Reviewerly/exHarmony huggingface dataset:
CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and Contour Guidance Supervision

Current state-of-the-art semi-supervised semantic segmentation (SSSS) methods typically adopt pseudo labeling and consistency regularization between multiple learners with different perturbations. Although the performance is desirable, many issues remain: (1) supervisions from a single learner tend to be noisy which causes unreliable consistency regularization (2) existing pixel-wise confidence-score-based reliability measurement causes potential error accumulation as the training proceeds. In this paper, we propose a novel SSSS framework, called CFCG, which combines cross-fusion and contour guidance supervision to tackle these issues. Concretely, we adopt both image-level and feature-level perturbations to expand feature distribution thus pushing the potential limits of consistency regularization. Then, two particular modules are proposed to enable effective semi-supervised learning under heavy coherent perturbations. Firstly, Cross-Fusion Supervision (CFS) mechanism leverages multiple learners to enhance the quality of pseudo labels. Secondly, we introduce an adaptive contour guidance module (ACGM) to effectively identify unreliable spatial regions in pseudo labels. Finally, our proposed CFCG achieves gains of mIoU +1.40%, +0.89% with a single learner and +1.85%, +1.33% by fusion inference on PASCAL VOC 2012 and on Cityscapes respectively under 1/8 protocols, clearly surpassing previous methods and reaching the state-of-the-art.
VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control

As the model size of pre-trained language models (PLMs) grows rapidly, full fine-tuning becomes prohibitively expensive for model training and storage. In vision-and-language (VL), parameter-efficient tuning (PET) techniques are proposed to integrate modular modifications (e.g., Adapter and LoRA) into encoder-decoder PLMs. By tuning a small set of trainable parameters, these techniques perform on par with full fine-tuning. However, excessive modular modifications and neglecting the functionality gap between the encoders and decoders can lead to performance degradation, while existing PET techniques (e.g., VL-Adapter) overlook these critical issues. In this paper, we propose a Vision-and-Language Parameter-Efficient Tuning (VL-PET) framework to impose effective control over modular modifications via a novel granularity-controlled mechanism. Considering different granularity-controlled matrices generated by this mechanism, a variety of model-agnostic VL-PET modules can be instantiated from our framework for better efficiency and effective-ness trade-offs. We further propose lightweight PET module designs to enhance VL alignment and modeling for the encoders and maintain text generation for the decoders. Extensive experiments conducted on four image-text tasks and four video-text tasks demonstrate the efficiency, effectiveness and transferability of our VL-PET framework. In particular, our VL-PET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large</inf> with lightweight PET module designs significantly outperforms VL-Adapter by 2.92% (3.41%) and LoRA by 3.37% (7.03%) with BART-base (T5-base) on image-text tasks. Furthermore, we validate the enhanced effect of employing our VL-PET designs on existing PET techniques, enabling them to achieve significant performance improvements. Our code is available at https://github.com/HenryHZY/VL-PET.
Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors

We introduce a novel framework for one-shot audio-driven talking head generation. Unlike prior works that require additional driving sources for controlled synthesis in a deterministic manner, we instead sample all holistic lip-irrelevant facial motions (i.e. pose, expression, blink, gaze, etc.) to semantically match the input audio while still maintaining both the photo-realism of audio-lip synchronization and overall naturalness. This is achieved by our newly proposed audio-to-visual diffusion prior trained on top of the mapping between audio and non-lip representations. Thanks to the probabilistic nature of the diffusion prior, one big advantage of our framework is it can synthesize diverse facial motion sequences given the same audio clip, which is quite user-friendly for many real applications. Through comprehensive evaluations of public benchmarks, we conclude that (1) our diffusion prior outperforms auto-regressive prior significantly on all the concerned metrics; (2) our overall system is competitive with prior works in terms of audio-lip synchronization but can effectively sample rich and natural-looking lip-irrelevant facial motions while still semantically harmonized with the audio input.
Boosting Semantic Segmentation from the Perspective of Explicit Class Embeddings

Semantic segmentation is a computer vision task that associates a label with each pixel in an image. Modern approaches tend to introduce class embeddings into semantic segmentation for deeply utilizing category semantics, and regard supervised class masks as final predictions. In this paper, we explore the mechanism of class embeddings and have an insight that more explicit and meaningful class embeddings can be generated based on class masks purposely. Following this observation, we propose ECENet, a new segmentation paradigm, in which class embeddings are obtained and enhanced explicitly during interacting with multi-stage image features. Based on this, we revisit the traditional decoding process and explore inverted information flow between segmentation masks and class embeddings. Furthermore, to ensure the discriminability and informativity of features from backbone, we propose a Feature Reconstruction module, which combines intrinsic and diverse branches together to ensure the concurrence of diversity and redundancy in features. Experiments show that our ECENet outperforms its counterparts on the ADE20K dataset with much less computational cost and achieves new state-of-the-art results on PASCALContext dataset. The code will be released at https://gitee.com/mindspore/models and https://github.com/Carol-lyh/ECENet.
Exploring the Research on Utilizing Machine Learning in E-Learning Systems

