Reviewerly/exHarmony huggingface dataset:
Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model

With the increase in the volume of news articles and headlines being generated, it is becoming more difficult for individuals to keep up with the latest developments and find relevant news articles in the Kurdish language. To address this issue, this paper proposes a novel data augmentation approach for improving the performance of Kurdish news headline classification using back-translation and a proposed deep learning Bidirectional Long Short-Term Memory (BiLSTM) model. The approach involves generating synthetic training data by translating Kurdish headlines into a target language in this context English language and back-translating them to the Kurdish language, resulting in an augmented dataset. The proposed BiLSTM model is trained on the augmented data and compared with baseline models SVM (Support-Vector-Machines) and Naïve Bayes an trained on the original data. The experimental results demonstrate that the proposed BiLSTM model outperforms the baseline model and other existing models, achieving state-of-the-art performance on the Kurdish news headline classification task. The findings suggest that the combination of back-translation and a proposed BiLSTM model is a promising approach for data augmentation in low-resource languages, contributing to the advancement of natural language processing in under-resourced languages. Moreover, having a Kurdish news headline classification model can improve access to news and information for Kurdish speakers. With the classification model, they can easily and quickly search for news articles that interest them based on their preferred categories, such as politics, sports, or entertainment.
On Feasibility of Server-side Backdoor Attacks on Split Learning

Split learning is a collaborative learning design that allows several participants (clients) to train a shared model while keeping their datasets private.In split learning, the network is split into two halves: clients have the initial part until the cut layer, and the remaining part of the network is on the server side.In the training process, clients feed the data into the first part of the network and send the output (smashed data) to the server, which uses it as the input for the remaining part of the network.Recent studies demonstrate that collaborative learning models, specifically federated learning, are vulnerable to security and privacy attacks such as model inference and backdoor attacks.While there have been studies regarding inference attacks on split learning, it has not yet been tested for backdoor attacks.This paper performs a novel backdoor attack on split learning and studies its effectiveness.Despite traditional backdoor attacks done on the client side, we inject the backdoor trigger from the server side.We provide two attack methods: one using a surrogate client and another using an autoencoder to poison the model via incoming smashed data and its outgoing gradient toward the innocent participants.The results show that despite using strong patterns and injection methods, split learning is highly robust and resistant to such poisoning attacks.While we get the attack success rate of 100% as our best result for the MNIST dataset, in most of the other cases, our attack shows little success when increasing the cut layer.
An image encryption algorithm based on cascade chaotic map and DNA coding

Abstract The 2D‐Cascade‐Transeform‐Logistic‐Sine Map (2D‐CTLSM) with good randomness and large parameter chaos range is obtained by improving the Logistic and Sine map in a cascade manner. By combining the chaotic map with DNA coding, an efficient encryption algorithm based on a chaotic map with chunking dislocation and alternate row diffusion is designed. The shortcomings of zigzag itself are overcome by performing different zigzag transformations in chunks while combining DNA encoding to accomplish the diffusion operation. Due to the excellent properties of DNA encoding, the diffusion operation is performed by the DNA computation method with alternate row diffusion so that each value can complete the diffusion operation twice. The simulation experimental results and security analysis show that the algorithm can effectively resist various typical attacks, such as the information entropy is close to the expected value of 8, the correlation coefficient is close to 0, and the NPCR and UACI values can meet the critical value range, and so on. This is a safe and reliable image encryption algorithm.
Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data

The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb × Zn/Cu × Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces.
Pixel Value Adjustment to Reduce the Energy Requirements of Display Devices

The media and entertainment industry consumes a significant amount of energy to create, distribute, and present video content to consumers. A large proportion of this energy is used by display devices, notably by the 1.9+ billion displays installed in consumers’ homes globally. The production of light within a display is the main reason for this energy use, independent of display technology. Reducing the global energy use of display devices by any means possible is, therefore, a topic of prime concern. Due to the very large number of displays currently in use, even small reductions per display may end up globally saving enormous amounts of energy. In this spirit, we present a video processing technique that can reduce energy requirements without altering the visual quality of the displayed material. For use cases whereby a viewer would accept a certain loss of visual quality, a user parameter could be exposed that further reduces display energy requirements.
Design of True Random Number Generator Based on Multi-Ring Convergence Oscillator Using Short Pulse Enhanced Randomness

The entropy source structure with embedded XOR gates in a ring oscillator (RO) as a true random number generator (TRNG) can improve the speed of accumulating jitter in the oscillator. However, the XOR gate has a certain response time to the input change, and when the input changes too fast, the XOR gate will output short pulses. In this paper, we propose a TRNG design based on a multi-ring convergence oscillator (MRCO) making use of the characteristics of short pulses. We study the output of the XOR gate when facing different inputs. By modeling the time of a fibonacci ring oscillator (FIRO) as an example, we find that the loss of short pulses in an inverter chain is the reason for making the FIRO enter into periodic oscillation. This phenomenon suppresses the accumulation of jitter and occurs periodically in existing structures. Our proposed structure uses independent sub-rings to accumulate jitter, allowing the main-ring to quickly generate short pulses to provide analog randomness. The proposed TRNG design is implemented in Xilinx Virtex-6 FPGA. The experimental results show that it has the highest ratio of throughput rate to hardware resources. The generated random sequence pass both NIST SP800-22 test and NIST SP800-90B test.
A Cross-Chain framework for Industry Collaboration and Transaction

