Reviewerly/exHarmony huggingface dataset:
The Parallelism Tradeoff: Limitations of Log-Precision Transformers

Abstract Despite their omnipresence in modern NLP, characterizing the computational power of transformer neural nets remains an interesting open question. We prove that transformers whose arithmetic precision is logarithmic in the number of input tokens (and whose feedforward nets are computable using space linear in their input) can be simulated by constant-depth logspace-uniform threshold circuits. This provides insight on the power of transformers using known results in complexity theory. For example, if L≠P (i.e., not all poly-time problems can be solved using logarithmic space), then transformers cannot even accurately solve linear equalities or check membership in an arbitrary context-free grammar with empty productions. Our result intuitively emerges from the transformer architecture’s high parallelizability. We thus speculatively introduce the idea of a fundamental parallelism tradeoff: any model architecture as parallelizable as the transformer will obey limitations similar to it. Since parallelism is key to training models at massive scale, this suggests a potential inherent weakness of the scaling paradigm.
LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems

&lt;abstract&gt;&lt;p&gt;Advanced wireless communication technologies, such as 5G, are faced with significant challenges in accurately estimating the transmitted signal and characterizing the channel. One of the major obstacles is the interference caused by the delay spread, which results from receiving multiple signal copies through different paths. To mitigate this issue, the orthogonal frequency division modulation (OFDM) technique is often employed. Efficient signal detection and optimal channel estimation are crucial for enhancing the performance of multi-carrier wireless communication systems. To this end, this paper proposes a Long Short Term Memory-Projected Layer (LSTM-PL) deep neural network(DNN) based channel estimator to detect received OFDM signal. The results show that the LSTM-PL algorithm outperforms traditional methods such as Least Squares(LS), Minimum Mean Square Error (MMSE) and other LSTM deep learning channel estimation methods like Long Short Term Memory(LSTM)-DNN and Bidirectional-LSTM(Bi-LSTM)-DNN, as evidenced by Symbol-Error Rate (SER) outcomes.&lt;/p&gt;&lt;/abstract&gt;
APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection

Abstract As a hearing disorder, sensorineural hearing loss (SNHL) can be effectively detected by magnetic resonance imaging (MRI). However, the manual detection of MRI scanning is subjective, time‐consuming, and unpredictable. An accurate and automatic computer‐aided diagnosis system is proposed for SNHL detection, providing reliable references for professionals. The system first employs a wavelet entropy layer to extract features of MRI images. Then, a neural network layer is proposed as the classifier consisting of a feedforward neural network (FNN) and an adaptive‐probability PSO (APPSO) algorithm. The authors prove the rotation‐variant property of the basic particle swarm optimization (PSO) by the algebraic property of matrix transformation. The property is unsuitable for optimising parameters of neural networks. Thus, in APPSO, the authors integrate the new update rules based on all‐dimensional variation and adaptive‐probability mechanism into the basic PSO, which can improve its searching ability without losing population diversity. The authors compare APPSO‐NN with FNN trained by five popular evolutionary algorithms. The simulation results show that APPSO performs best in training FNN. The method also compares with six state‐of‐the‐art methods. The simulation results show that the best performance in sensitivity and overall accuracy of hearing loss classification, which proves that the method is effective and promising for SNHL detection.
T5-Based Model for Abstractive Summarization: A Semi-Supervised Learning Approach with Consistency Loss Functions

Text summarization is a prominent task in natural language processing (NLP) that condenses lengthy texts into concise summaries. Despite the success of existing supervised models, they often rely on datasets of well-constructed text pairs, which can be insufficient for languages with limited annotated data, such as Chinese. To address this issue, we propose a semi-supervised learning method for text summarization. Our method is inspired by the cycle-consistent adversarial network (CycleGAN) and considers text summarization as a style transfer task. The model is trained by using a similar procedure and loss function to those of CycleGAN and learns to transfer the style of a document to its summary and vice versa. Our method can be applied to multiple languages, but this paper focuses on its performance on Chinese documents. We trained a T5-based model and evaluated it on two datasets, CSL and LCSTS, and the results demonstrate the effectiveness of the proposed method.
Systematic keyword and bias analyses in hate speech detection

