Reviewerly/exHarmony huggingface dataset:
Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation

Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. Since the teacher model perceives data in a way different from humans, existing KD methods only distill knowledge that is consistent with labels annotated by human expert while neglecting knowledge that is not consistent with human perception, which results in insufficient distillation and sub-optimal performance. In this paper, we propose inconsistent knowledge distillation (IKD), which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. We start by considering the teacher model's counter-intuitive perceptions of frequency and non-robust features. Unlike previous works that exploit fine-grained features or introduce additional regularizations, we extract inconsistent knowledge by providing diverse input using data augmentation. Specifically, we propose a sample-specific data augmentation to transfer the teacher model's ability in capturing distinct frequency components and suggest an adversarial feature augmentation to extract the teacher model's perceptions of non-robust features in the data. Extensive experiments demonstrate the effectiveness of our method which outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors (at most +1.0 mAP). Our codes will be made available at https://github.com/JWLiang007/IKD.git.
BEAMER: Behavioral Encoder to Generate Multiple Appropriate Facial Reactions

This paper presents a framework for generating appropriate facial expressions for a listener engaged in a dyadic conversation. The ability to produce contextually suitable facial gestures in response to user interactions may enhance the user experience for avatars and social robots interaction. We propose a Transformer and Siamese architecture-based approach for generating appropriate facial expressions. Positive and negative Speaker-Listener pairs are created, applying a contrastive loss to facilitate learning. Furthermore, an ensemble of reconstruction quality sensitive loss functions is added to the network for learning discriminative features. The listener's facial reactions are represented with a combination of the 3D Morphable Model's coefficients and affect-related attributes (facial action units). The inputs to the network are pre-trained Transformer-based feature MARLIN and affect-related features. Experimental analysis demonstrate the effectiveness of the proposed method across various metrics in the form of an increase in performance compared to a variational auto-encoder-based baseline.
H2V4Sports: Real-Time Horizontal-to-Vertical Video Converter for Sports Lives via Fast Object Detection and Tracking

We present H2V4Sports, a real-time horizontal-to-vertical video converter specifically designed for sports live broadcasts. With the increasing demand of smartphone users who prefer to watch sports events on their vertical screens anywhere, anytime, our platform provides a seamless viewing experience. We achieve this by fine-tuning and pruning an object detector and tracker, which enables us to provide real-time, accurate key-object tracking results despite the complexity of sports scenes. Additionally, we propose a video virtual director platform that captures the most informative vertical zones from horizontal video live frames using various director logic for a smooth frame-to-frame transition. We have successfully demonstrated our platform in two popular sports: basketball and diving, and the results indicate that our technology delivers high-quality vertical scenes that are beneficial for smartphone users and other vertical scenarios.
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization

Zero-shot skeleton-based action recognition aims to recognize actions of unseen categories after training on data of seen categories. The key is to build the connection between visual and semantic space from seen to unseen classes. Previous studies have primarily focused on encoding sequences into a singular feature vector, with subsequent mapping the features to an identical anchor point within the embedded space. Their performance is hindered by 1) the ignorance of the global visual/semantic distribution alignment, which results in a limitation to capture the true interdependence between the two spaces. 2) the negligence of temporal information since the frame-wise features with rich action clues are directly pooled into a single feature vector. We propose a new zero-shot skeleton-based action recognition method via mutual information (MI) estimation and maximization. Specifically, 1) we maximize the MI between visual and semantic space for distribution alignment; 2) we leverage the temporal information for estimating the MI by encouraging MI to increase as more frames are observed. Extensive experiments on three large-scale skeleton action datasets confirm the effectiveness of our method.
An Entropy-Balanced Orthogonal Learning Bamboo Forest Growth Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Capacitated Vehicle Routing Problem

The bamboo forest growth optimization (BFGO) algorithm combines the characteristics of the bamboo forest growth process with the optimization course of the algorithm. The algorithm performs well in dealing with optimization problems, but its exploitation ability is not outstanding. Therefore, a new heuristic algorithm named orthogonal learning quasi-affine transformation evolutionary bamboo forest growth optimization (OQBFGO) algorithm is proposed in this work. This algorithm combines the quasi-affine transformation evolution algorithm to expand the particle distribution range, a process of entropy increase that can significantly improve particle searchability. The algorithm also uses an orthogonal learning strategy to accurately aggregate particles from a chaotic state, which can be an entropy reduction process that can more accurately perform global development. OQBFGO algorithm, BFGO algorithm, quasi-affine transformation evolutionary bamboo growth optimization (QBFGO) algorithm, orthogonal learning bamboo growth optimization (OBFGO) algorithm, and three other mature algorithms are tested on the CEC2017 benchmark function. The experimental results show that the OQBFGO algorithm is superior to the above algorithms. Then, OQBFGO is used to solve the capacitated vehicle routing problem. The results show that OQBFGO can obtain better results than other algorithms.
Novel chaotic image cryptosystem using dynamic DNA coding

