Reviewerly/exHarmony huggingface dataset:
Blockchain and Beyond: Understanding Blockchains Through Prototypes and Public Engagement

This article presents an annotated portfolio of projects that seek to understand and communicate the social and societal implications of blockchains, DLTs and smart contracts. These complex technologies rely on human and technical factors to deliver cryptocurrencies, shared computation and trustless protocols but have a secondary benefit in providing a moment to re-think many aspects of society, and imagine alternative possibilities. The projects use design and HCI methods to relate blockchains to a range of topics, including global supply chains, delivery infrastructure, smart grids, volunteering and charitable giving, through engaging public, exploring ideas and speculating on possible futures. Based on an extensive annotated portfolio we draw out learning for the design of blockchain systems, broadening participation and surfacing questions around imaginaries, social implications and engagement with new technology. This paints a comprehensive picture of how HCI and design can shape understandings of the future of complex technologies.
Autonomous Single-Image Drone Exploration With Deep Reinforcement Learning and Mixed Reality

Autonomous exploration is a longstanding goal of the robotics community. Aerial drone navigation has proven to be especially challenging. The stringent requirements on cost, weight, maneuverability, and power consumption do not allow exploration approaches to easily be employed or adapted to different types of environments. End-to-End Deep Reinforcement Learning (DRL) techniques based on Convolutional Networks approximators, which grant constant-time computation, predefined memory usage, and deliver high visual perception capabilities, represent a very promising alternative to current state of the art solutions relying on metric environment reconstruction. In this work, we address the autonomous exploration problem with aerial robots with a monocular camera based on DRL. Specifically, we propose a novel asymmetric actor-critic model for drone exploration that efficiently leverages ground truth information provided by the simulator environment to speed up learning and enhance final exploration performances. Furthermore, in order to reduce the sim-to-real gap for exploration, we present a novel mixed reality framework that allows an easier, smoother, and safer simulation to real-world transition. Both aspects allow to further exploit the great potential of simulation engines and contribute to reducing the risk associated with directly deploying algorithms on a physical platform with no intermediate step between the simulation and the real world. This is well-known to create several safety concerns and be dangerous when deploying aerial vehicles. Experimental results with a drone exploring multiple environments show the effectiveness of the proposed approach.
Sentiment Analysis of Weibo Comments Based on Graph Neural Network

Weibo is one of the most important online social platforms.Currently, user comments are increasing rapidly, which makes data management difficult.Comments show the non-standardized and colloquial form of expression.Traditional sentiment analysis techniques are no longer applicable to unspecified sentence analysis tasks.To mitigate overreliance on text sequences, ignoring syntactic structure, and the poor interpretability of feature space that are typical of traditional classification models, a sentiment classification model based on a graph neural network (GNN) is developed in this study.For each comment text, the dependency syntax is used to construct the semantic graph of the short text.Aiming at the heterogeneity of the semantic graph, the spatial domain graph filter is designed for feature extraction.Concurrently, long short-term memory (LSTM) is used as a state updater to filter node noise.In this method, a graph neural network is used as a semantic parser to encode the syntactic dependency tree, which can extract the semantic and syntactic features of sentences concurrently.Experimental results show that GNN-LSTM has achieved superior performance in the Weibo comments dataset by achieving 95.25% accuracy and 95.22% F1 score.
Combining Conceptual Frameworks on Maternal Health in Indigenous Communities—Fuzzy Cognitive Mapping Using Participant and Operator-independent Weighting

A recurring issue in intercultural research is whose knowledge informs conceptualization and design of projects or interventions. Fuzzy cognitive mapping uses arrows and weights to represent stakeholder knowledge on causal relationships and can generate composite theories to inform research and action. Cognitive mapping is accessible across different cultures, but participant weighting is not always straightforward. We describe a procedure to combine and condense maps from different stakeholders and an alternative operator-independent weighting procedure adapted from Harris’s discourse analysis.
A multi- unmanned aerial vehicle dynamic task assignment method based on bionic algorithms

Because of the long calculation time in task allocation algorithms and the long travelled distance of the Unmanned Aerial Vehicle (UAV) during task allocation, a multi-UAV task allocation method based on three bionic algorithms, which are the ant colony algorithm, bat algorithm, and gray wolf algorithm was proposed in this paper. The Multi-UAV Dynamic Task Assignment Method Based on Bionic Algorithms (TABA) method dynamically allocates UAVs based on the number of task points and includes a comparison mechanism. The results of experiments demonstrate that, among the three algorithms, the proposed method has 30% improvement to the conventional methods, and the UAV travel time is about one-third that of the original algorithm on average, thereby revealing the effectively reduced algorithm complexity. Moreover, the algorithm can reduce the travel time of UAVs.
Scanning Hyperspectral Imaging for In Situ Biogeochemical Analysis of Lake Sediment Cores: Review of Recent Developments