Industrial Internet technology is developing rapidly. Besides revolutionizing the production end, the Industrial Internet has profound impact on the marketing and service end. However, the Industrial Internet faces challenges in both production collaboration and value exchange. In terms of production collaboration, the current peer-to-peer-based industrial data exchange is inefficient and expensive. And for value exchange, a single indicator like the price can cause information asymmetry in transactions. We propose a cross-chain framework, which consists of three main aspects: the Collaboration Chain (CChain), the Transaction Chain (TChain), and the Cross-chain mechanism that collects manufacturing information from CChain and generates evaluation parameters on TChain for information exchange and market regulation. A normal method to build a machine-learning-based credit model is proposed as the cross-chain function, which can integrate various data into one credit parameter that can be adjusted through market feedback simultaneously. To demonstrate its feasibility, a demo framework is built and evaluated. The results show that the data registration and the cross-chain mechanism can support practical applications. Based on the proposed framework, the production and transaction activities of the entire industrial chain can reach the state of self-organization, self-adaptation, and self-development, which can be considered the foundation of the Industrial Metaverse.
Cross-Chain Technologies: Challenges and Opportunties for Blockchain Interoperability

Blockchain technology has given rise to various blockchains, each with unique characteristics and applications. However, these isolated and heterogeneous blockchains pose challenges for value transfer and data communication, creating disconnected value islands. To address this issue, cross-chain technology provides blockchain interconnection, improving interoperability and scalability. This paper explores cross-chain technologies, discussing their background, significance, and research status. It also introduces, analyzes, and compares mainstream cross-chain technologies and projects. Additionally, the paper identifies current challenges in cross-chain technology and suggests solutions, promoting discussions on its development trends. The paper aims to provide researchers and practitioners with a comprehensive understanding of blockchain interoperability and relevant knowledge and application methods.
Non-adversarial Robustness of Deep Learning Methods for Computer Vision

Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shifts caused by natural variations in data. However, achieving this property is challenging because it is difficult to predict in advance the types of distribution shifts that may occur. To address this challenge, researchers have proposed various approaches, some of which anticipate potential distribution shifts, while others utilize knowledge about the shifts that have already occurred to enhance model generalizability. In this paper, we present a brief overview of the most recent techniques for improving the robustness of computer vision methods, as well as a summary of commonly used robustness benchmark datasets for evaluating the model’s performance under data distribution shifts. Finally, we examine the strengths and limitations of the approaches reviewed and identify general trends in deep learning robustness improvement for computer vision.
Anomaly Detection on Distributed Ledger Using Unsupervised Machine Learning

In recent years, blockchain technology has gained widespread attention for its distributed and immutable ledger system that ensures security and transparency. However, the decentralized nature of the blockchain network also presents unique challenges in detecting fraudulent activities, such as money laundering, phishing, and other illicit transactions that may be executed by malicious actors. The traditional detection methods, such as rule-based systems, may not be sufficient to capture the complex and evolving nature of these activities. This paper proposes an anomaly detection approach for significant entity identification within Worldwide Asset Exchange (WAX) blockchain network using unsupervised machine learning. The proposed approach was evaluated by utilizing three detection algorithms (CBLOF, AE, and IF) for assigning an anomaly score to each account in the dataset. The results indicate the presence of potentially fraudulent activities and the effectiveness of the anomaly ranking mechanism in identifying such cases.
Can an old fashioned feature extraction and a light-weight model improve vulnerability type identification performance?

Recent advances in automated vulnerability detection have achieved potential results in helping developers determine vulnerable components. However, after detecting vulnerabilities, investigating to fix vulnerable code is a non-trivial task. In fact, the types of vulnerability, such as buffer overflow or memory corruption, could help developers quickly understand the nature of the weaknesses and localize vulnerabilities for security analysis. In this work, we investigate the problem of vulnerability type identification (VTI). The problem is modeled as the multi-label classification task, which could be effectively addressed by “pre-training, then fine-tuning” framework with deep pre-trained embedding models. We evaluate the performance of the well-known and advanced pre-trained models for VTI on a large set of vulnerabilities. Surprisingly, their performance is not much better than that of the classical baseline approach with an old-fashioned bag-of-word, TF-IDF. Meanwhile, these deep neural network approaches cost much more resources and require GPU. We also introduce a lightweight independent component to refine the predictions of the baseline approach. Our idea is that the types of vulnerabilities could strongly correlate to certain code tokens (distinguishing tokens) in several crucial parts of programs. The distinguishing tokens for each vulnerability type are statistically identified based on their prevalence in the type versus the others. Our results show that the baseline approach enhanced by our component can outperform the state-of-the-art deep pre-trained approaches while retaining very high efficiency. Furthermore, the proposed component could also improve the neural network approaches by up to 92.8% in macro-average F1.
Generating paraphrase sentences for multimodal entity-category-sentiment triple extraction

