Reviewerly/exHarmony huggingface dataset:
Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However, the amount of data was limited, and prices were predicted for only a short period (less than two years).In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus, the input features used in the paper can be applied on Bitcoin price prediction.Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.
An artificial bee colony algorithm with a balance strategy for wireless sensor network

As a popular population based evolutionary computation algorithm, artificial bee colony (ABC) algorithms have attracted many researchers to do further works. In this paper, by utilizing a linkage detection strategy to distinguish nonseparable and separable functions, we present new balance strategies for the updating equations of ABC on the two types of functions respectively. First, for accelerating convergence rate, we propose a historic array to preserve the best individual which the population ever achieved. Second, for the different role of employed and onlooker bees during iterations, compared with the traditional algorithm, we present two updating equations for the two types of bees on the nonseparable and separable functions respectively. Third, a new multi-dimensional updating mechanism for the worst individual and a new updating strategy for scout bees are presented respectively. At last, for verifying its effectiveness, we apply the improved algorithm to optimize the power consumption of sensor nodes in a wireless sensor network. In this paper, verified by the test on the network, the basic benchmark functions and CEC2014, our algorithm shows superior performance with the compared modern algorithms.
A model-based infrastructure for the specification and runtime execution of self-adaptive IoT architectures

Abstract To meet increasingly restrictive requirements and improve quality of service (QoS), Internet of Things (IoT) systems have embraced multi-layered architectures leveraging edge and fog computing. However, the dynamic and changing IoT environment can impact QoS due to unexpected events. Therefore, proactive evolution and adaptation of the IoT system becomes a necessity and concern. In this paper, we present a model-based approach for the specification and execution of self-adaptive multi-layered IoT systems. Our proposal comprises the design of a domain-specific language (DSL) for the specification of such architectures, and a runtime framework to support the system behaviuor and its self-adaptation at runtime. The code for the deployment of the IoT system and the execution of the runtime framework is automatically produced by our prototype code generator. Moreover, we also show and validate the extensibility of such DSL by applying it to the domain of underground mining. The complete infrastructure (modeling tool, generator and runtime components) is available in a online open source repository.
Pillar-Based 3D Object Detection from Point Cloud with Multiattention Mechanism

Object detection in point clouds is a critical component in most autonomous driving systems. In this paper, in order to improve the effectiveness of image feature extraction and the accuracy of detection of point clouds, a pillar-based 3D point cloud object detection algorithm with multiattention mechanism is proposed, which includes three attention mechanisms SOCA, SOPA, and SAPI. The results show that the recognition accuracy of the optimized algorithm for cars, pedestrians, and cyclists on KITTI dataset is significantly improved on the detection benchmarks of BEV and 3D. Despite using only LiDAR, our algorithm outperforms PointPillars, which is one of the state-of-the-art algorithms for 3D object detection, with respect to both 3D and BEV view KITTI benchmarks while maintaining a relatively competitive speed.
A hue preservation lossless contrast enhancement method with RDH for color images

The contrast enhancement with reversible data hiding (CERDH) has become a hot topic in many fields. At present, most schemes applying CERDH methods to color images have the problem of color distortion. To solve this problem, this paper proposes a CERDH method for color images based on constant hue plane. It firstly separates the color images into min, mid and max channels. Then, a novel preprocessing is proposed to maintain the distribution of histogram for original image while vacate room for data hiding. After that, the mid channel is used for data hiding, and min and max channels are hue-preserved to avoid color distortion. Finally, the three channels compose a contrast-enhanced color image. The experimental results show that the proposed method achieves the best enhanced results based on CIEDE2000 compared with the state-of-the-art methods, and color distortion is also greatly reduced.
Online Video Super-Resolution With Convolutional Kernel Bypass Grafts

Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applications. These methods solely consider the distortion quality and ignore crucial requirements for online applications, e.g., low latency and low model complexity. In this paper, we focus on online video transmission in which VSR algorithms are required to generate high-resolution video sequences frame by frame in real time. To address such challenges, we propose an extremely low-latency VSR algorithm based on a novel kernel knowledge transfer method, named the convolutional kernel bypass graft (CKBG). First, we design a lightweight network structure that does not require future frames as inputs and saves extra time for caching these frames. Then, our proposed CKBG method enhances this lightweight base model by bypassing the original network with “kernel grafts”, which are extra convolutional kernels containing the prior knowledge of the external pretrained image SR models. During the testing phase, we further accelerate the grafted multibranch network by converting it into a simple single-path structure. The experimental results show that our proposed method can process online video sequences up to 110 FPS with very low model complexity and competitive SR performance.
Privacy-Preserving Split Learning for Large-Scaled Vision Pre-Training

