Reviewerly/exHarmony huggingface dataset:
Algorithm Substitution Attacks on Identity-Based Encryption

Algorithm substitution attack (ASA) was proposed by Bellare et al. at CRYPTO 2014 and has been studied in the context of various cryptographic primitives so far. In this work, we turn to study ASAs on identity-based encryption (IBE) scheme which has been widely considered as a useful encryption tool. We first provide ASAs models to capture the attacks against IBE and then present concrete ASAs on two IBE schemes where the key extraction algorithm and encryption algorithm are effectively subverted respectively. Our work shows that ASA on IBE could be dangerous and thus the proposal of effective countermeasures is highly desirable to deploy IBE securely in the real-world applications.
Energy and SLA-Aware VM Placement Policy for VM Consolidation Process in Cloud Data Centers

The use of cloud services in recent times increased drastically. Satisfying the appeal for cloud services requires an enormous number of data centers that use colossal energy. These data centers create high carbon dioxide by-products in the climate and high functional expenses for the cloud service provider. The strategy for lessening energy utilization in the server is called virtual machine consolidation (VMC). The VMC procedure helps to improve the performance of Physical Machine (PM) by closing down underutilized PM without infringement Service Level Agreement (SLA). The VMC procedure is divided into four policies: underload detection of the host, overload detection of a host, selection of the host for migration, and placement of VM on the destination host. The virtual machine placement (VMP) methodology is fundamental in controlling energy consumption and SLA infringement by choosing a precise host for VM placement. This work proposed energy and SLA-aware VM placement (ESVMP) policy, which identifies the placement based on medium utilizing PM instead of lower utilized PM. We execute and compare our proposed ESVMP policy with other existing placement policies like the first fit (FF), least fit (LF), modified best fit decreasing (MBFD), and power-aware best fit decreasing (PABFD). We execute our experiment using the PlanetLab dataset on the CloudSim toolkit and compare simulation results. The results indicate that our policy ESVMP policy outperforms compared to standard policies.
High-Order Subgraph Convolution Networks with Auxiliary Self-Supervised Task for Recommendation

In recent years, Graph Convolution Networks have achieved promising results on recommendation tasks. Starting from the influential Pinsage, many other works have been proposed and techniques such as Light Graph Convolutional Networks, self-supervised Graph Learning, and Interest-aware Message-Passing have gained popularity thanks to their good performance. While self-supervised Graph Learning explores the advantages of learning a so-called auxiliary self supervised tasks, Interest-aware Message-Passing finds its success by using high-order graph convolution inside subgraphs. The subgraph represents groups of similar users together with their item interactions. In this work, starting from these two novel techniques, we propose an approach that combines the high-order subgraph with an auxiliary self supervised task. Experimental results on benchmark datasets show that our technique achieves state-of-the-art results.
An Improved K-means Algorithm Based on the Bayesian Inference

As one of the most widely used algorithms in machine learning, the fs algorithm is known for its simplicity and efficiency. However, it also has certain limitations, including pre-selecting the K value, which leads to issues such as different cluster shapes and outlier effects. In addition, this clustering algorithm needs to select the initial cluster centers, which produces the phenomenon of local optimum. To alleviate these two types of problems, this paper propose a K-means variant combined with Bayesian inference, dubbed the Bayes-K-means. Firstly, use Bayesian parameter inference to identify parameters for initial cluster centers. According to the probability function of the prior distribution, recalculate the distance from the sample points to the cluster centers. Therefore, the algorithm realizes the global search in the iterative process, which helps to improve the global optimum ability of the algorithm and avoids the phenomenon of local search caused by the K-means algorithm. We verify the Bayes-K-means and theoretically analyze its convergence from the aspect of the posterior distribution and the expression of the clustering center, respectively. Finally, applying the collected and processed real datasets, experimental tests based on artificial datasets and UCI datasets, then comparing Bayes-K-means with K-means, K-means++ and Bisecting K-means experiments, aggregated the class results are consistent with the actual situation. The results show that the proposed algorithm can effectively improve the clustering accuracy, efficiency and generalization performance.
Factoring non-monic polynomials represented by black boxes

We aim to factor a sparse polynomial a ∈ Z[ x 1 , ···, x n ] represented by a black box. The authors have previously developed efficient sparse Hensel lifting algorithms for the monic and square-free case that outperforms the algorithm by Kaltofen and Trager in 1990. We complete this black box factorization problem for the non-monic case with a new algorithm that computes the factors of a using many non-monic bivariate Hensel lifts. Our algorithm handles all cases of input a ∈ Z[ x 1 , ···, x n ] including the non-square-free and the non-primitive cases. We have implemented the algorithm in Maple with all major subroutines coded in C for efficiency.
A Sample Size Calculation for Training and Certifying Targeting Policies

