Reviewerly/exHarmony huggingface dataset:
Accurate Phase Detection for ZigBee Using Artificial Neural Network

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks. Moreover, researchers have reported a positive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems, including ZigBee. Meanwhile, artificial neural network (ANN) and machine learning (ML) models outperformed results for predicting signals for detection and classification purposes. This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation. First, a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network (WPAN) standard. Then, Gaussian noise was injected into the BPSK simulation model. To reduce the noise of BPSK phase signals, a recurrent neural networks (RNN) model is implemented and integrated at the receiver side to estimate the BPSK’s phase signal. We evaluated our predictive-detection RNN model using mean square error (MSE), correlation coefficient, recall, and F1-score metrics. The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient (R-value) metric for different signal-to-noise (SNR) values. In addition, our RNN-based model scored 98.71% and 96.34% based on recall and F1-score, respectively.
Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

The term 'executed linguistics' corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights. The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users. The Automatic Text Summarization (ATS) process reduces the primary size of the text without losing any basic components of the data. The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning (ALTS-MLODL) model. The presented ALTS-MLODL technique aims to summarize the text documents in the English language. To accomplish this objective, the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features. Next, the MLO algorithm is used for the effectual selection of the extracted features. For the text summarization process, the Cascaded Recurrent Neural Network (CRNN) model is exploited whereas the Whale Optimization Algorithm (WOA) is used as a hyperparameter optimizer. The exploitation of the MLO-based feature selection and the WOA-based hyperparameter tuning enhanced the summarization results. To validate the performance of the ALTS-MLODL technique, numerous simulation analyses were conducted. The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.
Obstacle Avoidance for Quadcopters in Formation Flying Based on A* Algorithm

Quadcopters take precedence over fixed-wing aircraft within the UAV family, owing to their distinctive characteristics such as vertical take-off and landing, reduced size and weight, high maneuverability, and more. With recent technological advancements, UAVs have become viable for a wide range of applications ranging from military to civilian, including traffic monitoring, aerial photography, surveillance, payload carrying search and rescue, and much more, especially for tedious, filthy, and dangerous jobs that endanger people, such as building fires and observatories in the woods, military purposes. To complete some of these missions, a swarmswarm of Quadcopters must work together. Determining how a Quadcopter can autonomously achieve its goal position despite obstacles in its path is a complex issue. This paper uses the A* (A-star) algorithm to model path planning, trajectory generation, and autonomous control of a quadcopter. Path planning was thoroughly examined using various scenarios with various obstacle positions, and the dimensions of obstacles are much greater than the dimensions of the Quadcopter over the map. It was also observed whether the swarmswarm (3 Quadcopters in “vee” formation) maintains the necessary formation throughout the created path.
Cultivate Smart and Healthy Ageing

The essential criterion for developing an intelligent age-friendly environment is evaluating and monitoring the provision of services. Our research aims to allow the deployment of a broad range of digital healthcare solutions interconnected in an IoT network ecosystem. Through their interoperability, we will be able to cultivate age-friendly smart homes for older adults. Thus, we will enable interpreted living and wellbeing. The intelligent integrations of digital solutions will allow the acquisition and evaluation of health-related data. Standardization, interoperability and scalability of the integration will increase efficiency in healthcare, improving older adults' Quality of Life (QoL). Also, interpreting their needs will allow key stakeholders to optimize the Quality of Service (QoS). The proposed Internet of Health (IoH) framework will encapsulate sustainable and affordable innovative solutions and their benefits for a comfortable, meaningful, and independent life in an intelligent environment. Therefore, the frame mediated by edge computing, in-home and community settings will be able to interact with healthcare networks contributing to hospitalizations and institutional care.
A Composite Image Processing Technique to Enhance Segmentation of Ultrasound Images