The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, e.g., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradients, parameters, or activations, etc. However, there is little research on collaborative learning based on state-of-the-art and large-scaled models, mainly due to the high computation or communication overheads of the latest models. Training these models may be still unrealized for users’ terminals. In this paper, we make a first attempt at the sensitive image pre-training with large-scaled models in the collaborative learning scenario, and propose a new lightweight framework for split learning based on mask, Masked Split Learning (MaskSL). We further ensure its security by differential privacy. Besides, we model the computation and communication overheads of several collaborative learning approaches by deduction to illustrate advantages of our scheme. Finally, we design and conduct a series of experiments on real-world datasets, e.g., in face recognition and medical image classification tasks, to demonstrate the performance of MaskSL.
ATS-RRT*: an improved RRT* algorithm based on alternative paths and triangular area sampling

AbstractThe Rapidly Exploring Random Tree Star (RRT*) is a probabilistically complete algorithm. It is recognized as a better path planning algorithm, but its path quality and path planning speed still have room for improvement. This paper proposes an improved RRT* algorithm based on alternative paths and triangular area sampling (ATS-RRT*). The alternative paths strategy generates multiple initial paths based on whether the sample points can communicate with the target points and set the path with the smallest cost as the final initial path, which can speed up the initial path planning and improve the initial path finding rate. The triangular area sampling strategy combines every three adjacent nodes to generate some triangle areas and corresponding half-triangle areas. The path quality can be improved quickly by limiting the sampling in these triangle areas. In addition, the direct connection strategy with triangle nodes and the tabu table using in the Rewire process also speeds up the algorithm. Experiments show that the speed of finding the initial path and the success rate of finding the suboptimal path are improved by 2.3 and 1.45 times respectively compared with RRT*, Quick-RRT*, and Informed + Quick-RRT*.KEYWORDS: RRT*path planningsampling-based algorithmsdirect connection strategy Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant number 61873188, No. 32171902]; Natural Science Foundation of Tianjin [grant number 18JCYBJC19300].Notes on contributorsZhi-wei ZhangZhi W. Zhang is a mechanical engineering student at Tianjin University of Technology.Yun-wei JiaYun W. Jia is with the Tianjin University of Technology.Qi-qi SuQi Q. Su is a mechanical engineering student at Tianjin University of Technology.Xiao-tong ChenXiao T. Chen is a mechanical engineering student at Tianjin University of Technology.Bang-peng FuBang P. Fu is with the Tiandy Technologies Co., Ltd.
Information extraction from German radiological reports for general clinical text and language understanding

Abstract Recent advances in deep learning and natural language processing (NLP) have opened many new opportunities for automatic text understanding and text processing in the medical field. This is of great benefit as many clinical downstream tasks rely on information from unstructured clinical documents. However, for low-resource languages like German, the use of modern text processing applications that require a large amount of training data proves to be difficult, as only few data sets are available mainly due to legal restrictions. In this study, we present an information extraction framework that was initially pre-trained on real-world computed tomographic (CT) reports of head examinations, followed by domain adaptive fine-tuning on reports from different imaging examinations. We show that in the pre-training phase, the semantic and contextual meaning of one clinical reporting domain can be captured and effectively transferred to foreign clinical imaging examinations. Moreover, we introduce an active learning approach with an intrinsic strategic sampling method to generate highly informative training data with low human annotation cost. We see that the model performance can be significantly improved by an appropriate selection of the data to be annotated, without the need to train the model on a specific downstream task. With a general annotation scheme that can be used not only in the radiology field but also in a broader clinical setting, we contribute to a more consistent labeling and annotation process that also facilitates the verification and evaluation of language models in the German clinical setting.
Systemic stablecoin and the brave new world of digital money

