Reviewerly/exHarmony huggingface dataset:
Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation

Deep neural networksDeep Neural Network have achieved remarkable performance for artificial intelligence tasks. The success behind intelligent systems often relies on large-scale models with high computational complexity and storage costs. The over-parameterized networks are often easy to optimize and can achieve better performance. However, it is challenging to deploy them over resource-limited edge-devices. Knowledge DistillationKnowledge distillation (KD) aims to optimize a lightweight network from the perspective of over-parameterized training. The traditional offline KD transfers knowledge from a cumbersome teacher to a small and fast student network. When a sizeable pre-trained teacher network is unavailable, online KDKnowledge distillation can improve a group of models by collaborative or mutual learning. Without needing extra models, Self-KD boosts the network itself using attached auxiliary architectures. KDKnowledge distillation mainly involves knowledge extraction and distillation strategies these two aspects. Beyond KDKnowledge distillation schemes, various KDKnowledge distillation algorithms are widely used in practical applications, such as multi-teacher KD, cross-modal KD, attention-based KD, data-free KD and adversarial KD. This paper provides a comprehensive KDKnowledge distillation survey, including knowledge categoriesKnowledge category, distillation schemes and algorithms, as well as some empirical studies on performance comparison. Finally, we discuss the open challenges of existing KD works and prospect the future directions.
Development of Various Building Materials Based on Paludiculture Cattail

Besides the reduction of gases through the rewetting of peatlands that have been laid dry, cattails are optimally suited as raw material for industrial use due to their enormous growth performance and yield. Typha stands form resilient, natural monocultures with an annual production output of 15 to 20 tons of dry matter per hectare. Wet use of peatland ensures natural peatland functions such as nutrient and water retention and represents an appropriate, sustainable alternative land use for the future. Cultivation brings ecological benefits in terms of stabilisation and renaturation of rewetted fen areas. At the same time, water protection is also achieved through the cultivation of Typha. With the help of cultivated areas planted with Typha, large amounts of nutrients and pollutants can be removed from the surface water and the area can be protected from further progressive soil erosion. Due to the special structural properties of cattails, a building material was developed, the magnesite-bonded Typhaboard, which offers a unique combination of load-bearing capacity and insulation. This innovative building material has many other positive properties: Building material made from renewable raw material with a very high resistance to mold growth; easy workability with all common tools; low energy consumption during production and its recyclability. This product contains only vegetable ingredients and a purely mineral adhesive without any other additives. As a result, it is easily recyclable and even fully compostable and thus almost complies with the Cradle to Cradle concept. Starting from the Typhaboard already used in several buildings, new building materials based on cattail have been developed in the meanwhile. In addition to magnesite as a binder, other adhesives have been successfully tested which are also sustainable and do not stand in the way of composting. Even using clay, stable and at the same time, insulating boards can be produced for applications that are not exposed to any particular moisture load. Another example is the development of a board for on-roof insulation, which has a lower bulk density and thermal conductivity due to a special particle geometry and arrangement. Furthermore, a low-dust blown-in insulation made of cattail and a kind of OSB substitute with comparable strengths are developed as well as a special very slim ceiling structure made of Typha. The development of a high load-bearing wood substitute is also in progress.
Financial statements fraud identifiers

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Towards Privacy-Aware Causal Structure Learning in Federated Setting

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attached much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in federated learning setting.
VMSG: a video caption network based on multimodal semantic grouping and semantic attention

Network video typically contains a variety of information that is used by the video caption model to generate video tags. The process of creating video captions is divided into two steps—video information extraction and natural language generation. Existing models have the problem of redundant information in continuous frames when generating natural language, which affects the accuracy of the caption. As a result, this paper proposes a multimodal semantic grouping and semantic attention video caption model (VMSG). VMSG uses a novel semantic grouping method for decoding, which divides the video with the same semantics into a semantic group for decoding and predicting the next word, to reduce the redundant information of continuous video frames, which differs from the decoding mode of grouping by frame. Because the importance of each semantic group varies, we investigate a semantic attention mechanism to add weight to the semantic group and use a single-layer LSTM to simplify the model. Experiments show that VMSG outperforms some state-of-the-art models in terms of caption generation performance and alleviates the problem of redundant information in continuous video frames.
Control System for Humanoid Robot