In ultrasound images, the speckle noise phenomenon is a typical issue that limits the accuracy of the images obtained from any quantitative measurement. Noise elimination in ultrasound images in the pre-processing stage is an important step to make the image fit for the following steps involved in image segmentation. However, various noises produce a range of significant impacts on the quality of the image and thus tend to affect the interpretation of neural networks. In this study, 35 different image processing techniques, including denoising, feature descriptor, edge detection, miscellaneous, clustering, morphological, sharpening, and composite techniques were used to determine the most optimal ultrasound image in order to improve the segmentation of ultrasound images. The performance of image processing techniques was compared using a statistical parameter, peak signal-to-noise ratio (PSNR). All techniques were examined on deep learning segmentation approach. The segmentation performance was evaluated using mean intersection over union (mIoU) and mean pixel accuracy (mPA). The results showed that the composite image technique (FancyPCA + Bilateral + Gabor) slightly enhanced the segmentation performance, with the most ideal mIoU 0.96893%, mPA 0.97831%, and average PSNR 53.034 dB.
An Improved Binary Tomography Reconstruction

In this paper, the binary tomographic reconstruction problem is considered. Binary tomography aims to reconstruct binary images from their projections. Possible applications can be the field of human X-ray angiography, where the aim is to reconstruct images representing blood vessels or heart chambers, using X-ray tomography methods. Injecting a contrast agent with high linear attenuation coefficient into the part of the body being examined and seek for the presence or absence of the contrast agent in certain positions. Other common applications of this field are electron tomography and industrial non-destructive testing. To reconstruct a binary image from their projections, an improved algebraic approach is proposed in this paper. An energy-minimization reconstruction model is used; this model employs a loss function to be minimized. This loss function combines three features that can be extracted from the given projections. First, data fitting term based on the given projections; second, the first two image moments that are extracted from the given projections; and third, a term that enforces the binary solution. The first two terms are expressed in terms of a linear system and the third is expressed as anon linear cost function. The projected gradient descent algorithm is then employed for this minimization process. Experimental evaluations show that reasonable results can be obtained from minimal number of projections.
Deep Cross-modal Hashing Retrieval Based on Semantics Preserving and Vision Transformer

In response to the problem of similarity measure differences in different similarity coefficients that occur in cross-modal multi-label retrieval, this article uses an interval parameter to correct this bias. A new supervised hash method is proposed by introducing the transformer structure which performs well in CV and NLP tasks into cross-modal hash retrieval, called the Deep Semantics Preserving Vision Transformer Hashing (DSPVTH). This method uses network structures such as vision transformer to map different modal data into binary hash codes. It also uses the similarity relationship of multiple tags to maintain the semantic association between different modal data. Validation on four commonly used multimodal text datasets, Mirflickr25k, NUS-WIDE, COCO2014 and IAPR TC-12, shows a 2% to 8% improvement in average accuracy compared with the current optimal method, which means our method is robust and effective.
DEPA: Determining Exploit Primitives Automatically for Interactive Programs

Automated Exploit Generation (AEG) is a well-known difficult task, especially for heap vulnerabilities. The premise of this task is the determination of exploit primitives, and prior research efforts for exploit primitive determination are usually based on vulnerability identification. However, it is not always easy to discovery bugs using fuzzing or symbolic technologies for some programs.
Traffic Classification Method Based on Federated Semi-Supervised Learning

In order to protect the data privacy of network users and solve the training difficulties caused by traffic distribution, this paper based on federal semi-supervised learning presents a traffic classification method to solve the problem of a small number of labeled traffic distributed in server, and a large number of non-labeled traffic distributed independently and identically in clients and not shared. On the one hand, this paper adopts the parameter decomposition strategy to avoid interference between different tasks. On the other hand, this paper uses consistency regularization between clients to maximize consensus between similar segment clients to solve the learning problem of variable small sample data. In addition, method in this paper only transfer parameter differences during the federated learning parameter transfer process. The experimental results show that the accuracy gap between our method and the supervised learning training method is minimal, which can effectively protect user privacy and does not require a large amount of labeled data and communication costs.
Uncertainty-Aware Heterogeneous Representation Learning in POI Recommender Systems