Hate speech detection refers broadly to the automatic identification of language that may be considered discriminatory against certain groups of people. The goal is to help online platforms to identify and remove harmful content. Humans are usually capable of detecting hatred in critical cases, such as when the hatred is non-explicit, but how do computer models address this situation? In this work, we aim to contribute to the understanding of ethical issues related to hate speech by analysing two transformer-based models trained to detect hate speech. Our study focuses on analysing the relationship between these models and a set of hateful keywords extracted from the three well-known datasets. For the extraction of the keywords, we propose a metric that takes into account the division among classes to favour the most common words in hateful contexts. In our experiments, we first compared the overlap between the extracted keywords with the words to which the models pay the most attention in decision-making. On the other hand, we investigate the bias of the models towards the extracted keywords. For the bias analysis, we characterize and use two metrics and evaluate two strategies to try to mitigate the bias. Surprisingly, we show that over 50% of the salient words of the models are not hateful and that there is a higher number of hateful words among the extracted keywords. However, we show that the models appear to be biased towards the extracted keywords. Experimental results suggest that fitting models with hateful texts that do not contain any of the keywords can reduce bias and improve the performance of the models.
POPNASv3: A pareto-optimal neural architecture search solution for image and time series classification

The growing demand for machine learning applications in industry has created a need for fast and efficient methods to develop accurate machine learning models. Automated Machine Learning (AutoML) algorithms have emerged as a promising solution to this problem, designing models without the need for human expertise. Given the effectiveness of neural network models, Neural Architecture Search (NAS) specialises in designing their architectures autonomously, with results that rival the most advanced hand-crafted models. However, this approach requires significant computational resources and hardware investment, making it less attractive for real-world applications. This article presents the third version of Pareto-Optimal Progressive Neural Architecture Search (POPNASv3), a new NAS algorithm that employs Sequential Model-Based Optimisation and Pareto optimality. This choice makes POPNASv3 flexible to different hardware environments, computational budgets and tasks, as the algorithm can efficiently explore user-defined search spaces of varying complexity. Pareto optimality extracts the architectures that achieve the best trade-off with respect to the metrics considered, reducing the number of models sampled during the search and dramatically improving time efficiency without sacrificing accuracy. The experiments performed on image and time series classification datasets provide evidence that POPNASv3 can explore a large set of different operators and converge to optimal architectures suited to the type of data provided under different scenarios.1
A Toolbox for Migrating the Blockchain-Based Application From Ethereum to Hyperledger Fabric

Abstract The low transaction capacity, high transaction cost and long-term privacy concerns of the current Ethereum platform are notorious. Developers are seeking alternative blockchain platforms to migrate their blockchain-based applications to reduce their applications’ use-cost and improve their applications’ user experience. The Hyperledger Fabric (HLF) platform with resiliency, flexibility, scalability and confidentiality is preferred for developers to migrate their Ethereum blockchain-based applications. However, it is laborious for developers to migrate blockchain-based applications from the Ethereum platform to the HLF platform. In this paper, we first propose a complete and secure migration solution to ease the migration process. The main idea of our solution is to design a toolbox to help developers automatically eliminate the adverse effects that the differences between Ethereum and HLF may bring to the migrated application. Developers with the toolbox can migrate the application with little time and minimal modification. It is theoretically proved that the migrated application with the toolbox is secure. Besides, a prototype of the toolbox is implemented. The extensive experiments demonstrate that the time for the migration process is acceptable, and the toolbox has little impact on the migrated application’s performance.
Improving Image Captioning Systems With Postprocessing Strategies

Image captioning (IC) systems are generally based on encoder–decoder architecture where convolutional neural networks (CNNs) are employed to represent an image with discriminative features and recurrent neural networks (RNNs) sequentially generate a sentence description. Even though a lot of effort has been devoted lately to designing reliable IC systems, the task is far from being solved. The generated descriptions can be affected by different errors related to the attributes and the objects present in the scene. Moreover, once an error occurs, it can be propagated in the recurrent layers of the decoder generating non-accurate descriptions. To solve this problem, we propose two postprocessing strategies applied to the generated descriptions to rectify the errors and improve their quality. The proposed postprocessing strategies are based on hidden Markov models (HMMs) and Viterbi algorithm. The proposed postprocessing strategies can be applied to any encoder–decoder IC system. They are applied at test time once the IC system is trained. In particular, we propose to rectify a sentence once it is fully generated (post-generation strategy) or at each time instant of the generation process (in-generation strategy). Experiments conducted on four different IC datasets confirm the promising capabilities of the proposed postprocessing strategies to rectify the output of a simple encoder–decoder by generating more coherent descriptions. The achieved results are competitive and sometimes better than complex IC systems.
Multi-strategy and self-adaptive differential sine–cosine algorithm for multi-robot path planning