This study aims to solve the problem of small key space in image cryptosystems based on logistic mapping. First, a new one-dimensional (1D) chaotic system, with a wide continuous chaotic interval, a large Lyapunov exponent and obvious chaotic characteristics are presented. Subsequently, a novel image encryption algorithm based on the new 1D chaotic system and dynamic DNA encoding is designed. Compared with other DNA coding methods, the proposed image encryption algorithm encodes chaotic sequences and ensures that the sequence elements at different positions correspond to different DNA coding schemes. This will help to overcome the fixity of DNA coding and make the proposed dynamic DNA coding easy to operate and implement. Finally, the cipher image is obtained by scrambling and bit XOR operation based on the chaotic sequences. The fixed DNA coding method is compared with other chaotic image encryption schemes, and the experimental results indicate that the image encryption algorithm has higher security and can resist common attacks.
Big Data Application for Traffic Estimation on a Website: Big Daddy Case

&#x0D; &#x0D; &#x0D; &#x0D; While living under rapidly changing conditions innovation, flexibility and readiness to change are grounding prosperity of the firm. But any changes for companies should be reasoned and made on the basis of analytical approach. Big data usually could help in this situation without spending time and money on expensive research activities. Therefore, this paper is focused on big data application on customers’ behavior switching from one product to new its look. Modeling is based on few monthsˈ daily data with application of regression analysis and the least squares method. The major finding comes up with the estimation output that the new webpage is more popular among IOS and WEB users, although Android systems showing negative impact on switching to new website&#x0D; &#x0D; &#x0D; &#x0D;
A Deep Vision Learning-Based Intelligent Recognition Method for Dynamic Sports Gestures

Nowadays, artificial intelligence-assisted automatic technological analyses of sports matches have received a more general demand. Among them, the effective recognition of sports actions from videos or images acts as the most fundamental issue to be solved. As sports gestures are characterized by remarkable specialty and instantaneity, it is required to develop specific effective recognition algorithms for this purpose. As a result, this work takes the case of Taekwondo sports as an example, and introduces deep vision learning to develop a specific intelligent recognition method for dynamic sports gestures. After sampling the key frames from dynamic videos, vital joint points of limbs are first obtained as the basic bottom features. Then, gesture expertise of this domain is formed to construct a multiview feature extraction work flow to better analyze complex characteristics of each gesture. Finally, the extracted feature representation is input into a recurrent neural network structure to output the discriminative results. At last, experiments are conducted for assessment, whose results show that the proposal can reach an accuracy of 95.6% and can be well suitable for the investigated problem scenario.
Field and petrographic studies of Basement Complex rocks in Saigbe and Environs, North-Central, Nigeria

Geological mapping and petrographic studies of rocks provide useful information on texture and mineralogical composition of rocks which can be used in the exploration and exploitation of any associated mineralization. However, few regional studies have been conducted in Minna and environs. Here, preliminary field mapping and petrography of out crops in Saigbe environ were conducted to determine their identification, structural and mineralogical composition. Twenty-one rock samples were collected, eight of which were used for thin section analysis. Joint directions were measured and plotted on rose diagram. Results show that Saigbe is underlain by schist, amphibolite and granite with minor pegmatite intrusions. Joints, faults, fold and foliations constitute main structures. Joints in the schists trend NE-SW, while those in the granites trend NW-SE, suggesting different episodes of emplacement for both rocks. Petrography revealed that the schists comprised quartz, biotite, muscovite, plagioclase and opaque mineral, while the amphibolites are composed largely of quartz, hornblende, plagioclase feldspar and opaque minerals. The granites are composed of quartz, microcline, plagioclase feldspar, muscovite and opaque minerals. The pegmatite dykes in the granites comprised quartz, plagioclase feldspar, orthoclase feldspar, muscovite and gemstones, hence interpreted as product of residual melt. The pegmatites make interesting target for mineral exploration.
Mobile Apps-Based Cosmetic Equipment Selection Decision Support System Use Simple Additive Weighting (SAW) Method