Discovering interesting yet unvisited point-of-interests (POIs) is among the most practical applications but challenging problems in location-based social networks (LBSNs). Popular approaches face several issues, such as data sparsity and difficulties in modeling latent nonlinearity between users and POIs. Furthermore, the uncertainty in LBSNs poses additional obstacles to learning good representations of users’ general and current interests. To effectively address these issues, we postulate that fusing multiple sources of information is paramount. Toward that, we propose a novel deep generative recommender system—Wasserstein autoencoder for POI recommendation (WaPOIR). It unifies the information from users’ personal preference, social influence, and geographical data, and captures users’ general interests from historical check-ins, while modeling users’ current interests from recently visited POIs. Unlike previous methods, WaPOIR learns the latent distribution of data in the Wasserstein space as a potential representation for each POI and each user in LBSNs. This enables simultaneous maintenance of social and POI interactions and modeling the uncertainty of their relationships. WaPOIR is a stochastic recommendation approach that allows Bayesian inference and approximation of variational posterior distribution. Extensive experiments conducted on real-world LBSN datasets demonstrate that WaPOIR achieves better performance over the state-of-the-art approaches.
Deep reinforcement learning using least‐squares truncated temporal‐difference

Abstract Policy evaluation (PE) is a critical sub‐problem in reinforcement learning, which estimates the value function for a given policy and can be used for policy improvement. However, there still exist some limitations in current PE methods, such as low sample efficiency and local convergence, especially on complex tasks. In this study, a novel PE algorithm called Least‐Squares Truncated Temporal‐Difference learning (LST 2 D) is proposed. In LST 2 D, an adaptive truncation mechanism is designed, which effectively takes advantage of the fast convergence property of Least‐Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning (TD). Then, two feature pre‐training methods are utilised to improve the approximation ability of LST 2 D. Furthermore, an Actor‐Critic algorithm based on LST 2 D and pre‐trained feature representations (ACLPF) is proposed, where LST 2 D is integrated into the critic network to improve learning‐prediction efficiency. Comprehensive simulation studies were conducted on four robotic tasks, and the corresponding results illustrate the effectiveness of LST 2 D. The proposed ACLPF algorithm outperformed DQN, ACER and PPO in terms of sample efficiency and stability, which demonstrated that LST 2 D can be applied to online learning control problems by incorporating it into the actor‐critic architecture.
How to anchor design thinking in the future: Empirical evidence on the usage of strategic foresight in design thinking projects

Many organizations use design thinking (DT) to develop future products and services. DT is often used for its ability to serve as a common "language" and platform to enable market-facing departments and technology-oriented units to cocreate innovations. DT has been shown to be a powerful tool for helping to identify and connect the needs of average customers (personas) with technical solutions that form the basis for winning products. In this paper, we investigate the extent to which DT professionals already use strategic foresight (SF) methods that anticipate future customer needs and highlight emerging technologies to expand classical DT and anchor their projects in the future. Using survey data on 302 DT projects, we report on the extent to which SF methods are used in DT projects, the overriding types of SF methods in DT projects, and their impact on project success.
Seedable Conditions of Clouds Using Machine Learning Techniques

Cloud seeding, often called weather modification, is a scientific process intended to enhance rain and snow, reduce hail damage, and alleviate fog. In the Queensland Cloud Seeding Research Program (QSCRP) the researchers observed that the factors mentioned above, i.e., the wind parameters, cloud condensation nuclei (CCN) concentration, and size of particles should have high values to produce effective results of the seeding experiments but they failed to give a proper visualization as to what should the values be to classify the points from the rest of the values. So, in this study, we try to address this issue by providing an unsupervised Machine Learning based clustering method to determine the proper characteristics of a cloud that can differentiate it from the other characteristics which might not give a better result after seeding experiments.
From images to features: unbiased morphology classification via variational auto-encoders and domain adaptation

We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). We demonstrate the effectiveness of this approach using a sample of low redshift galaxies with detailed morphological type labels from the Galaxy-Zoo DECaLS project. We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. To further validate the effectiveness of our approach, we utilised a classical random forest (RF) classifier on the 40-dimensional latent variables to make detailed morphology feature classifications. This approach performs similarly to a direct neural network application on galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and BASS+MzLS, enabling the unbiased application of our model to galaxy images in both surveys. We observed that DA led to even better morphological feature extraction and classification performance. Overall, this combination of VAE and DA can be applied to achieve image dimensionality reduction, defect image identification, and morphology classification in large optical surveys.
A predictive model for understanding the role of emotion for the formation of presence in virtual reality

