Reviewerly/exHarmony huggingface dataset:
Research on PBFT consensus algorithm for grouping based on feature trust

Abstract The consensus mechanism is the core of the blockchain system, which plays an important role in the performance and security of the blockchain system . The Practical Byzantine Fault Tolerance (PBFT) algorithm is a widely used consensus algorithm, but the PBFT algorithm also suffers from high consensus latency, low throughput and performance. In this paper, we propose a grouped PBFT consensus algorithm (GPBFT) based on feature trust. First, the algorithm evaluates the trust degree of nodes in the transaction process through the EigenTrust trust model, and uses the trust degree of nodes as the basis for electing master nodes and proxy nodes. Then, the algorithm divides the nodes in the blockchain system into multiple groups, and the consensus within each independent group does not affect the other groups, which greatly reduces the communication overhead of the consensus process when the number of nodes in the system is large. Finally, we demonstrate through theoretical and experimental analysis that the GPBFT algorithm has a significant improvement in security and performance.
Real-world remote sensing image super-resolution via a practical degradation model and a kernel-aware network

Super-resolution is an essential task in remote sensing. It can enhance low-resolution remote sensing images and benefit downstream tasks such as building extraction and small object detection. However, existing remote sensing image super-resolution methods may fail in many real-world scenarios because they are trained on synthetic data generated by a single degradation model or on a limited amount of real data collected from specific satellites. To achieve super-resolution of real-world remote sensing images with different qualities in a unified framework, we propose a practical degradation model and a kernel-aware network (KANet). The proposed degradation model includes blur kernels estimated from real images and blur kernels generated from pre-defined distributions, which improves the diversity of training data and covers more real-world scenarios. The proposed KANet consists of a kernel prediction subnetwork and a kernel-aware super-resolution subnetwork. The former estimates the blur kernel of each image, making it possible to cope with real images of different qualities in an adaptive way. The latter iteratively solves two subproblems, degradation and high-frequency recovery, based on unfolding optimization. Furthermore, we propose a kernel-aware layer to adaptively integrate the predicted blur kernel into super-resolution process. The proposed KANet achieves state-of-the-art performance for real-world image super-resolution and outperforms the competing methods by 0.2–0.8 dB in the peak signal-to-noise ratio (PSNR). Extensive experiments on both synthetic and real-world images demonstrate that our approach is of high practicability and can be readily applied to high-resolution remote sensing applications.
Attention-based Frequency-aware Multi-scale Network for Sequential Recommendation

The key problem of sequential recommendation is how to capture user sequential patterns and enrich user sequential representations from historical interactions, mainly due to the uncertainty of user behavior and the limited information. The RNN-based methods capture the long- and short-term level patterns. The CNN-based methods treat the representation of the user’s historical interaction as an “image”, and discover the point-level patterns, union-level patterns and union-level with skip once. The attention-based methods mine the focus-level patterns. However, all the previous methods have only studied how to capture users’ sequential patterns in the time domain. In many cases, if we only consider the time domain information, these methods will have trouble in mining the user’s sequential patterns. To solve this problem, we consider the frequency domain to capture frequency-level patterns for the first time. Because a non-periodic historical behavior sequence in the time domain may be brutal to reflect the user’s intention but much more accessible in the frequency domain. In light of this, we propose a novel Attention-based Frequency-aware Multi-scale Network (AFMN) for Sequential Recommendation. We introduce Fourier transform to decompose the simple embedding vector, the representation of the user’s historical interaction, into a multi-frequency embedding vector to enrich the user’s behavior sequence representation. The frequency-aware attention layers adaptively focus on the important frequency components and output a refined multi-frequency embedding vector. Given the multi-frequency embedding vector, we develop a non-local attention module to aggregate attribute-level and item-level features of the previous L items. Empirical results on four public benchmark datasets show that our method can achieve a significant improvement over the state-of-the-art baselines.
Sentiment analysis tools in software engineering: A systematic mapping study

