Reviewerly/exHarmony huggingface dataset:
Simple but Effective: CLIP Embeddings for Embodied AI

Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to captioning and image manipulation. We investigate the effectiveness of CLIP visual backbones for Embodied AI tasks. We build incredibly simple baselines, named EmbCLIP, with no task specific architectures, inductive biases (such as the use of semantic maps), auxiliary tasks during training, or depth maps-yet we find that our improved baselines perform very well across a range of tasks and simulators. EmbCLIP tops the RoboTHOR ObjectNav leader-board by a huge margin of 20 pts (Success Rate). It tops the iTHOR 1-Phase Rearrangement leaderboard, beating the next best submission, which employs Active Neural Mapping, and more than doubling the % Fixed Strict metric (0.08 to 0.17). It also beats the winners of the 2021 Habitat ObjectNav Challenge, which employ auxiliary tasks, depth maps, and human demonstrations, and those of the 2019 Habitat PointNav Challenge. We evaluate the ability of CLIP's visual representations at capturing semantic information about input observations-primitives that are useful for navigation-heavy embodied tasks- and find that CLIP's representations encode these primitives more effectively than ImageNet-pretrained backbones. Finally, we extend one of our baselines, producing an agent capable of zero-shot object navigation that can navigate to objects that were not used as targets during training. Our code and models are available at https://github.com/allenai/embodied-clip.
Improved A-Star Algorithm for Long-Distance Off-Road Path Planning Using Terrain Data Map

To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm.
Locally Private Graph Neural Networks

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph. To address this problem, we propose a privacy-preserving, architecture-agnostic GNN learning framework with formal privacy guarantees based on Local Differential Privacy (LDP). Specifically, we develop a locally private mechanism to perturb and compress node features, which the server can efficiently collect to approximate the GNN's neighborhood aggregation step. Furthermore, to improve the accuracy of the estimation, we prepend to the GNN a denoising layer, called KProp, which is based on the multi-hop aggregation of node features. Finally, we propose a robust algorithm for learning with privatized noisy labels, where we again benefit from KProp's denoising capability to increase the accuracy of label inference for node classification. Extensive experiments conducted over real-world datasets demonstrate that our method can maintain a satisfying level of accuracy with low privacy loss.
Blockchain and the Evolving Financial Market: A Literature Review

Blockchain technology has gained widespread utility, particularly in the field of finance due to the popularity of bitcoin. There are other areas such as energy trade and securities market, which have evolved due to blockchain. These fields are new and building regulations and architecture to enhance their functionality. There are studies on blockchain finance and this paper aims to summarize a few of the pathbreaking studies and guide future research in this field. This paper reviews 77 journal publications related to blockchain from 2018 to 2021 available in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The selected papers have been grouped into 10 categories. This overview indicates that research in blockchain finance needs to focus on developing better privacy and security mechanism as well as better regulations regarding financial transactions not only to benefit from “Initial Coin Offerings”, but also to diversify investor’s risk.
FAQ chatbot and inclusive learning in massive open online courses

Recognizing the research gap involving the lack of equity considerations in new technology implementation, this study compares students' learning experiences when using an FAQ chatbot with using an FAQ webpage. We trained a natural language processing–based chatbot utilizing content from an FAQ webpage and deployed it in two journalism massive open online courses (MOOCs) with 46 students and compared their experiences with 74 students' experiences with the FAQ webpage as a baseline. There were equal numbers of male and female students, their ages ranged from 18 to 65+, and they hailed from 45 unique countries. Considering the importance of supporting students with an inclusive Q&A experience before implementing any new technology into real-world operation, this study investigates students' disparate Q&A experiences by measuring their intention to use the interface as well as perceived Q&A service quality, enjoyment, and barriers utilizing a between-subjects online experiment. The results indicate that the students preferred an FAQ webpage over an FAQ chatbot, and the chatbot users experienced a higher magnitude of barriers compared to the webpage users. For the chatbot users, we found that region and native language factors influenced their Q&A experiences significantly. We discussed the meaning of the students’ disparate experiences from multiple perspectives—namely, human-computer interaction, MOOC context, and technologies as social practice aspects. Lastly, we determined how feasible it is to provide an inclusive learning experience for the MOOC population with the FAQ chatbot, based on the contextualized meaning of MOOC inclusiveness in current literature. This study suggests multi-faceted aspects to consider when adopting new technologies in MOOCs to provide an inclusive learning experience, and underscores the need for more active research in chatbot use to serve diverse student needs in MOOCs.
A data-centric review of deep transfer learning with applications to text data

