Reviewerly/exHarmony huggingface dataset:
How to Explain Sentiment Polarity – A Survey of Explainable Sentiment Analysis Approaches

Sentiment analysis area has become more and more popular due to information and opinion overload (especially in social networks). With growth of efficient and high accuracy methods of artificial intelligence, it is easier to obtain satisfactory results using opinion mining. Usually, more complex structure, and more sophisticated methods give us better results, but it is hard to understand why such a result was returned. Many of these methods are black-box models – it is a reason for decreasing trust in the system. To manage the problem, explainable methods were developed. This paper presents a systematic survey of explainable artificial intelligence methods that are used in sentiment analysis area. We have analyzed and classified existing approaches to Explainable Sentiment Analysis (XSA) and indicated trends and challenges in this area.
Denoised Temporal Relation Network for Temporal Action Segmentation

Temporal relations among action segments play a crucial role in temporal action segmentation. Existing methods tend to employ the graph neural network to model the temporal relation. However, the performance is unsatisfactory and exhibits serious over-segmentation due to the generated noisy features. To solve the above issues, we present an action segmentation framework, termed a denoised temporal relation network (DTRN). In DTRN, a temporal reasoning module (TRM) models inter-segment temporal relations and conducts feature denoising jointly. Specifically, the TRM conducts an uncertainty-gated reasoning mechanism for noise-immune and utilizes a cross-attention-based structure to combine the informative clues from the discriminative enhance module which is trained under Selective Margin Plasticity (SMP) to ensure informative clues, SMP adjusts the decision boundary adaptively by changing specific margins in real-time. Our framework is demonstrated to be effective and achieves state-of-the-art performance of accuracy, edit score, and F1 score on the challenging 50Salads, GTEA, and Breakfast benchmarks.
PENGARUH PENGGUNAAN HANDPHONE TERHADAP HASIL BELAJAR SISWA DI KELAS IV SEKOLAH DASAR

The purpose of this study was to analyze and determine how much influence the use of cellphones on student learning outcomes in grade IV SD Negeri 060833 Medan Petisah District. This research is a quantitative research which is included in the ex-post facto research. The population taken was the fourth grade students with a total of 22 students, of which 8 male students and 14 female students at SD Negeri 060833 Medan Petisah sub-district, with a total sample size of the total population. Data analysis used the One Kolmogrov-Smirnov data normality test and hypothesis testing with Simple Regression (Simple Regression). Based on the results of hypothesis testing using simple regression analysis, the effect of the use of mobile phones on student learning outcomes, the value of tcount = 2.701 ≥ (t table) = 2.079 with a significance of 0.491&gt; 0.05, it can be concluded that there is an "Effect of Mobile Use on Student Learning Outcomes in SD Negeri 060833 Medan Petisah sub-district with a large contribution of 2.4%.
Asymmetric Attention Fusion for Unsupervised Video Object Segmentation

In this paper, we introduce a novel Asymmetric Attention Fusion Network (AAF-Net) based on an attention mechanism to complete unsupervised video object segmentation. Firstly, we propose an asymmetric attention fusion module (AAFM) to aggregate the two source inputs to exploit the complementary representations between optical flow and RGB images. The asymmetric attention structure of AAFM is equipped to enhance feature information. Next, we design a feature correction module (FCM) to balance the information ratio between motion features and appearance features. The results of experimental evaluation obtained on several well-known benchmarking datasets, including DAVIS16, FBMS, and SegTrack-V2, deliver outstanding performance compared to the other segmentation networks based on optical flow, reflecting the merit and advantage of the proposed approach.
RSID: A Remote Sensing Image Dehazing Network

Hazy images often lead to problems such as loss of image details and dull colors, which significantly affects the information extraction of remote sensing images, so it is necessary to research image dehazing. In the field of remote sensing, remote sensing images are characterized by large-size and rich information, so the processing of remote sensing images often has the problems of GPU memory overflow and difficult removal of non-uniform haze. For remote sensing image characteristics, an efficient and lightweight end-to-end dehazing method is proposed in this paper. We use the FA attention combined with smoothed dilated convolution instead as the main structure of the encoder, which can achieve imbalanced handling of hazy images with different levels of opacity while reducing parameter count. Channel weight fusion self-attention is added in the decoder part to realize the automatic learning and pixel-level processing of different receptive field features We tested the proposed method on both public datasets RESIDE and real large-size hazy remote sensing images. The proposed method achieved satisfactory results in our experiments, which proves the effectiveness of the proposed method.
Knowledge-based Capabilities of a Linguistic Neural Network