Naturally, you are already familiar with the phrase "E-Learning" in a time when information technology rules and everything is digital. electronic learning, e-learning. Through e-learning, anyone, at any time, can participate in the teaching and learning process. Distance and time are no longer impediments to completing activities, including learning in this situation, just like other online activity concepts. Nearly all schools and institutions today use eLearning in some capacity. The COVID-19 pandemic and the rapidly changing globe necessitate that everything is done online in addition to the world being entirely digital. This study employed the systematic literature review (SLR) methodology. The outcomes, which can be employed in a variety of Machine Learning (ML) applications, are acquired. Artificial intelligence (AI) in the form of machine learning (ML) enables software applications to predict outcomes more accurately even when they are not expressly programmed to do so. To forecast new output values, machine learning algorithms use historical data as input.
Implementasi Dompet Digital Polije Berbasis IoT dan Blockchain

urrently, the implementation of IoT devices has been widely utilized by various parties in supporting the increasingly complex aspects of human life. IoT devices have become a favorite among hardware and software developers due to their ease of use and flexibility in adapting to the evolving systems of today. One of the uses of IoT is in the realm of digital economics, where digital economy represents a new way of prioritizing technology and ease in the buying and selling transaction process between sellers and buyers. In the past, payment processes for goods or services used to involve paper money and coins, but nowadays, payments are made using digital media. However, the current digital payment methods still rely on centralized storage technology. Therefore, if the data held at the center is damaged or compromised, it can disrupt the flow of financial transactions, and user data privacy becomes a subsequent concern.
ParaMET: A Parallel Framework for Efficient Medical Data Extraction on Tianhe-NG Supercomputer

In the burgeoning realm of data-driven medical research, the escalating scale and intricacy of contemporary medical datasets frequently surpass the processing capabilities of traditional computational environments. Specifically, I/O bottlenecks have emerged as pivotal constraints in several research areas. In this paper, a data service framework is introduced that harnesses supercomputers to parallelly access multi-modal datasets and supports multi-node processing, called ParaMET. To enhance user accessibility, a web-based user interface has been integrated, allowing permitted researchers to effortlessly interact via their laptops, complemented by an API suite available through SDK for those adept with supercomputing for deeper data manipulation. Through extensive empirical validation, our framework manifests a remarkable performance elevation in multi-node supercomputing settings, achieving acceleration of up to approximately 1000x compared to existing methods.
Introducing Speculative Design, Gender, and AI in Lean UX for HCI Education

This paper presents a novel approach to HCI education that incorporates speculative design, gender, and artificial intelligence (AI) into the lean UX methodology. We argue that this approach is essential for creating more inclusive and innovative UX solutions that reflect the diversity of users in our increasingly complex world. The paper outlines the theoretical underpinnings of these concepts and how they can be integrated into the lean UX methodology. We present an innovative educational experience that exemplifies how these concepts have been applied in practice, including developing a speculative design workshop for students, using gender-inclusive language in UX design, and integrating AI tools into the lean UX process. Finally, we reflect on the potential impact of this approach on the future of HCI education, and how it can contribute to creating more inclusive, equitable, and effective UX designs. We argue that this approach is essential for preparing students to navigate the rapidly evolving UX design landscape and contribute to a more just and sustainable future.
A Hybrid CNN-Multi-Class SVM Framework for Biomedical Document Gene-Disease Datasets Classification

Healthcare investigators and clinicians need biomedical document classification to organize and handle the large volume of biomedical literature. Conventional classification methods use manually designed features, which may be timeconsuming and may not represent biomedical text complexity. Biomedical data’s high dimensionality and sparsity may also challenge current approaches. For big datasets, CNNs are computationally costly. Increasing feature extraction efficiency reduces training and inference durations. The proposed method intends to improve the accuracy of document classification in the biomedical sector considerably. It functions in two stages: feature extraction and classification. The proposed method employs a hybrid approach to biomedical document classification, focusing on the intricate interactions between genes, diseases, and chemical treatments via the use of a CNN Multi-class Support Vector Machine (M-SVM) model. CNN is utilized to extract features, while M-SVM is employed as a classifier. This work discusses Improved CNNs, which may extract more discriminative and informative features from input data, resulting in a more accurate representation of underlying patterns and connections. Error-Correcting Output Coding (ECOC) based on M-SVM is used to manage noisy data by merging the outputs of many binary classifiers, enabling it to recover from faults in individual classifiers and thereby lowering the risk of overfitting. The study’s results show that the proposed model is successful, with an accuracy of 99.28% and an F1-score of 99.84% across biomedical document datasets.
Visual Data Engineering for Conflict and Terrorism Prediction

