Reviewerly/exHarmony huggingface dataset:
A novel tree pattern-based violence detection model using audio signals

Physical violence detection using multimedia data is crucial for public safety and security. This is an important research area in information security and digital forensics. Research in video-based violence detection (VVD) has grown steadily in recent years with rapid increase in video surveillance systems worldwide. Verbal aggression detection technologies, on the other hand, are still limited due to the popularity of computer vision models. Thus, researchers have preferred to use computer vision models to detect violence using videos. We have presented a new automatic audio violence detection (AVD) model to fill this gap. Our AVD model is handcrafted and its details are as follows. This work collected a new audio dataset on verbal aggression from YouTube. A novel handcrafted model was proposed using multilevel feature extraction, feature selection, classification, and majority voting phases. A new local feature extraction function based on the binary tree was used to generate features from audio signals. We call this function tree pattern-23 (TreePat23), where 23 represents the number of wavelet bands/audio signals inputs. Wavelet bands were generated using tunable Q wavelet transform (TQWT) before being applied to our TreePat23 for feature extraction. The iterative neighborhood component analysis (INCA) and Chi2 were used to select the features. The selected features were classified using k nearest neighbors (kNN) and support vector machine (SVM) followed by iterative majority voting (IMV) method. The best-predicted vector was obtained by using a greedy algorithm. Finally, a new validation technique called leave one record out (LORO) cross-validation (CV) was used to validate the results. Our proposed TreePat23 model has attained classification accuracy of 89.68% and 89.75% with kNN and SVM, respectively. Our developed system has generated 14 results for each classifier and automatically selected the best result. Hence this model is a self-organized audio classification model which yielded over 89% classification accuracy for both classifiers using LORO CV strategy.
HUKB at the COLIEE 2022 Statute Law Task

HUKB group is participating in the Statute Law task (Tasks 3 and 4) in COLIEE 2022. For Task 3, we propose a method that utilizes three different IR systems. Our new proposed IR system utilizes the similarity of descriptions of judicial decisions between questions and articles. In addition to this new IR system, we also use an ordinal keyword-based IR system (BM25) and the BERT-based IR system proposed in COLIEE 2020. Because of the different characteristics of these systems, ensembled results have better recall without losing too much precision. Our system, which ensembles the results of our new proposed IR and keyword-based IR system, achieves the best performance for Task 3. For Task 4, we extend our previous BERT-based entailment system (the best performance system of COLIEE 2021) by using a new data augmentation method and a method to select relevant parts from the articles. We discuss the characteristics of the system using the submitted results.
A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer

Among all the cancers known today, prostate cancer is one of the most commonly diagnosed in men. With modern advances in medicine, its mortality has been considerably reduced. However, it is still a leading type of cancer in terms of deaths. The diagnosis of prostate cancer is mainly conducted by biopsy test. From this test, Whole Slide Images are obtained, from which pathologists diagnose the cancer according to the Gleason scale. Within this scale from 1 to 5, grade 3 and above is considered malignant tissue. Several studies have shown an inter-observer discrepancy between pathologists in assigning the value of the Gleason scale. Due to the recent advances in artificial intelligence, its application to the computational pathology field with the aim of supporting and providing a second opinion to the professional is of great interest.In this work, the inter-observer variability of a local dataset of 80 whole-slide images annotated by a team of 5 pathologists from the same group was analyzed at both area and label level. Four approaches were followed to train six different Convolutional Neural Network architectures, which were evaluated on the same dataset on which the inter-observer variability was analyzed.An inter-observer variability of 0.6946 κ was obtained, with 46% discrepancy in terms of area size of the annotations performed by the pathologists. The best trained models achieved 0.826±0.014κ on the test set when trained with data from the same source.The obtained results show that deep learning-based automatic diagnosis systems could help reduce the widely-known inter-observer variability that is present among pathologists and support them in their decision, serving as a second opinion or as a triage tool for medical centers.
Hate speech in adolescents: A binational study on prevalence and demographic differences