We propose an approach for determining the sample size required when using an experiment to train and certify a targeting policy. Calculating the rate at which the performance of a targeting model improves with additional training data is a complex problem. We approximate the problem by grouping customers into segments. This allows us to develop computationally efficient algorithms that calculate the required sample size using simulations or closed-form analytical approximations. We consider two problem formulations. The first formulation focuses on training a targeting policy that satisfies a predefined performance threshold. The second formulation involves out-of-sample certification of a trained targeting policy in a statistical test. We illustrate our analysis using data from a luxury fashion retailer.
Towards a methodological framework for production-ready AI-based software components

AI-based software systems have seen more widespread production deployment in recent years. However, many challenges currently complicate the integration of AI-based software components into production systems, and their operation and maintenance remains complex. In this research proposal, we summarize these challenges and propose a framework that enables the efficient design, integration, and operation of AI components.
Question Classification Based on Weak Supervision and Interrogative Pronouns Attention Mechanism

Question classification (QC) plays a vital role in the question answering system, particularly in understanding the user intent. The major challenges for QC are related to annotating the question corpora and extracting the valuable features in the short question text. Intuitively, a lack of annotation corpora has appeared in the QC task, especially in the low-resource languages (LRLs) scenario. This study first tries to augment the corpus with a weakly supervised approach. After that, we also introduce Kazakh QC models based on deep learning networks and interrogative pronouns attention. Namely, we construct the original QC corpora and augment it with synonym dictionaries. The interrogative pronouns matrix is constructed through the question dictionary to strengthen the meaning features in the question. The experiment results demonstrate that the proposed QC model significantly improves the performance of the QC task. Moreover, the corpora presented in this paper are extremely useful for further studies.
Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification

Automating video-based data and machine learning pipelines poses several challenges including metadata generation for efficient storage and retrieval and isolation of key-frames for scene understanding tasks. In this work, we present two semi-supervised approaches that automate this process of manual frame sifting in video streams by automatically classifying scenes for content and filtering frames for fine-tuning scene understanding tasks. The first rule-based method starts from a pre-trained object detector and it assigns scene type, uncertainty and lighting categories to each frame based on probability distributions of foreground objects. Next, frames with the highest uncertainty and structural dissimilarity are isolated as key-frames. The second method relies on the simCLR model for frame encoding followed by label-spreading from 20% of frame samples to label the remaining frames for scene and lighting categories. Also, clustering the video frames in the encoded feature space further isolates key-frames at cluster boundaries. The proposed methods achieve 64–93% accuracy for automated scene categorization for outdoor image videos from public domain datasets of JAAD and KITTI. Also, less than 10% of all input frames can be filtered as key-frames that can then be sent for annotation and fine tuning of machine vision algorithms. Thus, the proposed framework can be scaled to additional video data streams for automated training of perception-driven systems with minimal training images.
Multi-view Knowledge Graph for Explainable Course Content Recommendation in Course Discussion Posts

Recommendations form an integral part of instructional process and can be instrumental to promote or maintain student engagement in various course-offering platforms. Automated evaluation of a student’s discussion forum post and proactively generating a personalized recommendation to address the student’s learning requirement are of huge interest, specifically in an in-person classroom setting, which is still considered to be a dominant mode of mainstream learning. However, the task is growingly challenging due to the ever-expanding enrollment trend, where students from the wider socio-economic backgrounds become more of the norm. The traditional support structures, such as daytime-only office hours for student advisement, are typically inadequate. Toward this, we propose a multi-modal attentive learning framework that keeps track of the temporally evolving student learning patterns and their conversation dynamics in the course discussion board to automatically estimate relevant expression (e.g. ‘confusion’, ‘question’, ‘urgency’) reflected in forum posts. Based on the classifier evaluation, the consequential content recommendation module employs information propagation on a multi-view course specific knowledge graph to obtain a more context-aware entity embedding for recommendation. The system derives a personalized ranked list or relevant documents/video clippings augmented with the explainability score that enables the system to reveal the recommendation justifications for an improved student acceptance. The experimental results, which leverage our in-house course-specific multi-modal activity details from three large in-person Undergraduate and Postgraduate level STEM courses, demonstrate the effectiveness of our approach.
Categories of LB Technique in Cloud Infrastructure

