Reviewerly/exHarmony huggingface dataset:
Towards a logic for ‘because’

Abstract This paper explores the connective ‘because’, based on the idea that ‘ C because A ’ implies the acceptance/truth of the antecedent A as well as of the consequent C , and additionally that the antecedent makes a difference for the consequent. To capture this idea of difference-making a ‘relevantized’ version of the Ramsey Test for conditionals is employed that takes the antecedent to be relevant to the consequent in the following sense: a conditional is true/accepted in a state $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> just in case (i) the consequent is true/accepted when $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> is revised by the antecedent and (ii) the consequent fails to be true/accepted when $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> is revised by the antecedent’s negation. To extend this to a semantics for ‘because’, we add that (iii) the antecedent and (iv) the consequent are accepted/true in the state $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> . We get metaphysical or doxastic interpretations of these clauses, depending on what we mean by a model and a state. We introduce several semantics known from suppositional conditionals, which we reinterpret for difference-making conditionals and ‘because’. We present a minimal logic for ‘because’ sentences and show how it can be extended in ways that parallel the hierarchy of extensions of the logic of suppositional conditionals. We establish correspondence results between axioms for ‘because’ and properties of states, and prove that the specified logics are sound with respect to the semantics.
Review of Image Forensic Techniques Based on Deep Learning

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.
Transformer-Based Global PointPillars 3D Object Detection Method

The PointPillars algorithm can detect vehicles, pedestrians, and cyclists on the road, and is widely used in the field of environmental awareness in autonomous driving. However, its feature encoding network only uses a minimalist PointNet network for feature extraction of point cloud information, which does not consider the global context information of the point cloud, and the local structure features are not sufficiently extracted, and these feature losses can seriously affect the performance of the object detection network. To address this problem, this paper proposes an improved PointPillars algorithm named TGPP: Transformer-based Global PointPillars. After the point cloud is divided into several pillars, global context features and local structure features are extracted through a multi-head attention mechanism, so that the point cloud after feature coding has global context features and local structure features; the two-dimensional pseudo-image generated by this feature is used for feature learning using a two-dimensional convolutional neural network. Finally, the SSD detection head is used to achieve 3D object detection. It is demonstrated that the TGPP achieves an average accuracy improvement of 2.64% in the KITTI test set.
Research on Improved YOLOv5 for Low-Light Environment Object Detection

Object detection in low-light scenarios has been widely acknowledged as a significant research area in the field of computer vision, presenting a challenging task. Aiming at the low detection accuracy of mainstream single-stage object detection models in low-light scenarios, this paper proposes a detection model called DK_YOLOv5 based on YOLOv5, specifically designed for such scenarios. First, a low-light image enhancement algorithm with better results is selected to generate enhanced images that achieve relatively better visual effects and amplify target features. Second, the SPPF layer is improved to an R-SPPF module with faster inference speed and stronger feature expression ability. Next, we replace the C3 module with the C2f module and incorporate an attention mechanism to develop the C2f_SKA module, enabling richer gradient information flow and reducing the impact of noise features. Finally, the model detection head is replaced with a decoupled head suitable for the object detection task in this scenario to improve model performance. Additionally, we expand the Exdark dataset to include low-light data of underground mine scenario targets, named Mine_Exdark. Experimental results demonstrate that the proposed DK_YOLOv5 model achieves higher detection accuracy than other models in low-light scenarios, with an mAP0.5 of 71.9% on the Mine_Exdark dataset, which is 4.4% higher than that of YOLOv5.
Arabic Mispronunciation Recognition System Using LSTM Network

The Arabic language has always been an immense source of attraction to various people from different ethnicities by virtue of the significant linguistic legacy that it possesses. Consequently, a multitude of people from all over the world are yearning to learn it. However, people from different mother tongues and cultural backgrounds might experience some hardships regarding articulation due to the absence of some particular letters only available in the Arabic language, which could hinder the learning process. As a result, a speaker-independent and text-dependent efficient system that aims to detect articulation disorders was implemented. In the proposed system, we emphasize the prominence of “speech signal processing” in diagnosing Arabic mispronunciation using the Mel-frequency cepstral coefficients (MFCCs) as the optimum extracted features. In addition, long short-term memory (LSTM) was also utilized for the classification process. Furthermore, the analytical framework was incorporated with a gender recognition model to perform two-level classification. Our results show that the LSTM network significantly enhances mispronunciation detection along with gender recognition. The LSTM models attained an average accuracy of 81.52% in the proposed system, reflecting a high performance compared to previous mispronunciation detection systems.
Multi-Strategy Enhanced Dung Beetle Optimizer and Its Application in Three-Dimensional UAV Path Planning