In recent years, many applications are using various forms of deep learning models. Such methods are usually based on traditional learning paradigms requiring the consistency of properties among the feature spaces of the training and test data and also the availability of large amounts of training data, e.g., for performing supervised learning tasks. However, many real-world data do not adhere to such assumptions. In such situations transfer learning can provide feasible solutions, e.g., by simultaneously learning from data-rich source data and data-sparse target data to transfer information for learning a target task. In this paper, we survey deep transfer learning models with a focus on applications to text data. First, we review the terminology used in the literature and introduce a new nomenclature allowing the unequivocal description of a transfer learning model. Second, we introduce a visual taxonomy of deep learning approaches that provides a systematic structure to the many diverse models introduced until now. Furthermore, we provide comprehensive information about text data that have been used for studying such models because only by the application of methods to data, performance measures can be estimated and models assessed.
Kidney tumor segmentation from computed tomography images using DeepLabv3+ 2.5D model

Kidney cancer is a public health problem that affects thousands of people worldwide. Accurate kidney tumor segmentation is an important task that helps doctors to reduce the time to interpret images, diagnose, and plan the treatment of the diseases. However, because of the heterogeneity of tumors, manually segmenting is a time-consuming process and subject to variability among specialists. In the last years, convolutional neural networks (CNNs) have gained popularity for automatic medical image segmentation. Three-dimensional (3D) networks have a high segmentation capacity, but they are complex and have high computational costs. Thus, two-dimensional networks are the most used owing to the relatively low memory consumption, but they do not exploit 3D features. Therefore, in the present study, a 2.5D network that balances memory consumption and model complexity is proposed to assist doctors specialized in kidney tumor diagnosis in computed tomography (CT). There are three major steps in the proposed method: (1) preprocessing of the KiTS19 dataset, (2) initial segmentation using the DeepLabv3+ 2.5D model with DPN-131 encoder, and (3) reduction of false positives using image processing. The performance of the proposed method was assessed in 210 CTs from the available KiTS19 dataset. In the best scenario, the proposed method presented accuracy of 99.71%, sensitivity of 84.24%, specificity of 99.82%, Dice coefficient of 85.17%, Jaccard index of 75.62%, Hausdorff distance of 18.39 mm, and average surface distance of 3.36 mm. Thus, the 2.5D neural network approach is capable of efficiently solving the problem of segmentation of kidney tumors in CT images, presenting results comparable to those of high-performance 3D neural networks.
Perceptual metric-guided human image generation

Pose transfer, which synthesizes a new image of a target person in a novel pose, is valuable in several applications. Generative adversarial networks (GAN) based pose transfer is a new way for person re-identification (re-ID). Typical perceptual metrics, like Detection Score (DS) and Inception Score (IS), were employed to assess the visual quality after generation in pose transfer task. Thus, the existing GAN-based methods do not directly benefit from these metrics which are highly associated with human ratings. In this paper, a perceptual metrics guided GAN (PIGGAN) framework is proposed to intrinsically optimize generation processing for pose transfer task. Specifically, a novel and general model-Evaluator that matches well the GAN is designed. Accordingly, a new Sort Loss (SL) is constructed to optimize the perceptual quality. Morevover, PIGGAN is highly flexible and extensible and can incorporate both differentiable and indifferentiable indexes to optimize the attitude migration process. Extensive experiments show that PIGGAN can generate photo-realistic results and quantitatively outperforms state-of-the-art (SOTA) methods.
An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information

