Reviewerly/exHarmony huggingface dataset:
Exact and efficient solutions of the LMC Multitask Gaussian Process model

The Linear Model of Co-regionalization (LMC) is a very general model of multitask gaussian process for regression or classification. While its expressivity and conceptual simplicity are appealing, naive implementations have cubic complexity in the number of datapoints and number of tasks, making approximations mandatory for most applications. However, recent work has shown that under some conditions the latent processes of the model can be decoupled, leading to a complexity that is only linear in the number of said processes. We here extend these results, showing from the most general assumptions that the only condition necessary to an efficient exact computation of the LMC is a mild hypothesis on the noise model. We introduce a full parametrization of the resulting \emph{projected LMC} model, and an expression of the marginal likelihood enabling efficient optimization. We perform a parametric study on synthetic data to show the excellent performance of our approach, compared to an unrestricted exact LMC and approximations of the latter. Overall, the projected LMC appears as a credible and simpler alternative to state-of-the art models, which greatly facilitates some computations such as leave-one-out cross-validation and fantasization.
Discussion on the Application of Artificial Intelligence in Computer Network Technology

With the rapid development of science and technology, artificial intelligence technology has now been widely used in computer network technology, and has become an indispensable part of people's daily life, to meet people's increasing needs for data computing and information transmission. This paper systematically introduces artificial intelligence technology, analyzes the application of artificial intelligence technology in the development of computer networks, and explores the key points and development directions of technical applications, so as to provide ideas for better grasping the application of artificial intelligence technology in the development of computer networks.
Mediatization, Social Media, and Islamization: Exploring the Nexus of Influence and Violence in Contemporary Bangladesh Politics

Internet technology probably contains one of the most important discoveries of the contemporary era. Additionally, it is now undeniably evident that individuals are increasingly using social media platforms as their main mode of communication in day-to-day life. The phenomena of Islamization through the Internet and social media has seen a noteworthy upsurge in the current period of fast modernization, principally driven by the ubiquitous effect of mediatization. This effect may be seen not just in Bangladesh’s religious landscape but also in its political system. Religion’s influence on social media is startlingly similar to how it is used as a powerful factor in Bangladesh’s political dynamics. It is crucial to recognize that the procedure of islamization through digital platforms has also contributed to the spread of violence, which has primarily taken the form of targeted assaults on minority groups and those who support free-thinking beliefs. Further, it is crucial to take into account the elements at the national level that have influenced the disturbing trend of using social media as a weapon to incite violence. In order to promote the continuation of violence through digital channels, Islamist extremist organizations have carefully constructed these crucial elements, which are examined in this study. Additionally, this study seeks to bring the alarming trend of Islamist extremist groups blending politics and religion with ease while leveraging the potent mediatization power to instigate violence. This study seeks to clarify the methods used by these organizations to influence digital platforms for their evil ends through a number of thoroughly considered case studies. In the end, this essay makes the claim that political parties and Islamic extremist groups have a complicated and interconnected relationship, a dynamic that not only fosters but also promotes the inciting of violence through the strategic use of social media and digital communication channels.
Towards Robust Token Embeddings for Extractive Question Answering

Extractive Question Answering (EQA) tasks have gained intensive attention in recent years, while Pre-trained Language Models (PLMs) have been widely adopted for encoding purposes. Yet, PLMs typically take as initial input token embeddings and rely on attention mechanisms to extract contextual representations. In this paper, a simple yet comprehensive framework, termed perturbation for alignment (PFA), is proposed to investigate variations towards token embeddings. A robust encoder is further formed being tolerant against the embedding variation and hence beneficial to subsequent EQA tasks. Specifically, PFA consists of two general modules, including the embedding perturbation (a transformation to produce embedding variations) and the semantic alignment (to ensure the representation similarity from original and perturbed embeddings). Furthermore, the framework is flexible to allow several alignment strategies with different interpretations. Our framework is evaluated on four highly-competitive EQA benchmarks, and PFA consistently improves state-of-the-art models.
EEG-Based VR Video Quality Measurement for Resolution Reduction

