OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Reconfigurable Intelligent Surfaces for Small Data Packets: A Subarray Approach
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy consumption taking both the pilot and data transmission powers into account. By dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient, the pilot overhead can be tuned to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that there exists an energy-minimizing subarray size. For small data blocks and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using large subarrays. When the channel conditions to the RIS are better and the data blocks are large, it is preferable to use fewer elements per subarray and potentially configure the elements individually.
On the $α$-spectral radius of hypergraphs
Venue: arXiv cs.DM 2023
Year: 2023

For real $α\in [0,1)$ and a hypergraph $G$, the $α$-spectral radius of $G$ is the largest eigenvalue of the matrix $A_α(G)=αD(G)+(1-α)A(G)$, where $A(G)$ is the adjacency matrix of $G$, which is a symmetric matrix with zero diagonal such that for distinct vertices $u,v$ of $G$, the $(u,v)$-entry of $A(G)$ is exactly the number of edges containing both $u$ and $v$, and $D(G)$ is the diagonal matrix of row sums of $A(G)$. We study the $α$-spectral radius of a hypergraph that is uniform or not necessarily uniform. We propose some local grafting operations that increase or decrease the $α$-spectral radius of a hypergraph. We determine the unique hypergraphs with maximum $α$-spectral radius among $k$-uniform hypertrees, among $k$-uniform unicyclic hypergraphs, and among $k$-uniform hypergraphs with fixed number of pendant edges. We also determine the unique hypertrees with maximum $α$-spectral radius among hypertrees with given number of vertices and edges, the unique hypertrees with the first three largest (two smallest, respectively) $α$-spectral radii among hypertrees with given number of vertices, the unique hypertrees with minimum $α$-spectral radius among the hypertrees that are not $2$-uniform, the unique hypergraphs with the first two largest (smallest, respectively) $α$-spectral radii among unicyclic hypergraphs with given number of vertices, and the unique hypergraphs with maximum $α$-spectral radius among hypergraphs with fixed number of pendant edges.
Prospects for the implementation of an affordable VR/AR-content management tool for Learning Management Systems
Venue: arXiv cs.HC 2023
Year: 2023

The article discusses the prospects for introducing into educational practice the designer of electronic training courses based on virtual and augmented reality technologies for LMS Moodle. The requirements for the functions, interface, appearance of the module-designer being developed, the formation of VR/AR-content in terms of its use by unprepared users, such as teachers and develop-ers of training courses, are formulated.
Text revision in Scientific Writing Assistance: An Overview
Venue: arXiv cs.CL 2023
Year: 2023

Writing a scientific article is a challenging task as it is a highly codified genre. Good writing skills are essential to properly convey ideas and results of research work. Since the majority of scientific articles are currently written in English, this exercise is all the more difficult for non-native English speakers as they additionally have to face language issues. This article aims to provide an overview of text revision in writing assistance in the scientific domain. We will examine the specificities of scientific writing, including the format and conventions commonly used in research articles. Additionally, this overview will explore the various types of writing assistance tools available for text revision. Despite the evolution of the technology behind these tools through the years, from rule-based approaches to deep neural-based ones, challenges still exist (tools' accessibility, limited consideration of the context, inexplicit use of discursive information, etc.)
Binary self-orthogonal codes which meet the Griesmer bound or have optimal minimum distances
Venue: arXiv cs.IT 2023
Year: 2023

The purpose of this paper is two-fold. First, we characterize the existence of binary self-orthogonal codes meeting the Griesmer bound by employing Solomon-Stiffler codes and some related residual codes. Second, using such a characterization, we determine the exact value of $d_{so}(n,7)$ except for five special cases and the exact value of $d_{so}(n,8)$ except for 41 special cases, where $d_{so}(n,k)$ denotes the largest minimum distance among all binary self-orthogonal $[n, k]$ codes. Currently, the exact value of $d_{so}(n,k)$ $(k \le 6)$ was determined by Shi et al. (2022). In addition, we develop a general method to prove the nonexistence of some binary self-orthogonal codes by considering the residual code of a binary self-orthogonal code.
Emotional Framing in the Spreading of False and True Claims
Venue: arXiv cs.SI 2023
Year: 2023