Users' emotions may influence the formation of presence in virtual reality (VR). Users' expectations, state of arousal and personality may also moderate the relationship between emotions and presence. An interoceptive predictive coding model of conscious presence (IPCM) considers presence as a product of the match between predictions of interoceptive emotional states and the actual states evoked by an experience (Seth et al. 2012). The present paper aims to test this model's applicability to VR for both high-arousal and low-arousal emotions. The moderating effect of personality traits on the creation of presence is also investigated. Results show that user expectations about emotional states in VR have an impact on presence, however, expression of this relationship is moderated by the intensity of an emotion, with only high-arousal emotions showing an effect. Additionally, users' personality traits moderated the relationship between emotions and presence. A refined model is proposed that predicts presence in VR by weighting emotions according to their level of arousal and by considering the impact of personality traits.
CT-GAN: A conditional Generative Adversarial Network of transformer architecture for text-to-image

How to generate an image from a text description is an imaginative and challenging task. This study proposes a conditional generative adversarial network (GAN) of transformer architecture for text-to-image tasks called CT-GAN by employing the GAN generator based on transformer architecture. We also propose a filtering module suitable for non-end-to-end multi-stage models. This module can screen out the good images generated in the previous stage and allows only the good images to participate in the generation of the later stage. This method significantly improves the quality of the generated images. Furthermore, we designed a generator and discriminator based on symmetry. In the generator, we propose a shift self-attention technology to establish information communication between grids, reduce boundary loss, and improve image quality. We established two modes of local and global discriminations based on the grid, which can balance the performance of the generator and discriminator, improve the training stability, and accelerate the model convergence. We conducted several experiments on the widely used conditional datasets (CUB and COCO) and unconditional datasets (CelebA and LSUN church). The experimental results show that the proposed CT-GAN is superior to the most advanced convolution model in generating diversity and semantic consistency. Codes are available at: https://github.com/Jwtcode/CT-GAN.
Should you listen to crypto YouTubers?

YouTube content drives prices and trading volume of low cap cryptocurrencies. Using a sample of 305 YouTube videos, we find significant positive abnormal returns as well as abnormal trading volume in the wake of the coverage of a specific coin. These effects are transitory though, excess returns and excess volume are reversed shortly thereafter.
Web3 Sybil avoidance using network latency

Web3 is emerging as the new Internet-interaction model that facilitates direct collaboration between strangers without a need for prior trust between network participants and without central authorities. However, one of its shortcomings is the lack of a defense mechanism against the ability of a single user to generate a surplus of identities, known as the Sybil attack. Web3 has a Sybil attack problem because it uses peer sampling to establish connections between users. We evaluate the promising but under-explored direction of Sybil avoidance using network latency measurements, according to which two identities with equal latencies are suspected to be operated from the same node, and thus are likely Sybils. Network latency measurements have two desirable properties: they are only malleable by attackers by adding latency, and they do not require any trust between network participants. Our basic SybilSys mechanism avoids Sybil attackers using only network latency measurements if attackers do not actively exploit their malleability. We present an enhanced version of SybilSys that protects against targeted attacks using a variant of the flow correlation attack, which we name TrafficJamTrigger. We show how the message flows of Round-Trip Time measurements can be used to expose attack patterns and we propose and evaluate six classifiers to recognize these patterns. Our experiments show, through both emulation and real-world deployment, that enhanced SybilSys can serve a fundamental role for Web3, effectively establishing connections to real users even in the face of networks consisting of 99% Sybils.
Topic-aware multi-hop machine reading comprehension using weighted graphs

The problem of Machine Reading Comprehension (MRC) aims to answer a question based on a natural language context. Multi-hop MRC is a challenging task as it requires a deep comprehension and reasoning of disjoint pieces of information to find the answer. Recently, graph-based methods have become very popular in multi-hop MRC as they well model the problem and ease the reasoning task. However, most existing studies ignore some valuable information of the context. As a result, they focus on partial information instead of covering the full information of the context. To fill this gap, a new approach is presented in this study for the graph-based multi-hop MRC that takes more important information of the context into consideration, including the topic of sentences, the topic of relationships, and the importance and strength of relationships to generate and reason an enrich weighted graph. Several experiments have been conducted to demonstrate the usefulness of the proposed approach. Experiments on the HotpotQA benchmark show that our proposed approach has achieved the new state-of-the-art results.