Hate speech, or intentional derogatory expressions about people based on assigned group characteristics, has been studied primarily in online contexts. Less is known about the occurrence of this phenomenon in schools. As it has negative consequences for victims, perpetrators, and those who witness it, it is crucial to characterize the occurrence of offline (i.e., in the school) and online hate speech to describe similarities and differences between these two socialization contexts. The present study aimed to investigate the prevalence of hate speech witnessing, victimization, and perpetration, in a sample of 3,620 7–9th graders (51% self-identified as female) from 42 schools in Germany and Switzerland. We found that 67% of the students witnessed hate speech in their school, and 65% witnessed online hate speech at least once in the past 12 months. Approximately 21% of the students self-identified as offline perpetrators and 33% as offline victims, whereas these percentages were lower for online hate speech (13 and 20%, respectively). In both settings, skin color and origin were the most common group references for hate speech (50% offline and 63% online). Offline hate speech mainly came from classmates (88%), unknown sources (e.g., graffiti; 19%), or teachers (12%), whereas online hate speech mostly came from unknown persons (77%). The most frequent forms of offline hate speech were offensive jokes (94%) and the spread of lies and rumors about the members of a specific social group (84%). Significant differences by country, gender, and migration background were observed. Girls reported more offline victimization experiences, less perpetration, and a greater frequency of witnessing hate speech. This difference was larger in magnitude in the online setting. Students in Switzerland reported being exposed to hate speech more often than students in Germany. Students with a migration background reported higher hate speech victimization based on skin color and origin than students without a migration background. The high prevalence of hate speech highlights the need for school-based prevention programs. Our findings are discussed in terms of the practical implications.
Simplification of Deep Neural Network-Based Object Detector for Real-Time Edge Computing

This paper presents a method for simplifying and quantizing a deep neural network (DNN)-based object detector to embed it into a real-time edge device. For network simplification, this paper compares five methods for applying channel pruning to a residual block because special care must be taken regarding the number of channels when summing two feature maps. Based on the comparison in terms of detection performance, parameter number, computational complexity, and processing time, this paper discovers the most satisfying method on the edge device. For network quantization, this paper compares post-training quantization (PTQ) and quantization-aware training (QAT) using two datasets with different detection difficulties. This comparison shows that both approaches are recommended in the case of the easy-to-detect dataset, but QAT is preferable in the case of the difficult-to-detect dataset. Through experiments, this paper shows that the proposed method can effectively embed the DNN-based object detector into an edge device equipped with Qualcomm’s QCS605 System-on-Chip (SoC), while achieving a real-time operation with more than 10 frames per second.
AutoEncoder and LightGBM for Credit Card Fraud Detection Problems

This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank credit card feature data using the characteristics of an autoencoder which has a symmetrical network structure, enhancing the ability of feature representation learning. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. For this situation, the smote algorithm is used to resample the data before putting the extracted feature data into LightGBM, making the amount of fraud data and non-fraud data equal. After comparing the resampled and non-resampled data, it was found that the performance of the AED-LGB algorithm was not improved after resampling, and it was concluded that the AED-LGB algorithm is more suitable for imbalanced data. Finally, the AED-LGB algorithm is comparable with other commonly used machine learning algorithms, such as KNN and LightGBM, and it has an overall improvement of 2% in terms of the ACC index compared to LightGBM and KNN. When the threshold is set to 0.2, the MCC index of AED-LGB is 4% higher than that of the second-highest LightGBM algorithm and 30% higher than that of KNN. It shows that the AED-LGB algorithm has higher performance in accuracy, true positive rate, true negative rate, and Matthew’s correlation coefficient.
A novel technique for dental radiographic image segmentation based on neutrosophic logic

Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, ‘Panoramic Dental X-rays with Segmented Mandibles’ and ‘Digital Dental X-ray Database for Caries Screening,’ with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system.
Memory Access Characteristics of Neural Network Workloads and Their Implications