In recent years, explainable recommendation has been a topic of active study. This is because the branch of the machine learning field related to methodologies is enabling human understanding of the reasons for the outputs of recommender systems. The realization of explainable recommendation is widely expected to increase both user satisfaction and the demand for explainable recommendation systems. Explainable recommendation utilizes a wealth of side information (such as sellers, brands, user ages and genders, and bookmark information, among others) to expound the decision-making reasoning applied by recommendation models. In explainable recommendation, although learning side information containing numerous variables leads to rich interpretability, learning too many variables presents a challenge because decreases the amount of learning that a given computational resource can perform, and the accuracy of the recommendation model may be degraded. However, numerous and diverse variables are included in the side information stored by the actual companies operating massive real-world services. Hence, to realize practical applications of this valuable information, it is necessary to resolve problems such as computational cost. In this study, we propose a new framework for explainable recommendation based on an improved knowledge graph attention network model, which utilizes the side information of items and realizes high recommendation accuracy. The proposed framework enables direct interpretation by visualizing the reasons for the recommendations provided. Experimental results show that the proposed framework reduced computational time requirements by approximately 80%, while maintaining recommendation accuracy by enabling the model to learn the probabilistically given edges included in the graph structure. Moreover, the results show that the proposed framework exhibited richer interpretability than the conventional model. Finally, a multifaceted analysis suggests that the proposed framework is not only effective as an explainable recommendation model but also provides a powerful tool for planning various marketing strategies.
A survey on blockchain systems: Attacks, defenses, and privacy preservation

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.
RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic

Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model.
Fairness and accuracy in horizontal federated learning

In the horizontal federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in a horizontal federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning. This paper proposes an algorithm to achieve more fairness and accuracy in horizontal federated learning (FedFa). It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model. An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed. This process assists clients in aggregating at the server with a more fair weighting. Our results show that the proposed FedFa algorithm outperforms the baseline algorithm in terms of accuracy and fairness.
Low-depth quantum state preparation

A crucial subroutine in quantum computing is to load the classical data of $N$ complex numbers into the amplitude of a superposed $n=\ensuremath{\lceil}{log}_{2}N\ensuremath{\rceil}$-qubit state. It has been proven that any algorithm universally implementing this subroutine would need at least $O(N)$ constant weight operations. However, the proof assumes that only $n$ qubits are used, whereas the circuit depth could be reduced by extending the space and allowing ancillary qubits. Here we investigate this space-time tradeoff in quantum state preparation with classical data. We propose quantum algorithms with $O({n}^{2})$ circuit depth to encode any $N$ complex numbers using only single- and two-qubit gates, and local measurements with ancillary qubits. Different variances of the algorithm are proposed with different space and runtime. In particular, we present a scheme with $O({N}^{2})$ ancillary qubits, $O({n}^{2})$ circuit depth, and $O({n}^{2})$ average runtime, which exponentially improves the conventional bound. While the algorithm requires more ancillary qubits, it consists of quantum circuit blocks that only simultaneously act on a constant number of qubits, and at most $O(n)$ qubits are entangled. We also prove a fundamental lower bound $\mathrm{\ensuremath{\Omega}}(n)$ for the minimum circuit depth and runtime with an arbitrary number of ancillary qubits, aligning with our scheme with $O({n}^{2})$. The algorithms are expected to have wide applications in both near-term and universal quantum computing.
Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.
Contrastive learning based self-supervised time-series analysis

Deep learning architectures usually require large scale labeled datasets for achieving good performance on general classification tasks including computer vision and natural language processing. Recent techniques of self-supervised learning have opened up new a research frontier where deep learning architectures can learn general features from unlabeled data. The task of self-supervised learning is usually accomplished with some sort of data augmentation through which the deep neural networks can extract relevant information. This paper presents a novel approach for self-supervised learning based time-series analysis based on the SimCLR contrastive learning. We present novel data augmentation techniques, focusing especially on time-series data, and study their effect on the prediction task. We provide comparison with several fault classification approaches on benchmark Tennessee Eastman dataset and report an improvement to 81.43% in the final accuracy using our contrastive learning approach. Furthermore we report a new benchmark of 80.80%, 81.05% and 81.43% for self-supervised training on Tennesee Eastman where a classifier is only trained with 5%, 10% or 50% percent of the available training data. Hence, we can conclude that the contrastive approach is very successful in time-series problems also, and further suitable for usage with partially labeled time-series datasets.