The article presents the specification of the control system designed for humanoid robots. The agent approach was used and the basic concept of subsystem function implementation using Finite State Machines was presented. The basic components of the system consisting of the motion agent, scheduling agent, location and mapping agent and user interface agent are discussed. The main parts of the system have been implemented and tested on small humanoid robots.
Sea-YOLOv5s: A UAV image-based model for detecting objects in SeaDronesSee dataset

Unmanned aerial vehicles (UAVs) play a crucial role in maritime search and rescue missions, capturing images of open water scenarios and assisting in object detection. Previous object detection models have mainly focused on general scenarios. However, existing object detection models have mainly focused on general scenarios, while images captured by UAVs in vast ocean scenarios often contain numerous small objects that significantly degrade the performance of the original models. To address this challenge, we propose a model that can automatically detect objects in images captured by UAVs during maritime search and rescue missions. Our approach involves designing a new detection head with higher resolution feature maps and more comprehensive feature information to improve the detection of small objects. Additionally, we integrate Swin Transformer blocks into the small object detection head, which can improve the model’s ability to obtain abundant contextual information and thus improves the model’s ability to detect small objects. Moreover, we fuse the Convolutional Block Attention Model into the small object detection head to help the model focus on important features. Finally, we adopt a model ensemble strategy to further improve the mean average precision (mAP). Our proposed model achieves a 4.05% improvement in mAP compared to the baseline model. Furthermore, our model outperforms the previous state-of-the-art model on the SeaDronesSee dataset in terms of fewer parameters, lower training costs, and higher mAP.
Speaker anonymization using generative adversarial networks

The advent use of smart devices has enabled the emergence of many applications that facilitate user interaction through speech. However, speech reveals private and sensitive information about the user’s identity, posing several security risks. For example, a speaker’s speech can be acquired and used in speech synthesis systems to generate fake speech recordings that can be used to attack that speaker’s verification system. One solution is to anonymize the speaker’s identity from speech before using it. Existing anonymization schemes rely on using a pool of real speakers’ identities for anonymization, which may result in associating a speaker’s speech with an existing speaker. Hence, this paper investigates the use of Generative Adversarial Networks (GAN) to generate a pool of fake identities that are used for anonymization. Several GAN types were considered for this purpose, and the Conditional Tabular GAN (CTGAN) showed the best performance among all GAN types according to different metrics that measure the naturalness of the anonymized speech and its linguistic content.
MOOforest – Multi-objective Optimization to Form Decision Tree Ensemble

Multi-criteria optimization is increasingly used to build classifier ensembles, including for the imbalanced data classification task. Then we have the problem of optimizing at least two criteria related to the prediction quality of the minority and majority classes, or the so-called classification precision. The paper proposes MOOforest - a new method for building decision tree ensembles. It uses the MOEA/D optimization algorithm to return a diverse pool of base classifiers by selecting different feature subsets on which they are trained. From the pool of non-dominated solutions, the final ensemble is chosen using the promethee method. Modifying the weights of the promethee algorithm allows the user to select the appropriate solution in the context of the user’s expectations (i.e., it indicates how important each optimization criterion is to the user). It is worth noting that during the classifier ensemble training, the features selected for the base classifiers result from the optimization process, and not as in the case of popular algorithms employing the Random Subspace approach (such as Random Forest), where attributes are selected randomly. Thus, the proposed method has an advantage over the mentioned approach, confirmed through a comprehensive set of computer experiments.
An efficient image defogging algorithm based on dragonfly optimized gamma correction and stationary wavelet decomposition

Image dehazing is a revolutionary technique for restoring images with hazy or foggy landscapes, that has gotten a lot of focus in recent years since it gained importance in a surveillance system. However, the image processing by the traditional defogging algorithm has difficulties in integrating the depth of image detail and the color of the image. Therefore, in this paper, a novel framework based on wavelet decomposition and optimized gamma correction is proposed for efficaciously retrieving the fog-free image. The foggy image is first divided into low and high frequency sub-images using SWT (Stationary Wavelet Transform), which has the advantages of preserving temporal features so that information loss can be stopped. Then the low frequency and high frequency images are processed with defogging and denoising modules to remove fog and noise respectively. The DOGC (Dragonfly optimal Gamma Correction) algorithm in dehazing module dynamically enhanced the color detail information without human intervention so that observed scene contrast and visibility are well preserved. Lastly, fog-free image is reconstructed from sub-enhanced images. The experimental findings show that the proposed framework outperforms state-of-the-art methods in terms of both quantitative and qualitative assessment criteria using the established dataset. Furthermore, the proposed method efficiently removes fog while preserving the naturalness of fog images.
An evaluation metric for image retrieval systems, using entropy for grouped precision of relevant retrievals

