Reviewerly/exHarmony huggingface dataset:
Developing a geophysical framework for Neolithic land-use studies: in situ monitoring and forward modelling of electrical properties at "Valther-Tweeling" (NL)

Boosting Neolithic land-use studies in the Low Countries requires optimal geophysical survey. Hereby, forward modeling such subtle soil features in a sandy soil could support survey design. The models are parametrized through soil sample analyses and in situ data collection over a feature. Supplemented by long term in situ monitoring, these results reveal variable detectability. This approach enables a quantitative approach for geophysical survey design.
Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier

Abstract This study aims to assist farmers in monitoring soil nutrients, especially phosphorus. To measure the phosphorus content of paddy soil, the TCS3200 converter, as an intelligent sensor, was applied. The geographical information system (GIS) was also involved in this research to map the phosphorus content. In addition, the Naïve Bayes method was applied to classify lowland soil phosphorus status. The result of this study indicated that the Naïve Bayes algorithm could classify lowland soil phosphorus status with a probability of 0.34 for moderate phosphorus conditions and 0.66 for high phosphorus conditions. The sample testing results showed that the error rate was 3% and the success rate was 97%. Testing with a phosphorus-measuring instrument can be carried out by mapping the soil phosphorus status with the ArcGIS software, whereby seven points of medium-phosphorus-status paddy soil and 13 locations of high-phosphorus-status soil samples were determined. This research thus successfully mapped the soil phosphorus.
White-Box Cryptography with Global Device Binding from Message-Recoverable Signatures and Token-Based Obfuscation

Device binding for white-box cryptography ensures that a white-box program is only executable on one specific device and is unusable elsewhere. In this paper we ask the following: is it possible to design a global white-box program which is compiled once, but can be securely shared with multiple users and bound to each of their devices? Acknowledging this question, we define different flavours of security for such global white-boxes and provide corresponding constructions. We first consider families of strong global white-boxes which can be securely distributed and bound to users' devices without the need of sharing secrets between compiling entities and users. We then show how such strong global white-boxes can be constructed based on message recoverable signatures (MRS). To this end, we introduce puncturable MRS which we build based on puncturable pseudorandom functions and indistinguishability obfuscation. We later consider the use of Token-Based Obfuscation for constructing a simpler family of global white-boxes, and show new ways of building white-box crypto, from more accepted assumptions as previously considered in the literature.
Learning a Gaussian Mixture Model From Imperfect Training Data for Robust Channel Estimation

In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i.e., the training data are solely comprised of noisy and sparsely allocated pilot observations. In a practical application, recent pilot observations at the base station (BS) can be utilized for training. This is in sharp contrast to state-of-the-art machine learning (ML) techniques where a training dataset consisting of perfect channel state information (CSI) samples is a prerequisite, which is generally unaffordable. In particular, we propose an adapted training procedure for fitting the GMM which is a generative model that represents the distribution of all potential channels associated with a specific BS cell. To this end, the necessary modifications of the underlying expectation-maximization (EM) algorithm are derived. Numerical results show that the proposed estimator performs close to the case where perfect CSI is available for the training and exhibits a higher robustness against imperfections in the training data as compared to state-of-the-art ML techniques.
On Learning Mechanical Laws of Motion From Video Using Neural Networks

In computer vision, physics plays an important role in several applications. In this work, we teach a machine to detect the mechanical laws of motion of physical objects using video, and show how the results can be useful for computer vision tasks. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing equation (e.g. projectile motion) but also the existence of governing parameters (e.g. velocities). We evaluate our ability to represent the physical laws of motion in video, such as the movement of a projectile or circular motion, in both real and constructed videos. These elementary tasks have textbook governing equations and enable ground truth verification of our approach. To establish the importance of the proposed method, we show a real-world use case in the domain of object tracking in confounding scenes, where existing state-of-the-art algorithms fail. Incorporating physics into computer vision not only serves the purpose of curiosity-driven research, but also provides an inductive bias for computer vision applications like object tracking.
An Adaptive Federated Filter Based on Variational Bayes With Application to Multisource Navigation

Information fusion is one of the key technologies in airborne multisource navigation, where the federated filter is widely used due to its simple structure. However, the sensor measurement of multisource navigation system will become unreliable in some interferential environment, which leads to the state and measurement noise covariance matrix (MNCM) change over time and estimate difficultly. Inaccurate covariance matrices cause the loss of positioning accuracy in traditional federated filter. To address the problems, this article puts forward an adaptive federal Kalman filtering algorithm based on variational Bayesian and applies it to the inertial navigation system (INS)/global positioning system (GPS)/terrain/geomagnetic multisource navigation system. Simulation and experimental results show that the proposed filter outperforms the traditional federated filter in improving the positioning accuracy under the condition of time-varying or even unknown state and MNCM, which plays a positive role in unmanned aerial vehicle (UAV) multisource navigation.
MRFT: Multiscale Recurrent Fusion Transformer Based Prior Knowledge for Bit-Depth Enhancement

