Reviewerly/exHarmony huggingface dataset:
Police Organisational Behaviour and the Trend of Criminal Activities in Rivers State Nigeria

This study was carried out to investigate the extent to which the Nigeria Police Force (NPF) organizational behavior has reduced the trend of criminal activities in Rivers State. A descriptive survey research method of correlational research design was employed in this study to provide detailed investigation of the procedure used to establish the relationship between the Nigeria police force organizational behavior and the trend of criminal activities in Rivers State. The population of the study was 193,392,500 persons consisting of residents drawn from the cities and rural communities in Rivers State. A sample size of 400 respondents was determined, using Taro Yamene’s sample size techniques at 0.5 percent level of significance. A purposive sampling technique was adopted using a considerable percentage representation approach to ensure effective selection of the representative sample elements of the population from respondents who have adequate knowledge of the study under investigation. A structured instrument for data collection containing twenty (20) item questions was used for the study. The face and content validation of the instrument was obtained through the judgment of experts. A test-retest method was used to determine the reliability of the instrument and a reliability index of .83 was obtained. The data collected were analyzed using the descriptive statistics involving the mean score test and the percentage test method to answer the research questions, while the Z-score test of the inferential statistics method was used to test the hypothesized variables at .05 level of significance. The results obtained revealed that “Nigeria Police Force organizational behavior does not significantly reduce the trend of criminal activities in Rivers State.” The implication of the finding is that the discovered weakness of the Nigeria police force organizational behavior is that it lacks the needed proactive ethical improvement value to carry out its responsible assignments as an organization. Consequently, increasing the level of police compromise with many related criminal activities is considered mephitic enough to uplift or expand the trend of criminal activities in the state if more punitive measures are not employed by the government. It was therefore advisable that the federal government should employ legal assessor that will be used to form “Police Behavioral Monitoring Task Force (PBMT)” in every local government areas to oversee or monitor unethical and irresponsible behaviors of the Nigeria police officers in each state to curtail or curb police dysfunctional involvement in criminal related practices.
Applying Statistical Analysis and Economics Models to Unscramble the Depositional Signals from Chemical Proxies in Black Shales

The complex controls on the accumulation of organic-rich rocks remain elusive, despite their economic importance as source rocks and unconventional reservoirs, partially due to the multitude of factors that may impact production and preservation of organic matter in sediments. The complexity of Earth systems is comparable to the intricacies of Economics, and application of statistical and econometrics methods and models to analyze geological data may assist interpretation of the processes controlling organic burial. Chemical indices calculated for mudrock datasets from modern sediments and the Woodford Formation were used as proxies for detrital input, primary productivity, redox conditions, and upwelling, and a series of statistical analyses were run to test whether these methods were useful to discriminate different depositional conditions and establish the controls on total organic carbon (TOC) in the sediments. Model results showed that chemical proxies reliably predict not only TOC but also indicate correlations between indices. Our results suggest that detrital input, primary productivity and bottom-water anoxia are relevant drivers of organic content in the sediments, but the first two appear to have a more significant role in organic burial, illustrating the usefulness of these methods to assess depositional parameters in organic-rich rocks.
Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.
Data management method for building internet of things based on blockchain sharding and DAG

Sharding technology can address the throughput and scalability limitations that arise when single-chain blockchain are applied in the Internet of Things (IoT). However, existing sharding solutions focus on addressing issues like malicious nodes clustering and cross-shard transactions. Existing sharding solutions cannot adapt to the performance disparities of edge nodes and the characteristic of three-dimensional data queries in building IoT. This leads to problems such as shard overheating and inefficient data query efficiency. This paper proposes a dual-layer architecture called S-DAG, which combines sharded blockchain and DAG blockchain. The sharded blockchain processes transactions within the building IoT, while the DAG blockchain stores block headers from the sharded network. By designing an Adaptive Balancing Load Algorithm (ABLA) for periodic network sharding, nodes are divided based on their load performance values to prevent the aggregation of low-load performance nodes and the resulting issue of shard overheating. By combining the characteristics of the KD tree and Merkle tree, a block structure known as 3D-Merkle tree is designed to support three-dimensional data queries, enhancing the efficiency of three-dimensional data queries in building IoT. By deploying and conducting simulation experiments on various physical devices, we have verified the effectiveness of the solution proposed in this paper. The results indicate that, compared to other solutions, the proposed solution is better suited for building IoT data management. ABLA is effective in preventing shard overheating issue, and the 3D-Merkle tree significantly enhances data query efficiency.
Sparse least-squares Universum twin bounded support vector machine with adaptive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e21955" altimg="si10.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norms and feature selection

