Reviewerly/exHarmony huggingface dataset:
Improving Information Delivery Effectiveness and Efficiency Using WebVR Technology

In this digital era, attracting new students from outside the region to enter a college or university is difficult. This problem can be solved with good marketing and information dissemination techniques. Information is a type of data that has been processed and organized so that it has relevant meaning for the recipient. Useful information can be used to help make decisions, gain new knowledge, or solve problems. One medium for providing information is by using videos that can be uploaded to YouTube for dissemination. However, problems arise when the information depends on the point of view of the video content creator, who may not provide enough information sought by users in their videos. Virtual reality (VR) technology can be pursued as one of the solutions to overcome the lack of detail and control in videos for users to get the expected information. One of the technologies in VR that allows people to explore a three-dimensional virtual world on a web platform is the 360° WebVR technology. Since users have full control over what can be seen using WebVR, it is expected that this technology can assist users in getting deeper and detailed information related to their needs. This study aims to determine the effectiveness and efficiency of using YouTube videos and WebVR in distributing information and delivering detailed information to users. The results show that WebVR technology has the potential to provide greater control over more detailed information to users who use it, in contrast to traditional two-dimensional video playback technology.
Barad-dur: Near-Storage Accelerator for Training Large Graph Neural Networks

Graph Neural Networks (GNNs) enable effective machine learning on graph-structured data, but their performance and scalability are often limited by the irregular structure and large size of real-world graphs. Conventional accelerators such as GPUs suffer a sharp performance loss when the target graph exceeds its fast random-access memory, because the overhead of partitioning graphs into memory-size chunks quickly dominates performance. We address this issue with Barad-dur, a near-storage GNN accelerator which processes the entire graph in cost-efficient solid-state storage (SSD) to remove the partitioning overhead. Barad-dur minimizes the performance impact of using relatively slow SSDs instead of DRAM, via storage-optimized graph encodings, hardware-accelerated access reordering, and the higher internal storage bandwidth available to near-storage accelerators. We demonstrate that Barad-dur can better maintain computational throughput on larger graphs compared to in-memory CPU and GPU systems. On even moderately large graphs such as the Twitter graph, Barad-dur achieves an order of magnitude higher performance compared to a highly optimized Pytorch Geometric implementation running on the NVIDIA V100 GPU, while requiring a fraction of capital cost and energy.
SpecCheck: A Tool for Systematic Identification of Vulnerable Transient Execution in gem5

Speculative execution attacks leverage a processor's speculative execution optimization to leak secret information. Previous attempts to generalize transient execution attacks often analyze specific gadgets in software or look solely at mi-croarchitectural state artifacts to explain the fundamental logic behind these attacks. In this work, we present SPECCHECK, a systematic security verification for detecting potential transient data leakage. SPECCHECK is based on a description of a generic transient execution attack in the form of a register based Finite State Machine (FSM). SPECCHECK'S key insight is the fact that transient execution attacks involve both the software and the hardware to succeed and the only way to verify if a design is capable of mitigating such attacks is by considering both at verification time. The FSM is easily incorporated into commonly used processor simulators. As a proof of concept, we implement SPECCHECK'S FSM in the gem5 simulator to check for suspicious program flows during an arbitrary program's simulation and lay the groundwork for a robust and systematic hardware security verification tool. We show that SPECCHECK is able to identify known transient execution gadgets in two of the main Spectre variants, variant 1 (PHT) and 2 (BTB), with a 100% true positives and an average of 14% false positive rate for malicious sequences of code and an average of 19% vulnerable windows identified for the SPEC benchmark suite.
Medical Image Segmentation Using Discretized nmODE

In recent years, deep learning has been widely used in medical image segmentation, and many high-performance models have emerged. However, these models usually sacrifice simplicity in order to obtain more accurate results, which make them unsuitable for mobile health application that requires convenience. To address this issue, we propose a more efficient method. Using discretized Neural memory Ordinary Different Equation (NmODE) to replace the decoders of UNet and its variants. We validate the efficacy of the nmODE decoder through comprehensive evaluations on two distinct datasets-LiTS2017 in the 3D context and ISIC2017 in the 2D context. The results demonstrated the efficacy of nmODE in diminishing network parameters while maintaining, or in some cases, enhancing performance levels.
Enhancing Real-Time Strategy Games via Transformer Encoder with Patch Embedding