The explosive growth of online misinformation, such as false claims, has affected the social behavior of online users. In order to be persuasive and mislead the audience, false claims are made to trigger emotions in their audience. This paper contributes to understanding how misinformation in social media is shaped by investigating the emotional framing that authors of the claims try to create for their audience. We investigate how, firstly, the existence of emotional framing in the claims depends on the topic and credibility of the claims. Secondly, we explore how emotionally framed content triggers emotional response posts by social media users, and how emotions expressed in claims and corresponding users' response posts affect their sharing behavior on social media. Analysis of four data sets covering different topics (politics, health, Syrian war, and COVID-19) reveals that authors shape their claims depending on the topic area to pass targeted emotions to their audience. By analysing responses to claims, we show that the credibility of the claim influences the distribution of emotions that the claim incites in its audience. Moreover, our analysis shows that emotions expressed in the claims are repeated in the users' responses. Finally, the analysis of users' sharing behavior shows that negative emotional framing such as anger, fear, and sadness of false claims leads to more interaction among users than positive emotions. This analysis also reveals that in the claims that trigger happy responses, true claims result in more sharing compared to false claims.
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
Venue: arXiv cs.LG 2023
Year: 2023

This study presents a novel deep learning method, called GATv2-GCN, for predicting player performance in sports. To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay. We use a graph attention network to capture the attention that each player pays to each other, allowing for more accurate modeling of the dynamic player interactions. To handle the multivariate player statistics time series, we incorporate a temporal convolution layer, which provides the model with temporal predictive power. We evaluate the performance of our model using real-world sports data, demonstrating its effectiveness in predicting player performance. Furthermore, we explore the potential use of our model in a sports betting context, providing insights into profitable strategies that leverage our predictive power. The proposed method has the potential to advance the state-of-the-art in player performance prediction and to provide valuable insights for sports analytics and betting industries.
Multi-View Clustering via Semi-non-negative Tensor Factorization
Venue: arXiv cs.LG 2023
Year: 2023

Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability. However, existing NMF-based multi-view clustering methods perform NMF on each view data respectively and ignore the impact of between-view. Thus, they can't well exploit the within-view spatial structure and between-view complementary information. To resolve this issue, we present semi-non-negative tensor factorization (Semi-NTF) and develop a novel multi-view clustering based on Semi-NTF with one-side orthogonal constraint. Our model directly performs Semi-NTF on the 3rd-order tensor which is composed of anchor graphs of views. Thus, our model directly considers the between-view relationship. Moreover, we use the tensor Schatten p-norm regularization as a rank approximation of the 3rd-order tensor which characterizes the cluster structure of multi-view data and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point. Extensive experiments on various benchmark datasets indicate that our proposed method is able to achieve satisfactory clustering performance.
Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters or uses extra blocks, we design a more capable parameter-sharing architecture based on matrix product operator (MPO). MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts: the major part that contains the major information (central tensor) and the supplementary part that only has a small proportion of parameters (auxiliary tensors). Based on such a decomposition, our architecture shares the central tensor across all layers for reducing the model size and meanwhile keeps layer-specific auxiliary tensors (also using adapters) for enhancing the adaptation flexibility. To improve the model training, we further propose a stable initialization algorithm tailored for the MPO-based architecture. Extensive experiments have demonstrated the effectiveness of our proposed model in reducing the model size and achieving highly competitive performance.
Dialogue-to-Video Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialogue-to-video retrieval uses structured queries in the form of user-generated dialogue as the search descriptor. We present a novel dialogue-to-video retrieval system, incorporating structured conversational information. Experiments conducted on the AVSD dataset show that our proposed approach using plain-text queries improves over the previous counterpart model by 15.8% on R@1. Furthermore, our approach using dialogue as a query, improves retrieval performance by 4.2%, 6.2%, 8.6% on R@1, R@5 and R@10 and outperforms the state-of-the-art model by 0.7%, 3.6% and 6.0% on R@1, R@5 and R@10 respectively.
Towards Understanding the Effect of Pretraining Label Granularity
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we study how the granularity of pretraining labels affects the generalization of deep neural networks in image classification tasks. We focus on the "fine-to-coarse" transfer learning setting, where the pretraining label space is more fine-grained than that of the target problem. Empirically, we show that pretraining on the leaf labels of ImageNet21k produces better transfer results on ImageNet1k than pretraining on other coarser granularity levels, which supports the common practice used in the community. Theoretically, we explain the benefit of fine-grained pretraining by proving that, for a data distribution satisfying certain hierarchy conditions, 1) coarse-grained pretraining only allows a neural network to learn the "common" or "easy-to-learn" features well, while 2) fine-grained pretraining helps the network learn the "rarer" or "fine-grained" features in addition to the common ones, thus improving its accuracy on hard downstream test samples in which common features are missing or weak in strength. Furthermore, we perform comprehensive experiments using the label hierarchies of iNaturalist 2021 and observe that the following conditions, in addition to proper choice of label granularity, enable the transfer to work well in practice: 1) the pretraining dataset needs to have a meaningful label hierarchy, and 2) the pretraining and target label functions need to align well.
CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Stream
Venue: arXiv cs.LG 2023
Year: 2023