Path planning is a challenging, computationally complex optimization task in high-dimensional scenarios. The metaheuristic algorithm provides an excellent solution to this problem. The dung beetle optimizer (DBO) is a recently developed metaheuristic algorithm inspired by the biological behavior of dung beetles. However, it still has the drawbacks of poor global search ability and being prone to falling into local optima. This paper presents a multi-strategy enhanced dung beetle optimizer (MDBO) for the three-dimensional path planning of an unmanned aerial vehicle (UAV). First, we used the Beta distribution to dynamically generate reflection solutions to explore more search space and allow particles to jump out of the local optima. Second, the Levy distribution was introduced to handle out-of-bounds particles. Third, two different cross operators were used to improve the updating stage of thief beetles. This strategy accelerates convergence and balances exploration and development capabilities. Furthermore, the MDBO was proven to be effective by comparing seven state-of-the-art algorithms on 12 benchmark functions, the Wilcoxon rank sum test, and the CEC 2021 test suite. In addition, the time complexity of the algorithm was also analyzed. Finally, the performance of the MDBO in path planning was verified in the three-dimensional path planning of UAVs in oil and gas plants. In the most challenging task scenario, the MDBO successfully searched for feasible paths with the mean and standard deviation of the objective function as low as 97.3 and 32.8, which were reduced by 39.7 and 14, respectively, compared to the original DBO. The results demonstrate that the proposed MDBO had improved optimization accuracy and stability and could better find a safe and optimal path in most scenarios than the other metaheuristics.
SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.
Maximizing Annual Energy Yield in a Grid-Connected PV Solar Power Plant: Analysis of Seasonal Tilt Angle and Solar Tracking Strategies

Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The study investigates the impact of fixed tilt, seasonal tilt, SAHST (single-axis horizontal solar tracking), and SAVST (single-axis vertical solar tracking) on energy yield, considering technical, economic, and environmental aspects. In the first scenario, a fixed tilt angle of 31 degrees was employed. The second scenario explored the use of seasonal tilt angles, with a summer tilt angle of 15 degrees and a winter tilt angle of 30 degrees. The third scenario analyzed SAHST. Finally, the fourth scenario focused on implementing SAVST. SAVST proved to be an exceptional solution, showcasing a remarkable increase in annual energy yield, and generating an additional 6680 MWh/year, 6336 MWh/year, and 5084 MWh/year compared to fixed, seasonal, and SAHST scenarios, respectively. As a result, surplus energy yielded an income of USD 554,440.00 per year compared to fixed tilt. However, the investment cost for the solar tracking system amounted to USD 1,451,932, accompanied by an annual operation and maintenance cost of 0.007 USD/W/year. The analysis revealed a promising payback period of 3 years, confirming the economic feasibility of this investment. The findings underscore the effectiveness of different strategies for optimizing solar power generation in the Kandahar region. Notably, the installation of SAVST emerged as an influential solution, significantly increasing power production. These research outcomes bear practical implications for solar tracking strategies for addressing the load challenges faced by Kandahar province and offer valuable insights for the operators and operation of solar power plants in similar regions.
Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.
Text3D: 3D Convolutional Neural Networks for Text Classification

Convolutional Neural Networks (CNNs) have demonstrated promising performance in many NLP tasks owing to their excellent local feature-extraction capability. Many previous works have made word-level 2D CNNs deeper to capture global representations of text. Three-dimensional CNNs perform excellently in CV tasks through spatiotemporal feature learning, though they are little utilized in text classification task. This paper proposes a simple, yet effective, approach for hierarchy feature learning using 3D CNN in text classification tasks, named Text3D. Text3D efficiently extracts rich information through text representations structured in three dimensions produced by pretrained language model BERT. Specifically, our Text3D utilizes word order, word embedding and hierarchy information of BERT encoder layers as features of three dimensions. The proposed model with 12 layers outperforms the baselines on four benchmark datasets for sentiment classification and topic categorization. Text3D with a different hierarchy of output from BERT layers demonstrates that the linguistic features from different layers have varied effects on text classification.
The Use of Ontologies to Accelerate the Behavioral Sciences: Promises and Challenges