We delve into the potential applications of transformer encoder within the domain of real-time strategy games. We adopt the Gym-µRTS environment, where the observation domain is divided into different patches and sequentially input to the transformer encoder to get patch embedding. The training of the agent is carried out utilizing an Asynchronous Advantage Actor-Critic framework, employing Proximal Policy Optimization against scripted AI across diverse maps. In contrast with the approaches that combine feature maps and standard transformer encoder, our method achieves a higher success rate in the evaluation and effectively alleviates the challenge of overfitting associated with the encoding of raw observations, thereby enhancing generalization. Consequently, the fusion of patch embedding's transformer encoder enables the final agent to achieve a higher win rate against different AIs.
YOLOv5s-AAEA: An Effective Approach for Accurate Face Mask Wearing Detection in Complex Scenes

In numerous scenarios, particularly amid the on-going global health crisis, the proper wearing of masks is crucial for public health and safety. As a result, the accurate detection of mask usage has become an essential surveillance measure for public space security. Addressing the challenges of missed detections and false alarms in face mask wearing detection, especially in complex backgrounds and densely packed small target scenes, this paper proposes the YOLOv5s-AAEA algorithm for accurate face mask wearing detection. Firstly, we redefine the sizes of prior boxes using the K-means method on annotated dataset target boxes, matching them to their respective feature layers to enhance the accuracy of feature extraction. Subsequently, the ASFF module is incorporated into the Neck layer of the YOLOv5s model, facilitating better fusion of features at different scales, thereby addressing the issue of low accuracy in complex background detection. Attention mechanisms are introduced separately into the backbone network of the YOLOv5s model, suppressing irrelevant information and enhancing the information representation capability of feature maps, thereby addressing the challenge of detecting densely packed small targets. Finally, considering the issues of slow convergence and poor performance in handling incomplete target objects exhibited by the CIOU loss function, the EIOU_Loss is employed to ensure faster convergence of predicted boxes during training. Experimental results demonstrate that the proposed algorithm achieves an average accuracy increase of 3.3 percentage points over the original algorithm while maintaining a rapid detection rate. Moreover, compared to existing mainstream detection algorithms, the proposed method exhibits significantly improved accuracy, making it well-suited for real-time monitoring of mask-wearing status among individuals.
Financial Time Series Prediction via Neural Ordinary Differential Equations Approach

This paper considers the prediction problem of financial time series, namely, the prediction of exchange rate for four currencies with the Chinese Yuan (CNY). A novel approach is developed for this challenging topic by neural ordinary differential equations (NODEs). The comparing simulations with long short-term memory (LSTM) and gated recurrent unit (GRU) show that NODEs have better precision and accuracy in exchange rate prediction. Especially, for the drastic change case, the NODEs approach gives more flexible technique.
Application models for a fiscal blockchain: The Brazilian tax system

Electronic tax documents represent an important advance in the process of digital transformation of tax administrations in Brazil. However, the current system still presents several challenges to be overcome. In the technological aspect, vulnerabilities and inconsistencies typical of a centralized model can be mentioned. In addition, from a functional perspective, the lack of control over taxes payments is a major concern. In this context, Blockchain technology emerges as a possibility to complement the current model, thanks to its characteristics, such as traceability, immutability, security and transparency, which are essential in the tax environment. This work proposes four application models based on Blockchain aimed at monitoring the payment of taxes, using different types of integration with existing systems. After the presentation and description of the models, they are discussed and compared, in order to reinforce their main characteristics, facilitating decision making in possible future applications.
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. However, data heterogeneity, e.g., non-independently identically distributions and different sizes of training datasets among clients, poses major challenges to wireless FL. Limited communication resources complicate the implementation of fair scheduling which is required for training on heterogeneous data, and further deteriorate the overall performance. To address this issue, this paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. Specifically, we first develop a closed-form expression for an upper bound on the FL loss function, with a particular emphasis on data heterogeneity described by a dataset size vector and a data divergence vector. Then we formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, uplink transmission power, channel allocation and the number of local epochs. Next, via the Lyapunov drift technique, we transform the optimization problem into a series of tractable problems. Extensive experiments on real-world datasets demonstrate that our method outperforms other benchmarks in terms of the learning accuracy and energy consumption.
Probabilistic Linguistic Petri Nets for Knowledge Representation and Acquisition with Dynamic Consensus Reaching Process

