (DIF). To address the above two challenges, this article proposes to take advantage of the stability of person attributes and the complementarity of person attributes and the corresponding low-level visual features to guide the learning of discriminative DIF. Specifically, the proposed solution contains the generation of latent attribute-correlated visual features (GLAVF), DIF learning under the guidance of person attributes, and the alignment of person attributes corresponding to the local regions of pedestrian images. Due to the gap between person attributes and visual features, person attributes are first converted into latent attribute-correlated visual features (LAVF) without any specific domain information in GLAVF, and then LAVF are used as the substitutions of person attributes to guide the learning of DIF. To enhance the discrimination of learned features, the proposed solution mainly explores the alignment between person attributes and corresponding local regions, and the alignment of the same person attributes across multiple pedestrian images. A fully connected layer is used to achieve the above two types of alignment in the proposed framework, which reduces the adverse impacts of inference information and ensures the semantic consistency between person attributes and corresponding local regions across multiple pedestrian images. The effectiveness of the proposed solution is confirmed on four existing datasets by comparative experiments.
The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond

Abstract Background The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. Methods Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
EDN: Salient Object Detection via Extremely-Downsampled Network

Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. High-level features, which although have long proven effective for many other tasks, yet have been barely studied for SOD. In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well. To this end, we introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image, leading to accurate salient object localization. To accomplish better multi-level feature fusion, we construct the Scale-Correlated Pyramid Convolution (SCPC) to build an elegant decoder for recovering object details from the above extreme downsampling. Extensive experiments demonstrate that EDN achieves state-of-the-art performance with real-time speed. Our efficient EDN-Lite also achieves competitive performance with a speed of 316fps. Hence, this work is expected to spark some new thinking in SOD. Code is available at https://github.com/yuhuan-wu/EDN.
Popularity-Opportunity Bias in Collaborative Filtering

This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias. That is, conditioned on user preferences that a user likes both items, the more popular item is more likely to be recommended (or ranked higher) to the user than the less popular one. This type of bias is harmful, exerting negative effects on the engagement of both users and item providers. Thus, we conduct a three-part study: (i) By a comprehensive empirical study, we identify the existence of the popularity-opportunity bias in fundamental matrix factorization models on four datasets; (ii) coupled with this empirical study, our theoretical study shows that matrix factorization models inherently produce the bias; and (iii) we demonstrate the potential of alleviating this bias by both in-processing and post-processing algorithms. Extensive experiments on four datasets show the effective debiasing performance of these proposed methods compared with baselines designed for conventional popularity bias.
Microwaves in Quantum Computing

Quantum information processing systems rely on a broad range of microwave technologies and have spurred development of microwave devices and methods in new operating regimes. Here we review the use of microwave signals and systems in quantum computing, with specific reference to three leading quantum computing platforms: trapped atomic ion qubits, spin qubits in semiconductors, and superconducting qubits. We highlight some key results and progress in quantum computing achieved through the use of microwave systems, and discuss how quantum computing applications have pushed the frontiers of microwave technology in some areas. We also describe open microwave engineering challenges for the construction of large-scale, fault-tolerant quantum computers.
Blockchain for Public Services: A Systematic Literature Review

Blockchain is heralded as being “the next big thing” that promises to radically transform society and the economy in near-future applications. While scholarly literature on blockchain has largely focused on bitcoin and cryptofinance, in recent years, a body of scholarship has started to emerge on blockchain in the public sector. The characteristics of blockchain have made it a promising technology to transform many activities related to public policy and public service provision, such as administrative processes, welfare provision and regulation practices. This article provides, to the best of our knowledge, the first systematic literature review of the use of blockchain across all the main public services. This systematic review identifies the public services most likely to be impacted by the introduction of blockchain. It also highlights the main potential benefits, costs and risks of blockchain for government, civil servants and citizens. Governments are found to benefit mainly from improvements in efficiency and traceability, while regulatory uncertainty and questions around scalability represent major costs and risks for them. Civil servants, the least studied actor in the literature, could benefit from blockchain through the reduction of red tape and improvements in coordination between agencies. Their lack of blockchain knowledge and skills represent a major cost as regards adoption. Regarding citizens, security and transparency are identified as being the most important benefits, while risks are mainly associated with data security concerns. The article concludes by noting several limitations in the literature and providing suggestions towards fruitful lines of research.
A Research Survey on Applications of Consensus Protocols in Blockchain