Bit-depth enhancement (BDE) plays an important role in providing high bit-depth data support for high-dynamic range (HDR) display. Although convolutional neural network (CNN) based BDE methods have achieved top performance, multiscale feature extraction and fusion still suffer from some inherent architectural flaws. Moreover, the training-data-scarce scene has not been effectively explored. To this end, this paper proposes an innovative multiscale recurrent fusion transformer (MRFT) framework, which contains three key components, i.e. multiscale transformer feature encoder, recurrent feature fusion module, and prior knowledge injection. Specifically, the multiscale transformer feature encoder consists of a prior-injected context encoder (PICE) and a multiscale local feature encoder (MLFE). PICE leverages the vanilla self-attention mechanism to extract the global context correlating spatially-distant contents for distinguishing long-distance false contours. MLFE exploits the local self-attention mechanism with varied window sizes to capture different-scale detail features. Then, a hierarchical recurrent decoder (HRD) is proposed as the recurrent feature fusion module to fuse multiscale visual information with global guidance. Via the circular query-key mechanism, global-to-local information is progressively fused. Furthermore, we propose a two-stage alternating optimization strategy for prior knowledge injection. By pre-parameterizing the global auxiliary priors, the training dilemma on the data-scarce domain is significantly alleviated. Extensive analyses on multiple benchmark datasets demonstrate the superiority of our MRFT in terms of quantitative measures and aesthetic effects.
Integration of Simulated Quantum Annealing in Parallel Tempering and Population Annealing for Heterogeneous-Profile QUBO Exploration

Simulated Quantum Annealing (SQA) is a heuristic algorithm which can solve Quadratic Unconstrained Binary Optimization (QUBO) problems by emulating the exploration of the solution space done by a quantum annealer. It mimics the quantum superposition and tunnelling effects through a set of correlated replicas of the spins system representing the problem to be solved and performing Monte Carlo steps. However, the effectiveness of SQA over a classical algorithm strictly depends on the cost/energy profile of the target problem. In fact, quantum annealing only performs well in exploring functions with high and narrow peaks, while classical annealing is better in overcoming flat and wide energy-profile barriers. Unfortunately, real-world problems have a heterogeneous solution space and the probability of success of each solver depends on the size of the energy profile region compatible with its exploration mechanism. Therefore, significant advantages could be obtained by exploiting hybrid solvers, which combine SQA and classical algorithms. This work proposes four new quantum-classical algorithms: Simulated Quantum Parallel Tempering (SQPT), Simulated Quantum Population Annealing (SQPA), Simulated Quantum Parallel Tempering - Population Annealing v1 (SQPTPA1) and Simulated Quantum Parallel Tempering - Population Annealing v2 (SQPTPA2). They are obtained by combining SQA, Parallel Tempering (PT), and Population Annealing (PA). Their results are compared with those provided by SQA, considering benchmark QUBO problems, characterized by different profiles. Even though this work is preliminary, the obtained results are encouraging and prove hybrid solvers’ potential in solving a generic optimization problem.
Automatic Loss Function Search for Adversarial Unsupervised Domain Adaptation

Unsupervised domain adaption (UDA) aims to reduce the domain gap between labeled source and unlabeled target domains. Many prior works exploit adversarial learning that leverages pre-designed discriminators to drive the network for aligning distributions between domains. However, most of them do not consider the degeneration of the domain discriminators caused by the gradually dominating gradients of aligned target samples during training, and they still suffer from the cross-domain semantic mismatch problem in the learned feature space. Hence, this paper attempts to understand and solve both issues from the lens of optimization loss and propose an automatic loss function search for adversarial domain adaptation (ALSDA). First, we extend the common adversarial loss by adding an adjustable hyper-parameter that can re-weight the gradients assigned to target samples, so that the domain discriminator can impose consecutive and influential driving forces for domain alignment. Meanwhile, we upgrade the traditional orthogonality loss with class-wisely adjustable hyper-parameters that can strengthen the cross-domain feature separation. Since manually determining the optimal loss functions requires expensive expert efforts, we leverage the popular AutoML to automatically search for the optimal loss functions from a pre-defined novel and unique search space for UDA. Further, to enable the loss function search when the target domain is unlabeled, we introduce a simple-but-effective entropy-guided search strategy with the aid of REINFORCE learning. Extensive experiments on various typical baselines and benchmark datasets such as Office-Home, Office-31, and Birds-31 have been conducted, and the results validate the generalization and superiority of the proposed ALSDA.
Literacy of Lower Classes Students Primary School in the 2013 Curriculum during the Pandemic COVID-19

