Reviewerly/exHarmony huggingface dataset:
A Novel Method for Solving the Multi-Commodity Flow Problem on Evolving Networks

The minimum cost multi-commodity flow (MMCF) problem on evolving networks is the latest challenge to solve the min-cost network flow problem of multi-commodity flowing from Multi-source and multi-sink on an evolving network. The researches focused on the solution of the MMCF problem of static networks. Meanwhile, the researchers largely ignored the MMCF problem of the network structure dynamically changes over time. Evolving networks have a dynamically changing and unpredictable network structure. The existing methods need to recalculate when the network structure changes, leading to high computational complexity. In this manuscript, we propose a dynamic graph computing model for adaptive dynamic path selection and optimization. First, the model selects the path with the least cost for each commodity, assuming that the route can transport a specified number of such commodities to find the optimal path faster. Second, we design path selection and flow update strategies for each node in the graph to adapt to changes in the graph structure. At the same time, we can prevent multiple commodity conflicts from exceeding the marginal capacity limit. Finally, the optimal path of each commodity is dynamically pruned based on the original optical path to obtain the tree structure and determine the absolute path to meet the specified transportation quantity of each commodity. Performance analysis results show that our method can efficiently cope with the dynamic changes of the network structure, which provides ideas for solving the multi-commodity network flow problem in dynamic networks.
Exploiting Deepfakes by Analyzing Temporal Feature Inconsistency

In recent years, the rapid advancement of image generation technology has facilitated the creation of counterfeit images and videos, posing significant challenges for content authenticity verification. Malefactors can easily extract videos from social networks and generate their own deceptive renditions using state-of-the-art techniques. The latest Deepfake face forgery videos have reached an unprecedented level of sophistication, making it exceptionally difficult to discern signs of manipulation. While several methods have been proposed for detecting fraudulent media, they often target specific aspects, and as new attack methods emerge, these approaches tend to become obsolete. This paper presents a novel detection approach that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). Initially, CNN is employed to extract image features from each frame of the input facial video, capturing subtle alterations and irregularities in manipulated content. Subsequently, the extracted feature sequence is used to train the LSTM network, mimicking the temporal consistency of human visual perception and enhancing the effectiveness of counterfeit video detection. To validate this methodology, a comprehensive evaluation is conducted using the FaceForensic++ dataset, affirming its proficiency in identifying Deepfake counter-feit videos.
A Stochastic Event-Triggered Robust Unscented Kalman Filter-Based USV Parameter Estimation

This article aims to address the remote estimation of states and model parameters for a class of unmanned surface vehicle (USVs) with unknown noise parameters and stochastic event-triggered communication mechanism. Specifically, the heavy-tailed process noises and Gaussian distributed measurement noises with unknown covariance matrices are considered. By utilizing variational Bayesian technique, a new class of online estimation approach is developed to achieve the goal of jointly estimating the states, USV model parameters, and noise parameters in a remote manner. Due to the inherent nonlinearity of the augmented system, the unscented transformation is incorporated into the estimator design. In addition, to balance the tradeoff between estimation effectiveness and communication rate, the objective of joint estimation is realized under the event-triggered mechanism with the help of Gaussianity. Finally, the performance of the proposed event-triggered robust unscented Kalman filter is demonstrated by practical experiments as well as numerical simulations.
Peningkatan Keterampilan Komputer dan Pengenalan Perangkat Lunak Microsoft Office Bagi Guru dan Siswa SD N 17 Kota Bengkulu

The development of information technology does not only play a role in the economic field, but also in the field of education. Today every teacher and school student at almost all levels of education is required to be technology literate, especially information technology because every job has been in contact with information technology. In addition to supporting educational activities, information technology literacy also supports teachers in solving administrative problems and for students to support the learning system. One of the software that can be used to support education and administrative issues is Microsoft Word, Microsoft Excel and Microsoft PowerPoint. Microsoft Word, Microsoft Excel and Microsoft PowerPoint software has many features that are still not familiar to some users, especially elementary school teachers, especially elementary school students. This is also experienced by the teachers of SD 17 Bengkulu City who are not familiar with the many tools provided in Microsoft Word, Microsoft Excel and Microsoft PowerPoint software. Some of the obstacles that are often encountered are the less than optimal use of the merge function in Word, the function of conditional formulas in Excel and animation and multimedia in PowerPoint. This is the background for the dedication team to provide training "Improving the Skills in Using Microsoft Office Software for Teachers and Students at SDN 17 Bengkulu City". This activity was held in the classroom of SD N 17 Bengkulu city with lecture methods and direct practice. The participants of this training were teachers and grade 6 students, all of whom were teachers and students of SDN 17 Bengkulu City
SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition

3D modeling from satellite imagery is essential in areas of environmental science, urban planning, agriculture, and disaster response. However, traditional 3D modeling techniques face unique challenges in the remote sensing context, including limited multi-view baselines over extensive regions, varying direct, ambient, and complex illumination conditions, and time-varying scene changes across captures. In this work, we introduce SUNDIAL, a comprehensive approach to 3D reconstruction of satellite imagery using neural radiance fields. We jointly learn satellite scene geometry, illumination components, and sun direction in this single-model approach, and propose a secondary shadow ray casting technique to 1) improve scene geometry using oblique sun angles to render shadows, 2) enable physically-based disentanglement of scene albedo and illumination, and 3) determine the components of illumination from direct, ambient (sky), and complex sources. To achieve this, we incorporate lighting cues and geometric priors from remote sensing literature in a neural rendering approach, modeling physical properties of satellite scenes such as shadows, scattered sky illumination, and complex illumination and shading of vegetation and water. We evaluate the performance of SUNDIAL against existing NeRF-based techniques for satellite scene modeling and demonstrate improved scene and lighting disentanglement, novel view and lighting rendering, and geometry and sun direction estimation on challenging scenes with small baselines, sparse inputs, and variable illumination.
Merging Vision Transformers from Different Tasks and Domains

This work targets to merge various Vision Transformers (ViTs) trained on different tasks (i.e., datasets with different object categories) or domains (i.e., datasets with the same categories but different environments) into one unified model, yielding still good performance on each task or domain. Previous model merging works focus on either CNNs or NLP models, leaving the ViTs merging research untouched. To fill this gap, we first explore and find that existing model merging methods cannot well handle the merging of the whole ViT models and still have improvement space. To enable the merging of the whole ViT, we propose a simple-but-effective gating network that can both merge all kinds of layers (e.g., Embedding, Norm, Attention, and MLP) and select the suitable classifier. Specifically, the gating network is trained by unlabeled datasets from all the tasks (domains), and predicts the probability of which task (domain) the input belongs to for merging the models during inference. To further boost the performance of the merged model, especially when the difficulty of merging tasks increases, we design a novel metric of model weight similarity, and utilize it to realize controllable and combined weight merging. Comprehensive experiments on kinds of newly established benchmarks, validate the superiority of the proposed ViT merging framework for different tasks and domains. Our method can even merge beyond 10 ViT models from different vision tasks with a negligible effect on the performance of each task.
Revisiting Knowledge Distillation under Distribution Shift

Knowledge distillation transfers knowledge from large models into small models, and has recently made remarkable achievements. However, few studies has investigated the mechanism of knowledge distillation against distribution shift. Distribution shift refers to the data distribution drifts between training and testing phases. In this paper, we reconsider the paradigm of knowledge distillation by reformulating the objective function in shift situations. Under the real scenarios, we propose a unified and systematic framework to benchmark knowledge distillation against two general distributional shifts including diversity and correlation shift. The evaluation benchmark covers more than 30 methods from algorithmic, data-driven, and optimization perspectives for five benchmark datasets. Overall, we conduct extensive experiments on the student model. We reveal intriguing observations of poor teaching performance under distribution shifts; in particular, complex algorithms and data augmentation offer limited gains in many cases.
More than Correlation: Do Large Language Models Learn Causal Representations of Space?