Evaluating the performance of Content-Based Image Retrieval (CBIR) systems is a challenging and intricate task, even for experts in the field. The literature presents a vast array of CBIR systems, each applied to various image databases. Traditionally, automatic metrics employed for CBIR evaluation have been borrowed from the Text Retrieval (TR) domain, primarily precision and recall metrics. However, this paper introduces a novel quantitative metric specifically designed to address the unique characteristics of CBIR. The proposed metric revolves around the concept of grouping relevant images and utilizes the entropy of the retrieved relevant images. Grouping together relevant images holds great value from a user perspective, as it enables more coherent and meaningful results. Consequently, the metric effectively captures and incorporates the grouping of the most relevant outcomes, making it highly advantageous for CBIR evaluation. Additionally, the proposed CBIR metric excels in differentiating between results that might appear similar when assessed using other metrics. It exhibits a superior ability to discern subtle distinctions among retrieval outcomes. This enhanced discriminatory power is a significant advantage of the proposed metric. Furthermore, the proposed performance metric is designed to be straightforward to comprehend and implement. Its simplicity and ease of use contribute to its practicality for researchers and practitioners in the field of CBIR. To validate the effectiveness of our metric, we conducted a comprehensive comparative study involving prominent and well-established CBIR evaluation metrics. The results of this study demonstrate that our proposed metric exhibits robust discrimination power, outperforming existing metrics in accurately evaluating CBIR system performance.
Artificial Intelligence and Human-Induced Seismicity: Initial Observations of ChatGPT

Abstract Freely available artificial intelligence (AI) tools, such as the Chat Generative Pre-trained Transformer (ChatGPT), offer an alternative method to online search engines for scientific results and opinions to be automatically collated into concise summary prose. We applied this approach to human-induced seismicity by asking ChatGPT common, earthquake-related questions before asking questions about natural, induced, and debated earthquakes. ChatGPT provided rudimentary descriptive distinction between natural and induced seismicity, and for clear-cut cases of each type provided a response giving the scientific consensus. For debated cases, a natural origin was implied by responses focusing on fault rupture and tectonic setting. The inclusion of the word “induced” in the question phrasing led to discussion on possible induced origins, but not all response information was consistent with our knowledge of the scientific literature. ChatGPT did not provide an answer to a case more recent than its training data. For most questions, ChatGPT tended to include irrelevant information to increase response length. Online AI tools could become a mainstream technology, particularly for nonspecialists, to obtain concise summaries of published science. However, it is important to recognize the limitations of the current technologies, particularly sensitivity to question wording and inability to correctly reference scientific material, especially where a definitive answer does not yet exist to the question asked.
Analysis of Keypoint-based Copy-Move Forgery Detection in Digital Images

The images are used as the element that is primary of in multitudinous operations and the dupe- move phony is considerably used to induce the forged images by manipulating the content of digital images. With the advancement of various image editing software Photoshop that is including Draw, GIMP, etc make it's easier to manipulate the digital images. Thus, Copy- Move Forgery Detection is a task that is challenging. Some of the dupe- move phony discovery styles analogous as Symmetry predicated features that are original SIFT- Symmetry, Clustering with J- relation Algorithm, SIFT- KAZE crossbred Features, point Matching predicated on frippery- bin-First Algorithm, Binary Discriminant Featuresetc. are compared and analysed in this paper.
Russian Size Adjectives Analyses by Corpus Linguistics Methods