Currently, more and more types of cosmetic products are appearing on the market, thus making cosmetic users confused in choosing cosmetics that suit their skin type and usage. Not a few people who use cosmetics wrongly which has a lot of bad effects on their face and has to be touched up repeatedly. This application facilitates users with 5 types of skin types, lip types and types of use that can be selected to make it easier for users to choose cosmetics that suit their skin type and usage. The application was built with a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to make calculations for selecting the most appropriate Make Over cushion and lipstick product for the user. In this application, the user can enter their skin type and then the system will calculate using the SAW method to get the Make Over cushion and lipstick that is most suitable for the user. The final result of the system will display the Make Over cushion and lipstick and their description with the highest calculated value to the user. Based on testing, this application is able to display cushion and lipstick products whose compatibility with users reaches 98%.
An epidemic protection design of vehicle

Aiming at the difficulty of human material distribution under the background of normal epidemic prevention and control, a new pneumatic robot design scheme was proposed which can remote control. The robot structure was designed by using SolidWorks 3D software, and a prototype was made through Simulation analysis of key parts. The test results show that: The size of the robot model prototype is 600×600×600mm, the extension size is 1200×1200×1000mm, and the maximum weight is no more than 35KG. The test has proved that the epidemic prevention and control robot can be applied in the fields of material handling, building patrol, epidemic control and so on. This design for the robot intelligence has a certain promotion and display function.
Small object detection based on attention mechanism and enhanced network

Small object detection has a broad application prospect in image processing of unmanned aerial vehicles, autopilot and remote sensing. However, some difficulties exactly exist in small object detection, such as aggregation, occlusion and insufficient feature extraction, resulting in a great challenge for small object detection. In this paper, we propose an improved algorithm for small object detection to address these issues. By using the spatial pyramid to extract multi-scale spatial features and by applying the multi-scale channel attention to capture the global and local semantic features, the spatial pooling pyramid and multi-scale channel attention module (SPP-MSCAM) is constructed. More importantly, the fusion of the shallower layer with higher resolution and a deeper layer with more semantic information is introduced to the neck structure for improving the sensitivity of small object features. A large number of experiments on the VisDrone2019 dataset and the NWPU VHR-10 dataset show that the proposed method significantly improves the Precision, mAP and mAP50 compared to the YOLOv5 method. Meanwhile, it still preserves a considerable real-time performance. Undoubtedly, the improved network proposed in this paper can effectively alleviate the difficulties of aggregation, occlusion and insufficient feature extraction in small object detection, which would be helpful for its potential applications in the future.
Design of ART program language and interpreter based on Java

In the paper the Android runtime (ART) program language and its interpreter are designed based on Java. The grammar rules of ART language are designed in detail from lexical analysis, grammatical analysis and semantic analysis, and a simple but powerful scripting language is implemented, which supports object-oriented programming, functional programming, operator overloading and other functions. The ART interpreter is written based on the Java standard library. The experimental results show that it can be easily embedded into most JVM based applications including the Android platform. It improves the current problem of too few simple and easy to use scripting languages and relying on third-party library data.
EFL Teacher's Strategies in Online Learning Environment at SMK Nasional Makassar

The purpose of this research is to explore the strategies used by English teachers in implementing learning during online classes. This research is qualitative research with a case study approach through interviews. The participants in this study were 2 English teachers from Makassar National Vocational School. The data in this study were obtained through 3 parts of interviews with a total of ten questions. The findings show that the strategies used by English teachers in implementing learning during online classes are diagnostic assessment and discovery learning using several applications including WhatsApp, Zoom, Google Classroom, YouTube, Google Forms, and the Google search engine. Teachers used several other strategies involving peer tutoring, using school facilities, implementing synchronous online learning, and parental counseling to overcome challenges. Therefore, it is recommended for teachers to continue to use or apply a combination of strategies that they have implemented. In addition, because there are also several strategies discussed in this study, readers of this study, especially teachers, can also use other strategies and technologies to increase the variety of their learning activities. Finally, during the observation of the application of discovery learning, the teacher used it to recount text materials with a combination of writing and speaking projects. It has the potential to be used differently in different material focuses and skills. So, it is also suggested that future researchers be more specific with certain aspects of teaching English such as language skills, or certain materials to enrich data related to that field.
The Characteristics of Good Language Learners in Indonesia EFL Context