The concept of blockchain, widely known as virtual currencies, saw a massive surge in popularity in recent times. As far as the security of the blockchain is concerned, consensus algorithms play a vital role in the blockchain. Research has been done separately, or comparisons between a few of them have been presented previously. In this paper, we have discussed widely used consensus algorithms in the blockchain. The consensus protocols covered in this paper include PoW (Proof of Work), PoS (Proof of Stake), DPoS (Delegated Proof of Stake), PoET (Proof of Elapsed Time), PBFT (Practical Byzantine Fault Tolerance), and PoA (Proof of Authority). For each consensus, we have reviewed the properties, applications, and performance in the blockchain.
Network traffic classification for data fusion: A survey

Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking services, network traffic classification has attracted increasing attention. Many new traffic classification techniques have been developed and widely applied. However, the existing literature lacks a thorough survey to summarize, compare and analyze the recent advances of network traffic classification in order to deliver a holistic perspective. This paper carefully reviews existing network traffic classification methods from a new and comprehensive perspective by classifying them into five categories based on representative classification features, i.e., statistics-based classification, correlation-based classification, behavior-based classification, payload-based classification, and port-based classification. A series of criteria are proposed for the purpose of evaluating the performance of existing traffic classification methods. For each specified category, we analyze and discuss the details, advantages and disadvantages of its existing methods, and also present the traffic features commonly used. Summaries of investigation are offered for providing a holistic and specialized view on the state-of-art. For convenience, we also cover a discussion on the mostly used datasets and the traffic features adopted for traffic classification in the review. At the end, we identify a list of open issues and future directions in this research field.
Effective Management for Blockchain-Based Agri-Food Supply Chains Using Deep Reinforcement Learning

In agri-food supply chains (ASCs), consumers pay for agri-food products produced by farmers.During this process, consumers emphasize the importance of agri-food safety while farmers expect to increase their profits.Due to the complexity and dynamics of ASCs, the effective traceability and management for agri-food products face huge challenges.However, most of the existing solutions cannot well meet the requirements of traceability and management in ASCs.To address these challenges, we first design a blockchain-based ASC framework to provide product traceability, which guarantees decentralized security for the agri-food tracing data in ASCs.Next, a Deep Reinforcement learning based Supply Chain Management (DR-SCM) method is proposed to make effective decisions on the production and storage of agri-food products for profit optimization.The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed blockchain-based framework and the DR-SCM method under different ASC environments.The results show that reliable product traceability is well guaranteed by using the proposed blockchain-based ASC framework.Moreover, the DR-SCM can achieve higher product profits than heuristic and Q-learning methods.
A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm

In classification problems, normally there exists a large number of features, but not all of them contributing to the improvement of classification performance. These redundant features make the classification problem time consuming and often result in poor performance. Feature selection methods have been proposed to reduce the number of features, minimize computational cost, and maximize classification accuracy. As a wrapper-based approach, evolutionary algorithms have been widely applied in feature subset selection tasks. However, some of them trap into local minima, especially when the number of features increases, while others are not efficient in computational time. This paper proposes a Modified Differential Evolution (DE) approach to Feature Selection (MDEFS) by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy. Some modifications are made to the standard DE crossover and its key control parameters to enhance the proposed algorithm’s capabilities further. The proposed method has been compared to several state-of-the-art methods utilizing standard datasets from the UCI repository and results of the experiments demonstrate the superiority of the proposed approach.
Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.
CE-FPN: enhancing channel information for object detection