Today, linguistic neural networks are penetrating all spheres of human activity, including science. This fact is generally considered positively, as it yields a clear economic payoff. According to popular predictions, the current generation of people will already face the emergence of advanced artificial intelligence (AI) developed on the basis of the GPT line of linguistic neural networks. They say that AI will surpass the human intelligence in all respects. However, these expectations seem to be inflated. The main reason lies in the fact that the domain of modern linguistic models of artificial intelligence is the language of human communication, but languages of intuitive thinking, without which the generation of new knowledge obviously does not occur, are not yet amenable to formalization by means of information technology. The purpose of the work was to evaluate the knowledge-based capabilities of the modern neural network ChatGPT-3.5 in the course of discussion of phenomena of different scales: the control means and the foundations of modern science in the West and in Russia and the problem of reversibility and irreversibility of time in physics reflected in the theories of crystal growth. Initially, a linguistic model is aimed at making an assertion that is as anticipated as possible. And this feature leads to the eclecticism of the whole set of responses related to the broad theme of a dialog. The results of communication with the neural network on a narrowly specialized topic have demonstrated its ignorance of a well-known physics problem and, more importantly, its inability to apply it to a theory of crystal growth where this problem is the key one. Preoccupations, unreasonable optimism or fears about AI that characterize the moods of contemporary society have so far had little to do with scientific practice, apart from the harm caused by the ever-increasing information noise in which neural networks are becoming involved.
Promosi Website di RW 013 Cipinang Melayu Jakarta Timur

A website is an information system service that is very important in providing and informing about a product or service to a company, agency, or state and educational institution. This activity aims to provide understanding to the residents of RW 013 Cipinang Melayu, East Jakarta regarding the use and benefits of websites in providing information and services. The output targeted in this activity is to help residents understand the benefits of websites and help them in creating websites.
Ispol'zovanie neirosetei v prepodavanii vysshei matematiki v tekhnicheskom vuze

Due to the rapid development of information technology and artificial intelligence, the use of neural networks is becoming more widespread and significant in various fields of research and training. In higher mathematics, neural networks can be used to improve the learning process of students. For example, they can help create online courses tailored to the individual needs of students. Neural networks can analyze the results of students' work and offer individual recommendations for studying specific topics or problem areas. In addition, neural networks can be used to develop intelligent systems for automatic task verification and pattern recognition. This can help teachers in the process of evaluating students' work and providing feedback. Neural networks can also diagnose problems in understanding the material and offer additional materials or exercises to consolidate skills. Neural networks can also be used to study complex mathematical problems and develop new approaches and methods in higher mathematics. Their ability to process large amounts of data and find complex patterns can help in the study of mathematical structure and the creation of new mathematical models. It is obvious that the use of neural networks in teaching higher mathematics opens up opportunities for more effective and individualized student learning, as well as for the development of mathematics itself. Therefore, this topic is relevant and interesting for further research and applications.
CE$$^2$$: A Copula Entropic Mutual Information Estimator for Enhancing Adversarial Robustness

Deep neural networks are vulnerable to adversarial examples, which exploit imperceptible perturbations to mislead classifiers. To improve adversarial robustness, recent methods have focused on estimating mutual information (MI). However, existing MI estimators struggle to provide stable and reliable estimates in high-dimensional data. To this end, we propose a Copula Entropic MI Estimator (CE $$^2$$ ) to address these limitations. CE $$^2$$ leverages copula entropy to estimating MI in high dimensions, allowing target models to harness information from both clean and adversarial examples to withstand attacks. Our empirical experiments demonstrate that CE $$^2$$ achieves a trade-off between variance and bias in MI estimates, resulting in stable and reliable estimates. Furthermore, the defense algorithm based on CE $$^2$$ significantly enhances adversarial robustness against multiple attacks. The experimental results underscore the effectiveness of CE $$^2$$ and its potential for improving adversarial robustness.
Ranking Variance Reduced Ensemble Attack with Dual Optimization Surrogate Search

Deep neural networks have achieved remarkable success, but they are vulnerable to adversarial attacks. Previous studies have shown that combining transfer-based and query-based attacks in ensemble attacks can generate highly transferable adversarial examples. However, simply aggregating the output results of various models without considering the gradient variance among different models will lead to a suboptimal result. Moreover, fixed weights or inefficient methods for model weight updates can result in excessive query iterations. This paper proposes a novel method called Ranking Variance Reduced Ensemble attack with Dual Optimization surrogate search (RVREDO) as an enhanced ensemble attack method for improving adversarial transferability. Specifically, RVREDO mitigate the influence of gradient variance among models to improve the attack success rate of generated adversarial examples during the attack process. Simultaneously, a dual optimization weight updating strategy is employed to dynamically adjust the surrogate weight set, enhancing the efficiency of perturbation optimization. Experimental results on the ImageNet dataset demonstrate that our method outperforms previous state-of-the-art methods in terms of both attack success rate and average number of queries.
Experience Using Drawing as a Tool Diagnostic Work of a Psychologist WITH Students With Health Disabilities