This study aims to describe the application of reading literacy in the 2013 Curriculum for the COVID-19 pandemic era. This study uses a descriptive content analysis study and literature studies. The findings showed that educators carried out various activities to implement students' reading literacy through blended learning activities, both at school and home. In conclusion, teachers monitor the increase in reading literacy through assignments performed by students in learning from home. Then, teachers and student guardians work together in online learning to improve students' reading literacy.&#x0D; Keywords: COVID-19, Curriculum 2013, Literacy, Lower Class, Primary School
Computational estimation of drug’s concentration distribution through a microporous membrane using artificial intelligence approach

Recently, the emergence of promising membrane-based processes in disparate related disciplines of pharmaceutical industries (i.e., biochemical engineering and biotechnology) has paved the way towards concentration and purification of therapeutic entities, molecular synthesis, and drug delivery. The strategy of this investigation is to employ various artificial intelligence (AI)-based computational models as a novel method to estimate the concentration distribution value of a medical solute inside the membrane. To achieve this, a data set with two inputs and one output was analyzed using bagging ensemble method with some distinct base models. The base models are Multi-layer Perceptron (MLP), Linear Regression (LR), and K-Nearest Neighbor (KNN). The hyperparameters of the methods were developed through performing a grid search and the final models were examined. Based on this, as expected, the use of bagging along with the optimization of hyperparameters led to strong models. All three bagging models with the R-square measure have scores higher than 0.98. Also, in terms of RMSE bagging LR, bagging MLP, and bagging KNN have error rates of 9.25 × 101, 2.29 × 101, 9.57 × 101, respectively.
Levi and Harper identities for non-prioritized belief base change

In this paper, we investigate the relation between shielded base contraction postulates and credibility-limited (CL) base revision postulates. More precisely, we identify (i) the relation between the postulates satisfied by a shielded base contraction operator and the postulates satisfied by the CL base revision operator that is defined from it by means of the consistency-preserving Levi identity and (ii) the relation between the postulates satisfied by a CL base revision operator and the postulates satisfied by the shielded base contraction operator that is defined from it by means of the Harper identity. Furthermore, we show that the consistency-preserving Levi identity and the Harper identity establish a one-to-one correspondence between the twenty classes of shielded base contractions presented in [21] and the twenty classes of credibility-limited base revisions presented in [22].
Graph context-attention network via low and high order aggregation

Graph attention networks (GATs) have been shown effectively for representation learning. However, existing GATs only employ the first-order attention mechanism and thus fail to fully exploit and learn node’s contextual feature representations. To address this issue, we propose a novel Graph Context-Attention Network (GCAN) via low and high order aggregation for representation learning. The proposed model fully exploits both low-order and high-order information of nodes for representation learning by employing high-order attention mechanism and adversary regularization constraints. The main benefit of the proposed method is that it can effectively capture the discriminative feature differences among node representations while enhancing the diversity and richness of node features. Thus it can alleviate the issue of over-smoothing in the network learning. We perform extensive experiments on nine datasets and the experimental results demonstrate the effectiveness and better performance of our approach.
Semantical video coding: Instill static-dynamic clues into structured bitstream for AI tasks

Traditional media coding schemes typically encode image or video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream level. Semantically Structured Image Coding (SSIC) (Sun et al., 2020) makes the first attempt to enable partial-decoding image intelligent task analysis via a Semantically Structured Bitstream (SSB). However, the SSIC considers image coding and its generated SSB only contains the static object information. In this paper, we propose an advanced Semantically Structured Video Coding (SSVC). Video signals contain more rich dynamic motion information and redundancy. Thus, we present a reformulation of semantically structured bitstream (SSB) in SSVC which contains both static object characteristics and dynamic motion clues. Specifically, we introduce optical flow to encode continuous motion information and reduce cross-frame redundancy via a predictive coding architecture, then the optical flow and residual information are reorganized into SSB, which enables the proposed SSVC could better adaptively support video-based downstream intelligent applications. Extensive experiments on various vision tasks demonstrate that the proposed SSVC framework could directly support multiple intelligent tasks just depending on a partially decoded bitstream, saving bitrate consumption for intelligent analytics.
HSGS: A hybrid of harmony search algorithm and golden section for data clustering