Hyperspectral imaging (HSI) in situ core scanning has emerged as a valuable and novel tool for rapid and non-destructive biogeochemical analysis of lake sediment cores. Variations in sediment composition can be assessed directly from fresh sediment surfaces at ultra-high-resolution (40−300 μm measurement resolution) based on spectral profiles of light reflected from sediments in visible, near infrared, and short-wave infrared wavelengths (400−2500 nm). Here, we review recent methodological developments in this new and growing field of research, as well as applications of this technique for paleoclimate and paleoenvironmental studies. Hyperspectral imaging of sediment cores has been demonstrated to effectively track variations in sedimentary pigments, organic matter, grain size, minerogenic components, and other sedimentary features. These biogeochemical variables record information about past climatic conditions, paleoproductivity, past hypolimnetic anoxia, aeolian input, volcanic eruptions, earthquake and flood frequencies, and other variables of environmental relevance. HSI has been applied to study seasonal and inter-annual environmental variability as recorded in individual varves (annually laminated sediments) or to study sedimentary records covering long glacial−interglacial time-scales (>10,000 years).
DoH Tunneling Detection System for Enterprise Network Using Deep Learning Technique

In spite of protection mechanisms for Domain Name System (DNS), such as IP blacklist and DNS Firewall, DNS still has privacy issues in reality, since DNS is a plain-text protocol. Recently, to resolve this problem, an encrypted DNS, called DNS-over-HTTPS (DoH), has been developed, and is becoming more widespread. As the secured version of DNS, DoH guarantees privacy and security to prevent various attacks such as eavesdropping and manipulating DNS data by using the HTTPS protocol to encrypt the data between the DoH client and the DoH-based DNS resolver. DoH is one of the best security options for an enterprise network where more sensitive data protection is required. However, DoH may cause an unintended security breach, i.e., information leakage via malicious DoH tunneling. Since the DoH traffic is encrypted and indistinguishable from other HTTPS traffic, data hidden inside DoH packets can be easily leaked out of an enterprise network. Although some countermeasures to detect DoH tunneling attacks have been proposed, they still have limitations. Previous research used Supervised Machine Learning methods to detect DoH tunneling, which required a high volume of labeled data. In practice, collecting and labeling all of the data is an impossible task, especially in DoH, when all of the data are encrypted. Furthermore, Supervised Machine Learning methods rely heavily on human-engineered feature extraction, which makes classifying encrypted DoH traffic difficult. Furthermore, no previous research has mentioned a complete functional DoH detection applied to network infrastructure. Therefore, we propose a detection system for DoH tunneling attacks based on Transformer to detect a malicious DoH tunneling and build a fully functional DoH detection system that can be integrated with the security operation system of an enterprise network. The experiment results show a significant improvement compared with previous works.
Research on Distributed Energy Consensus Mechanism Based on Blockchain in Virtual Power Plant

Virtual power plant (VPP) composed of a large number of distributed energy resources (DERs) has become a regional multienergy aggregation model to realize the large-scale integration of renewable energy generation into the grid. Due to the characteristics of centralized management, the existing energy operation mode is difficult to simply apply to distributed energy resources transactions. The decentralization, transparency, contract execution automation and traceability of blockchain technology provide a new solution to the aggregation of decentralized resources and the opacity of transactions in VPP. In this paper, the existing problems of virtual power plants are analyzed, and the virtual power plant trading model is designed, which realizes the transparent benefit distribution and message transmission of virtual power plants. The virtual power plant blockchain network based on blockchain technology in this model solves the DERs coordination problem in VPP and the security and efficiency problems in information transmission. Combined with the actual situation of virtual power plant, the blockchain network collaboration mechanism (BNCM), which is convenient to reach agreement, is designed. Compared with the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, this mechanism can make DERs reach a consensus quickly. Finally, simulation experiments on the consensus algorithm show that the algorithm can reduce the collaboration time between DERs under the premise of ensuring the same fault tolerance rate and is more suitable for VPP scenarios with a large number of DERs.
Keypoint Communities