Multimodal entity-based sentiment analysis (MEBSA) is an emerging task in sentiment analysis that aims to identify three key elements (entity, entity category, and sentiment polarity) from a pair of sentence and image. However, most existing studies have primarily focused on one or two MEBSA subtasks, ignoring the fact that these subtasks are closely related with one another. Moreover, previous studies focused on the detection of coarse-grained entity categories, which failed to provide sufficient information to disambiguate entities. To address these two issues, we introduced a new task called multimodal entity-category-sentiment triple extraction (MECSTE) to extract entities, their corresponding fine-grained entity categories, and sentiment polarities simultaneously. We constructed two datasets for this task based on two existing Twitter corpora. Moreover, we developed a generative multimodal approach based on a pre-trained sequence-to-sequence model that formulates the MECSTE task as a paraphrase generation problem by linearising all the entity-category-sentiment triples into a natural language sentence. Extensive experiments on the annotated Twitter datasets demonstrate the superiority of the proposed method.
AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation

The ever-increasing concerns on data security and user privacy have significantly impacted the current centralized mechanism of intelligent systems in bridging private data islands and idle computing resources commonly dispersed at the edge. To resolve that, a novel distributed learning paradigm, called Federated Learning (FL), which can learn a global model in a collaborative and privacy-preserving manner, has been proposed and widely discussed. Furthermore, to tackle the data heterogeneity and model adaptation issues faced by FL, meta-learning starts to be applied together with FL to rapidly train a global model with high generalization. However, since federated meta-learning is still in its infancy to collaborate with participants in synchronous mode, straggler and over-fitting issues may impede its application in ubiquitous intelligence, such as smart health and intelligent transportation. Motivated by this, this paper proposes a novel asynchronous federated meta-learning mechanism, called AFMeta, that can measure the staleness of local models to enhance model aggregation. To the best of our knowledge, AFMeta is the first work studying the asynchronous mode in federated meta-learning. We evaluate AFMeta against state-of-the-art baselines on classification and regression tasks. The results show that it boosts the model performance by 44.23% and reduces the learning time by 86.35%.
A Web Service Classification Method Based on Graph Neural Network Knowledge Distillation

With the increasing number of Web services, service discovery becomes a significant challenge, and service classification methods based on Graph Neural Network (GNN) are considered to be an efficient way for service discovery. However, these methods rarely exploit the structure-based and feature-based prior knowledge of services. To address this problem, this paper proposes a Web service classification method based on graph neural network knowledge distillation. In this method, firstly, it employs Word2Vec to extract the semantic feature vector from service descriptions, and then builds the service relationship network based on the shared tag annotation relationship of service. Secondly, a graph neural network knowledge distillation framework for Web service classification consisting of a teacher model and a student model is designed. The teacher model extracts the knowledge of GNN model, and the student model obtains the structure-based and feature-based prior knowledge of the service relationship network from teacher model by neighbor feature aggregation and feature transformation. Finally, GNN models (GCN, GAT, SAGE, APPNP, SGC, and GCNII) as teacher models for accuracy comparison are used in the experiment. From the experiment result, the classification accuracy of the student model is 11.64%~23.10% higher than that of the teacher model.
CGFNet: 3D Convolution Guided and Multi-scale Volume Fusion Network for fast and robust stereo matching

Nowadays, although significant progress has been made by convolutional neural network, it is still difficult to realize accurate and robust stereo matching in real time. In this article, we study how to achieve more accurate and robust disparity estimation based on real-time requirement. For this reason, a Multi-scale Volume Fusion (MVF) module was proposed and embedded to improve the matching accuracy. To achieve real-time performance, an innovative way to use 3D convolution is proposed. The 3D convolution is used during training for guidance and supervision, making the inference lightweight. Based on these two structures, we designed an end-to-end stereo matching method called 3D Convolution Guided and Multi-scale Cost Volume Fusion Network (CGFNet). Experimental results showed that our CGFNet has better generalization performance on cross-domain datasets, which achieves more accurate disparity estimation without additional fine tuning process in challenging regions. On KITTI benchmark, CGFNet reached D1-all=1.98% with substantial improvement among the State-Of-The-Art (SOTA) real-time models and runs a pair of images within 38 ms (26 fps). The results are notable when considering both matching accuracy and real-time performance.
SteadySketch: Finding Steady Flows in Data Streams

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub.
A Method of Identifying Causes of Prediction Errors to Accelerate MLOps

MLOps, techniques for solving operational problems in machine learning, have attracted attention in recent years. In MLOps, understanding why mispredictions occur is essential to improving operational models. However, misprediction analysis is currently a time-consuming process performed manually by data scientists. In this paper, we propose a flowchart-structured analysis method (called AIEDF) that automatically identifies the causes of mispredictions during model operation. Thanks to the flowchart structure, AIEDF's analyses are comprehensive and explainable. In addition, AIEDF is flexible in implementation and can be model agnostic. Through experiments with synthetic and real data, we have demonstrated that AIEDF accurately identifies root causes and provides valuable insights for model improvement.