The real world has been changed into a virtual one as a result of the pandemic scenario. Cloud computing plays a major role for storing and computing the data using remote computing infrastructure for solving the day to day activities. The LB Techniques are used in the cloud computing process. LB techniques distributing the system's load among its various nodes in order to maximise resource efficiency and job response time. The major issues in load balancing are Geographical Distributed system, Single point of failure (SPoF), live Migration of instance, Heterogeneous device, Storage device Management, Scale-out/scale-in and Complexity of Algorithm. Load balancing ensures that each node in the network shortens reaction times, reduces expenses, and boosts performance. This paper address the various cloud computing balancing algorithm survey.
Research and Implementation of Cloud Gateway System for Environmental Data Acquisition Based on Power Internet of Things

At present, power grid enterprises widely carry out the construction of Power Internet of Things. The State Grid Corporation of China has uniformly built an Internet of things management platform in provincial companies to access the provincial power data acquisition equipment. At present, the platform has been widely used in user power data acquisition. With the continuous development of environmental protection business, realizing environmental protection monitoring through power data analysis is an innovative and effective means, but it is necessary to further collect environmental protection data such as equipment power consumption of key sewage enterprises. At present, the Power Internet Of Things Management Platform has selected MQTT data acquisition protocol as the platform IOT access technical specification, The environmental protection department selects HJ212 protocol as the application layer protocol technical standard for environmental protection data acquisition, and the environmental protection data acquisition equipment cannot be directly connected to the Power Internet Of Things Management Platform. Based on the Power IOT Management Platform, this paper studies and designs an Cloud Gateway System for Environmental Data Acquisition, which is used to environmental protection data acquisition. As a functional component and front-end service of the power IOT management platform, it solves the problem that HJ212 protocol equipment cannot access the power IOT management platform, Realize the access support and data acquisition of HJ212 protocol equipment by the Power IOT Management Platform, and provide data and platform support for power environmental protection monitoring.
Deep Reinforcement Learning with Parametric Episodic Memory

Deep Reinforcement Learning methods are widely acknowledged to be sample inefficient, while incorporating episodic memory significantly improves it through rapidly latching onto successful experiences to guide the action of agents. Previous episodic methods, utilizing discrete memory, cannot well accommodate the continuous control tasks and have limited generalization ability to aggregate the experience across trajectories. We propose an improved episodic memory-based RL algorithm, combining the one-step method in off-policy algorithm with Parametric Episodic Memory (PEM), which leverages the discrete memory by neural networks, and thereby enhances both sample efficiency and generalization ability. Moreover, an adaptive k-nearest-neighbors is used in determining the volume of retrieved memory, further improving its efficiency. Our algorithm, evaluated on various MuJoCo continuous control tasks, outperforms the model-free baseline methods and latest episodic memory-based RL algorithms.
SISTEM INFORMASI GEOGRAFIS PERSEBARAN MASYARAKAT MISKIN DI KOTA PALANGKA RAYA

The Palangka Raya City as Capital of Central Kalimanta, has the secondlowest of amount poor people (3.75%) after Sukamara (3.73%) (CentralKalimantan BPS, 2017).In desig and build the system use the Simple Additive Weight (SAW)method as a decision support system and apply in GeographicInformation System to the visualization of data. In this researchmethodology using systematic implementation process to illustrate,calculate and evaluate data. In this reserch use the methodology throughseveral stages, likely research design, database design, interface design,coding and system testing.The Testing of this system use Blackbox test system. The results of testwith the Blackbox system indicate the system create to runningaccording to the researcher expectation.
NeRFingXR: An Interactive XR Tool Based on NeRFs

With the rise of the metaverse, XR technologies have gained wide attention. However, traditional XR technologies require a high precision explicit 3D scene appearance and geometric model representation to achieve a more realistic visual fusion effect, which will make XR technologies require high computational power and memory capacity. Recent studies have shown that it is feasible to implicitly encode 3D scene appearance and geometric models by position-based MLPs, of which NeRF is a prominent representative. In this demo, we propose an XR tool based on NeRF that enables convenient and interactive creation of the XR environments. Specifically, we first train the NeRF model of XR content using Instant-NGP to achieve an efficient implicit 3D representation of XR content. Second, we contribute a depth-awareness scene understanding approach that automatically adapts different plane surfaces for XR content placement and more realistic real-virtual occlusion effects. Finally, we propose a multi-nerf joint rendering method to achieve natural XR content occlusion from each other. This demo shows the final result of our interactive XR tool.
A Set-based Motion Planning Algorithm for Aerial Vehicles in the Presence of Obstacles Exhibiting Hybrid Dynamics