Entering the 5G era, the virtual reality (VR) business is developing rapidly, resulting in many new applications and new scenarios. However, due to insufficient bandwidth and other reasons, VR video will undergo adaptive resolution reduction, which is one of the important factors affecting the quality of user experience (QoE). However, due to the unique look and feel and visual experience of VR, the video user experience quality evaluation model established on the basis of 2D screen is not applicable. At the same time, because VR services are less popular, it is difficult to use big data to build and correct big data in the short term. The way of subjective scoring (based on MOS) also brings biases such as subjective factors to the model itself. Therefore, we apply electroencephalography (EEG), a physiological measurement method, to measure the quality of experience of users in the face of VR video resolution reduction, using two methods for quantitative analysis. Finally, an objective EEG score model of user experience quality under single factor VR video resolution reduction was obtained.
Poster: E3PO - An Open Platform for 360° Video Streaming Simulation and Evaluation

The simulation and evaluation of diverse 360° video streaming systems present inherent challenges due to the varied design objectives, streaming strategies, and evaluation metrics. This poster introduces E3PO, a versatile and extensible simulation and evaluation platform for 360° video streaming systems. E3PO excels in simulating all proposed variations of 360° video streaming methods, while also generating the precise view that users experience. We have implemented E3PO and developed a variety of examples that simulate different streaming approaches. The promising results from our evaluation of these simulated scenarios demonstrate E3PO's substantial potential to assist researchers in testing new designs, fine-tuning parameters, and comparing performance with their counterparts.
Analisis Kualitas Pelayanan Sistem Informasi dan Budaya Lapor di Lingkungan Ombudsman RI Dengan Metode SERVQUAL

The rapid internet technological development makes web-based services develop rapidly. Web-based services are very useful in accelerating the supervision of public services. The Ombudsman of the Republic of Indonesia (ORI) is a state institution that has the authority to oversee the implementation of public services. However, many people still report on media instead of using the Ombudsman RI website. The purpose of this study is to analyze the service quality of reporting system on the Ombudsman information systems web. The quality measurement method uses the Service Quality (SERVQUAL) method that consists of five dimensions of service quality modified, usability, information quality, services reliability, assurance, and empathy. The research instrument uses primary data through the questionnaires and is processed quantitatively. The results showed a negative gap between expectations and perceptions in measuring the quality of the information system of Ombudsman RI. It shows that the web information system has not met the expectations of its users yet. So it can affect the culture of public reporting through the Ombudsman RI website.
Prediksi Peringkat Aplikasi di Google Play Menggunakan Metode Random Forest

Application developers and users are the keys to the market impact on application development. In application development, developers need to predict applications in the market accurately, accurate prediction results are very important in showing user ratings that affect the success of an application. Ratings are given by users to judge that the application is good or not. The higher the rating given by the user, it means that the user likes the application and can be a benchmark for other users to download the application. It is undeniable that there are so many apps available on the google play store, it is impossible for users to select one by one app on the google play store. Therefore, a rating prediction system is needed to determine the right application based on the rating given by the user to an application. Predictions will be made using the random forest algorithm as the method used to predict application ratings. This study using the Google Play Store dataset. This dataset has 10840 rows and 13 attributes. The results of this study can be seen from the use of the random forest algorithm with an average accuracy of 93.8%.
ANALISA WEBSITE W3SCHOOLS MENGGUNAKAN SYSTEM USABILITY SCALE

Website is the main choice of learning web programming for a beginner, with the website can facilitate the learning process. The website used to learn programming tutorials is W3schools. W3schools is a web engineer data website, with instructional exercises and references connected with web improvement subjects like HTMLS, CSS, JavaScript, PHP, SQL, and Jquery. The webpage gives a reference guide that covers numerous ascept of web programming. This website there are many categories of tutorials to choose from but there are some users who do not really understand the feature contained in the W3schools website. Website usability according to Nielsen is learnbility, efficiency, memorability, errors, and satisfaction High convenience on site, for example, W3schools is capable draw in light of a legitimate concern for clients on the grounds that the utilization of the site is thought of as simple. The consequence of this paper are as quality investigation and ease of of W3schools of W3schools website based on the perception of various people who use W3schools website, so as to help W3schools website developers to improve the website that can provide satisfactions for its users.
Merging Generated and Retrieved Knowledge for Open-Domain QA