The chapter raises the problem of using a projective approach in the activities of a school psychologist working within the framework of the model of psychological-pedagogical support of first-graders, as well as newly arrived students in other classes of primary school, at the stage of their adaptation. It is shown that psychological and pedagogical support solves important tasks of prevention, prevention and correction of many mental phenomena that can be actualized during the adaptation period. It is clear that isotherapy is a tool that allows you to understand and express hidden feelings and fears, which is the first step on the way to the child's mental health and well-being. Special attention is paid to the use of drawing as a method of isotherapy and a means of diagnostic work of a psychologist in the specified period of the school life of a child with limited health opportunities. The effectiveness of the application of the analysis of color choice and the drawing process itself, used in the diagnosis of the psycho-emotional sphere of a child with special needs, during the period of adaptation to school education, has been confirmed.
Healthcare 3.0: How to Transform Machine Learning Prototypes into Functional Healthcare Applications for Diagnostic Assistance?

Abstract Background As the number of elderly people is rapidly increasing, we are faced with the challenge that diagnostic services are demanded more frequently. At the same time, the number of medical centers and available experts remains almost constant. Tools for diagnostic assistance are urgently needed to improve the effectiveness of healthcare. In four externally funded projects, we will investigate aspects and strategies of how machine learning prototype systems can be translated into functional healthcare applications. Method In the ongoing project “ ExplAInation ” funded by the German research foundation (DFG), we are developing an artificial neural network framework to generate visual and textual explanations to improve the comprehensibility and interpretability of deep learning models. This effort includes participatory research with clinical users. In the complementary project “ TESIComp ” funded by the Federal Ministry of Education and Research (BMBF), we will investigate ethical and social aspects of the emerging field of computational psychiatry. Patients, caregivers, and medical doctors will be interviewed, which will be qualitatively analyzed. Within the German Medical Informatics Initiative, we will contribute to the project “ Open Medical Inference ” ( OMI ), which will develop a network of distributed machine learning evaluation services. We also participate in the international “ Clinical AI‐based Diagnostics ” ( CAIDX ) consortium, which receives funding from the European Interreg Baltic Sea Region program. Result We developed a deep learning application for the detection of dementia atrophy patterns in brain MRI scans. Derived relevance maps highlight diagnostically important brain areas for further evaluation by the radiologists. Interviews with clinicians will provide information on expectations, key requirements and the functional utility of machine learning‐based assistance. Interviews with patients and caregivers will elucidate future changes and challenges in the doctor’s role and responsibilities. The OMI network will allow hospitals to use distributed machine learning evaluation services remotely, without the need of operating all the tools locally. In CAIDX , we will develop best‐practice guidelines for the overarching process of integrating machine learning prototypes and commercial tools into the hospital. Conclusion In “Healthcare 3.0”, the digital transformation will change current diagnostic procedures and roles. Our activities focus on the involved stakeholders, regulatory aspects and implementation strategies to better steer this process.
“We Learn Through Mistakes”: Perspectives of Social Media Creators on Copyright Moderation in the European Union

Artists promoting their cultural production on social media platforms must navigate an algorithmic environment that involves visibility, content distribution, and growing algorithmic copyright moderation. The latter was experienced by EU-based social media creators when Article 17 of the Copyright in the Digital Single Market Directive was enforced in the EU (Directive [EU] 2019/790, 2019). This research focuses on the understanding and experiences of such creators regarding copyright content moderation of their work on social media platforms. Semi-structured interviews were conducted with 14 artists from EU countries, who were surveyed on digitization and digital access to cultural content. A multimodal framework was employed to analyze the copyright governance of creative practices and products, focusing on the regulative (i.e., the adoption of Article 17), normative (assumptions about legitimate and illegitimate behavior in specific communities), and discursive dimensions, as well as the influence of technological affordance on creative work. The findings contribute to the fields of cultural production and platform governance, as well as having policy implications. The anticipation of punishments from platforms due to copyright-related fears and the inability to use appeal processes directly influenced cultural production. Most interviewees employed self-censorship, avoidance, and content adjustment in their creative work before posting on social media platforms.
Exploring Visual Relationships via Transformer-based Graphs for Enhanced Image Captioning

