Reviewerly/exHarmony huggingface dataset:
TAOTF: A Two-Stage Approximately Orthogonal Training Framework in Deep Neural Networks

The orthogonality constraints, including the hard and soft ones, have been used to normalize the weight matrices of Deep Neural Network (DNN) models, especially the Convolutional Neural Network (CNN) and Vision Transformer (ViT), to reduce model parameter redundancy and improve training stability. However, the robustness to noisy data of these models with constraints is not always satisfactory. In this work, we propose a novel two-stage approximately orthogonal training framework (TAOTF) to find a trade-off between the orthogonal solution space and the main task solution space to solve this problem in noisy data scenarios. In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization. In the second stage, unlike other existing methods, we apply soft orthogonal constraints for all layers of DNN model. We evaluate the proposed model-agnostic framework both on the natural image and medical image datasets, which show that our method achieves stable and superior performances to existing methods. Supplementary materials can be found in https://github.com/nonameinformation/anonymous/tree/main.
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction

In radio astronomy, signals from radio telescopes are transformed into images of observed celestial objects, or sources. However, these images, called dirty images, contain real sources as well as artifacts due to signal sparsity and other factors. Therefore, radio interferometric image reconstruction is performed on dirty images, aiming to produce clean images in which artifacts are reduced and real sources are recovered. So far, existing methods have limited success on recovering faint sources, preserving detailed structures, and eliminating artifacts. In this paper, we present VIC-DDPM, a Visibility and Image Conditioned Denoising Diffusion Probabilistic Model. Our main idea is to use both the original visibility data in the spectral domain and dirty images in the spatial domain to guide the image generation process with DDPM. This way, we can leverage DDPM to generate fine details and eliminate noise, while utilizing visibility data to separate signals from noise and retaining spatial information in dirty images. We have conducted experiments in comparison with both traditional methods and recent deep learning based approaches. Our results show that our method significantly improves the resulting images by reducing artifacts, preserving fine details, and recovering dim sources. This advancement further facilitates radio astronomical data analysis tasks on celestial phenomena. Our code is available at https://github.com/RapidsAtHKUST/VIC-DDPM.
Degradation-Resistant Offline Optimization via Accumulative Risk Control

Offline optimization aims to elaborately construct a solution that optimizes a black-box function with only access to the offline dataset. A typical manner of constructing the solution is to train a surrogate model of the black-box function on the offline dataset and optimize the solution guided by the surrogate model. However, this manner often encounters a fundamental challenge that the surrogate model could erroneously estimate out-of-distribution (OOD) solutions. Therefore, the optimizer would be misled to produce inferior solutions for online applications, i.e., degradation of performance. To this end, this paper formalizes the risk of degradation for OOD solutions and proposes an accumulative risk controlled offline optimization (ARCOO) method. Specifically, ARCOO learns a surrogate model in conjunction with an energy model. The energy model characterizes the risk of degradation by learning on high-risk solutions and low-risk ones contrastively. In the optimization procedure, the behavior of the optimizer in each step is controlled by a risk suppression factor calculated via the energy model, which leads to the controllable accumulative risk. Theoretically, we justify the efficacy of energy for accumulative risk control. Extensive experiments on offline optimization tasks show that ARCOO surpasses state-of-the-art methods in both degradation-resistance and optimality of the output solution.
On constacyclic codes of length 9ps over 𝔽pm and their optimal codes

The problem of classifying constacyclic codes over a finite field, both the Hamming distance and the algebraic structure, is an interesting problem in algebraic coding theory. For the repeated-root constacyclic codes of length [Formula: see text] over [Formula: see text], where [Formula: see text] is a prime number and [Formula: see text] does not divide [Formula: see text], the problem has been solved completely for all [Formula: see text] and partially for [Formula: see text]. In this paper, we solve the problem for [Formula: see text] and all primes [Formula: see text] different from [Formula: see text] and [Formula: see text]. In particular, we characterize the Hamming distance of all repeated-root constacyclic codes of length [Formula: see text] over [Formula: see text]. As an application, we identify all optimal and near-optimal codes with respect to the Singleton bound of these types, namely, MDS, almost-MDS, and near-MDS codes.
Improving the Effectiveness of Eigentrust in Computing the Reputation of Social Agents in Presence of Collusion