Open-domain question answering (QA) systems are often built with retrieval modules. However, retrieving passages from a given source is known to suffer from insufficient knowledge coverage. Alternatively, prompting large language models (LLMs) to generate contextual passages based on their parametric knowledge has been shown to improve QA performance. Yet, LLMs tend to "hallucinate" content that conflicts with the retrieved knowledge. Based on the intuition that answers supported by both sources are more likely to be correct, we propose COMBO, a Compatibility-Oriented knowledge Merging for Better Open-domain QA framework, to effectively leverage the two sources of information. Concretely, we match LLM-generated passages with retrieved counterparts into compatible pairs, based on discriminators trained with silver compatibility labels. Then a Fusion-in-Decoder-based reader model handles passage pairs to arrive at the final answer. Experiments show that COMBO outperforms competitive baselines on three out of four tested open-domain QA benchmarks. Further analysis reveals that our proposed framework demonstrates greater efficacy in scenarios with a higher degree of knowledge conflicts.
Fast Fourier transform via automorphism groups of rational function fields

The Fast Fourier Transform (FFT) over a finite field $\mathbb{F}_q$ computes evaluations of a given polynomial of degree less than $n$ at a specifically chosen set of $n$ distinct evaluation points in $\mathbb{F}_q$. If $q$ or $q-1$ is a smooth number, then the divide-and-conquer approach leads to the fastest known FFT algorithms. Depending on the type of group that the set of evaluation points forms, these algorithms are classified as multiplicative (Math of Comp. 1965) and additive (FOCS 2014) FFT algorithms. In this work, we provide a unified framework for FFT algorithms that include both multiplicative and additive FFT algorithms as special cases, and beyond: our framework also works when $q+1$ is smooth, while all known results require $q$ or $q-1$ to be smooth. For the new case where $q+1$ is smooth (this new case was not considered before in literature as far as we know), we show that if $n$ is a divisor of $q+1$ that is $B$-smooth for a real $B>0$, then our FFT needs $O(Bn\log n)$ arithmetic operations in $\mathbb{F}_q$. Our unified framework is a natural consequence of introducing the algebraic function fields into the study of FFT.
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support

The posterior in probabilistic programs with stochastic support decomposes as a weighted sum of the local posterior distributions associated with each possible program path. We show that making predictions with this full posterior implicitly performs a Bayesian model averaging (BMA) over paths. This is potentially problematic, as model misspecification can cause the BMA weights to prematurely collapse onto a single path, leading to sub-optimal predictions in turn. To remedy this issue, we propose alternative mechanisms for path weighting: one based on stacking and one based on ideas from PAC-Bayes. We show how both can be implemented as a cheap post-processing step on top of existing inference engines. In our experiments, we find them to be more robust and lead to better predictions compared to the default BMA weights.
Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning

Spatial reasoning over text is challenging as the models not only need to extract the direct spatial information from the text but also reason over those and infer implicit spatial relations. Recent studies highlight the struggles even large language models encounter when it comes to performing spatial reasoning over text. In this paper, we explore the potential benefits of disentangling the processes of information extraction and reasoning in models to address this challenge. To explore this, we design various models that disentangle extraction and reasoning(either symbolic or neural) and compare them with state-of-the-art(SOTA) baselines with no explicit design for these parts. Our experimental results consistently demonstrate the efficacy of disentangling, showcasing its ability to enhance models' generalizability within realistic data domains.
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment'' (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2% ~ 12% DTW improvements.
Probabilistic Integral Circuits