Researchers have conducted studies using various metaheuristic algorithms for multi-robot path planning recently. One of these algorithms, sine–cosine algorithm (SCA) cannot produce satisfactory results in path planning problems, due to a single update strategy. It is necessary to adopt multiple update strategies and improve its performance for a wider set of problems. We have proposed a new multi-strategy self-adaptive differential sine–cosine algorithm (sdSCA) that uses a pool of strategies and allows for more frequent selection of strategies that lead to better solutions. Thus, dependency of SCA on a single strategy has been removed and it has become a more stable for a wider set of problems and convergence of SCA is improved. Firstly, effectiveness of sdSCA was tested in CEC2015 benchmark functions and CEC2020 real-world optimization problems. Performance of sdSCA at these tests is satisfactory. Secondly, sdSCA was applied to online multi-robot path planning in complex environments with static and dynamic obstacles. In this path planning simulation, the proposed algorithm achieved an average improvement of 42% compared to SCA. It also appears to produce results superior to state-of-the-art metaheuristic algorithms.
Extending compositional data analysis from a graph signal processing perspective

Traditional methods for the analysis of compositional data consider the log-ratios between all different pairs of variables with equal weight, typically in the form of aggregated contributions. This is not meaningful in contexts where it is known that a relationship only exists between very specific variables (e.g. for metabolomic pathways), while for other pairs a relationship does not exist. Modeling absence or presence of relationships is done in graph theory, where the vertices represent the variables, and the connections refer to relations. This paper links compositional data analysis with graph signal processing, and it extends the Aitchison geometry to a setting where only selected log-ratios can be considered. The presented framework retains the desirable properties of scale invariance and compositional coherence. A real data example from bioinformatics underlines the usefulness of this approach.
Mapping education 4.0 research trends

The Fourth Industrial Revolution (4IR) has profoundly impacted education systems globally, including in Africa. However, there is a dearth of research on Education 4.0 in Africa. To fill this gap, a bibliometric analysis of Scopus database data was conducted, with VOSviewer used to analyze the data. The study found that African research outputs on Education 4.0 have increased linearly between 2015 and 2022, but most are published outside Africa. Limited collaboration among African institutions and academics was observed, with African authors citing more academics outside Africa than within. E-learning, machine learning, cloud computing, and artificial intelligence were the most frequently researched areas. This study provides crucial information to researchers and policymakers, allowing them to map out future research and policies on Education 4.0.
Token Incentives in a Volatile Crypto Market: The Effects of Token Price Volatility on User Contribution

Crypto tokens, issued and managed via smart contracts, function as rewards in blockchain systems to encourage user participation. Distinct from monetary incentives, token incentives are uncertain in reward magnitude due to the large swings in token prices on crypto markets. By focusing on token price volatility, this study investigates how the reward uncertainty affects user contribution in a tokenized digital platform. Our empirical setting is Steemit, a platform where bloggers write posts and share token rewards based on their posts’ popularity. We find that while high token price volatility induces a large volume of blog posts, it diminishes post quality. The dichotomous effects are explained via two mechanisms: users’ direct reactions to reward uncertainties and their indirect reactions, mediated by altered token preference amid volatility shocks. Deeply exploring this dynamic process, our results reveal that token price volatility facilitates a platform’s network short-term effect but impairs long-term user creativity. Our empirical findings thus extend the literature on blockchain economics and cryptocurrencies and have practical implications for the design of incentive mechanisms on tokenized digital platforms.
Visual assessment of matrix‐variate normality

Summary In recent years, the analysis of three‐way data has become ever more prevalent in the literature. It is becoming increasingly common to analyse such data by means of matrix‐variate distributions, the most prevalent of which is the matrix‐variate normal distribution. Although many methods exist for assessing multivariate normality, there is a relative paucity of approaches for assessing matrix‐variate normality. Herein, a new visual method is proposed for assessing matrix‐variate normality by means of a distance–distance plot. In addition, a testing procedure is discussed to be used in tandem with the proposed visual method. The proposed approach is illustrated via simulated data as well as an application on analysing handwritten digits.
Multi-stream Global–Local Motion Fusion Network for skeleton-based action recognition

Skeleton-based action recognition is widely used in varied areas such as human–machine interaction and virtual reality. Benefit from the powerful expression ability to depict structural data, graph convolutional networks (GCNs) have been developed to address this task by modeling the human body skeletons as spatial–temporal graphs. However, most existing GCN-based methods usually ignore the diversity of the motion information between channels of the input feature. And how to enhance the ability to capture the long-term global correlations in spatial and temporal dimensions is also a fundamental challenge. In this work, we propose a novel multi-stream framework Global–Local Motion Fusion Network (GLMFN), which integrates the global and local motion information of spatial–temporal dimensions. Specifically, we design a grouping graph convolution module to enforce the ability to aggregate local spatial motion information. Besides, to learn richer semantic features, we propose two modules based on the self-attention operator: a spatial self-attention module and a temporal self-attention module. The former is responsible for extracting spatial long-term motion relationships, while the latter aims to capture temporal long-term motion relationships. Moreover, we present a multi-stream fusion strategy with a series of treatments for body joints to achieve a better recognition effect. To validate the efficacy and efficiency of the proposed model, we perform exhaustive experiments on the NTU-RGBD dataset and NTU-RGBD-120 dataset, and our method achieves the state-of-the-art performance on both datasets.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEAKTIFAN BELAJAR SISWA SEKOLAH DASAR