The paper analyzes the functioning of size adjectives, exact as well as average size adjectives in Russian. Accurate measurement requires a numeral and also an adjective corresponding to the unit of measure. A parametric adjective is non-obligatory, while the designated parameter is derived from the semantics of the defined noun. The parametric noun in the required case form is included in the noun group, which performs the function of the attribute. Average size adjectives with abstract parameter nouns form complex attributes, taking pre and post positions to the defined noun. The adjective cредний /average is mainly combined with height and length parameters with the dominance of the noun size. Size adjectives короткий / short and длинный / long have a 2.4 asymmetrical ratio index of their functioning. It should be noted that roads and streets are more often characterized by positive and negative parameters, in turn. Adjectives of a positive width parameter dominate in garment characterization. This survey's primary objective is a qualitative analysis of the structure and semantics of size adjectives of exact measurement.
Human-Cantered Design Approaches in Mechatronics for Improved User Experience

In the rapidly evolving field of mechatronics, human-cantered design (HCD) approaches have gained significant attention as a means to enhance user experience. Mechatronics, which combines mechanical engineering, electronics, computer science, and control systems, aims to develop intelligent and interactive systems. However, the success of mechatronic systems relies not only on their technical functionalities but also on how well they align with users' needs and expectations. This abstract explores the significance of human-cantered design approaches in mechatronics and their potential to improve user experience.In the outcome, human-cantered design approaches play a crucial role in mechatronics for improving user experience. By involving users from the early stages, employing iterative design cycles, and considering usability and emotional factors, mechatronic systems can be developed to be more user-friendly, efficient, and engaging. The integration of HCD approaches leads to systems that align with users' needs, preferences, and expectations, ultimately resulting in improved user experience and increased user acceptance of mechatronic systems. As mechatronics continues to advance, the adoption of human-cantered design approaches will remain essential in ensuring the successful development and deployment of user-centric mechatronic systems.
Algorithms and Methods for Fault-tolerant Control and Design of a Self-balanced Scooter

Due to complex operation and surrounding conditions, faults are inevitable during the use of complex technical systems such as two-wheel scooters. This paper explains algorithms and methods which allow the accommodation of faults through the design and control of such systems on the functional level of product concretization. A main component of the approach is a fuzzy decision engine. This engine combines the evaluation of residuals and the current state and is based on specialist’s expertise.
Application of data mining based on improved ant colony algorithm in college students’ employment and entrepreneurship education

Under the background of the new era of economy, the employment and entrepreneurship education of college students has also ushered in new problems. In the construction of student employment and entrepreneurship data mining model, data solving efficiency and association rule mining play a key role in the performance of the model. How to optimize and improve the mathematical model and improve the accuracy of student employment and entrepreneurship data analysis needs further research and analysis. This paper constructs the mathematical model of ant colony algorithm, introduces the logical process in solving practical problems, provides setting suggestions for the value of algorithm parameters, and improves the solution efficiency. This paper improves the decision tree classification method and ant colony algorithm. The association rule mining based on ant colony has more perfect reference value. From the combination and interest analysis of college students’ employment and entrepreneurship content, it provides efficient, practical and useful information for the school. Finally, after constructing the prediction model, this paper analyzes the four factors affecting the choice of college students’ homework entrepreneurial intention, which are individual, family, school and society, and puts forward five measures to improve college students’ entrepreneurship and employment education.
Remediation of heavy metal-contaminated soil and urban environmental monitoring based on remote sensing images

In order to improve people's quality of life, it is necessary to establish a complete environmental monitoring system outdoors. In the organizational structure of the environmental monitoring system, monitoring nodes are placed according to actual monitoring requirements to meet the needs of environmental monitoring and data information collection in the area. It mainly analyzes ZigBee technology and 4G technology in detail. In order to overcome the short-distance transmission shortcomings of ZigBee technology, the system combines short-distance ZigBee technology and long-distance 4G technology to achieve short-distance data collection and long-distance transmission. This paper selects the tin mining area as a representative research field and processes the images through professional remote sensing image processing software to extract land and plant pollution caused by tailings and mining areas. Then, enter the sample point to be tested to select typical main vegetation mining areas, complete the reflectance spectrum detection on the spot, and then conduct a comprehensive analysis of the spectral characteristics of some test data, and then check the plant reflectance and pollution status based on image processing. Through studying the polluted materials extracted by spectroscopy, the tailings ecological restoration plan was finally developed.