Reviewerly/exHarmony huggingface dataset:
An active semi-supervised deep learning model for human activity recognition

Abstract Human activity recognition (HAR), which aims at inferring the behavioral patterns of people, is a fundamental research problem in digital health and ambient intelligence. The application of machine learning methods in HAR has been investigated vigorously in recent years. However, there are still a number of challenges confronting the task, where one significant barrier lies in the longstanding shortage of annotations. To address this issue, we establish a new paradigm for HAR, which integrates active learning and semi-supervised learning into one framework. The main idea is to reduce the annotation cost by actively selecting the most informative samples for annotation, as well as leveraging the unlabelled instances in a semi-supervised way. In particular, we propose to utilize the massive unlabelled data via temporal ensembling of convolutional neural networks (CNN), which yields robust consensus predictions by aggregating the outputs of the training networks on different epochs. We conducted extensive experiments on three public benchmark datasets. The proposed method achieves Macro F1 values of 0.76, 0.45 and 0.91 in a low annotation scenario on PAMAP2, USCHAD and UCIHAR datasets respectively, outperforming a multitude of state-of-the-art deep models. The ablation study proves the effectiveness of the two components of the framework, i.e., active learning-based sample selection and semi-supervised model training with temporal ensembling, in alleviating the issue of insufficient labels. Cross-validation and statistical significance experiments further demonstrate the robustness and generalization ability of the proposed method. The source codes are available at https://github.com/HaixiaBi1982/ActSemiCNNAct.
Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a metaheuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to three other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work.
Natural Images Allow Universal Adversarial Attacks on Medical Image Classification Using Deep Neural Networks with Transfer Learning

Transfer learning from natural images is used in deep neural networks (DNNs) for medical image classification to achieve a computer-aided clinical diagnosis. Although the adversarial vulnerability of DNNs hinders practical applications owing to the high stakes of diagnosis, adversarial attacks are expected to be limited because training datasets (medical images), which are often required for adversarial attacks, are generally unavailable in terms of security and privacy preservation. Nevertheless, in this study, we demonstrated that adversarial attacks are also possible using natural images for medical DNN models with transfer learning, even if such medical images are unavailable; in particular, we showed that universal adversarial perturbations (UAPs) can also be generated from natural images. UAPs from natural images are useful for both non-targeted and targeted attacks. The performance of UAPs from natural images was significantly higher than that of random controls. The use of transfer learning causes a security hole, which decreases the reliability and safety of computer-based disease diagnosis. Model training from random initialization reduced the performance of UAPs from natural images; however, it did not completely avoid vulnerability to UAPs. The vulnerability of UAPs to natural images is expected to become a significant security threat.
Eliminating Sandwich Attacks with the Help of Game Theory

Predatory trading bots lurking in Ethereum's mempool present invisible taxation of traders on automated market makers (AMMs). AMM traders specify a slippage tolerance to indicate the maximum price movement they are willing to accept. This way, traders avoid automatic transaction failure in case of small price movements before their trade request executes. However, while a too-small slippage tolerance may lead to trade failures, a too-large slippage tolerance allows predatory trading bots to profit from sandwich attacks. These bots can extract the difference between the slippage tolerance and the actual price movement as profit. In this work, we introduce the sandwich game to analyze sandwich attacks analytically from both the attacker and victim perspectives. Moreover, we provide a simple and highly effective algorithm that traders can use to set the slippage tolerance. We unveil that most broadcasted transactions can avoid sandwich attacks while simultaneously only experiencing a low risk of transaction failure. Thereby, we demonstrate that a constant auto-slippage cannot adjust to varying trade sizes and pool characteristics. Our algorithm outperforms the constant auto-slippage suggested by the biggest AMM, Uniswap, in all performed tests. Specifically, our algorithm repeatedly demonstrates a cost reduction exceeding a factor of 100.
A Fast Text-to-Image Encryption-Decryption Algorithm for Secure Network Communication