In response to the escalating global conflicts, predictive models have become extremely important for peacekeeping initiatives. The proliferation of “Hybrid Threats,” including terrorism and unconventional warfare, necessitates innovative strategies for enhancing peace and security. This paper outlines a collaborative effort with the United Nations Global Service Center (UNGSC) to develop a predictive tool for domestic conflicts in Africa, using diverse open-source datasets. The study employs data engineering, visualization, and integration techniques to explore commonalities and discrepancies among the datasets. Notably, data inconsistencies emerge, underscoring the significance of verifying information sources. While horizontal and vertical data integration possibilities are identified, challenges related to data anomalies and miscommunication are highlighted. To build a reliable predictive model, rigorous data analysis, expert insights, and a multidimensional approach are indispensable, ultimately contributing to conflict prevention and sustainable peacekeeping.
WhatsApp-based anemia e-leaflet for young women as a media for adolescent health promotion

BACKGROUND: Adolescence is the right time to intervene in overcoming anemia because it is an important period for growth and development. One of the causes of anemia is nutritional disorders, which can be caused by a lack of information about nutrition or the inability to apply the information obtained in daily life. The implementation of health promotion cannot be separated from the media. E-leaflets based on WhatsApp Messenger are one of the media that is easily accessed and understood by teenagers to reduce the number of anemia problems in young women. Therefore, this study was conducted to produce WhatsApp-based e-leaflet products as a medium for health promotion in adolescents. MATERIALS AND METHODS: This research is a type of research and development (RnD). The research subjects were media experts and 20 young women who met the inclusion criteria using a purposive sampling technique. Validation sheets by media experts and response questionnaires for young women were used to test the feasibility of the product. A quantitative descriptive analysis technique was applied for expert validation. RESULTS: The results showed that the WhatsApp-based anemia e-leaflet for young women was very feasible as media for promoting youth health based on the results of validation from media experts (96%) and small group trials (88%). CONCLUSION: WhatsApp-based anemia e-leaflets for young women are very appropriate as a medium for promoting adolescent health. It is recommended to do a trial in a large group and test the effectiveness of the WhatsApp-based anemia e-leaflet product for young women as a medium for promoting adolescent health.
Perbandingan Algoritma C4.5 dan Naive Bayes Untuk Menentukan Penerima Bantuan Bedah Rumah di Kabupaten Tanggamus

A livable house is a basic human need, a livable house is said to be if the home owner has a sense of peace and comfort living in that house. A livable house is interpreted as a house that has facilities to meet human needs in carrying out daily activities. The government assistance has not been distributed evenly to the homes of the residents of Tiga Dolok Village, because the program that has been running so far is very complicated, the system that runs is still manual in processing community data. Therefore, the authors provide a solution to classify the eligibility level of the house using the C4.5 algorithms and the Naïve Bayes algorithm. This algorithm was chosen because it is one of the methods in the Decision Tree that is widely used to make predictions about a case. Thus, government subsidies for renovating uninhabitable houses can be channeled in an appropriate manner. The research objective is to use a decision tree based on the C4.5 algorithms, which is expected to increase the accuracy of receiving housing feasibility renovation assistance. The research method used is Data Mining. Analysis and calculations using this algorithm really help the decision-making process to determine people who are eligible to receive housing renovation assistance and people who are not eligible to receive housing renovation assistance. Keywords: Livable House; C4.5 Algorithm; Decision tree, Naïve
Audio Noise Reduction Using Different Wavelet and Filtering Procedures