The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.
Modal Logic Characterizations of Forward, Reverse, and Forward-Reverse Bisimilarities

Reversible systems feature both forward computations and backward computations, where the latter undo the effects of the former in a causally consistent manner.The compositionality properties and equational characterizations of strong and weak variants of forward-reverse bisimilarity as well as of its two components, i.e., forward bisimilarity and reverse bisimilarity, have been investigated on a minimal process calculus for nondeterministic reversible systems that are sequential, so as to be neutral with respect to interleaving vs. truly concurrent semantics of parallel composition.In this paper we provide logical characterizations for the considered bisimilarities based on forward and backward modalities, which reveals that strong and weak reverse bisimilarities respectively correspond to strong and weak reverse trace equivalences.Moreover, we establish a clear connection between weak forward-reverse bisimilarity and branching bisimilarity, so that the former inherits two further logical characterizations from the latter over a specific class of processes.
The Effect of Hyper-parameters in Model-contrastive Federated Learning Algorithm

Federated learning enables all parties to perform data analysis and machine learning through the updated use of global models. The key difficulty in federation learning is data heterogeneity, and there are numerous studies that address this challenge but with poor results. The model-contrastive federated learning (MOON) algorithm described in this paper, an improvement on FedAvg, is a Model-level federation learning algorithm that is concise and efficient. The core idea is to train a local model by comparing between global models, which in turn updates the global model without the need for privacy data uploads while model-level training solves the non-IID problem. Widely tests are performed by the authors to confirm the MOON algorithm’s efficacy. Contrasting various image databases, lr, µ and other different parameters reveals that MOON outperforms other federal learning algorithms in all aspects such as learning accuracy, convergence speed and training time. It demonstrates its advanced and robustness.
A Hybrid LSTM and Decision Tree Model: A Novel Machine Learning Architecture for Complex Data Classification

We propose a novel hybrid machine learning architecture that synergistically combines the temporal data processing capabilities of Long Short-Term Memory (LSTM) networks with the categorical data handling and interpretability features of decision trees. This architecture addresses the complex problem of classifying mixed sequential and non-sequential data, a common challenge in various computer science domains. Our approach harnesses the strength of LSTM networks in processing sequential patterns and the power of decision trees in handling categorical data. To evaluate the effectiveness of the hybrid model, we conducted rigorous experiments using a two-year dataset from a university. Benchmark tests against standalone LSTM and decision tree models showed that our hybrid architecture consistently outperforms in terms of accuracy, precision, recall, and F1 score. The results attest to the potential of hybrid models in managing complex data classification tasks, thus making a significant contribution to the growing field of machine learning model fusion.
Low-Dimensional Space Modeling-Based Differential Evolution: A Scalability Perspective on bbob-largescale suite

Scalability is a challenge for Large Scale Optimization Problems (LSGO). Improving the scalability of efficient Differential Evolution algorithms (DE) has been a research focus due to their successful application to high-dimensional problems. Recently, a DE-based algorithm called LSMDE (Low-dimensional Space Modeling-based Differential Evolution) has shown promising results in solving LSGO problems on the CEC’2013 large-scale global optimization suite. LSMDE uses dimensionality reduction to generate an alternative search space and Gaussian mixture models to deal with the information loss caused by uncertainty from space transformation. This paper aims to extend the initial research through the scalability analysis of the LSMDE’s performance compared with its main competitors, SHADE-ILS and GL-SHADE, on bbob-largescale suite functions. The results show that although all competing algorithms perform worse as dimensionality increases, LSMDE outperforms the competition and is robust to dimensionality expansion in search spaces with diverse characteristics, achieving a target hit rate between $$40\%$$ and $$80\%$$ .
Searchable encryption algorithm based on key aggregation of multiple data owners in data sharing

Searchable encryption allows data owners to outsource their encrypted data to cloud servers and provide a searchable encryption service without exposing their sensitive information. Existing searchable encryption schemes provide the service for users to query the encrypted data of a single data owner. However, these schemes ignore the problem of query matching efficiency when users query the encrypted data of multiple data owners simultaneously. Specifically, for the same content encrypted by different data owners, users need to generate different query trapdoors in the query phase and match the different query trapdoors and indexes in the matching phase. In this paper, we propose a searchable encryption algorithm based on the key aggregation of multiple data owners. The algorithm first generates keys for multiple data owners using the mutual inversion of elements in a finite field to ensure the consistency of ciphertext processing. Then, it generates index keys to build encrypted indexes and ensure the searchability of the ciphertext. Finally, it generates query trapdoors based on multi-key aggregation to match the encrypted indexes of multiple data owners. The theoretical analysis and simulation results show that the proposed algorithm improves the query efficiency and protects the query indexes and trapdoors.
DBTrans: A Dual-Branch Vision Transformer for Multi-Modal Brain Tumor Segmentation