This paper proposes a set-based motion planning algorithm to drive a quadrotor to a target location while avoiding multiple moving obstacles that have hybrid dynamics. By exploiting a library of motion primitives, the proposed planner is capable of rapidly generating trajectories that avoid obstacles with unknown angular velocities that instantaneously change at unknown impact times. The planner employs the reachable sets of the vehicle and of the obstacles to generate a set of safe trajectories from which a trajectory with lowest cost is selected. Simulations and experimental validation of the motion planner are presented.
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System

The recommendation system plays a vital role in many areas, especially academic fields, to support researchers in submitting and increasing the acceptance of their work through the conference or journal selection process. This study proposes a transformer-based model using transfer learning as an efficient approach for the paper submission recommendation system. By combining essential information (such as the title, the abstract, and the list of keywords) with the aims and scopes of journals, the model can recommend the Top K journals that maximize the acceptance of the paper. Our model had developed through two states: (i) Fine-tuning the pre-trained language model (LM) with a simple contrastive learning framework. We utilized a simple supervised contrastive objective to fine-tune all parameters, encouraging the LM to learn the document representation effectively. (ii) The fine-tuned LM was then trained on different combinations of the features for the downstream task. This study suggests a more advanced method for enhancing the efficiency of the paper submission recommendation system compared to previous approaches when we respectively achieve 0.5173, 0.8097, 0.8862, 0.9496 for Top 1, 3, 5, 10 accuracies on the test set for combining the title, abstract, and keywords as input features. Incorporating the journals' aims and scopes, our model shows an exciting result by getting 0.5194, 0.8112, 0.8866, 0.9496 respective to Top 1, 3, 5, and 10. We provide the implementation and datasets for further reference at https://github.com/hduc-le/SimCPSR .
ACEs: Unsupervised Multi-label Aspect Detection with Aspect-category Experts

Unsupervised aspect detection (UAD) aims to identify the aspect categories mentioned in product reviews automatically. Existing unsupervised methods mainly focus on single-label prediction which cannot work well for the realistic application scene since a review usually contain multiple aspect categories. Recent attempts alleviate this issue by setting a threshold. However, the imbalance of the number of category-related words in review segments makes these methods difficult to find optimal thresholds to recall all categories, which is a common but neglected phenomenon. In this paper, we propose a novel unsupervised method termed Aspect-Category Experts (ACEs) to address this problem. Our goal is to train a set of aspect-category experts to encode the sentence in parallel, where experts and aspect categories correspond one-to-one. Experts in different aspects weight the embedding of representative words with aspect-specific attention to avoid the negative impact of accumulation. Besides, to enhance the complementarity between different experts to reduce inter-class feature entanglement, we construct a novel mutual exclusion loss (ME loss) to improve the aspect detection performance. Extensive experimental results on four datasets demonstrate that our proposed ACEs model outperforms the previous state-of-the-art methods.
LRTD: A Low-rank Transformer with Dynamic Depth and Width for Speech Recognition

Though Transformer-based models have achieved great success in the automatic speech recognition (ASR) field, they are generally resource-hungry and computation-intensive which makes them difficult to deploy in resource-restricted devices. In this paper, we propose LRTD, a lightweight Transformer for end-to-end ASR. LRTD compresses the model size using matrix decomposition during training and further reduces the depth and width of the trained model dynamically leveraging two structured pruning strategies. The performance of the pruned model reply on two additional structured dropout methods and two search techniques that can recognize the important layers and attention heads. The experimental results show that our proposed model can achieve competitive performance on Aishell-1 even with about 2.27 × fewer model parameters compared to the baseline transformer model. Moreover, we experimentally demonstrate that the matrix decomposition technique achieves a higher compression rate while two pruning methods can adjust the model size flexibly.
Deep Attention-based Lightweight Network For Aerial Image Deblurring

Aiming to challenge that current image deblurring methods fail to deal with complex scenes well and are computationally time-consuming, we present an attention-based lightweight network (MALNET) that restores sharp images in a pyramid learning pattern. The model introduces the lightweight chained residual unit as the core building block to construct the multi-path refinement network and embeds the simple yet efficient attention unit to obtain rich contextual information. Moreover, we explore multiple training strategies such as multi-scale joint loss and parameter sharing, making MALNET more flexible and compatible in various application scenarios. The experimental results sampled from the DOTA and GoPro datasets demonstrate that MALNET can enjoy 5 times faster speed and 7 times fewer bulk than those state-of-the-art approaches while still providing a competitive performance both quantitatively and qualitatively.