Reviewerly/exHarmony huggingface dataset:
Twitter Sentiment Analysis-Based Adjustment of Cryptocurrency Action Recommendation Model for Profit Maximization

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit.Two research approaches have been adopted.Studies adopting the first approach directly predict the future cryptocurrency price.Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach.Further, studies adopting the second approach recommend actions to investors to maximize profits, such as ''Sell'', ''Buy'', and ''Wait.''In this approach, classification models are used and results are derived based on probabilities.However, these action recommendation models do not consider the quality of the result.For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%.To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis.The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.
Knowledge-based machine learning for mineral classification in a complex tectonic regime of Yingxiu-Beichuan fault zone, Sichuan basin

The present study aims to better understand the mineralogy and thermal structure of the Yingxiu-Beichuan fault zone (YBFZ), Sichuan basin, China, which was lacking previously. The previous research on the Wenchuan earthquake Fault Scientific Drilling (WFSD) project was focused on mineral classification, fault analysis, and geochemical research utilizing original logs using WFSD-1 well and at a shallow depth of 700 meters. No investigations are conducted at a depth of around 1550 meters utilizing multiple dimensionality reduced well-logs. Thereafter, we sought to categorize the minerals along the YBFZ using Machine Learning (ML) technologies and concentration-number (C–N) modeling using multiple WFSD-1 and WFSD-2 wells. In the categorization of rocks, three classifiers are discussed: Support Vector Machines (SVM), Feed Forward Back Propagation (BPNN), and Radial Basis Function Neural Networks (RBFN). Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to normalize the log data in a geologically complicated region. By using the BPNN classifier to PCA, LDA, and all 17 logs, we achieved respective accuracy rates of 75.54%, 86.32%, and 72.54%, which are higher than the accuracy rates of RBFN (76.03%, 77.01%, and 65.3%), and SVM (74.45%, 78.03%, and 70.06%). These results suggest that BPNN shows improved accuracy rates for mineral classification in a complex tectonic regime. In addition, concentration-number (C–N) fractal model technique and log-log plots are also used to characterize geothermal features and the results of C–N modeling supports the results of ML models. High gamma ray (GR) ranges of 349.7API, heat production (HP) ranges of 5.5 μW/m3, and low thermal conductivity (TC) ranges of 0.28 W/km show that the fault region is associated with comparatively strong radiogenic activity compared to its surroundings.
Deep adversarial autoencoder recommendation algorithm based on group influence

Recommender systems are crucial in the big data era, effectively mitigating information overload. Existing recommendation methods are limited on highly sparse data and have mediocre recall performance. Group influence aggregates knowledge from different users or organizations to generate decisions, improving information fusion efficiency and group decision-making quality. In this paper, a group influence-based deep adversarial autoencoder (GI-AAE), is proposed for top-N recommendation. It leverages group influence to strengthen autoencoder latent features and address sparse data and uses adversarial learning from GANs to enhance reconstruction. The group influence based deep autoencoder (GI-AE) is the generative model for the GI-AAE. Experimental results show that the proposed algorithm is competitive and gets higher recall values.
Knowledge Representation and Reasoning

Humans are the most adapt at comprehending, making sense of, and applying knowledge. Man has knowledge, or understanding, of some things, and he acts in the real world in diverse ways in accordance with this understanding.
An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment

Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) devices. IoT data are prone to anomalies due to various factors such as malfunctioning of sensors, low-cost devices, etc. Following the AIoT paradigm, this work explores anomaly detection in IoT urban noise sensor networks using a Long Short-Term Memory Autoencoder. Two autoencoder models are trained using normal data from two different sensors in the sensor network and tested for the detection of two different types of anomalies, i.e. point anomalies and collective anomalies. The results in terms of accuracy of the two models are 99.99% and 99.34%. The trained model is quantised, converted to TensorFlow Lite format and deployed on the ESP32 microcontroller (MCU). The inference time on the microcontroller is 4 ms for both models, and the power consumption of the MCU is 0.2693 W ± 0.039 and 0.3268 W ± 0.015. Heap memory consumption during the execution of the program for sensors TA120-T246187 and TA120-T246189 is 528 bytes and 744 bytes respectively.
A Robust Chaotic Map and Its Application to Speech Encryption in Dual Frequency Domain