Continuous latent variables (LVs) are a key ingredient of many generative models, as they allow modelling expressive mixtures with an uncountable number of components. In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units. Unlike continuous LV models, PCs provide tractable inference but are limited to discrete LVs with categorical (i.e. unordered) states. We bridge these model classes by introducing probabilistic integral circuits (PICs), a new language of computational graphs that extends PCs with integral units representing continuous LVs. In the first place, PICs are symbolic computational graphs and are fully tractable in simple cases where analytical integration is possible. In practice, we parameterise PICs with light-weight neural nets delivering an intractable hierarchical continuous mixture that can be approximated arbitrarily well with large PCs using numerical quadrature. On several distribution estimation benchmarks, we show that such PIC-approximating PCs systematically outperform PCs commonly learned via expectation-maximization or SGD.
Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance

Graph Neural Network (GNN) research has produced strategies to modify a graph's edges using gradients from a trained GNN, with the goal of network design. However, the factors which govern gradient-based editing are understudied, obscuring why edges are chosen and if edits are grounded in an edge's importance. Thus, we begin by analyzing the gradient computation in previous works, elucidating the factors that influence edits and highlighting the potential over-reliance on structural properties. Specifically, we find that edges can achieve high gradients due to structural biases, rather than importance, leading to erroneous edits when the factors are unrelated to the design task. To improve editing, we propose ORE, an iterative editing method that (a) edits the highest scoring edges and (b) re-embeds the edited graph to refresh gradients, leading to less biased edge choices. We empirically study ORE through a set of proposed design tasks, each with an external validation method, demonstrating that ORE improves upon previous methods by up to 50%.
LP-OVOD: Open-Vocabulary Object Detection by Linear Probing

This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. However, this method has a critical issue: many low-quality boxes, such as over- and under-covered-object boxes, have the same similarity score as high-quality boxes since CLIP is not trained on exact object location information. To address this issue, we propose a novel method, LP-OVOD, that discards low-quality boxes by training a sigmoid linear classifier on pseudo labels retrieved from the top relevant region proposals to the novel text. Experimental results on COCO affirm the superior performance of our approach over the state of the art, achieving $\textbf{40.5}$ in $\text{AP}_{novel}$ using ResNet50 as the backbone and without external datasets or knowing novel classes during training. Our code will be available at https://github.com/VinAIResearch/LP-OVOD.
Quantum Teleportation and Entanglement of the 5-Qubit State Over a GHZ-Like Channel

Quantum information processing has a wide range of uses, including the solution of complex algorithms, cryptography, dense coding, quantum computation, and quantum teleportation. Quantum entanglement, a phenomenon that is crucial to many different applications of quantum computation, is an essential resource for quantum computation, and the need for quantum circuits that generate entangled states is one of the ongoing needs for constructing quantum computers. In this study, we present a general approach to the design of quantum circuits that produce entangled states. The approach can be applied to the design of various entanglement circuits with any number of qubits (n-qubit systems), and it makes use of a set of CNOT and Hadamard gates, where the number of CNOT gates should always be (n-1) and each gate connects the two adjacent qubits. In this paper, a three-entangled teleportation scheme of a GHZ-like state (named after its inventor Greenberger-Horne-Zeilinger) through three particles as a quantum channel is presented. The probability of successful teleportation depends on the degree of entanglement of GHZ-like states. A 5-qubit quantum teleportation over a GHZ-like channel has also been used. And single-qubit gates are defined, which are Pauli gates. These gates are represented by arrays I, X, Y, and Z. The results in this paper are good and promising theoretical results in the field of quantum entanglement and quantum teleportation using the Mathematica program.
Application of cloud edge collaborative architecture in power information system

With the acceleration of the digital transformation and upgrading of power, more and more edge terminal devices are connected to the power system, and the cloud data center of the power system is faced with a series of challenges such as network overload, service delay, and low resource utilization. Aiming at the above problems, this paper expounds the cloud edge collaboration structure based on SDN, analyzes its key technologies and applies it to the power system, designs the application scenarios of cloud edge collaboration in the power system, verifies the feasibility of the method through experiments, and provides references for the system construction and application of cloud edge collaboration technology in the power system.