The background of this research is to describe the factors that influence the learning activity of elementary school students. This research is a qualitative descriptive study. The subjects of this study were fifth grade students of SD Negeri 004 Bangkinang Kota. Methods of data collection using observation and interviews. The data analysis technique uses data reduction, data presentation, and drawing conclusions. The data validity technique uses source and technique triangulation. The results showed that the internal factors that affect the learning activity of fifth grade students are (1) physiological factors, namely physical (sensory) and physical conditions, (2) psychological factors, namely attention, memory, and responses. External factors that affect the learning activity of fifth graders are (1) social factors, namely teachers and peers, (2) non-social factors, namely places and facilities. The dominant factor that affects the learning activity of class V students is the student's psychological factor.
Self-regulatory Fault Forbearing and Recuperation Scheduling Model in Uncertain Cloud Context

Cloud computing has become inevitable in own way with its offering multi-services, deployment, resource provisioning, data management, etc. Efficient resource consumption proves the dignity of a reliable cloud service provider in the market. However, the cloud environment often witnesses the uncertainties with respect to the resource provisioning. The most significant uncertainty encountered is the fault that paves way for a failure in the total process. Existing methods fail to incorporate such uncertainty into the picture leading to performance degradation of the cloud service requested by cloud user. The proposed scheme, robust fault detection and recuperation (RFDR), is a reactive fault-tolerant strategy that monitors the system continuously and recovers it from failures caused by VMs, host, and PEs. The proposed mechanism is run through baseline scheduling algorithm such as first come first service (FCFS), shortest job first (SJF), priority scheduling (PS), min-min, and max–min scheduling for the performance evaluation. The experiments indicate that the proposed method efficiently handles the fault detection and recover with 99% of resource availability and reliability.
Data Security using Multi-Level Encryption

In the present paper the authors have suggested a unique symmetric key cryptographic technique where three modern cipher, namely transposition cipher, rotation cipher and XOR ciphers have been used. The cipher text thus obtained is hard to crack because a teraflop super computer may need upto 3400 Years to find all possible permutations even for a plain text consisting of 10 characters.
The current state of cryptocurrency forensics

This paper examines the current state of cryptocurrency forensics focusing on digital forensics investigation supporting law enforcement activities. We begin with a brief overview of cryptocurrency and blockchain technologies, and with that understanding, we explore criminal and malicious activities enabled by these technologies which drive the need for cryptocurrency forensics. Followed by this, the paper will explore current cryptocurrency forensics techniques and challenges pertaining to mobile forensics, computer & memory forensics, and blockchain & network forensics. We will analyze the current body of research and identify gaps in the area. Also, current researchers and practicing investigators indicate that cryptocurrency forensics seems to be lagging and needs improved inter-agency coordination along with forensic techniques to counter the existing and emerging threats.
P <sub>2</sub> O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants

Wastewater treatment plants (WWTPs) are critical infrastructures responsible for processing wastewater before discharging effluent to rivers and other potential uses. WWTPs use large, connected deep tunnels for storing sanitary and wet-weather flows for treatment. However, wastewater in those systems cannot exceed safe tunnel levels in order to prevent overflows of untreated wastewater into the environment. Further, WWTPs are among the 16 national lifeline infrastructure sectors in which the utilization of sensor technology has increased, making the sectors vulnerable to all forms of cyber threats. Considering these challenges, the work presented in this manuscript uncovers the role of AI at WWTPs by focusing on two problems: tunnel water-level prediction and detection of security threats. This is done by proposing an AI framework: P2O (prediction, protection, and optimization). The prediction module forecasts the tunnel water level using deep-learning models based on the current wastewater flow in the tunnel and other inputs from the sensors and gauges. The protection module focuses on classifying the intentionality of an anomaly, i.e., whether an attack is adversarial in nature or merely an outlier, using recurrent neural network models. Last, the optimization module aims to provide actionable recommendations to pump operators using a genetic algorithm. The experimental results of P2O indicate that the prediction module can predict the tunnel water level with 85% accuracy, and the protection module can detect about 97% of intentional attacks on WWTPs. AI models within P2O are evaluated; the experimental results are presented and discussed.