With the recent advances in machine learning and many-core computing technologies, neural networks are widely used in various service domains of the 4th industrial revolution. As the data set of neural network is becoming increasingly large, it is important to analyze the memory access characteristics of neural network workloads. In this paper, we perform a comprehensive analysis of memory access behaviors in four types of neural network configurations, i.e., CNN (convolutional neural networks), RNN (recurrent neural networks), DNN (deep neural networks), and ANN (artificial neural networks). From this analysis, we observe the following characteristics, which are quite different from traditional desktop and smartphone memory accesses. First, we analyze the access bias of memory locations and find that most memory accesses occur in a certain limited memory locations. Second, the identity of these hot locations is the data and heap regions, which account for over 90% of total memory accesses. Third, the bias of memory access in neural network workloads is relatively weaker than other desktop or smartphone workloads, specially for write operations. Fourth, write operations account for about twice of read operations regardless of neural network types. Fifth, in predicting re-access likelihood, temporal locality provides better information than access frequency in read operations, but combining the two properties is necessary for accurate estimation in write operations. We anticipate that the analysis of this study will be a good guideline for designing an efficient memory system for neural network workloads.
A new YOLO-based method for social distancing from real-time videos

The coronavirus disease (COVID-19) is primarily disseminated through physical contact. As a precaution, it is recommended that indoor spaces have a limited number of people and at least one meter apart. This study proposes a real-time method for monitoring physical distancing compliance in indoor spaces using computer vision and deep learning techniques. The proposed method utilizes YOLO (You Only Look Once), a popular convolutional neural network-based object detection model, pre-trained on the Microsoft COCO (Common Objects in Context) dataset to detect persons and estimate their physical distance in real time. The effectiveness of the proposed method was assessed using metrics including accuracy rate, frame per second (FPS), and mean average precision (mAP). The results show that the YOLO v3 model had the most remarkable accuracy (87.07%) and mAP (89.91%). On the other hand, the highest fps rate of up to 18.71 was achieved by the YOLO v5s model. The results demonstrate the potential of the proposed method for effectively monitoring physical distancing compliance in indoor spaces, providing valuable insights for future use in other public health scenarios.
FinBERT–MRC: Financial Named Entity Recognition Using BERT Under the Machine Reading Comprehension Paradigm

Financial named entity recognition (FinNER) is a challenging task in the field of financial text information extraction, which aims to extract a large amount of financial knowledge from unstructured texts. It is widely accepted to use the sequence tagging framework to implement the FinNER tasks. However, such sequence tagging models cannot fully take advantage of the semantic information in the texts. Instead, we formulate the FinNER task as a machine reading comprehension (MRC) problem and propose a new model termed FinBERT–MRC. This formulation introduces significant prior information by utilizing well-designed queries, and extracts the start index and end index of the target entities without decoding modules such as conditional random fields (CRFs). We conduct experiments on a publicly available Chinese financial dataset ChFinAnn and a real-world business dataset AdminPunish. FinBERT–MRC achieves average $${F}_{1}$$ scores of 92.78% and 96.80% on two datasets, respectively, with average $${F}_{1}$$ gains + 3.75% and + 0.68% over some sequence tagging models including BiLSTM–CRF, BiGRU–CRF, BiLSTM–CNN–CRF, FinBERT–Tagger, and FinBERT–CRF. The source code is available at https://github.com/zyz0000/FinBERT-MRC .
Multisensor Satellite Data and Field Studies for Unravelling the Structural Evolution and Gold Metallogeny of the Gerf Ophiolitic Nappe, Eastern Desert, Egypt