3D Spatially Aligned Multi-modal MRI Brain Tumor Segmentation (SAMM-BTS) is a crucial task for clinical diagnosis. While Transformer-based models have shown outstanding success in this field due to their ability to model global features using the self-attention mechanism, they still face two challenges. First, due to the high computational complexity and deficiencies in modeling local features, the traditional self-attention mechanism is ill-suited for SAMM-BTS tasks that require modeling both global and local volumetric features within an acceptable computation overhead. Second, existing models only stack spatially aligned multi-modal data on the channel dimension, without any processing for such multi-channel data in the model's internal design. To address these challenges, we propose a Transformer-based model for the SAMM-BTS task, namely DBTrans, with dual-branch architectures for both the encoder and decoder. Specifically, the encoder implements two parallel feature extraction branches, including a local branch based on Shifted Window Self-attention and a global branch based on Shuffle Window Cross-attention to capture both local and global information with linear computational complexity. Besides, we add an extra global branch based on Shifted Window Cross-attention to the decoder, introducing the key and value matrices from the corresponding encoder block, allowing the segmented target to access a more complete context during up-sampling. Furthermore, the above dual-branch designs in the encoder and decoder are both integrated with improved channel attention mechanisms to fully explore the contribution of features at different channels. Experimental results demonstrate the superiority of our DBTrans model in both qualitative and quantitative measures. Codes will be released at https://github.com/Aru321/DBTrans .
Geology, Pb and S Isotope Geochemistry, and Genesis of the Na Bop-Pu Sap Lead-Zinc Deposit in the Cho Don area, Northeastern Vietnam

The Na Bop-Pu Sap Pb-Zn ore bodies represent a typical vein-type lead-zinc deposit situated in the Cho Don area and are currently being extracted for their lead and zinc resources. This deposit is characterized by its significant scale and quality and is considered one of the prominent lead-zinc deposits in the Cho Don area. Despite its significance, this deposit has not received adequate attention, resulting in limited knowledge of its geology, mineralization, and deposit genesis model. To address this knowledge gap, our study utilized several methodologies, including field surveying, ore mineral analysis under a microscope, and S and Pb isotopic geochemistry. By employing these approaches, we were able to obtain specific insights into the origin of mineralization and the deposit model. Our field survey suggests that the ore deposits are formed as Pb-Zn-bearing veins along Devonian shale, claystone, and limestone faults. Microscopic analyses of the veins reveal the presence of galena, sphalerite, chalcopyrite, pyrite, arsenopyrite, and pyrrhotite as ore minerals, and quartz, calcite, dolomite, and chalcedony as gangue minerals. Sulfur-isotope values (δ34SCDT) of galena 5.3 to 0.1‰ (average 2.8‰), sphalerite 6.8 to 2.5‰ (average 5.3‰), and pyrite 5.8 to 4.1‰ (average 4.9‰) indicate that the sulfide mineralization may be related to a deep source, possibly originating from magmatic activity in the region and contaminated by carbonate-bearing marine sedimentary rocks. Lead-isotope studies indicate a model age of 598-424 Ma for the lead reservoir, consistent with the possible presence of local source rocks containing sulfur. The lead and sulfur in the ore veins were probably contaminated by Devonian carbonate-bearing marine sedimentary rocks and leached from Neoproterozoic to Cambrian magmatic activity. The lead-zinc deposits in Na Bop-Pu Sap do not display any Mississippi valley-type (MVT) or Sedimentary exhalative (SEDEX) lead-zinc deposit characteristics, as they appear to be related to shear zone-hosted lead-zinc deposits.
Dynamic deep neural network inference via adaptive channel skipping