Abstract New forms of money invite informed speculation regarding future possibilities. In this extended commentary, we explore five issue-areas that the growth of cryptocurrency and, more particularly, stablecoin have evoked. This new form of digital money has the potential to change the form and functioning of payments technologies and thus alter not just how something is paid for but what can be paid for. Moreover, as the now shelved plans for Facebook/Meta’s Libra/Diem indicate, there is scope for a major corporation or coalition of corporations to issue their own stablecoin and this greatly increases the likelihood of a ‘systemic’ stablecoin. This, in turn, could change where power resides and who exercises it in banking, finance and society. Concern with power leads to issues regarding the nature of change and thus to concern with possible financial, economic and social disruptions ranging across the nature of trust, bank business models, the effectiveness of central bank policy and security of payments systems. Given these issues, cryptocurrency and stablecoin have become a growing concern for regulators and this concern extends to the case for a retail central bank digital currency (CBDC). Finally, a new form of money invites discussion of its implications for the nature of money and this leads to matters of philosophical or social theory interest.
Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through ( <i>k</i> , 𝒫)-Core based Embedding

Expert finding is crucial for a wealth of applications in both academia and industry. Given a user query and trove of academic papers, expert finding aims at retrieving the most relevant experts for the query, from the academic papers. Existing studies focus on embedding-based solutions that consider academic papers’ textual semantic similarities to a query via document representation and extract the top- n experts from the most similar papers. Beyond implicit textual semantics, however, papers’ explicit relationships (e.g., co-authorship) in a heterogeneous graph (e.g., DBLP) are critical for expert finding, because they help improve the representation quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this article, we study expert finding on heterogeneous graphs by considering both the explicit relationships and implicit textual semantics of papers in one model. Specifically, we define the cohesive ( k , 𝒫)-core community of papers w.r.t. a meta-path 𝒫 (i.e., relationship) and propose a ( k , 𝒫)-core based document embedding model to enhance the representation quality. Based on this, we design a proximity graph-based index (PG-Index) of papers and present a threshold algorithm (TA)-based method to efficiently extract top- n experts from papers returned by PG-Index. We further optimize our approach in two ways: (1) we boost effectiveness by considering the ( k , 𝒫)-core community of experts and the diversity of experts’ research interests, to achieve high-quality expert representation from paper representation; and (2) we streamline expert finding, going from “extract top- n experts from top- m ( m&gt; n ) semantically similar papers” to “directly return top- n experts”. The process of returning a large number of top- m papers as intermediate data is avoided, thereby improving the efficiency. Extensive experiments using real-world datasets demonstrate our approach’s superiority.
Evaluation Metrics Research for Explainable Artificial Intelligence Global Methods Using Synthetic Data

In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to understand the logic of how machine learning models work, and in order to compare the methods, it is necessary to evaluate them. The paper analyzes various approaches to the evaluation of XAI methods, defines the requirements for the evaluation system and suggests metrics to determine the various technical characteristics of the methods. A study was conducted, using these metrics, which determined the degradation in the explanation quality of the SHAP and LIME methods with increasing correlation in the input data. Recommendations are also given for further research in the field of practical implementation of metrics, expanding the scope of their use.
Prototyping an Online Virtual Simulation Course Platform for College Students to Learn Creative Thinking

With the rapid development of science and technology, the ability to creative thinking has become an essential criterion for measuring talents. Current creative thinking courses for college students are affected by COVID-19 and are challenging to conduct. This study aimed to explore practical ways to teach creative thinking knowledge online and explored design opportunities for working on this teaching activity online. Through qualitative interviews, we found that the factors that influenced the design of the online virtual simulation course platform were focused on five dimensions: information presentation, platform characteristics, course assessment, instruction design, and presentation format. Through the analysis of user requirements, we obtained six corresponding design guidelines. Based on the knowledge system of design thinking, we set up eight modules in the course platform and developed a prototype including 100 user interfaces. We invited three experts and 30 users to conduct cognitive walk-through sessions and made design iterations based on the feedback. After user evaluation, dimensions of attractiveness, efficiency, dependability, and novelty reached excellent rating and were recognized by users.
Cluster-Based Secure Aggregation for Federated Learning