Fuzzy Petri nets (FPNs) are widely used in various fields for knowledge representation and reasoning. Nevertheless, the original FPN model has many limitations when applied in the practical situations. In response, this paper aims to develop a new FPN model, named as probabilistic linguistic Petri nets (PLPNs), for representing and acquiring knowledge based on a dynamic consensus reaching process. First, to avoid losing the initial information, the probabilistic linguistic term sets (PLTSs) are adopted to represent the professional knowledge of domain experts. Subsequently, a knowledge acquisition approach is proposed to acquire the knowledge parameters of PLPNs. Then, a dynamic consensus reaching method is introduced during the knowledge acquisition process to increase the group consensus level among experts. Finally, a real-world risk assessment example concerning subway fire system is presented to validate the usefulness and effectiveness of the proposed PLPNs. The results show that the new PLPNs are efficient and practical to represent and acquire expert knowledge with conflict and inconsistent opinions.
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg

Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other’s privately held data. The characteristics of non-i.i.d. data across the network, low device participation, high communication costs, and the mandate that data remain private bring challenges in understanding the convergence of FL algorithms, particularly regarding how convergence scales with the number of participating devices. In this paper, we focus on Federated Averaging (FedAvg), one of the most popular and effective FL algorithms in use today, as well as its Nesterov accelerated variant, and conduct a systematic study of how their convergence scale with the number of participating devices under non-i.i.d. data and partial participation in convex settings. We provide a unified analysis that establishes convergence guarantees for FedAvg under strongly convex, convex, and overparameterized strongly convex problems. We show that FedAvg enjoys linear speedup in each case, although with different convergence rates and communication efficiencies. For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings. Empirical studies of the algorithms in various settings have supported our theoretical results.
Celebrities’ Debunking Rumours and Misinterpretations: A Case Study of BTS’ Namjoon’s Letter on Weverse Through Pragmastylistic Analysis

The phenomenon of debunking rumours and misinterpretations has been investigated in its general terms without considering celebrities’ emotions through linguistic expressions and speech acts. This research aimed to conduct a case study incorporating pragmastylistic analysis of BTS’ RM’s letter (2022) on Weverse to debunk BTS’ disbandment rumours and misinterpretations about BTSFESTA. The study also determined to scrutinize the types of speech acts used in order to clear certain propositions and assumptions. The study employed a qualitative research method to address the research questions. The theoretical framework was based on Speech Act theory (Austin, 1962) to analyse the speech acts in the letter under felicity conditions (Leech, 1962). The findings of the study suggested that Namjoon used simple and declarative statements performing hybrid speech acts such as expressive, commissive, directive, expositive, verdictive, etc. Mostly, the speech acts were inclined towards expositive and commissive expressions to clear certain ambiguities and make commitments to manage to do this in future as well. This study was methodologically significant for incorporating pragmastylistic analysis to study the language of celebrities’ debunking rumours on social media. The study recommended the future researchers to take both celebrities’ voices and pragmastylistic analysis together to reach various conclusions and novel findings.
“HOW DO THE BROCHURES SPEAK?” SYSTEMIC FUNCTIONAL LINGUISTICS ANALYSIS IN ONLINE PARENTING BROCHURES

Online brochures are one of the preferred choices in describing the advertisement’s worth of many linguistic features. ‘Parenting’ strategies are one of the topics which are discussed in the online brochures. We can find the quotes and problem-solving about ‘parenting’ spread in cyberspace. Online parenting brochures are made by a personal or community to share that importance. The clause in parenting brochures discourse has a semantic meaning of its speech function. The language of the brochures was examined by using Halliday’s theory of Systemic Functional Linguistics (SFL), then it was found that the language in the brochures was formulated interpersonally. The meanings of the clause are developed in any interactive features in exchanging the giving and demanding move of speech function. This research was conducted qualitatively by analyzing 48 clauses in the brochures. It was found that the most commonly used linguistic clause in the brochures was the statement (71%) as a way of giving information to the readers. Then the rest was the command (29%) for the parents or the childminder to be applied in nurturing the children. Besides, multimodal strategies were also used to make the brochures more eye-catching. The semiotics modes used in the brochures were supported by the meaning making of the discourse. The readers were also invited to comprehend the urgent situation of the topic discussed.
Query Reformulator And Contrastive Answer Ranker For Conversational Question Answering Over Knowledge Graph