Data security is the science of protecting data in information technology, including authentication, data encryption, data decryption, data recovery, and user protection. To protect data from unauthorized disclosure and modification, a secure algorithm should be used. Many techniques have been proposed to encrypt text to an image. Most past studies used RGB layers to encrypt text to an image. In this paper, a Text-to-Image Encryption-Decryption (TTIED) algorithm based on Cyan, Magenta, Yellow, Key/Black (CMYK) mode is proposed to improve security, capacity, and processing time. The results show that the capacity increased from one to four times compared to RGB mode. Security was also improved due to a decrease in the probability of an adversary discovering keys. The processing time ranged between 0.001 ms (668 characters) and 31 s (25 million characters), depending on the length of the text. The compression rate for the encrypted file was decreased compared to WinRAR. In this study, Arabic and English texts were encrypted and decrypted.
An Enhanced Key Schedule Algorithm of PRESENT-128 Block Cipher for Random and Non-Random Secret Keys

The key schedule algorithm (KSA) is a crucial element of symmetric block ciphers with a direct security impact. Despite its undeniable significance, the KSA is still a less focused area in the design of an encryption algorithm. PRESENT is a symmetric lightweight block cipher that provides the optimal balance between security, performance, and minimal cost in IoT. However, the linear functions in KSA lead to a slow and predictable bit transition, indicating the relationship between round keys. A robust KSA should produce random and independent round keys irrespective of the secret key. Therefore, this research aims to improve the KSA PRESENT-128 block cipher with enhanced randomness, round key bit difference, and the avalanche effect. The experiments on round keys and ciphertext with random, low density and high-density secret key datasets endorse the expected improvements. Moreover, the results show that the improved KSA produces random round keys that successfully pass the NIST randomness test. The bit transition from one round key to another is increased from 20% to 40%, where a greater inclination of the avalanche effect has an increased effect with 50% bit change. On the other hand, the improved KSA PRESENT requires an additional 0.001871 s to generate round keys, as a security cost trade-off.
Accessibility of educational virtual reality for children during the COVID-19 pandemic.

This article provides an exploratory inquiry into children's use of educational virtual reality (VR) at home, thereby complementing prior experimental research about the effects of VR on children.In order to assess the potentially innovative role that VR can play in remote instruction, this study collected data from parents and legal guardians reporting on their children's VR use at home during the first wave of the shelter-in-place measures resulting from the coronavirus disease (COVID-19) pandemic.From April to July 2020, parents and legal guardians who own VR devices participated in a survey (n = 311), longitudinal follow-up surveys (n = 60), and in-depth interviews (n = 20).The results indicate how VR can function as an innovative tool for socioemotional learning in a situation of remote instruction by (a) enhancing school materials and (b) sparking conversations about current affairs.Additionally, the results highlight two main barriers obstructing children's learning with VR.First, VR technology is gendered and may hinder the usage of both women and girls.Second, educational content is hard to find and lacks contextualizing complementary materials.With regard to the first barrier, the authors argue that the gender issue should be addressed in order to make VR more accessible to all children.This article addresses the second barrier by providing a database of educational VR applications.Ultimately, educational VR applications should be complemented with contextualizing materials to reach VR's potential as an innovative learning tool.
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data distribution may change in unpredictable ways, which is known as the concept drift in the literature. To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data. However, there are still many cases that some underlying factors of environment evolution are predictable, making it possible to model the future concept drift trend of the streaming data, while such cases are not fully explored in previous work. In this paper, we propose a novel method DDG-DA, that can effectively forecast the evolution of data distribution and improve the performance of models. Specifically, we first train a predictor to estimate the future data distribution, then leverage it to generate training samples, and finally train models on the generated data. We conduct experiments on three real-world tasks (forecasting on stock price trend, electricity load and solar irradiance) and obtained significant improvement on multiple widely-used models.
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning

Logical reasoning is of vital importance to natural language understanding. Previous studies either employ graph-based models to incorporate prior knowledge about logical relations, or introduce symbolic logic into neural models through data augmentation. These methods, however, heavily depend on annotated training data, and thus suffer from over-fitting and poor generalization problems due to the dataset sparsity. To address these two problems, in this paper, we propose MERIt, a MEta-path guided contrastive learning method for logical ReasonIng of text, to perform self-supervised pre-training on abundant unlabeled text data. Two novel strategies serve as indispensable components of our method. In particular, a strategy based on meta-path is devised to discover the logical structure in natural texts, followed by a counterfactual data augmentation strategy to eliminate the information shortcut induced by pre-training. The experimental results on two challenging logical reasoning benchmarks, i.e., ReClor and LogiQA, demonstrate that our method outperforms the SOTA baselines with significant improvements.
Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances

Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.
Geochemistry of geothermal fluids in the Zhangjiakou-Penglai Fault Zone, North China: Implications for structural segmentation

The Zhangjiakou-Penglai Fault Zone (ZPFZ) is a large-scale WNW-trending deep fault zone across the North China Craton, which is composed of four segments. There are abundant hot springs and geothermal wells in the ZPFZ and vicinity, from which 191 water and gas samples were collected and chemically analyzed with the aim of characterizing spatial variations of geothermal fluids and unraveling the influence of regional and local tectonics on fluid compositions. The investigations indicated that: (1) the geothermal waters were classified into 3 chemical types: HCO3-Ca·Mg, HCO3-Na and SO4·Cl-Na. The waters exhibited spatially significant discrepancies in physical-chemical parameters along four segments of ZPFZ. (2) The waters in different tectonic segments underwent different geochemical processes, including water-rock interactions, mixture of waters from different aquifers, dissolution of gases of different origins from geothermal water, continuous gas-water interactions. (3) Hydrogen and oxygen isotope compositions of the water samples indicated meteoric origin with different geographical effects on geothermal waters. Carbon and helium isotope compositions of the geothermal gas samples indicated that underlying mantle-derived magmas acting as heat source rise along the conduits induced by the crossing of NWN- and NE-trending faults to shallow reservoirs. (4) Intense interplay of fault-fluid relationships has been found along the four segments of ZPFZ. (5) Based on these findings, a conceptual model was established to tentatively simulate the origins and evolution processes of geothermal fluids.
A Unified Forensic Model Applicable to the Database Forensics Field

The Database Forensics Investigation (DBFI) field is focused on capturing and investigating database incidents. DBFI is a subdomain of the digital forensics domain, which deals with database files and dictionaries to identify, acquire, preserve, examine, analyze, reconstruct, present, and document database incidents. Several frameworks and models have been offered for the DBFI field in the literature. However, these specific models and frameworks have redundant investigation processes and activities. Therefore, this study has two aims: (i) conducting a compressive survey to discover the challenges and issues of the DBFI field and (ii) developing a Unified forensic model for the database forensics field. To this end, the design science research (DSR) method was used in this study. The results showed that the DBFI field suffers from many issues such as the lack of standardization, multidimensional nature, heterogeneity, and ambiguity, making it complex for those working in this domain. In addition, a model was proposed in this paper, called the Unified Forensic Model (UFM), which consists of five main stages: initialization stage, acquiring stage, investigation stage, restoring and recovering stage, and evaluation stage. Each stage has several processes and activities. The applicability of UFM was evaluated from two perspectives: completeness and implementation perspectives. UFM is a novel model covering all existing DBFI models and comprises two new stages: the recovering and restoring stage and the evaluation stage. The proposed UFM is so flexible that any forensic investigator could employ it easily when investigating database incidents.
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation

Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, evaluating whether the graph contrastive learning is robust to adversarial attacks is still an open problem because most existing graph adversarial attacks are supervised models, which means they heavily rely on labels and can only be used to evaluate the graph contrastive learning in a specific scenario. For unsupervised graph representation methods such as graph contrastive learning, it is difficult to acquire labels in real-world scenarios, making traditional supervised graph attack methods difficult to be applied to test their robustness. In this paper, we propose a novel unsupervised gradient-based adversarial attack that does not rely on labels for graph contrastive learning. We compute the gradients of the adjacency matrices of the two views and flip the edges with gradient ascent to maximize the contrastive loss. In this way, we can fully use multiple views generated by the graph contrastive learning models and pick the most informative edges without knowing their labels, and therefore can promisingly support our model adapted to more kinds of downstream tasks. Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction. We further show that our attack can be transferred to other graph representation models as well.
Blockchain-based Internet of Musical Things

Blockchain technology is impacting several industries, including the creative industries and those operating in the Internet of Things (IoT). Lately, researchers' attention has been devoted to the application of blockchain in the recorded music industry. However, thus far, no research has investigated the use of such technology in the Internet of Musical Things (IoMusT). The IoMusT is a new area emerging in industry and academy as an extension of the IoT to the musical domain. The IoMusT itself, as the IoT, is a distributed network of musical things, which are objects augmented with information and communication technologies serving a musical purpose. The IoMusT vision requires, above all, IoT features such as decentralization, seamless authentication, transparency, data integrity and privacy, and self-maintenance, as well as the musical domain features such as efficient handling of copyrights and speed of royalties payment. Such features can be brought by blockchain. In this paper, we investigate the integration of blockchain technology with the IoMusT, and we name such synthesis "Blockchain-based IoMusT". We present a vision for this new paradigm in terms of the novel opportunities that are enabled, and we propose a set of application scenarios enabled by technological integration. Finally, we outline the open research directions in this promising area.
Optimal Photovoltaic Panel Direction and Tilt Angle Prediction Using Stacking Ensemble Learning

Renewable energy sources produce electricity without causing increment in pollution, and solar energy is one of the primary renewable sources. Switching to renewable electricity is particularly impactful for companies whose emissions from purchased energy are the primary source. The Renewable Energy (RE100) initiative provides awareness to governments and the general public. Therefore, organizations must now move from renewable energy sources to clean energy sources. Solar panels are the primary source of renewable energy. However, a harsh environment or solar panel malfunction can lead to missing data, which causes various problems, such as data processing complexity, severe biases, and commitment to data quality. Optimal orientation and tilt angle for solar panels effectively get more energy from the solar panels. We have used machine learning to predict the optimal angle for a solar panel according to the season and time. This article studies solar panel data’s photovoltaic energy generation value and proposes a machine learning model based on the stacking ensemble learning technique. Three ML models, including catboost, XGboost, and random forest, are ensebmled. Experimental data are obtained by setting up sixteen solar panels with different combinations of tilt and direction. The performance of the proposed method is compared with other ML and statistical models. We obtained a regression score ( R 2 ) of 0.86 and a mean absolute percentage error (MAPE) of 2.54%.
Improved Rapidly Exploring Random Tree with Bacterial Mutation and Node Deletion for Offline Path Planning of Mobile Robot

The path-planning algorithm aims to find the optimal path between the starting and goal points without collision. One of the most popular algorithms is the optimized Rapidly exploring Random Tree (RRT*). The strength of RRT* algorithm is the collision-free path. It is the main reason why RRT-based algorithms are used in path planning for mobile robots. The RRT* algorithm generally creates the node for randomly making a tree branch to reach the goal point. The weakness of the RRT* algorithm is in the random process when the randomized nodes fall into the obstacle regions. The proposed algorithm generates a new random environment by removing the obstacle regions from the global environment. The objective is to minimize the number of unusable nodes from the randomizing process. The results show better performance in computational time and overall path-planning length. Bacterial mutation and local search algorithms are combined at post-processing to get a better path length and reduce the number of nodes. The proposed algorithm is tested in simulation.