We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation. We model all keypoints belonging to a human or an object –the pose– as a graph and leverage insights from community detection to quantify the independence of keypoints. We use a graph centrality measure to assign training weights to different parts of a pose. Our proposed measure quantifies how tightly a keypoint is connected to its neighborhood. Our experiments show that our method outperforms all previous methods for human pose estimation with fine-grained keypoint annotations on the face, the hands and the feet with a total of 133 keypoints. We also show that our method generalizes to car poses.
Disentangled Lifespan Face Synthesis

Age group 5 Age group 0 (0-2) Age group 1 (3-6) Age group 2 (7-9) Age group 3 (15-19) Age group 4 (30-39) Age group 5 (50-69) Ours LATS InGan Figure 1: Examples of generated face images using our lifespan face synthesis model and two state-of-the-art alternatives.InGAN [43] generates valid texture transformation but fails in shape transformation and identity preservation.LATS [24]improves on shape transformation and identity preservation, but is poor in reconfiguration (the re-generated image in the same age group 5 as the reference looks very different).In contrast, our model overcomes all these limitations, yielding the most plausible effects of aging whilst being identity-preserving.
A Semi-supervised Deep Learning Method for Cervical Cell Classification

Currently, the Thinprep cytologic test (TCT) is the most popular cervical cancer cytology test technique. It can detect precancerous conditions and microbial infections. However, this technique entirely relies on manual operation and doctors' naked eye observation, resulting in a heavy workload and low accuracy rate. Recently, automatic pathological diagnosis has been developed to solve this problem. Cervical cell classification is a key technology in the intelligent cervical cancer diagnosis system. Training a deep neural network-based classification model requires a large amount of data. However, cervical cell labeling requires specialized physicians and the cost of labeling is high, resulting in a lack of sufficient labeling data in this field. To address this problem, we propose a method to ensure high accuracy in cervical cell classification with a small amount of labeled data by introducing manual features and a voting mechanism to achieve data expansion in semi-supervised learning. The method consists of three main steps, using a clarity function to filter out high-quality cervical cell images, annotating a small amount of them, and balancing the training data using a voting mechanism. With a small amount of labeled data, the accuracy of the proposed method in this paper can reach to 91.94%.
Decentralized Storage Of Educational Assets Using NFTs And Blockchain Technology

In today's world, education is an integral part of the societal life. Where the students' records are vulnerable to mutability, hacking and tampering due to the centralized storage of the educational assets. Addressing these problems, this paper aims to solve them by the application of a decentralized education model using the Blockchain Technology. The student's educational assets are cryptographically hashed and represented using the Non-Fungible Tokens(NFTs) to uniquely identify each educational asset, which are stored on the Ethereum Blockchain, Due to the limitations of cost and data storage on the blockchain, Interplanetary File System (IPFS) is implemented, where the original data is stored and the unique Id returned by the IPFS i.e., Content Identifier(CID) is used by the users to access the Educational Asset.
Q-Match: Iterative Shape Matching via Quantum Annealing

Finding shape correspondences can be formulated as an ${\mathcal{N}}{\mathcal{P}} - hard$ quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density. A promising research direction is to tackle such quadratic optimization problems over binary variables with quantum annealing, which allows for some problems a more efficient search in the solution space. Unfortunately, enforcing the linear equality constraints in QAPs via a penalty significantly limits the success probability of such methods on currently available quantum hardware. To address this limitation, this paper proposes Q-Match, i.e., a new iterative quantum method for QAPs inspired by the α-expansion algorithm, which allows solving problems of an order of magnitude larger than current quantum methods. It implicitly enforces the QAP constraints by updating the current estimates in a cyclic fashion. Further, Q-Match can be applied iteratively, on a subset of well-chosen correspondences, al-lowing us to scale to real-world problems. Using the latest quantum annealer, the D-Wave Advantage, we evaluate the proposed method on a subset of QAPLIB as well as on isometric shape matching problems from the FAUST dataset.
Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre

The level of difficulty that can be envisioned in a cloud data center will not grow with convention. As a result, all hosts should have a standard and pervasive collection of memory and communication characteristics in order to lower ownership costs and operate virtual machine instances. This solution includes fundamental foundations and integrated component basics that will allow an IT or federal agency to embrace cloud computing domestically via private virtual cloud data centers. These private cloud data centers would later be developed to purchase and develop IT services on the outside. They are well aware of the obstacles to cloud computing’s acceptance, including concerns about credibility, privacy, interoperability, and marketplaces. In addition, this procedure describes critical standards and collaborations to address these issues. Ultimately, it offers a coherent response to deploying safe data centers using cloud computing services from both a technological and an IT strategic standpoint. To foster creativity, invention, learning, and enterprise, a private data center and cloud computing must be established to combine the activities of different research teams. In the framework of energy-efficient distribution of resources in private cloud data center architecture, we focus on system structure investigations. On the other hand, we want to equip private cloud providers with the current design and performance analysis for energy-efficient resource allocation. The methodology should be adaptable enough to support a wide range of computing systems, as well as on-demand and extensive resource providing approaches, cloud environment scheduling, and bridging the gap between private cloud users and a complete image of offers.
Detecting Human-Object Relationships in Videos

We study a crucial problem in video analysis: human-object relationship detection. The majority of previous approaches are developed only for the static image scenario, without incorporating the temporal dynamics so vital to contextualizing human-object relationships. We propose a model with Intra- and Inter-Transformers, enabling joint spatial and temporal reasoning on multiple visual concepts of objects, relationships, and human poses. We find that applying attention mechanisms among features distributed spatio-temporally greatly improves our understanding of human-object relationships. Our method is validated on two datasets, Action Genome and CAD-120-EVAR, and achieves state-of-the-art performance on both of them.
Knowledge distillation-based deep learning classification network for peripheral blood leukocytes

Leukocytes are a critical component of the human immune system. Many diseases can be diagnosed by analyzing the morphology and number of leukocytes. Due to the extensive application of convolutional neural networks (CNNs) in computer vision (CV), computer-aided automated methods have become the preferred methods for medical image diagnoses. Recently, Transformer has emerged in CV with performance comparable to CNN. Assisted diagnoses are often performed on resource-limited computing devices. The deployments of deep learning (DL) models are limited by the number of parameters and the computation. This study provides a DL training framework that introduces a model compression method of knowledge distillation (KD) in the classification of leukocytes, using small models instead of large ones, to achieve accurate results. Firstly, large models with CNN or Transformer structure are pre-trained on the mixed leukocyte dataset with 25,830 original images. Then, the dark knowledge of the pre-trained large models is extracted by KD, and the small models are trained. Finally, the best performing small model is selected as the final prediction model, which achieves 98.31% testing accuracy on the mixed dataset. The proposed framework on the enhanced BCCD dataset achieves 99.88% testing accuracy, which is better than other methods. It effectively combines the advantages of large and small models to meet the requirements of low resource consumption and high accuracy.
Low-light image enhancement using inverted image normalized by atmospheric light

Images captured under low-light conditions are characterized by problems, including low brightness, low contrast, and color distortion. Low-light image enhancement, aims to enhance visual effects for the benefit of subsequent image processing and computer vision tasks, but it presents a challenging problem. This paper proposes an efficient and fast algorithm for low-light image enhancement. First, it derives a mathematical connection between the Retinex theory and the atmospheric scattering model by normalizing an inverted image to atmospheric light. Next, medium transmission is derived as a function of the saturation of the scene radiance only, and the image-adaptive saturation of scene radiance is estimated via a simple stretch function according to the average saturation of the low-light image. The proposed algorithm is fast because it requires no training, prior knowledge, or refinement. The simulation results confirm that in terms of both computational complexity and enhancement efficiency, the proposed low-light enhancement scheme outperforms other state-of-the-art approaches.
Study of energy improvement with the insertion of bifacial modules and solar trackers in photovoltaic installations in Brazil

To improve the performance of photovoltaic systems, solar trackers have been widely used in photovoltaic power plants. Recently, bifacial modules also have started being widely used in the photovoltaic market for the same purpose. Bifacial technology is currently being widely discussed by the scientific community due to the considerable challenges and possibilities, such as modelling and mismatch of irradiance on the rear side of the module, albedo databases, optimum orientation and heights, and the meagre amount of measurement and study data for specific locations. This work presents and compares simulation results of a photovoltaic plant with monofacial and bifacial modules installed on a fixed structure and solar trackers in seven localities in Brazil. Bifacial gains, tracker gains, and bifacial-tracker gains were calculated and compared for each location. A comparison of system gains was also made by varying ground coverage ratio and clearance height in two locations. The results showed that the bifacial gain varies from 3.78% to 8.16%, and the tracker gain varies from 13.40% and 18.20% depending on weather conditions and system configuration. The total gain from the application of bifacial modules and trackers varies between 19.39% and 27.39%. Therefore, the potential of combining these technologies to enhance the technical and economic viability of photovoltaic plants is emphasised.