Deep neural networks have recently made remarkable achievements in computer vision applications. However, the high computational requirements needed to achieve accurate inference results can be a significant barrier to deploying DNNs on resource-constrained computing devices, such as those found in the Internet-of-things. In this work, we propose a fresh approach called adaptive channel skipping (ACS) that prioritizes the identification of the most suitable channels for skipping and implements an efficient skipping mechanism during inference. We begin with the development of a new gating network model, ACS-GN, which employs fine-grained channel-wise skipping to enable input-dependent inference and achieve a desirable balance between accuracy and resource consumption. To further enhance the efficiency of channel skipping, we propose a dynamic grouping convolutional computing approach, ACS-DG, which helps to reduce the computational cost of ACS-GN. The results of our experiment indicate that ACS-GN and ACS-DG exhibit superior performance compared to existing gating network designs and convolutional computing mechanisms, respectively. When they are combined, the ACS framework results in a significant reduction of computational expenses and a remarkable improvement in the accuracy of inferences
Rollout Heuristics for Online Stochastic Contingent Planning

Partially observable Markov decision processes (POMDP) are a useful model for decision-making under partial observability and stochastic actions. Partially Observable Monte-Carlo Planning is an online algorithm for deciding on the next action to perform, using a Monte-Carlo tree search approach, based on the UCT (UCB applied to trees) algorithm for fully observable Markov-decision processes. POMCP develops an action-observation tree, and at the leaves, uses a rollout policy to provide a value estimate for the leaf. As such, POMCP is highly dependent on the rollout policy to compute good estimates, and hence identify good actions. Thus, many practitioners who use POMCP are required to create strong, domain-specific heuristics. In this paper, we model POMDPs as stochastic contingent planning problems. This allows us to leverage domain-independent heuristics that were developed in the planning community. We suggest two heuristics, the first is based on the well-known h_add heuristic from classical planning, and the second is computed in belief space, taking the value of information into account.
RAINFALL PREDICTION IN JEMBER REGENCY WITH ADAPTIVE NEURO FUZZY INFERENCE SYSTEM BASED ON GSMaP SATELLITE DATA

Rainfall is very influential in daily life, including in agriculture. According to the Jember Regency Government, the majority of the economic activities of the Jember people come from the agricultural sector. Significant changes in rainfall conditions will adversely affect the agricultural sphere. The Water Resources Office of Jember Regency measures rainfall directly. Precipitation measurement can also be made indirectly using the Global Satellite Mapping of Precipitation (GSMaP), a project promoted by the Japan Aerospace Exploration Agency (JAXA) to produce rainfall accumulation globally. Rainfall predictions are urgently needed to address rainfall-related issues. The Adaptive Neuro-Fuzzy Inference System (ANFIS) method is an effective method for prediction because its working principle combines adaptive methods of artificial neural networks and fuzzy logic. The RMSE in the ANFIS training and testing process on daily rainfall was 12.7464 and 14.6268. Furthermore, RMSE in ANFIS training and testing on monthly rainfall was 7.6336 and 8.1456. The predicted daily rainfall in Jember Regency on January 1, 2023, is 3.1971 mm. Meanwhile, the predicted monthly rainfall in Jember Regency in January 2023 is 19.9114 mm.
MODELLING EARTHQUAKE DISASTER DAMAGE DUE DEPTH OF EPICENTER AND MAGNITUDE USING SPATIAL REGRESSION

East Java Province is geographically close to the Eurasian and Indo-Australian Plate subduction zones, resulting in frequent earthquakes East Java Province has a high population density, so it is very risky if disaster occurs. One preventive solution to reduce this impact is estimating damage when an earthquake occurs. The purpose of this study was to determine the best modeling of damages due to earthquakes in East Java Province, using the amount of house damage as a response variable, while depth of the epicenter and the strength of the earthquake as predictor variables. It is suspected that there is a spatial dependency effect in this case, so the solution is to use regression with an area approach, namely the Spatial Durbin Model (SDM). The amount of house damage is collected from BNPB, the epicenter and the magnitude of earthquake collected from BMKG in 2021. The result shows that SDM is good at explaining the dependency relationship between response and predictor variables. The significant predictor variables are the depth of epicenter and the strength of the earthquake. It is meaning that the magnitude and the depth of the epicenter of the earthquake in an area have an impact on other adjacent areas. There is a relationship between the amount of house damage in one area and other adjacent areas. The Regency will have a high number of damaged houses if it is adjacent to a Regency that has a high number of damaged houses