Conversational KGQA (Knowledge Graph Question Answering) is a sequential process of question-answering over a knowledge graph (KG). Conversational KGQA involves follow-up questions that are presented in anaphoric or abbreviated form, known as ellipsis. Another challenge is the difficulty in selecting corrects answer for follow-up questions from a large number of candidate answers from KGs. This paper aims to tackle the challenge of alleviating the ellipsis phenomena and selecting correct answers from copious candidates. We present a module called CSR, which effectively merges information-retrieval-based method with pretrained language models. This module aims to identify the most relevant entity for elliptical questions based on instructional templates and subsequently rephrase the questions. Additionally, we propose a contrastive answer ranker to address the challenge of selecting the correct answer from copious candidates by leveraging the relationship between the questions and answers. Leveraging fewer trainable parameters, our method outperforms baselines on ConvQuestions and surpass the F1-Score metric of state-of-the-art on ConvRef dataset.
Cost-Effective Tweet Classification through Transfer Learning in Low-Resource NLP Settings

Recently, pre-trained Transformer-based Language Models (TLM) for specific task has led to significant advancements in Natural Language Processing (NLP) tasks, achieving state-of-the-art results. Despite the ability of TLM and its benefits for low-resource languages where labelled data is scarce. Limited research has been conducted on investigating the applicability of TLM in low-resource settings for French tweets classification. In this paper, our focus is on exploring the effectiveness of Transfer Learning using fine-tuned Transformer language models, specifically FlauBERT and XLM-RoBERTa applied to French tweet dataset. We investigate their ability to achieve higher performance in text classification tasks while requiring less training data compared to traditional text classification methods.Experiments conducted on our labeled dataset consisting of 4000 French tweets reveal that FlauBERT outperforms its rival, achieving an average F1-score of 0.84 compared to XLM-R’s 0.79. However, in scenarios with even fewer available data, XLM-R has the potential to surpass FlauBERT.Furthermore, the environmental sustainability is considered while highlighting the potential CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions reduction through manipulating the layers of FlauBERT without compromising the performance.
Intelligent Suppression of Interferences Based on Retroactive-DQN

This paper aims to investigate intelligent interference suppression strategies through reinforcement learning. Particularly, the Retroactive-Deep Q Network (R-DQN) is devised as the principle of updating suppression strategies by introducing a backtracking Q value, which links the evaluations in each step of training. At the analysis stage, three interferences, including the Interrupted Sampling Repeater Jamming (ISRJ), the SMeared SPectrugram (SMSP) jamming and repeater jamming, and five suppression methods are considered. Moreover, the selection probabilities of the implementation sequence for interference suppression between the R-DQN and the conventional DQN are compared. Numerical results corroborate the effectiveness and robustness of the considered suppression strategies.
An FPGA-Based Efficient NTT Accelerator for Post-Quantum Cryptography CRYSTALS-Kyber

CRYSTALS-Kyber has emerged as a significant lattice-based post-quantum cryptography (PQC) scheme. As one of the four finalists at round three in NIST's PQC standardization, CRYSTALS-Kyber uses number theoretic transform (NTT) to optimize polynomial multiplication, which is the most complex operation in CRYSTALS-Kyber. This work proposes an FPGA-based efficient NTT accelerator with a novel butterfly unit and an efficient modular polynomial multiplier. This work adopts a radix-4-based configurable NTT design, which can achieve forward and inverse NTT operations on a unified architecture. The proposed architecture on the Xilinx Virtex-7 FPGA platform is 1.5x faster than the traditional radix-4 NTT architecture and 2.3x faster than the traditional radix-2 NTT architecture.
Intelligence Business Model Canvas Recommender for B40 Women Entrepreneurs

Smart SuriPreneur empowers B40 women entrepreneurs with a comprehensive platform for online business management. By integrating intelligent systems and leveraging the Malaysian GDP Index, the platform evaluates the impact of business programs on B40 women entrepreneurs. It provides tailored training, financing options, coaching, and tools to address their specific needs. Despite receiving training, B40 women entrepreneurs face challenges in creating suitable Business Model Canvases (BMCs) using existing templates. To overcome this, the research proposes iBMCSuriPreneur, an intelligent BMC recommendation system. iBMCSuriPreneur utilizes content-based filtering and similarity methods to categorize and recommend relevant BMCs based on business profiles and goals. The system ranks the recommendations by similarity and suggests specific elements for each BMC component to provide guidance and generate new ideas. Validation involves five experiments with 25 sets of BMCs, verified by expert coaches. The results demonstrate the system's accuracy in recommending BMCs that align with business goals and similarity metrics. iBMCSuriPreneur supports B40 women entrepreneurs and fosters business growth through intelligent systems. Future research can explore additional features and evaluate the system's performance in different contexts, benefiting a wider range of users. With its comprehensive services and intelligent BMC recommendations, Smart SuriPreneur is crucial in empowering B40 women entrepreneurs and enabling their success in the business world.