In order to protect each node’s local learning parameters from model inversion attacks, secure aggregation has become the essential technique for federated learning so that the federated learning server knows only the combined result of all local parameters. In this paper, we introduced a novel cluster-based secure aggregation model that effectively deals with dropout nodes while reducing communicational and computational overheads. Specifically, we considered a federated learning environment with heterogeneous devices deployed across the country. The computing power of each node and the amount of training data can be heterogeneous. Because of this, each node had a different local processing time, and the response time to the server is also different. To clearly determine the dropout nodes in this environment, our model clusters nodes with similar response times based on each node’s local processing time and location and then performs the aggregation on a pre-cluster basis. In addition, we propose a new practical additive sharing-based masking protocol to hide the actual local updates of nodes during aggregation. The new masking protocol makes it easy to remove the share of dropout nodes from the aggregation without using a (t, n) threshold scheme, and updates from dropout nodes are still secure even if they are delivered to the server after the dropout shares have been revealed. In addition, our model provides mask verification for reliable aggregation. Nodes can publicly verify the correctness and integrity of the masks received from others using a discrete logarithm problem before the aggregation. As a result, the proposed aggregation model is robust to dropout nodes and ensures the privacy of local updates if at least three honest nodes are alive in each cluster. Since the masking process is performed on a cluster basis, our model effectively reduces the overhead of generating and sharing the masking value. For an average cluster size C and a total number of nodes N, the computation and communication cost of each node is O(C), the computation cost of the server is O(N), and the communication cost is O(NC). We analyzed the security and efficiency of our protocol by simulating diverse dropout scenarios. The simulated results showed that our cluster-based secure aggregation outputs about a 91% learning accuracy regardless of dropout rate with four clusters for one hundred nodes.
Authentication-Chains: Blockchain-Inspired Lightweight Authentication Protocol for IoT Networks

Internet of Things networks (IoT) are becoming very important in industrial, medical, and commercial applications. The security aspect of IoT networks is critical, especially the authentication of the devices in the network. The current security model in IoT networks uses centralized key exchange servers that present a security weak point. IoT networks need decentralized management for network security. Blockchain, with its decentralized model of authentication, can provide a solution for decentralized authentication in IoT networks. However, blockchain authentication models are known to be computationally demanding because they require complex mathematical calculations. In this paper, we present an Authentication-Chains protocol which is a lightweight decentralized protocol for IoT authentication based on blockchain distributed ledger. The proposed protocol arranges the nodes in clusters and creates an authentication blockchain for each cluster. These cluster chains are connected by another blockchain. A new consensus algorithm based on proof of identity authentication is adapted to the limited computational capabilities of IoT devices. The proposed protocol security performance is analyzed using cryptographic protocols verifier software and tested. Additionally, a test bed consisting of a Raspberry Pi network is presented to analyze the performance of the proposed protocol.
A Compact and Powerful Single-Stage Network for Multi-Person Pose Estimation

Multi-person pose estimation generally follows top-down and bottom-up paradigms. The top-down paradigm detects all human boxes and then performs single-person pose estimation on each ROI. The bottom-up paradigm locates identity-free keypoints and then groups them into individuals. Both of them use an extra stage to build the relationship between human instance and corresponding keypoints (e.g., human detection in a top-down manner or a grouping process in a bottom-up manner). The extra stage leads to a high computation cost and a redundant two-stage pipeline. To address the above issue, we introduce a fine-grained body representation method. Concretely, the human body is divided into several local parts and each part is represented by an adaptive point. The novel body representation is able to sufficiently encode the diverse pose information and effectively model the relationship between human instance and corresponding keypoints in a single-forward pass. With the proposed body representation, we further introduce a compact single-stage multi-person pose regression network, called AdaptivePose++, which is the extended version of AAAI-22 paper AdaptivePose. During inference, our proposed network only needs a single-step decode operation to estimate the multi-person pose without complex post-processes and refinements. Without any bells and whistles, we achieve the most competitive performance on representative 2D pose estimation benchmarks MS COCO and CrowdPose in terms of accuracy and speed. In particular, AdaptivePose++ outperforms the state-of-the-art SWAHR-W48 and CenterGroup-W48 by 3.2 AP and 1.4 AP on COCO mini-val with faster inference speed. Furthermore, the outstanding performance on 3D pose estimation datasets MuCo-3DHP and MuPoTS-3D further demonstrates its effectiveness and generalizability on 3D scenes.