Software development is a collaborative task. Previous research has shown social aspects within development teams to be highly relevant for the success of software projects. A team’s mood has been proven to be particularly important. It is paramount for project managers to be aware of negative moods within their teams, as such awareness enables them to intervene. Sentiment analysis tools offer a way to determine the mood of a team based on textual communication. We aim to help developers or stakeholders in their choice of sentiment analysis tools for their specific purpose. Therefore, we conducted a systematic mapping study (SMS). We present the results of our SMS of sentiment analysis tools developed for or applied in the context of software engineering (SE). Our results summarize insights from 106 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools, (5) the usage of already existing tools, and (6) the difficulties researchers face. We analyzed in more detail which tools and approaches perform how in terms of their performance. According to our results, sentiment analysis is frequently applied to open-source software projects, and most approaches are neural networks or support-vector machines. The best performing approach in our analysis is neural networks and the best tool is BERT . Despite the frequent use of sentiment analysis in SE, there are open issues, e.g. regarding the identification of irony or sarcasm, pointing to future research directions. We conducted an SMS to gain an overview of the current state of sentiment analysis in order to help developers or stakeholders in this matter. Our results include interesting findings e.g. on the used tools and their difficulties. We present several suggestions on how to solve these identified problems.
Coronavirus Pandemic (COVID-19)

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.
Scalable neural indoor scene rendering

We propose a scalable neural scene reconstruction and rendering method to support distributed training and interactive rendering of large indoor scenes. Our representation is based on tiles. Tile appearances are trained in parallel through a background sampling strategy that augments each tile with distant scene information via a proxy global mesh. Each tile has two low-capacity MLPs: one for view-independent appearance (diffuse color and shading) and one for view-dependent appearance (specular highlights, reflections). We leverage the phenomena that complex view-dependent scene reflections can be attributed to virtual lights underneath surfaces at the total ray distance to the source. This lets us handle sparse samplings of the input scene where reflection highlights do not always appear consistently in input images. We show interactive free-viewpoint rendering results from five scenes, one of which covers an area of more than 100 m 2 . Experimental results show that our method produces higher-quality renderings than a single large-capacity MLP and five recent neural proxy-geometry and voxel-based baseline methods. Our code and data are available at project webpage https://xchaowu.github.io/papers/scalable-nisr.
Rapid design of articulated objects

Designing articulated objects is challenging because, unlike with static objects, it requires complex decisions to be made regarding the form, parts, rig, poses, and motion. We present a novel 3D sketching system for rapidly authoring concepts of articulated objects for the early stages of design, when designers make such decisions. Compared to existing CAD software, which focuses on slowly but elaborately producing models consisting of precise surfaces and volumes, our system focuses on quickly but roughly producing models consisting of key curves through a small set of coherent pen and multi-touch gestures. We found that professional designers could easily learn and use our system and author compelling concepts in a short time, showing that 3D sketching can be extended to designing articulated objects and is generally applicable in film, animation, game, and product design.
A multi-input with multi-function activated weights and structure determination neuronet for classification problems and applications in firm fraud and loan approval

Neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to resolve the shortcomings of traditional back-propagation neuronets such as slow training speed and local minimum. A multi-input multi-function activated WASD neuronet (MMA-WASDN) model is introduced in this paper, combined with a novel multi-function activated WASD (MA-WASD) algorithm, for handling binary classification problems. Using multiple power activation functions, the MA-WASD algorithm finds the optimal weights and structure of the MMA-WASDN and uses cross-validation to address bias and prevent being stuck in local optima during the training process. As a result, neuronets trained with the MA-WASD algorithm have higher precision and accuracy than neuronets trained with traditional WASD algorithms. Applications on firm fraud and loan approval classification validate our MMA-WASDN model in order to demonstrate its outstanding learning and predicting performance. Since these applications use real-world datasets that include strings and missing values, an algorithmic method for preparing data is also suggested to make them manageable from the MMA-WASDN. A comparison of the MMA-WASDN model to five other high-performing neuronet models is included, as well as a MATLAB package that is publicly available through GitHub to support and promote the findings of this research. • Study of a novel weights-and-structure-determination (WASD) algorithm. • Study of a 4-layer multi-input multi-function activated WASD neuronet. • The algorithm handles multiple activation functions. • Three new power activation functions for the WASD algorithms are defined. • Applications on firm fraud and loan approval classification are presented.
Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm

Abstract ** This paper presents a novel fast algorithm for feedforward neural networks training. It is based on the Recursive Least Squares (RLS) method commonly used for designing adaptive filters. Besides, it utilizes two techniques of linear algebra, namely the orthogonal transformation method, called the Givens Rotations (GR), and the QR decomposition, creating the GQR (symbolically we write GR + QR = GQR) procedure for solving the normal equations in the weight update process. In this paper, a novel approach to the GQR algorithm is presented. The main idea revolves around reducing the computational cost of a single rotation by eliminating the square root calculation and reducing the number of multiplications. The proposed modification is based on the scaled version of the Givens rotations, denoted as SGQR. This modification is expected to bring a significant training time reduction comparing to the classic GQR algorithm. The paper begins with the introduction and the classic Givens rotation description. Then, the scaled rotation and its usage in the QR decomposition is discussed. The main section of the article presents the neural network training algorithm which utilizes scaled Givens rotations and QR decomposition in the weight update process. Next, the experiment results of the proposed algorithm are presented and discussed. The experiment utilizes several benchmarks combined with neural networks of various topologies. It is shown that the proposed algorithm outperforms several other commonly used methods, including well known Adam optimizer.
Robust stacking ensemble model for darknet traffic classification under adversarial settings

Encrypted traffic tunnelled by Tor or VPN is referred to as darknet traffic. The ability to detect, identify, and characterize darknet traffic is critical for detecting network traffic generated by a cyber-attack. Darknet classification models based on Machine Learning / Deep Learning (ML/DL) usually demonstrate high False Positive Rate (FPR) and lower F1-score which are essential metrics for network traffic analysis. Additionally, ML/DL models used in such tasks are susceptible to adversarial perturbed samples that can cause the network security solution to malfunction. This work proposes a Stacking Ensemble (SE) model to combine the predictions of three base learners, 1) Random Forest, 2) K-Nearest Neighbors, and 3) Decision Tree in the most efficient way for improving the overall performance of darknet characterization. A two-layered autoencoder-based defence mechanism consisting of a detector and denoiser is devised to increase the robustness of the network security system against adversarial attacks. The performance of the proposed model is demonstrated through extensive experiments using the CIC-Darknet-2020 dataset. The robustness of the model is tested against three highly transferable adversarial attacks, 1) Fast Gradient Sign Method (FGSM), 2) Basic Iterative Method (BIM), 3) DeepFool and a realistic 4) Boundary attack. The experimental results show that the SE model outperforms the baseline Deep Image and other competitor models by achieving an accuracy score of 98.89% and an FPR of 0.43% in the case of darknet traffic identification. Furthermore, an accuracy of 97.88% is achieved in the case of darknet traffic flow characterization. In addition, the statistical properties of the packet flow are identified as the most commonly affected features by adversarial attacks through the Mean Adversarial Perturbation (MAP) metric.
Fighting hate speech from bilingual hinglish speaker’s perspective, a transformer- and translation-based approach.

Many people have begun to use social media platforms due to the increased use of the Internet over the previous decade. It has a lot of benefits, but it also comes with a lot of risks and drawbacks, such as Hate speech. People in multilingual societies, such as India, frequently mix their native language with English while speaking, so detecting hate content in such bilingual code-mixed data has drawn the larger interest of the research community. The majority of previous work focuses on high-resource language such as English, but very few researchers have concentrated on the mixed bilingual data like Hinglish. In this study, we investigated the performance of transformer models like IndicBERT and multilingual Bidirectional Encoder Representation(mBERT), as well as transfer learning from pre-trained language models like ULMFiT and Bidirectional encoder Representation(BERT), to find hateful content in Hinglish. Also, Transformer-based Interpreter and Feature extraction model on Deep Neural Network (TIF-DNN), is proposed in this work. The experimental results found that our proposed model outperforms existing state-of-art methods for Hate speech identification in Hinglish language with an accuracy of 73%.
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector

Plasticity-stability dilemma is a main problem for incremental learning, where plasticity is referring to the ability to learn new knowledge, and stability retains the knowledge of previous tasks. Many methods tackle this problem by storing previous samples, while in some applications, training data from previous tasks cannot be legally stored. In this work, we propose to employ mode connectivity in loss landscapes to achieve better plasticity-stability trade-off without any previous samples. We give an analysis of why and how to connect two independently optimized optima of networks, null-space projection for previous tasks and simple SGD for the current task, can attain a meaningful balance between preserving already learned knowledge and granting sufficient flexibility for learning a new task. This analysis of mode connectivity also provides us a new perspective and technology to control the trade-off between plasticity and stability. We evaluate the proposed method on several benchmark datasets. The results indicate our simple method can achieve notable improvement, and perform well on both the past and current tasks. On 10-split-CIFAR-100 task, our method achieves 79.79% accuracy, which is 6.02% higher. Our method also achieves 6.33% higher accuracy on TinyImageNet. Code is available at https://github.com/lingl1024/Connector.
Analyzing Greece 2010 Memorandum’s Impact on Macroeconomic and Financial Figures through FCM

The financial crisis of 2008 has caused a series of drawbacks to economies around the world. Greek economy has been hit twice at 2009, since its credibility worsened, provoking the implication of harsh fiscal measures from the 2010 Memorandum of Understanding (MoU). The effects of these measures to Greek macroeconomic figures have been widely criticized. Authors aim to estimate these effects at the macroeconomic figures of Greece through utilization of Decision Support Systems, and propose accurate insights regarding their efficacy. By capitalizing on regression analysis and Fuzzy Cognitive Mapping processes, specific results from 2010 Memorandum’s measures arise. It has been calculated that measures implied by 2010 Memorandum have been harsh and posed a negative effect on key Greek macroeconomic figures like GDPR, public debt, etc., especially with the ongoing 2008 financial crisis.
Blockchain Intra- and Interoperability

We introduce blockchains and distributed ledgers and study their intra- and interoperability. Blockchain intraoperability allows one to swap different assets defined on the same blockchain supporting smart contracts. Blockchain interoperability enables one to exchange or move assets residing on different blockchains. Finding practical mechanisms for intra- and interoperability is of paramount importance for the ultimate success of blockchain technology. We recommend using automated market makers for intraoperability and gateways and atomic swaps for interoperability.
Multibiosignal chaotic encryption scheme based on spread spectrum and global diffusion process for e-health

E-health provides several remote health services highly convenient for patients. Nevertheless, inherent security risks are present in e-health such as data privacy since insecure communication channels are used to transmit data between patients and physicians or to cloud medical data storage. Nowadays, medical body area network provides a solution to continuous health monitoring with real-time updates of multibiosignal records in multiuser communication schemes for monitoring and early detection of medical conditions or diseases. In this paper, we propose to use chaos-based sequence direct spread spectrum technology and chaos-based cryptography to provide data privacy, high security and efficiency in monitoring multibiosignals in multiuser networks for e-health applications. First, each plain biosignal is modulated with sequence direct spread spectrum by using the chaotic Hénon map. Then, all modulated biosignals are summed to produce the modulated cryptogram. The modulated cryptogram is finally encrypted by using global diffusion process with the chaotic Hénon map to increase security by reducing the high autocorrelation of the modulated cryptogram. The experimental results are presented in a simulated multiuser scenario with 4 users (or patients) and 2 specialists (neurologist and cardiologist), with 12 biosignals acquired from database on the Internet, such as electroencephalograms, electromyograms, blood pressure signals, and electrocardiograms. A comprehensive security study is presented to show the high security, the high speed encryption, and the high noise interference robustness. According with the results, the proposed method can be used for secure multibiosignal monitoring in multiuser networks for e-health applications with high efficiency and security.
An Integrated Visual System for Unmanned Aerial Vehicles Following Ground Vehicles: Simulations and Experiments

The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG-based deep neural network for object detection and tracking, which has great advantages in robustness to object scale and illuminations compared with typical deep network-based approaches. Then, we have also improved based on the original Kalman filter and designed an iterative multi-model-based filter to tackle the problem of unknown dynamics in real circumstances of motion estimations. Next, we have implemented the whole system and do ROS Gazebo-based testing in two complicated circumstances to verify the effectiveness of our design. Finally, we have deployed the proposed detection, tracking, and motion estimation strategies into real applications to do UAV tracking of a pillar and do obstacle avoidance. It is demonstrated that our system shows great accuracy and robustness in real applications.