This research aims to see and identify the characteristics of good language learners according to English language education students, at the Faculty of Languages and Literature, Makassar State University in 2023. The subjects of this research were fourth-semester students of the English Education Study Program, Department of English, because in that semester there were Learning and Teaching courses. This research wants to see what the student's language learning model are students in the fourth semester who took this course in 5 classes consisted of 210 students. However, this research was only carried out in 2 (two) classes, namely classes D and E with a total of 82 students. This research uses a qualitative approach, namely Qualitative Descriptive research. The results of this research show that Characteristics of good language learners (Good Language Learners) according to students include: Continuously practicing to hone their abilities/consistent/enthusiastic/patient/disciplined/diligent; Utilizing technology; Self-confident; Choosing a suitable and comfortable/adaptable environment; Adjustment; Motivation to learn/enthusiasm; Risk Taker; Self-habituation; Adapting the right learning style/strategy/method; Know the concept of language skills and language elements; Not easily give up; Have a Growth Mindset; Implementation of knowledge/ application/ practice/ active/ brave; Not afraid of being wrong; Search for solutions; Self-evaluation/self-reflection; High curiosity; Communication; Having a loud and loud voice; Know at least 3 languages; Creative; Use good/friendly/humble/polite/attitude language; Open to new cultures/adaptations; High awareness (Awareness); Long term commitment; Role models; Professional; Smart; Increase vocabulary; and read diligently.
Types of interaction between the media and the audience

The ongoing transformations in modern society require rethinking from the angle of different theoretical and methodological approaches. The development of society and the world community cannot be imagined with- out the influence of mass communication and information. It is impossible to imagine the institute of mass media in modern society without an audience. For the successful functioning of the media, it is necessary to study information interaction, social characteristics of the audience and its feedback. The main tool for solving these problems will make it possible to better systematize the existing knowledge about the theory of interaction between the media and the audience, to trace its features in the upcoming professional practice and work in the media. Today, the process of systematization in these concepts can hardly be said to be logically complete. In theoretical publications, the mass media are interpreted ambiguously as a source of distributing information to the public. However, we believe that the mass media can also transmit a function as a way to create an individual.
CCD-ASQP: A Chinese Cross-Domain Aspect Sentiment Quadruple Prediction Dataset

This work present CCD-ASQP, a cross-domain Aspect Sentiment Quadruple prediction(ASQP) dataset in chinese. Based on e-commerce scenario, this dataset lables 15,878 sentiment quadruples out of 3,700 reviews across 6 life domain and 10 product entities. Multiple baselines have been test on CCD-ASQP in terms of ASQP task, and performances have been compared with ChatGPT. Deep learning models’ dramatic decline of accuracy of when shifting to out-of-distribution data shows the lack of domain adaptiveness. ChatGPT achieves relatively consistent cross-domain performance in few-shot setup. Error analysis suggests effect of Chinese language forms on ASQP task. CCD-ASQP leaves great space for sentiment analysis tasks in Chinese language and perspectives from other disciplines are helpful.
Advanced PromptCBLUE Performance: A Novel Approach Leveraging Large Language Models

PromptCBLUE is a Chinese medical NLP benchmark that converts sixteen different medical NLP tasks into text generation task through prompt learning. It requires completing all tasks with just one large language model backbone and keeping the fine-tuning parameters under 1% of the model size, posing huge challenges. To address this issue, we have proposed an innovative approach. Initially a two-stage fine-tuning process is employed. For intricate tasks, a task-decomposition prompting construction method is introduced, enhancing algorithmic efficacy. Moreover, the integration of focus loss is explored to further amplify algorithm performance. Using this approach, a score of 72.28 was achieved in the CCKS2023 PromptCBLUE general competition track. This research contributes new insights to the advancement of large language models in the medical field.
A Novel Homogenized Chaotic System of Compressed Sensing Image Encryption Algorithm

Aimed at the problems of limited range, uneven distribution, and insufficient complexity of traditional one-dimensional chaotic mapping. In this paper, a method for constructing chaotic measurement matrices is presented. An experimental analysis of the chaotic properties of the proposed map is presented. Firstly, the Chebyshev map is homogeneously reconstructed, and the distributional characteristics of the original sequences are altered, in order to make the generated time series more consistent with information security requirements. Second, the Lyapunov exponent, information entropy, spectral entropy, and the NIST randomness test of pseudo random sequences are analyzed, and the results of the analysis show that the newly generated sequences have a good homogenized distribution, complex pseudo random sequences, and good ergodic properties. Then, based on that mapping, a measurement matrix for compressed sensing is generated. Finally, based on this measurement matrix, the image is compressed and encrypted. Simulation results show that the system has improved image reconstruction capability and complexity, and can withstand statistical and other common adversarial attacks.