In data analysis, when attempting to solve classification problems, we may encounter a large number of features. However, not all features are relevant for the current classification, and including irrelevant features can occasionally degrade learning performance. As a result, selecting the most relevant features is critical, especially for high-dimensional data sets in classification problems. Feature selection is an effective method for resolving this issue. It attempts to represent the original data by extracting relevant features containing useful information. In this research, our aim is to propose a p-norm least-squares Universum twin bounded support vector machine (LSp-UTBSVM) to perform classification and feature selection at the same time. Indeed, the proposed method, which outperforms the traditional least-squares Universum twin bounded support vector machine, can achieve good classification accuracy in a reasonable amount of time while also providing a sparse solution. The model we propose is an adaptive learning procedure with p-norm (0<p<1), where the parameter p can be automatically selected by the data set. The algorithm we use to find the approximate solution of this model involves solving systems of linear equations. Furthermore, we obtain new bounds for the absolute values of non-zero components of a local optimal solution. These bounds allow us to remove the zero components from an arbitrary numerical solution. Setting the parameter p, LSp-UTBSVM improves classification accuracy and selects the relevant features. Numerical experiments on a handwritten digit recognition, University of California Irvine (UCI) benchmark, Normally Distributed Clusters (NDC) and high dimensional data sets confirm the superiority of the proposed method in the accuracy of classification and the selection of relevant features in comparison with some popular methods.
Research on Unmanned Aerial Vehicle Path Planning

As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.
An adaptive density clustering approach with multi-granularity fusion

The real-world dataset exhibits diversity, incorporating instances with complex shapes and significant differences in density hierarchy, potentially disrupted by noise. However, most clustering algorithms typically rely on single-granularity fusion, requiring the pre-setting of global parameters for the entire dataset. Nevertheless, these global parameters may not adequately adapt to clusters with varying hierarchies or shapes, consequently reducing the clustering effectiveness. Therefore, we propose an adaptive density clustering approach with multi-granularity fusion. This approach characterizes the dataset with multi-granularity, forming natural granular-ball. After processing these natural granular-ball, overlapping ones are fused to yield the final clustering result. The entire approach adeptly identifies datasets with significant differences in shape or density hierarchy and exhibits a certain level of robustness. All codes have been released at https://github.com/xjnine/NGBC.
STEFF: Spatio-temporal EfficientNet for dynamic texture classification in outdoor scenes

<h2>Abstract</h2> In recent years, dynamic texture classification has become an important task for computer vision. This is a challenging task due to the unknown spatial and temporal nature of dynamic texture. To overcome this challenge, we investigate the potential of deep learning approaches and propose a novel spatio-temporal approach (STEFF) for dynamic texture classification that combines the representation power of motion and appearance using the difference and average operators between video sequences. In this work, we extract deep texture features from outdoor scenes and integrate both spatial and temporal features into a pre-trained Convolutional Neural Network model, namely EfficientNet, with a fine-tuning and regularization process. The robustness of the proposed approach is reflected in the promising result when comparing our method to the proposed architectures and other existing models. The experimental results on three datasets demonstrate the effectiveness and efficiency of the proposed approach. The accuracy percentages are 95.95%, 94.09%, and 98.01% on the outdoor scenes of Yupenn, DynTex++, and Yupenn++ datasets, respectively.
Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.
Privacy-Preserving Multi-Party Cross-Chain Transaction Protocols

Cross-chain transaction technologies have greatly promoted the scalability of cryptocurrencies, which then facilitates the development of Metaverse applications. However, existing solutions rely heavily on centralized middleware (notary) or smart contracts. These schemes lack privacy considerations, and users’ cross-chain transactions are easy to master by other parties. Some signature-based payment schemes have good privacy but do not support multi-party cross-chain protocols or rely heavily on some time assumptions. The uncertainty of user behavior makes it difficult to design a secure multi-party cross-chain protocol. To solve these problems, we investigate how to design a secure multi-party cross-chain transaction protocol with offline tolerance. We propose a new signature algorithm called the pre-adaptor signature scheme, an extension of the adaptor signature scheme. The pre-adaptor signature scheme combines the multi-signature and adaptor signature schemes, which can realize the secret transmission channel between multiple parties. To provide offline tolerance, we encode our protocol into the P2SH script. Our protocol provides better privacy due to no dependence on smart contracts. The performance evaluation was conducted with ten participants. For each participant of our cross-chain protocol, the initialization and execution process can be performed in 3 milliseconds and with 6 k bytes of communication overhead at most. The cost increases linearly with the increase in the number of participants.
The recommender problem with convex hulls