When chaotic systems are used for speech encryption, their chaotic performance largely determines the security of speech encryption. However, traditional chaotic systems have problems such as parameter discontinuity, easy occurrence of chaos degradation, low complexity, and the existence of periodic windows in chaotic intervals. In real applications, chaotic mappings may fall into periodic windows, which is extremely unfavorable for security. In this paper, a new chaotic mapping 2D-LMSM is proposed by improving the chaotic logistic and sine mappings, and applied to speech encryption. Performance evaluation shows that this map can effectively generate robust chaotic signals in a wide parameter range. The 2D-LMSM achieves better robustness and desired chaotic properties than several existing two-dimensional chaotic maps. We propose a novel speech encryption algorithm using this map. First, it performs Fast Fourier Transform (FFT) on the input speech signal to obtain real and imaginary values, which are encrypted by one-time scrambling encryption and XOR diffusion encryption with pseudorandom numbers generated by chaos; then, it performs secondary scrambling encryption by Discrete Wavelet Transform (DWT) and 2D-LMSM; finally, it obtains encrypted speech data by Discrete Wavelet Inverse Transform (IDWT) and Fast Fourier Inverse Transform (IFFT). Experimental results show that this algorithm has good encryption and decryption performances and ensures system security.
Leveraging Searchable Encryption through Homomorphic Encryption: A Comprehensive Analysis

The widespread adoption of cloud infrastructures has revolutionized data storage and access. However, it has also raised concerns regarding the privacy of sensitive data. To address these concerns, encryption techniques have been widely used. However, traditional encryption schemes limit the efficient search and retrieval of encrypted data. To tackle this challenge, innovative approaches have emerged, such as the utilization of Homomorphic Encryption (HE) in Searchable Encryption (SE) schemes. This paper provides a comprehensive analysis of the advancements in HE-based privacy-preserving techniques, focusing on their application in SE. The main contributions of this work include the identification and classification of existing SE schemes that utilize HE, a comprehensive analysis of the types of HE used in SE, an examination of how HE shapes the search process structure and enables additional functionalities, and the identification of promising directions for future research in HE-based SE. The findings reveal the increasing usage of HE in SE schemes, particularly Partially Homomorphic Encryption. The popularity of this type of HE schemes, especially Paillier’s cryptosystem, can be attributed to its simplicity, proven security properties, and widespread availability in open-source libraries. The analysis also highlights the prevalence of index-based SE schemes using HE, the support for ranked search and multi-keyword queries, and the need for further exploration in functionalities such as verifiability and the ability to authorize and revoke users. Future research directions include exploring the usage of other encryption schemes alongside HE, addressing omissions in functionalities like fuzzy keyword search, and leveraging recent advancements in Fully Homomorphic Encryption schemes.
Performance and Scalability Analysis of Ethereum and Hyperledger Fabric

Blockchain emerged in the last decade as a promising technology with possible applications in numerous fields such as healthcare, supply chain, and finance.Its immutability, transparency, security, and decentralisation gained significant attention in academia and industry.One technology that blockchain can support is the Internet of Things (IoT).However, there are still challenges hindering the real-world adoption of blockchain due to concerns about its performance, scalability, and complexity.This study contributes to a comparative study that analyses blockchain platforms in terms of their performance and scalability, with specific reference to IoT applications.We focus on the Ethereum and Hyperledger Fabric blockchain platforms.As part of the implementation, an IoT healthcare use case is developed.We conducted performance and scalability tests on private platform networks to measure the throughput and latency parameters.To evaluate scalability, we examined the behaviour of the studied platforms in response to an increase in the number and rate of transactions.Hyperledger Caliper is used to collect these parameters.Experiment analysis shows that Fabric outperforms Ethereum in terms of latency and throughput.As for performance and scalability analysis, Fabric was found to be more suitable than Ethereum for private networks such as IoT healthcare ecosystems.
A comprehensive dual-layer architecture for phishing and spam email detection

The widespread use of email communication for sharing personal, professional, and financial data has rendered it vulnerable to cyber-attacks. Detecting untrustworthy emails with minimal errors is crucial to counter these threats. This research paper focuses on classifying spam and phishing emails, commonly employed to steal confidential information by impersonating legitimate sources. The scale of such attacks is alarmingly high, causing substantial financial losses across sectors like banking, healthcare, technology, and other businesses. This research aims to classify both spam and phishing emails, addressing the limitations of existing studies that only focus on one type and consider either the email body or content for feature selection. This research incorporates features from both the email body and content during model training. The authors propose a novel approach that ensures highly accurate classification while effectively handling the common issue of data imbalance in email phishing and spam classification. The approach utilizes a dual-layer architecture, with each layer containing a trained or pre-trained model that classifies data instances into their respective classes. Layer 1 classifies the phishing class, while Layer 2 classifies the spam class. Building upon the proven effectiveness of deep learning techniques for text classification and analysis, the proposed architecture employs models like Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN). Experimental evaluation demonstrates the approach's remarkable accuracy, recall, precision, and F1-score, achieving 99.51%, 99.68%, 99.5%, and 99.52%, respectively. This signifies its high efficacy in detecting and classifying malicious emails with minimal errors, thus holding great promise in enhancing system security against cyber-attacks in email communication.
General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox

This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and cam-era projection models, requires only one pairing of LiDAR and camera data without a calibration target, and is fully automatic. For automatic initial guess estimation, we employ the Super-Glue image matching pipeline to find 2D-3D correspondences between LiDAR and camera data and estimate the LiDAR-camera transformation via RANSAC. Given the initial guess, we refine the transformation estimate with direct LiDAR-camera registration based on the normalized information distance, a mutual information-based cross-modal distance metric. For a handy calibration process, we also present several assistance capabilities (e.g., dynamic LiDAR data integration and user interface for making 2D-3D correspondence manually). The experimental results show that the proposed toolbox enables calibration of any combination of spinning and non-repetitive scan LiDARs and pinhole and omnidirectional cameras, and shows better calibration accuracy and robustness than those of the state-of-the-art edge-alignment-based calibration method.
MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion

Multi-view radar-camera fused 3D object detection provides a farther detection range and more helpful features for autonomous driving, especially under adverse weather. The current radar-camera fusion methods deliver kinds of designs to fuse radar information with camera data. However, these fusion approaches usually adopt the straightforward concatenation operation between multi-modal features, which ignores the semantic alignment with radar features and sufficient correlations across modals. In this paper, we present MVFusion, a novel Multi-View radar-camera Fusion method to achieve semantic-aligned radar features and enhance the cross-modal information interaction. To achieve so, we inject the semantic alignment into the radar features via the semantic-aligned radar encoder (SARE) to produce image-guided radar features. Then, we propose the radar-guided fusion transformer (RGFT) to fuse our radar and image features to strengthen the two modals' correlation from the global scope via the cross-attention mechanism. Extensive experiments show that MVFusion achieves state-of-the-art performance (51.7% NDS and 45.3% mAP) on the nuScenes dataset. We shall release our code and trained networks upon publication.
Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot

This paper presents an efficient and safe method to avoid static and dynamic obstacles based on LiDAR. First, point cloud is used to generate a real-time local grid map for obstacle detection. Then, obstacles are clustered by DBSCAN algorithm and enclosed with minimum bounding ellipses (MBEs). In addition, data association is conducted to match each MBE with the obstacle in the current frame. Considering MBE as an observation, Kalman filter (KF) is used to estimate and predict the motion state of the obstacle. In this way, the trajectory of each obstacle in the forward time domain can be parameterized as a set of ellipses. Due to the uncertainty of the MBE, the semi-major and semi-minor axes of the parameterized ellipse are extended to ensure safety. We extend the traditional Control Barrier Function (CBF) and propose Dynamic Control Barrier Function (D-CBF). We combine D-CBF with Model Predictive Control (MPC) to implement safety-critical dynamic obstacle avoidance. Experiments in simulated and real scenarios are conducted to verify the effectiveness of our algorithm. The source code is released for the reference of the community <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/jianzhuozhuTHU/MPC-D-CBF..
GDIP: Gated Differentiable Image Processing for Object Detection in Adverse Conditions

Detecting objects under adverse weather and lighting conditions is crucial for the safe and continuous operation of an autonomous vehicle, and remains an unsolved problem. We present a Gated Differentiable Image Processing (GDIP) block, a domain-agnostic network architecture, which can be plugged into existing object detection networks (e.g., Yolo) and trained end-to-end with adverse condition images such as those captured under fog and low lighting. Our pro-posed GDIP block learns to enhance images directly through the downstream object detection loss. This is achieved by learning parameters of multiple image pre-processing (IP) techniques that operate concurrently, with their outputs combined using weights learned through a novel gating mechanism. We further improve GDIP through a multi-stage guidance procedure for progressive image enhancement. Finally, trading off accuracy for speed, we propose a variant of GDIP that can be used as a regularizer for training Yolo, which eliminates the need for GDIP-based image enhancement during inference, resulting in higher throughput and plausible real-world deployment. We demonstrate significant improvement in detection performance over several state-of-the-art methods through quantitative and qualitative studies on synthetic datasets such as PascalVOC, and real-world foggy (RTTS) and low-lighting (ExDark) datasets.
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation

Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D sensing capa-bilities of self-driving vehicles. However, it intrinsically relies upon the photometric consistency assumption, which hardly holds during nighttime. Although various supervised night-time image enhancement methods have been proposed, their generalization performance in challenging driving scenarios is not satisfactory. To this end, we propose the first method that jointly learns a nighttime image enhancer and a depth estimator, without using ground truth for either task. Our method tightly entangles two self-supervised tasks using a newly proposed uncertain pixel masking strategy. This strategy originates from the observation that nighttime images not only suffer from underexposed regions but also from overexposed regions. By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally. We benchmark the method on two established datasets: nuScenes and RobotCar and demonstrate state-of-the-art performance on both of them. Detailed ablations also reveal the mechanism of our proposal. Last but not least, to mitigate the problem of sparse ground truth of existing datasets, we provide a new photo-realistically enhanced nighttime dataset based upon CARLA. It brings meaningful new challenges to the community. Codes, data, and models are available at https://github.com/ucaszyp/STEPS.
Grounding Language with Visual Affordances over Unstructured Data

Recent works have shown that Large Language Models (LLMs) can be applied to ground natural language to a wide variety of robot skills. However, in practice, learning multi-task, language-conditioned robotic skills typically requires large-scale data collection and frequent human intervention to reset the environment or help correcting the current policies. In this work, we propose a novel approach to efficiently learn general-purpose language-conditioned robot skills from unstructured, offline and reset-free data in the real world by exploiting a self-supervised visuo-lingual affordance model, which requires annotating as little as 1% of the total data with language. We evaluate our method in extensive experiments both in simulated and real-world robotic tasks, achieving state-of-the-art performance on the challenging CALVIN benchmark and learning over 25 distinct visuomotor manipulation tasks with a single policy in the real world. We find that when paired with LLMs to break down abstract natural language instructions into subgoals via few-shot prompting, our method is capable of completing long-horizon, multi-tier tasks in the real world, while requiring an order of magnitude less data than previous approaches. Code and videos are available at http://hulc2.cs.uni-freiburg.de.
Exploiting a knowledge hypergraph for modeling multi-nary relations in fault diagnosis reports

Knowledge graphs (KG) demonstrate superiority in storing and exploiting massive data and lesson-learned knowledge collected from equipment operation and maintenance management, and deliver a higher level of cognitive intelligence for fault diagnosis. However, for some indivisible multi-nary relations that connect more than two entities in complex fault diagnosis scenarios, ordinary KG has encountered many challenges in preserving their completeness and correctness with triples of < head entity, relation, tail entity >. To this end, this paper evolves the ordinary KG into Knowledge hypergraph (KHyG), based on the differentiation of schema-level and instance-level hyperedges. A pipeline for constructing KHyG using fault diagnosis reports is accordingly proposed, which adopts an end-to-end tabular information extraction manner and hyperedge-enabled schema. A two-channel KHyG embedding manner is then articulated for multi-nary relations, so as to strengthen the performance of troubleshooting solution generation in KHyG applications. To validate the proposed KHyG, real-world fault diagnosis was conducted on bridge cranes in steel factories, which demonstrated that the improvement in the modeling of multi-nary relations showed effectiveness and advances in fault investigation and troubleshooting process. It is anticipated that this study can provide several practical insights into applying more advanced KG models and techniques toward futuristic cognitive intelligence-enabled fault diagnosis.
User-centric privacy preserving models for a new era of the Internet of Things

New concepts based on the Internet of Things propose the integration of the human factor as a key component of novel interconnected ecosystems, to offer them new services and features. However, the data acquisition process, carried out by the devices that allow the deployment of these new paradigms, leads to concerns about what kind of information these entities are able to collect and/or infer. In fact, in most cases, users are not aware that their information is being gathered, nor can they properly control these data flows. Nowadays, the European Union's General Data Protection Regulation calls for the adoption of transparent practices comprising, inter alia, adequate notification mechanisms, proper consent management schemes, and the development of models to empower the user as an active participant, with full control over the data flows, along with privacy preservation techniques. In this specific context, the aim of this manuscript is to present a review of existing user-centric privacy preservation models. This is, as far as we know, one of the few papers that addresses this particular privacy preserving dimension within an IoT context. Finally, the paper identifies and summarizes open issues, including a discussion of research opportunities in this area.