Speech signal analysis is one of the most important fields of study in multi-media applications. Using automated channels, it is possible to successfully eliminate the undesired noise in the speech signal's higher or lower pitch frequency portions. Various cutting-edge channels are used in this study to strengthen the dialogue. To denoize the sound flags and improves speech and sound signs, several wavelet types, DWT with thresholding, and necessary straight channels are utilized. Our main goal is to lessen the framework noise, which greatly depends on the application and connection. As a result, it must either make the broad speech more understandable or strengthen its discernment. Following extensive deliberation and research, the conclusion that sound reduction technology should be used to reduce unwanted foundation noise. It uses two separate systems with variables like SNR, PSNR, MSE, and time to lessen the need for blaring signals to get people to move. The sign-to-noise ratio (SNR), mean square error (MSE), threshold values, and slid-by-time analysis can all be used to analyze the denoised flag. The Chevshevby Type-1 Filter (seventh request) is now the best alternate channel since it has a greater SNR and a shorter elapsed time than other channels. But despite the fact that the Butterworth channel directly contributes to the high PSNR, the SNR is low and even negative in the Butterworth channel. Currently, the DWT Coif wavelet with delicate limit cannot compete with the coif hard edge's performance. On the other hand, the DWT Style wavelet with sensitive breaking point is widely used.
A Compilation Toolchain of Neural Networks for FPGA Backend

As neural networks are widely used in embedded and end-device applications, power consumption and performance optimization for specific applications, such as edge computing, are gaining increasing attention. FPGAs have been used in embedded neural network applications due to their high parallelism, energy efficiency, reconfigurability, and customizability. However, traditional FPGA programming techniques fail to meet the rapid and iterative development requirements of embedded applications, which also limit the development of FPGAs. Researchers have attempted to break through the language-level barriers and implement end-to-end flow. Xilinx’s Vitis and LeFlow are proposed to import and execute neural network applications on FPGAs. However, existing works are all focused on chips from foreign manufacturers such as Xilinx, and there lacks the support for domestic chips. This paper proposes a complete set of neural network compilation toolchain for domestic FPGA backend, which consist of a developer-friendly Python front-end and a complete FPGA back-end development flow that relieves FPGA developers from the cumbersome hardware programming tasks, and therefore improves the development efficiency of embedded neural network applications.The main contributions of this paper are listed as follows. (1) We propose a complete FPGA neural network toolchain; (2) We summarize a code specification friendly for high-level synthesis process, and based on this specification we propose a code generation algorithm based on the operator template. Experimental results show that the proposed general compilation toolchain works correctly for adapting the neural network on the backend of domestic FPGA, with a reduction of about 64% of the trigger resources compared with the generated code from the state-of-the-art compilation framework.
Featured articles from James Huston et al.

&lt;p&gt;In this issue, we are honored to feature a collection of articles from Professor James Hutson and his co-authors. Prof. James Hutson is an esteemed art history expert at Lindenwood University’s College of Arts and Humanities, whose recent interest is in AI generated art and the future potential for visual communication. He has provided us with a relatively comprehensive exploration of the Metaverse’s impact on education. In addition, he and his colleague also explored the ethical issues of the use of AI technologies, specifically the ethical considerations of digital necromancy. The decision to include four articles by the same author is a deliberate choice, reflecting the depth and breadth of Hutson’s research and the multifaceted nature of both the Metaverse in education and the possible ethical concerns of the use of AI technologies.&lt;/p&gt;
Fuzzy And LDR Based Sun Follower System

The amount of sun irradiation has a significant impact on the energy produced by these arrays. The work being done made use of dual-axis solar monitoring that relied on comparing the amount of light using fuzzy reasoning. Four Lights Depend Ratio (LDR) detectors & a single Arduino UNO microprocessor make up the circuitry of the framework, which measures the highest incident sun irradiation concentration. To look for the heading of the celestial body, two energy casement propellers (just one per the direction that is horizontal as well as a second per the one that runs vertically) operate in tandem at first. Every direction of movement's method operates on the fuzzy logic controlling device operates the electric screen motors in accordance with the signals received provided by the Light monitors. The microchip gets an optimal conclusion about which way the photovoltaic (PV) module ought to turn and where it must remain with the aid of the controller using fuzzy logic. By the end, the photovoltaic surveillance system functions at minimal energy intake and produces light with an outstanding efficiency.
Creative Intent on the Windward Slopes

SMPTE was formed in 1916 as narrative performance was ushered onto the technological stage allowing for mass distribution and transmission. While there is no need to list those advantages, there is one weakness with audiovisual media as compared to the traditional stage, which media still has not overcome. This is real-time audience feedback, and the ability to adjust the performance based on differing audience reactions. This paper motivates the use of biosensors in media by highlighting the problem of signal loss due to playback technology. A metadata system is proposed that allows creatives to steer signal modifications as a function of audience emotion and cognition as determined by biosensor analysis This is needed because today's audiovisual ecosystem includes such a wide variety of playback devices that the audience's experience can differ substantially for the same source content. Metadata for narrative and emotional expectation as inserted by creatives during the content production stages combines with the assessment to adapt the rendering. As a result, the system allows for creative intent to be scalable as best as possible across many types of playback systems in a manner analogous to real-time stage presentation.