The gold mineralization located in the southern Eastern Desert of Egypt mostly occurs in characteristic geologic and structural settings. The gold-bearing quartz veins and the alteration zones are confined to the ductile shear zones between the highly deformed ophiolitic blocks, sheared metavolcanics, and gabbro-diorite rocks. The present study attempts to integrate multisensor remotely sensed data, structural analysis, and field investigation in unraveling the geologic and structural controls of gold mineralization in the Gabal Gerf area. Multispectral optical sensors of Landsat-8 OLI/TIRS (L8) and Sentinel-2B (S2B) were processed to map the lithologic rock units in the study area. Image processing algorithms including false color composite (FCC), band ratio (BR), principal component analysis (PCA), minimum noise fraction (MNF), and Maximum Likelihood Classifier (MLC) were effective in producing a comprehensive geologic map of the area. The mafic index (MI) = (B13-0.9147) × (B10-1.4366) of ASTER (A) thermal bands and a combined band ratio of S2B and ASTER of (S2B3+A9)/(S2B12+A8) were dramatically successful in discriminating the ophiolitic assemblage, that are considered the favorable lithology for the gold mineralization. Three alteration zones of argillic, phyllic and propylitic were spatially recognized using the mineral indices and constrained energy minimization (CEM) approach to ASTER data. The datasets of ALSO PALSAR and Sentinel-1B were subjected to PCA and filtering to extract the lineaments and their spatial densities in the area. Furthermore, the structural analysis revealed that the area has been subjected to three main phases of deformation; (i) NE-SW convergence and sinistral transpression (D2); (ii) ~E-W far field compressional regime (D3), and (iii) extensional tectonics and terrane exhumation (D4). The gold-bearing quartz veins in several occurrences are controlled by D2 and D3 shear zones that cut heterogeneously deformed serpentinites, sheared metavolcanic rocks and gabbro-diorite intrusions. The information extracted from remotely sensed data, structural interpretation and fieldwork were used to produce a gold mineralization potential zones map which was verified by reference and field observations. The present study demonstrates the remote sensing capabilities for the identification of alteration zones and structural controls of the gold mineralization in highly deformed ophiolitic regions.
Economic sectors and globalization channels to gender economic inclusion in sub-Saharan Africa

This study complements the extant literature by assessing economic sector and globalization channels for gender economic inclusion. The study is focused on 35 countries in sub-Saharan Africa for the period 1995–2019 and the empirical evidence is based on fixed effects regressions. The following findings are established. First, economic and political globalization positively affect female employment in agriculture and the positive effect of economic globalization is driven by the trade globalization dynamic while social globalization negatively affects female employment in agriculture and the negative effect of social globalization is driven by cultural and informational globalization dynamics. Second, aggregate globalization and sub-components (i.e. economic globalization, social globalization and political globalization) negatively affect gender employment in the industry and the negative effect is driven by the financial globalization sub-component of economic globalization and by the informational and cultural components of social globalization. Third, aggregate globalization and sub-components positively affect gender employment in the service sector and the corresponding positive effect is driven by the trade globalization sub-component of economic globalization and by all sub-components (i.e. interpersonal, informational and cultural dimensions) of social globalization. In the terms of policy implications, policy makers should focus on promoting dimensions of globalization that are established to positively influence female employment as well as put in place measures that are designed to reverse the negative incidence of globalization dynamics that have been established to affect female employment. Moreover, policy makers should also be aware of the fact that when formulating the corresponding policies, the effect of globalization is contingent on globalization dynamics as well as on various economic sectors.
Complementary Parts Contrastive Learning for Fine-Grained Weakly Supervised Object Co-Localization

The aim of weakly supervised object co-localization is to locate different objects of the same superclass in a dataset. Recent methods achieve impressive co-localization performance by multiple instance learning and self-supervised learning. However, these methods ignore the common part information shared by fine-grained objects and the influence of the complementary parts on the co-localization of the fine-grained objects. To solve these issues, we propose a complementary parts contrastive learning method for fine-grained weakly supervised object co-localization. The proposed method follows such an assumption that fine-grained object parts with the same/different semantic meaning should have similar/dissimilar feature representations in the feature space. The proposed method tackles two critical issues in this task: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i)$ </tex-math></inline-formula> how to spread the model’s attention and suppress the complex background noise, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii)$ </tex-math></inline-formula> how to leverage the cross-category common parts information to mitigate the context co-occurrence problem. To address <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i)$ </tex-math></inline-formula> , we attempt to integrate local and context cues via three types of attention including self-supervised attention, channel, and spatial attention to spread the model’s attention toward automatically identifying and localizing most discriminative parts of objects in the fine-grained images. To solve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii)$ </tex-math></inline-formula> , we propose a cross-category object complementarity part contrastive learning module to identify the extracted part regions with different semantic information by pulling the same part features closer and pushing different part features away, which can mitigate the confounding bias caused by the co-occurrence surroundings within specific classes. Extensive qualitative and quantitative evaluations demonstrate the effectiveness of the proposed method on four fine-grained co-localization datasets: CUB-200–2011, Stanford Cars, FGVC-Aircraft, and Stanford Dogs. Code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Zhao-fan/CPCL</uri> .