Concept drift detection has attracted considerable attention due to its importance in many real-world applications such as health monitoring and fault diagnosis. Conventionally, most advanced approaches will be of poor performance when the evaluation criteria of the environment has changed (i.e. concept drift), either can only detect and adapt to virtual drift. In this paper, we propose a new approach to detect real-drift in the chunk data stream with limited annotations based on concept confusion. When a new data chunk arrives, we use both real labels and pseudo labels to update the model after prediction and drift detection. In this context, the model will be confused and yields prediction difference once drift occurs. We then adopt cosine similarity to measure the difference. And an adaptive threshold method is proposed to find the abnormal value. Experiments show that our method has a low false alarm rate and false negative rate with the utilization of different classifiers.
BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification
Venue: arXiv cs.CL 2023
Year: 2023

Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.
Applications of Causality and Causal Inference in Software Engineering
Venue: arXiv cs.SE 2023
Year: 2023

Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.
Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams
Venue: arXiv cs.CL 2023
Year: 2023

The present study aims to explore the capabilities of Language Models (LMs) in tackling high-stakes multiple-choice tests, represented here by the Exame Nacional do Ensino Médio (ENEM), a multidisciplinary entrance examination widely adopted by Brazilian universities. This exam poses challenging tasks for LMs, since its questions may span into multiple fields of knowledge, requiring understanding of information from diverse domains. For instance, a question may require comprehension of both statistics and biology to be solved. This work analyzed responses generated by GPT-3.5 and GPT-4 models for questions presented in the 2009-2017 exams, as well as for questions of the 2022 exam, which were made public after the training of the models was completed. Furthermore, different prompt strategies were tested, including the use of Chain-of-Thought (CoT) prompts to generate explanations for answers. On the 2022 edition, the best-performing model, GPT-4 with CoT, achieved an accuracy of 87%, largely surpassing GPT-3.5 by 11 points. The code and data used on experiments are available at https://github.com/piresramon/gpt-4-enem.
Overcoming Challenges to Continuous Integration in HPC
Venue: arXiv cs.DC 2023
Year: 2023

Continuous integration (CI) has become a ubiquitous practice in modern software development, with major code hosting services offering free automation on popular platforms. CI offers major benefits, as it enables detecting bugs in code prior to committing changes. While high-performance computing (HPC) research relies heavily on software, HPC machines are not considered "common" platforms. This presents several challenges that hinder the adoption of CI in HPC environments, making it difficult to maintain bug-free HPC projects, and resulting in adverse effects on the research community. In this article, we explore the challenges that impede HPC CI, such as hardware diversity, security, isolation, administrative policies, and non-standard authentication, environments, and job submission mechanisms. We propose several solutions that could enhance the quality of HPC software and the experience of developers. Implementing these solutions would require significant changes at HPC centers, but if these changes are made, it would ultimately enable faster and better science.
ACM with Overlapping Partitions: Implementation and Periodicity Analysis
Venue: arXiv cs.CR 2023
Year: 2023