Behavioral scientists produce a vast amount of research every year yet struggle to produce cumulative knowledge that is easily translated in applied settings. This article summarizes a National Academies of Sciences, Engineering, and Medicine consensus report on the development and use of ontologies to accelerate the behavioral sciences. The report examines key challenges in the behavioral and psychological sciences motivating an evaluation of ontology use and development in the behavioral sciences. The advantages of ontologies, including enhanced organization and retrieval of research evidence, improved scientific communication, reduction of duplication, and enhanced scientific replicability, are highlighted. Challenges that may impede the development and use of ontologies in the behavioral sciences are also considered. The article concludes with future directions for fulfilling the promise of ontologies to accelerate the behavioral and psychological sciences.
The digital augmentation of extremism: Reviewing and guiding online extremism research from a sociotechnical perspective

Abstract Online extremism remains a persistent problem despite the best efforts of governments, tech companies and civil society. Digital technologies can induce group polarization to promote extremism and cause substantial changes to extremism (e.g., create new forms of extremism, types of threats or radicalization approaches). Current methods to counter extremism induce undesirable side‐effects (e.g., ostracize minorities, inadvertently promote extremism) or do not leverage the full potential of digital technologies. Extremism experts recognize the need for researchers from other disciplines, like information systems, to contribute their technical expertise for understanding and countering online extremism. This article aims to introduce the field of information systems to the issue of online extremism. Information systems scholars address technology‐related societal issues from a sociotechnical perspective. The sociotechnical perspective describes systems through a series of interactions between social (structure, people) and technical components (physical system, task). We apply the sociotechnical perspective to (1) summarize the current state‐of‐the‐art knowledge of 222 articles in a systematic multi‐disciplinary literature review and (2) propose specific research questions that address two questions (How do digital technologies augment extremism? How can we successfully counter online extremism?).
Multilingual Chatbots to Collect Patient-Reported Outcomes

With spoken language interfaces, chatbots, and enablers, the conversational intelligence became an emerging field of research in man-machine interfaces in several target domains. In this paper, we introduce the multilingual conversational chatbot platform that integrates Open Health Connect platform and mHealth application together with multimodal services in order to deliver advanced 3D embodied conversational agents. The platform enables novel human-machine interaction with the cancer survivors in six different languages. The platform also integrates patients’ reported information as patients gather health data into digital clinical records. Further, the conversational agents have the potential to play a significant role in healthcare, from assistants during clinical consultations, to supporting positive behavior changes, or as assistants in living environments helping with daily tasks and activities.
Quantum Metropolis Solver: a quantum walks approach to optimization problems

Abstract The efficient resolution of optimization problems is one of the key issues in today’s industry. This task relies mainly on classical algorithms that present scalability problems and processing limitations. Quantum computing has emerged to challenge these types of problems. In this paper, we focus on the Metropolis-Hastings quantum algorithm, which is based on quantum walks. We use this algorithm to build a quantum software tool called Quantum Metropolis Solver (QMS). We validate QMS with the N-Queen problem to show a potential quantum advantage in an example that can be easily extrapolated to an Artificial Intelligence domain. We carry out different simulations to validate the performance of QMS and its configuration.
Fisher information of sequential measurements and the optimization in composite systems

In parameter estimation, the saturation of the quantum Cram\'er-Rao bound (QCRB) requests one to perform projective measurements on the eigenvectors of the symmetric logarithmic derivative (SLD). However, it maybe infeasible to saturate the QCRB in composite systems on account of the technological difficulties. In practice, it is necessary to perform sequential measurements on different subsystems to obtain a precise estimation. In this paper, we derive the expression of Fisher information (FI) for a sequential measurements scheme in composite systems with $N$ interacting subsystems. The result shows that the more subsystems used, the higher FI obtained due to the nonnegative property of FI. We illustrate the optimal measurements for different conditional density matrices in every subsystems are nonidentical, which depends on the previous outcomes. In addition, utilizing the convexity property of the quantum FI, we prove that the optimal projective measurements on the eigenvectors of the SLD of the subsystem reduced density matrix can be regarded as a trade off of the nonidentical optimal measurements of the conditional density matrices in every subsystems. In addition, importantly, we show the measurement sequence of the subsystem should be considered in the optimization to obtain a precise estimation for the sequential measurements scheme. Finally, we apply the theory to a nuclear spins control model where we concern the estimation of nuclear Larmor frequency. We should point out that the precision obtained by the sequential measurements scheme is no better than the theoretical result in the global system limited by the QCRB but more realistic, and better than the local measurements in one of the subsystems.