Feature pyramid network (FPN) has been an efficient framework to extract multi-scale features in object detection. However, current FPN-based methods mostly suffer from the intrinsic flaw of channel reduction, which brings about the loss of semantical information. And the miscellaneous feature maps may cause serious aliasing effects. In this paper, we present a novel channel enhancement feature pyramid network (CE-FPN) to alleviate these problems. Specifically, inspired by sub-pixel convolution, we propose sub-pixel skip fusion (SSF) to perform both channel enhancement and upsampling. Instead of the original 1 × 1 convolution and linear upsampling, it mitigates the information loss due to channel reduction. Then we propose sub-pixel context enhancement (SCE) for extracting stronger feature representations, which is superior to other context methods due to the utilization of rich channel information by sub-pixel convolution. Furthermore, we introduce a channel attention guided module (CAG) to optimize the final integrated features on each level. It alleviates the aliasing effect only with a few computational burdens. We evaluate our approaches on Pascal VOC and MS COCO benchmark. Extensive experiments show that CE-FPN achieves competitive performance and is more lightweight compared to state-of-the-art FPN-based detectors.
COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization

Abstract The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question–answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system ( http://einstein.ai/covid ) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.
Simulation of Eye Tracking Control based Electric Wheelchair Construction by Image Segmentation Algorithm

In this fast-paced world, it is very challenging for the elderly and disabled population to move independently to their desire places at any convenient time. Fortunately, some of the people have good eyesight and physically strong to take care of their survival. Nevertheless, Electric wheelchair (EWC) can provide them a better lifestyle with commendable confidence. At the same time, the hand, head and voice recognition-based EWC meet many limitations. Despite, the eye-tracking-based EWC provides a better smartness in their lifestyle. This research article discusses better accuracy achievement and minimizes the delay response time in the proposed system. The proposed eye-tracking EWC is differed from another existing system with good validation parameters of the controller and it introduces edge detection to identify the eye pupil position in the face. The proposed method includes a PID controller to control the DC motor, which in turn controls the rotation of wheel in EWC. This research article is mainly focused on the cost-effectiveness and improvement in the system performance. The display system is mounted in front of the sitting position of EWC users. The camera captures eye pupil position and it determines the direction of the EWC movement by controlling DC motor with the help of a PID controller. When derivative (D) control is used in the proposed system, the system response is quite faster and it reduces the delay time between the user and system reaction. This pupil of eye position is determined by a canny edge detector, which provides good results when compared with other edge detection approaches. Object detection in front of the EWC is an added advantage of the proposed system. The proposed article integrates all the activities and measures the system performance. The proposed model achieves an accuracy of about 90% and response time is least compared with the existing methods.
Grayscale Enhancement Colorization Network for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is an emerging and challenging cross-modality image matching problem because of the explosive surveillance data in night-time surveillance applications. To handle the large modality gap, various generative adversarial network models have been developed to eliminate the cross-modality variations based on a cross-modal image generation framework. However, the lack of point-wise cross-modality ground-truths makes it extremely challenging to learn such a cross-modal image generator. To address these problems, we learn the correspondence between single-channel infrared images and three-channel visible images by generating intermediate grayscale images as auxiliary information to colorize the single-modality infrared images. We propose a grayscale enhancement colorization network (GECNet) to bridge the modality gap by retaining the structure of the colored image which contains rich information. To simulate the infrared-to-visible transformation, the point-wise transformed grayscale images greatly enhance the colorization process. Our experiments conducted on two visible-infrared cross-modality person re-identification datasets demonstrate the superiority of the proposed method over the state-of-the-arts.
A survey of deep learning techniques for vehicle detection from UAV images

“Unmanned aerial vehicles” (UAVs) are now being used for a wide range of surveillance applications. Specifically, the detection of on-ground vehicles from UAV images has attracted significant attention due to its potential in applications such as traffic management, parking lot management, and facilitating rescue operations in disaster zones and rugged terrains. This paper presents a survey of deep learning techniques for performing on-ground vehicle detection from aerial imagery captured using UAVs (also known as drones). We review the works in terms of their approach to improve accuracy and reduce computation overhead and their optimization objective. We show the similarities and differences of various techniques and also highlight the future challenges in this area. This survey will benefit researchers in the area of artificial intelligence, traffic surveillance, and applications of UAVs.