The Arnold Cat Map (ACM) is a popular chaotic map used in image encryption. Chaotic maps are known for their sensitivity to initial conditions and their ability to mix, or rearrange, pixels. However, ACM is periodic, and the period is relatively short. This periodicity decreases the effective key space for a cryptosystem. Further, ACM can only be performed on square matrices. For non-square images, this issue can be solved by performing ACM on multiple square partitions of the image. If these partitions overlap, the periodicity will greatly increase. The resulting system will be referred to as overlapping ACM or OACM. This paper will cover the implementation and periodicity analysis for these overlapping systems, which previous papers involving similar overlapping block partitions did not. Viewing OACM as a scan as opposed to a map allows for faster implementation and period analysis.
Lockless Blockchain Sharding with Multiversion Control
Venue: arXiv cs.DC 2023
Year: 2023

Sharding is used to address the performance and scalability issues of the blockchain protocols, which divides the overall transaction processing costs among multiple clusters of nodes. Shards require less storage capacity and communication and computation cost per node than the existing whole blockchain networks, and they operate in parallel to maximize performance. However, existing sharding solutions use locks for transaction isolation which lowers the system throughput and may introduce deadlocks. In this paper, we propose a lockless transaction method for ensuring transaction isolation without using locks, which improves the concurrency and throughput of the transactions. In our method, transactions are split into subtransactions to enable parallel processing in multiple shards. We use versions for the transaction accounts to implement consistency among the shards. We provide formal proof for liveness and correctness. We also evaluate experimentally our proposed protocol and compare the execution time and throughput with lock-based approaches. The experiments show that the transaction execution time is considerably shorter than the lock-based time and near to the ideal (no-lock) execution time.
The Shapley-Hodge Associated Game
Venue: arXiv cs.GT 2023
Year: 2023

In cooperative game theory, associated games allow for providing meaningful characterizations of solution concepts. Moreover, generalized values allow computing an influence or power index of each coalition in a game. In this paper, we view associated games through the lens of game maps and we define the novel Shapley-Hodge Game, briefly ``SHoGa''. We characterize SHoGa via an axiomatic approach as a generalized value, and we thoroughly discuss the consistency properties of its associated game. Furthermore, we describe the Hodge decomposition of an oriented graph representing the transitive closure of the Hasse diagram of coalitions in the game. Finally, we show how SHoGa is linked to the solution of the Poisson equation derived from such a decomposition.
Weighted Scheduling of Time-Sensitive Coflows
Venue: arXiv cs.DC 2023
Year: 2023

Datacenter networks commonly facilitate the transmission of data in distributed computing frameworks through coflows, which are collections of parallel flows associated with a common task. Most of the existing research has concentrated on scheduling coflows to minimize the time required for their completion, i.e., to optimize the average dispatch rate of coflows in the network fabric. Nevertheless, modern applications often produce coflows that are specifically intended for online services and mission-crucial computational tasks, necessitating adherence to specific deadlines for their completion. In this paper, we introduce \wdcoflow,~ a new algorithm to maximize the weighted number of coflows that complete before their deadline. By combining a dynamic programming algorithm along with parallel inequalities, our heuristic solution performs at once coflow admission control and coflow prioritization, imposing a $σ$-order on the set of coflows. With extensive simulation, we demonstrate the effectiveness of our algorithm in improving up to $3\times$ more coflows that meet their deadline in comparison the best SoA solution, namely $\mathtt{CS\text{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes, \wdcoflow~ is able to improve the admission per class up to $4\times$, while increasing the average weighted coflow admission rate.