This paper proposes an improved variant of the harmony search (HS) algorithm called HSGS for solving clustering problems. The HSGS generates two new harmonies at each iteration. The first is produced by the harmony improvisation procedure of the canonical HS and promotes exploration ability. The second is generated by one of two new phases added to HS to enhance the exploitation capability with different ratios. Each phase employs the mathematical properties of a famous pattern found in nature, called golden section, to search around a specific harmony. In addition, these phases utilize different information of data points that need to be clustered. In each cycle of the HSGS, a self-adaptive selection mechanism selects a phase to properly adjust the exploitation ratio during the search process. A harmony with a better objective value between the two new harmonies competes with the current worst harmony to update the harmony memory. The performance of HSGS for clustering several well-known benchmark data sets, gene expression data sets, and noisy versions of gene expression data sets is compared with two modified variants of HSGS, seven HS-based clustering algorithms, k-means++, and six evolutionary clustering algorithms. In general, the results indicate that HSGS is superior to all considered algorithms in all data sets.
Mixed-integer trajectory optimization with no-fly zone constraints for a hypersonic vehicle

This paper focuses on mixed-integer trajectory optimization of no-fly zones avoidance for a hypersonic vehicle. When there are multiple no-fly zones, the trajectory optimization problem has many local optimal solutions, and the performance of the solution depends on the selection of an initial guess. Essentially, the selection of the initial guess can be abstracted as a discrete path decision. To deal with this issue, this paper first integrates the discrete path decision and continuous trajectory optimization for no-fly zones avoidance and models these as a mixed-integer optimal control problem (MIOCP). As the corner stone, a creative directed graph is established to model the path decision problem, which is then formulated by integer constraints. Combined with the minimum effort optimal control problem (Problem 1), we form the MIOCP (Problem 2), thus integrating the path decision and trajectory optimization to improve global performance. Additionally, to solve the MIOCP, an effective iterative mixed-integer convex programming (IMICP) algorithm is customized by adding two ad-hoc techniques, relaxation and a proximity term, thus remedying the artificial infeasibility. Simulation results show that this approach is practically independent of the initial guess and has better performance than existing methods.
A Systematic Literature Review of Virtual Reality Implementation in Sports

Virtual Reality is a major step in the technology field. Recently, Virtual Reality has been used in various fields, one of which is in the field of sports. In the field of sports, the relationship between experimental and subject behavior in real conditions is a major problem. Virtual Reality can be used in sports because of its situational productivity and animation control in situations that are similar to the real world. This paper presents a research review to understand the use of Virtual Reality in sports. This paper uses a systematic literature review (SLR) as a research method. Research Question(RQ) was determined in the first step. Popular database journals included IEEE Xplore, Science Direct, ACM Digital Library, Springer Open, JSTOR. Twenty-five related articles were produced from the search, then reviewed. The study concluded that there were four purposes of using virtual reality for sports, three types of Virtual Reality Devices used, and three types of approaches.
A Mini Literature Review on Integrating Cybersecurity for Business Continuity

Governments, organizations, and companies shall implement some measures to ensure the security and continuity of their business operations especially those with high criticality and availability. If cyber threats can bring down a company for a long time and create reputational impacts which cause painful financial losses of hundreds of thousands or millions of Saudi riyals and significant disruptions to business operations, then we should think of business continuity. As a result, as organizations build their business continuity and disaster recovery plans and procedures, cybersecurity measures must be integrated throughout the entire process. This paper focuses on falling the gaps between cybersecurity and business continuity and integrating them in order to establish resilience systems and enable organizations to ensure that the proper processes are being put in place and resources are allocated to help facilitate a smooth transition as they recover from cyber incidents.
Robustness of Deep Learning enabled IoT Applications Utilizing Higher Order QAM in OFDM Image Communication System

In a standard Orthogonal Frequency Division Multiplex (OFDM)-based communication system, higher order M-ary Quadrature Amplitude Modulation (M-QAM) increases throughput and link capacity significantly due to the larger number of bits per symbol. Such a system is suitable to transmit large volumes of image data for real-time Internet of Things (IoT) applications. However, higher order M-QAM increases the noise margin and Bit Error Rate (BER) in the received signal. Although the error bits corrupt the individual pixels values, the global contents of the image may remain intact and can be interpreted by Deep Learning (DL) models. Therefore, the current study analyzes the robustness of DL models for image communication systems utilizing higher order M-QAM. The performance analysis of the system is presented in terms of the BER and image quality measures. In the analysis, distortions in the reconstructed images decrease significantly when using higher Es/N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> across all M-QAM. For higher Eb/No in the 1024-QAM system, the performance of the model improved by 11% and obtained an accuracy of 86%, lagging by 6% compared to the accuracy obtained on the original images. Overall, the model maintained a high accuracy range between 83 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 90% at Eb/No 10 dB across all M-QAM.