We introduce a novel approach for the user-based, collaborative filtering, recommender problem for selecting neighbors and weighting their ratings to make a prediction. The proposed approach is based on computational geometry using convex hulls and linear programming (LP). Computational testing is used to compare our approach with one of the current standard methods based on Pearson correlations using three data sets: Jester, Yahoo! Music User Ratings of Musical Artists (Yahoo!Artists), and MovieLens. The comparisons reveal that the method based on convex hulls and LPs can reduce the RMSE for predicted ratings to up to onethird of that of Pearson's correlations. The results make a new method based on polyhedral geometry available to attack other machine learning problems.
Scattering expansion for localization in one dimension: From disordered wires to quantum walks

We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. We apply this expansion to several examples, including the Anderson model, a general class of periodic-on-average-random potentials, and a two-component discrete-time quantum walk, showing analytically in the latter case that the localization length can depend nonmonotonically on the strength of phase disorder (whereas expanding in weak disorder yields monotonic decrease). More generally, we obtain to all orders in the expansion a particular nonseparable form for the joint probability distribution of the transmission coefficient logarithm and reflection phase. Furthermore, we show that for weak local reflection strength, a version of the scaling theory of localization holds: the joint distribution is determined by just three parameters.
“What action should l take?”: Help-seeking behaviours of those targeted by romance fraud

Fraud uses deception to obtain a financial reward from its victim. There are strong negative stereotypes and victim blaming discourses towards fraud victims, with research indicating that fraud victims are unlikely to report incidents for many reasons, including a high degree of shame and stigmatisation. In this way, it can be assumed that fraud victims are averse to seeking help in the aftermath of an incident. Drawing from romance fraud reports lodged with Scamwatch (an Australian online reporting portal for fraud) between 1 July 2018 and 31 July 2019, this article examines the help-seeking behaviours of those targeted by romance fraud. The analysis found a variety of explicit and implicit help-seeking behaviours, encompassing both personal justice and altruistic reasons. The analysis demonstrates that despite the victim blaming that exists, this did not always act as a barrier to reporting or requesting help. Overall, this article challenges some of the perceived aversity to help-seeking behaviour of fraud victims. It also provides a foundation for improving responses and support to those who actively engage in help-seeking behaviour.
Ultra-early medical treatment-oriented system identification using High-Dimension Low-Sample-Size data

Ultra-early detection of diseases with High-Dimension Low-Sample-Size (HDLSS) data has been effectively addressed by the Dynamical Network Biomarkers (DNBs) theory. After ultra-early detection, it is crucial to consider ultra-early medical treatment for the detected disease. From the viewpoint of control engineering, ultra-early medical treatment is achieved by increasing the system’s stability and preventing the bifurcation, called re-stabilization. To implement effective re-stabilization, the system matrix is necessary. However, the available data in biological systems are often HDLSS, which is insufficient to identify the system matrix. In this paper, to realize HDLSS-based ultra-early medical treatment, we investigate an HDLSS data-based system matrix estimation method. First, HDLSS data is applied to compute the sample covariance matrix of the steady state. By assuming that the system matrix is sparse and the structure of the system matrix is known, it can utilize the Lyapunov equation to estimate the system matrix from the covariance matrix. The Lyapunov equation-based method gives a unique optimal estimation if the covariance matrix is full-rank. Otherwise, the optimal estimation is not unique. The sample covariance matrix computed from the HDLSS data is not full-rank. Thus, we apply shrinkage estimation to overcome the under-determined issue to obtain a well-conditioned covariance matrix with full rank. In addition, we confirm the effectiveness of the proposed method through numerical simulations.
Goal-conditioned offline reinforcement learning through state space partitioning

Abstract Offline reinforcement learning (RL) aims to create policies for sequential decision-making using exclusively offline datasets. This presents a significant challenge, especially when attempting to accomplish multiple distinct goals or outcomes within a given scenario while receiving sparse rewards. Prior methods using advantage weighting for offline goal-conditioned learning improve policies monotonically. However, they still face challenges from distribution shift and multi-modality that arise due to conflicting ways to reach a goal. This issue is especially challenging in long-horizon tasks, where the presence of multiple, often conflicting, solutions makes it hard to identify a single optimal policy for transitioning from a state to a desired goal. To address these challenges, we introduce a complementary advantage-based weighting scheme that incorporates an additional source of inductive bias. Given a value-based partitioning of the state space, the contribution of actions expected to lead to target regions that are easier to reach, compared to the final goal, is further increased. Our proposed approach, Dual-Advantage Weighted Offline Goal-conditioned RL, outperforms several competing offline algorithms in widely used benchmarks. Furthermore, we provide a theoretical guarantee that the learned policy will not be inferior to the underlying behavior policy.
Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues

The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.