Image captioning (IC), bringing vision to language, has drawn extensive attention. A crucial aspect of IC is the accurate depiction of visual relations among image objects. Visual relations encompass two primary facets: content relations and structural relations. Content relations, which comprise geometric positions content ( i.e. , distances and sizes) and semantic interactions content ( i.e. , actions and possessives), unveil the mutual correlations between objects. In contrast, structural relations pertain to the topological connectivity of object regions. Existing Transformer-based methods typically resort to geometric positions to enhance the visual relations, yet only using the shallow geometric content is unable to precisely cover actional content correlations and structural connection relations. In this paper, we adopt a comprehensive perspective to examine the correlations between objects, incorporating both content relations ( i.e. , geometric and semantic relations) and structural relations, with the aim of generating plausible captions. To achieve this, firstly, we construct a geometric graph from bounding box features and a semantic graph from the scene graph parser to model the content relations. Innovatively, we construct a topology graph that amalgamates the sparsity characteristics of the geometric and semantic graphs, enabling the representation of image structural relations. Secondly, we propose a novel unified approach to enrich image relation representations by integrating semantic, geometric, and structural relations into self-attention. Finally, in the language decoding stage, we further leverage the semantic relation as prior knowledge to generate accurate words. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our model, with improvements of CIDEr from 128.6% to 136.6%. Codes have been released at https://github.com/CrossmodalGroup/ER-SAN/tree/main/VG-Cap.
DPDFormer: a Coarse-to-Fine Model for Monocular Depth Estimation

Monocular depth estimation attracts great attention from computer vision researchers for its convenience in acquiring environment depth information. Recently classification-based MDE methods show its promising performance and begin to act as an essential role in many multi-view applications such as reconstruction and 3D object detection. However, existed classification-based MDE models usually apply fixed depth range discretization strategy across a whole scene. This fixed depth range discretization leads to the imbalance of discretization scale among different depth ranges, resulting in the inexact depth range localization. In this paper, to alleviate the imbalanced depth range discretization problem in classification-based monocular depth estimation (MDE) method we follow the coarse-to-fine principle and propose a novel depth range discretization method called depth post-discretization (DPD). Based on a coarse depth anchor roughly indicating the depth range, the DPD generates the depth range discretization adaptively for every position. The depth range discretization with DPD is more fine-grained around the actual depth, which is beneficial for locating the depth range more precisely for each scene position. Besides, to better manage the prediction of the coarse depth anchor and depth probability distribution for calculating the final depth, we design a dual-decoder transformer-based network, i.e., DPDFormer, which is more compatible with our proposed DPD method. We evaluate DPDFormer on popular depth datasets NYU Depth V2 and KITTI. The experimental results prove the superior performance of our proposed method.
Depth-Quality Purification Feature Processing for Red Green Blue-Depth Salient Object Detection

With the advances in deep learning technology, Red Green Blue-Depth (RGB-D) Salient Object Detection (SOD) based on convolutional neural networks (CNNs) is gaining more and more attention. However, the accuracy of current models is challenging. It has been found that the quality of the depth features profoundly affects the accuracy. Several current RGB-D SOD techniques do not consider the quality of the depth features and directly fuse the original depth features and Red Green Blue (RGB) features for training, resulting in enhanced precision of the model. To address this issue, we propose a depth-quality purification feature processing network for RGB-D SOD, named DQPFPNet. First, we design a depth-quality purification feature processing (DQPFP) module to filter the depth features in a multi-scale manner and fuse them with RGB features in a multi-scale manner. This module can control and enhance the depth features explicitly in the process of cross-modal fusion, avoiding injecting noise or misleading depth features. Second, to prevent overfitting and avoid neuron inactivation, we utilize the RReLU activation function in the training process. In addition, we introduce the pixel position adaptive importance (PPAI) loss, which integrates local structure information to assign different weights to each pixel, thus better guiding the network’s learning process and producing clearer details. Finally, a dual-stage decoder is designed to utilize contextual information to improve the modeling ability of the model and enhance the efficiency of the network. Extensive experiments on six RGB-D datasets demonstrate that DQPFPNet outperforms recent efficient models and delivers cutting-edge accuracy.
DetTrack: An Algorithm for Multiple Object Tracking by Improving Occlusion Object Detection

Multi-object tracking (MOT) is an important problem in computer vision that has a wide range of applications. Currently, object occlusion detecting is still a serious challenge in multi-object tracking tasks. In this paper, we propose a method to simultaneously improve occluded object detection and occluded object tracking, as well as propose a tracking method for when the object is completely occluded. First, motion track prediction is utilized to improve the upper limit of occluded object detection. Then, the spatio-temporal feature information between the object and the surrounding environment is used for multi-object tracking. Finally, we use the hypothesis frame to continuously track the completely occluded object. Our study shows that we achieve competitive performances compared to the current state-of-the-art methods on popular multi-object tracking benchmarks such as MOT16, MOT17, and MOT20.