Recent work found high mutual information between the learned representations of large language models (LLMs) and the geospatial property of its input, hinting an emergent internal model of space. However, whether this internal space model has any causal effects on the LLMs' behaviors was not answered by that work, led to criticism of these findings as mere statistical correlation. Our study focused on uncovering the causality of the spatial representations in LLMs. In particular, we discovered the potential spatial representations in DeBERTa, GPT-Neo using representational similarity analysis and linear and non-linear probing. Our casual intervention experiments showed that the spatial representations influenced the model's performance on next word prediction and a downstream task that relies on geospatial information. Our experiments suggested that the LLMs learn and use an internal model of space in solving geospatial related tasks.
A Comprehensive Study of Object Tracking in Low-Light Environments

Accurate object tracking in low-light environments is crucial, particularly in surveillance and ethology applications. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges. This paper presents a comprehensive study examining the impact of these distortions on automatic object trackers. Additionally, we propose a solution to enhance tracking performance by integrating denoising and low-light enhancement methods into the transformer-based object tracking system. Experimental results show that the proposed tracker, trained with low-light synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN.
An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development

Causal networks are widely used in many fields, including epidemiology, social science, medicine, and engineering, to model the complex relationships between variables. While it can be convenient to algorithmically infer these models directly from observational data, the resulting networks are often plagued with erroneous edges. Auditing and correcting these networks may require domain expertise frequently unavailable to the analyst. We propose the use of large language models such as ChatGPT as an auditor for causal networks. Our method presents ChatGPT with a causal network, one edge at a time, to produce insights about edge directionality, possible confounders, and mediating variables. We ask ChatGPT to reflect on various aspects of each causal link and we then produce visualizations that summarize these viewpoints for the human analyst to direct the edge, gather more data, or test further hypotheses. We envision a system where large language models, automated causal inference, and the human analyst and domain expert work hand in hand as a team to derive holistic and comprehensive causal models for any given case scenario. This paper presents first results obtained with an emerging prototype.
Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs

The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent's lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs.
On the complexity of Cayley graphs on a dihedral group

In this paper, we investigate the complexity of an infinite family of Cayley graphs $\mathcal{D}_{n}=Cay(\mathbb{D}_{n}, b^{\pm\beta_1},b^{\pm\beta_2},\ldots,b^{\pm\beta_s}, a b^{\gamma_1}, a b^{\gamma_2},\ldots, a b^{\gamma_t} )$ on the dihedral group $\mathbb{D}_{n}=\langle a,b| a^2=1, b^n=1,(a\,b)^2=1\rangle$ of order $2n.$ We obtain a closed formula for the number $\tau(n)$ of spanning trees in $\mathcal{D}_{n}$ in terms of Chebyshev polynomials, investigate some arithmetical properties of this function, and find its asymptotics as $n\to\infty.$ Moreover, we show that the generating function $F(x)=\sum\limits_{n=1}^\infty\tau(n)x^n$ is a rational function with integer coefficients.
A Survey on Super Resolution for video Enhancement Using GAN

This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks. The studies covered in these summaries provide fresh techniques to addressing the issues of improving image and video quality, such as recursive learning for video super-resolution, novel loss functions, frame-rate enhancement, and attention model integration. These approaches are frequently evaluated using criteria such as PSNR, SSIM, and perceptual indices. These advancements, which aim to increase the visual clarity and quality of low-resolution video, have tremendous potential in a variety of sectors ranging from surveillance technology to medical imaging. In addition, this collection delves into the wider field of Generative Adversarial Networks, exploring their principles, training approaches, and applications across a broad range of domains, while also emphasizing the challenges and opportunities for future research in this rapidly advancing and changing field of artificial intelligence.
How Robust are LLMs to In-Context Majority Label Bias?

In the In-Context Learning (ICL) setup, various forms of label biases can manifest. One such manifestation is majority label bias, which arises when the distribution of labeled examples in the in-context samples is skewed towards one or more specific classes making Large Language Models (LLMs) more prone to predict those labels. Such discrepancies can arise from various factors, including logistical constraints, inherent biases in data collection methods, limited access to diverse data sources, etc. which are unavoidable in a real-world industry setup. In this work, we study the robustness of in-context learning in LLMs to shifts that occur due to majority label bias within the purview of text classification tasks. Prior works have shown that in-context learning with LLMs is susceptible to such biases. In our study, we go one level deeper and show that the robustness boundary varies widely for different models and tasks, with certain LLMs being highly robust (~90%) to majority label bias. Additionally, our findings also highlight the impact of model size and the richness of instructional prompts contributing towards model robustness. We restrict our study to only publicly available open-source models to ensure transparency and reproducibility.
FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs

Graph Neural Network (GNN) ushered in a new era of machine learning with interconnected datasets. While traditional neural networks can only be trained on independent samples, GNN allows for the inclusion of inter-sample interactions in the training process. This gain, however, incurs additional memory cost, rendering most GNNs unscalable for real-world applications involving vast and complicated networks with tens of millions of nodes (e.g., social circles, web graphs, and brain graphs). This means that storing the graph in the main memory can be difficult, let alone training the GNN model with significantly less GPU memory. While much of the recent literature has focused on either mini-batching GNN methods or quantization, graph reduction methods remain largely scarce. Furthermore, present graph reduction approaches have several drawbacks. First, most graph reduction focuses only on the inference stage (e.g., condensation and distillation) and requires full graph GNN training, which does not reduce training memory footprint. Second, many methods focus solely on the graph's structural aspect, ignoring the initial population feature-label distribution, resulting in a skewed post-reduction label distribution. Here, we propose a Feature-Label COnstrained graph Net collapse, FALCON, to address these limitations. Our three core contributions lie in (i) designing FALCON, a topology-aware graph reduction technique that preserves feature-label distribution; (ii) implementation of FALCON with other memory reduction methods (i.e., mini-batched GNN and quantization) for further memory reduction; (iii) extensive benchmarking and ablation studies against SOTA methods to evaluate FALCON memory reduction. Our extensive results show that FALCON can significantly collapse various public datasets while achieving equal prediction quality across GNN models. Code: https://github.com/basiralab/FALCON
A robust hybrid receiver for binary phase-shift keying discrimination in the presence of phase noise

We address the problem of coherent state discrimination in the presence of phase diffusion. We investigate the role of the hybrid near-optimum receiver (HYNORE) we proposed in [J. Opt. Soc. Am. B 40, 705-714 (2023)] in the task of mitigating the noise impact. We prove the HYNORE to be a robust receiver, outperforming the displacement photon-number-resolving (DPNR) receiver and beating the standard quantum limit in particular regimes. We introduce the maximum tolerable phase noise $\sigma_{\mathrm{max}}$ as a figure of merit for the receiver robustness and show that HYNORE increases its value with respect to the DPNR receiver.
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion

Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges. However, their reliance on substantial computational resources and extensive data collection remains a bottleneck. On the other hand, the integration of existing diffusion models, each specialized for different controls and operating in unique latent spaces, poses a challenge due to incompatible image resolutions and latent space embedding structures, hindering their joint use. Addressing these constraints, we present "PanGu-Draw", a novel latent diffusion model designed for resource-efficient text-to-image synthesis that adeptly accommodates multiple control signals. We first propose a resource-efficient Time-Decoupling Training Strategy, which splits the monolithic text-to-image model into structure and texture generators. Each generator is trained using a regimen that maximizes data utilization and computational efficiency, cutting data preparation by 48% and reducing training resources by 51%. Secondly, we introduce "Coop-Diffusion", an algorithm that enables the cooperative use of various pre-trained diffusion models with different latent spaces and predefined resolutions within a unified denoising process. This allows for multi-control image synthesis at arbitrary resolutions without the necessity for additional data or retraining. Empirical validations of Pangu-Draw show its exceptional prowess in text-to-image and multi-control image generation, suggesting a promising direction for future model training efficiencies and generation versatility. The largest 5B T2I PanGu-Draw model is released on the Ascend platform. Project page: $\href{https://pangu-draw.github.io}{this~https~URL}$
Evolutionary Swarm Robotics: Dynamic Subgoal-Based Path Formation and Task Allocation for Exploration and Navigation in Unknown Environments

This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. Simulation experiments conducted in the Argos simulator demonstrate the successful formation of paths in the majority of trials. Furthermore, the paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. To mitigate this issue, a task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication. The strategy evaluates the distance between points and determines the required number of robots for the path formation task, reducing unwanted exploration and traffic congestion. The performance of the sub-goal-based path formation and task allocation strategy is evaluated by comparing path length, time, and resource reduction against the A* algorithm. The simulation experiments demonstrate promising results, showcasing the scalability, robustness, and fault tolerance characteristics of the proposed approach.