Reviewerly/exHarmony huggingface dataset:
Layer-Level Progressive Transformer With Modality Difference Awareness for Multi-Modal Neural Machine Translation

Multi-modal neural machine translation (MNMT) aims to translate sentences from the source language into the target language with the aid of corresponding images. Unfortunately, there is a considerable modality gap between the semantic-related images and texts in terms of data form and semantic expression. How to fully incorporate visual information into texts to enhance the performance of machine translation is one of the critical issues for MNMT. However, the initial visual and textual features are generally extracted with their modality-specific models; Consequently, there is a considerable representation gap between images and texts. Most previous MNMT works prefer only to adopt the feature-level fusion strategies to learn multi-modal representation, while the modality representation gap is often ignored. To this end, this paper proposes a progressive multi-modal Transformer (ProMul-Trans) with Modality Difference-Aware (MDA) to address the visual-to-textual fusion problem raised in MNMT. We first employ MDA to capture the modality-consistency information by taking visual and textual representations as inputs in each Transformer layer. Then a layer-level progressive fusion (Layer-ProFusion) strategy is adopted to progressively align visual and textual representations layer-by-layer to enhance machine translation performance. Experiment results on the Multi30k dataset are conducted, and the results show that the proposed approach outperforms the compared state-of-the-art (SOTA) methods on English <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> German (En <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> De), English <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> French (En <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> Fr) and English <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> Czech (En <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> Cs) tasks. We release the code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JunjieYe-MMT/HierProMul-Trans</uri> .
Support Vector Machines

Support vector classification finds a separating line (more generally: a hyperplane) between two classes such that the distance between the separating line and the nearest observation of either class is maximized. Often the classes are not separable, i.e., they do not form separate clouds in x-space. In that case, a cost parameter allows for a certain amount of classification error. By deriving additional x-variables (e.g., quadratic terms), we can increase the number of x-variables. We can usually find a separating hyperplane in the expanded x-space with less classification error because the observations appear more sparse in higher dimensions. The so-called kernel trick makes expanding the x-space computationally efficient. Support vector classification can be adapted to work for multi-class and regression problems. A case study predicts the popularity of online news.
Boosting

We introduce gradient boosting. We discuss the MART algorithm and give details for Gaussian gradient boosting, logit boosting, and multinomial boosting. We talk about variable influence as a way of computing the contribution of individual variables and contrast this approach with variable importance as used in random forests. We discuss tuning parameters and the effect of individual tuning parameters on computing time. We also introduce an increasingly popular regularized generalization of gradient boosting called XGBoost. Our case study is about an open-ended survey question where respondents give advice to “Patient Joe” in a hypothetical situation. We classify the text answers into one of four classes. The case study illustrates pre-processing, tuning, bar charts of variable influence, and prediction.
Nearest Neighbors

We discuss k-nearest neighbor (kNN) classification and regression. We introduce several distance and similarity metrics and explain how to resolve ties that can arise for prediction. kNN can easily accommodate highly nonlinear behavior and approximates the Bayes classifier. However, kNN is slow for large data sets and memory hungry. The tuning parameter k regulates the bias-variance tradeoff, and kNN is sensitive to scaling. The case study classifies reasons for quitting smoking from the Waterloo Smoker Helpline.
A Method for Generating True Random Numbers With Multiple Distribution Characteristics

In this paper, a method of generating true random numbers obeying multiple distribution characteristics is proposed. First, two resistance-capacitance (RC) self-excited oscillation circuits are used to generate two jittered, periodically unstable square wave signals, and then a high-precision and high-frequency quartz crystal oscillator is used to sample and measure these two jittered signals to obtain their periods. Due to the randomness of the external environment, there is Gaussian white noise in the circuit, so the periods of these two signals are random variables subject to the Gaussian distribution. Finally, we use a field-programmable gate array (FPGA) to perform secondary processing on these two random signals, which can generate true random number sequences that conform to the distribution characteristics of Gaussian distribution, Poisson distribution, 0-1 distribution, uniform distribution, etc. The random numbers generated by the circuit can be applied to the field of intelligent control related to automatic control and machine learning. This method uses a physical entropy source to generate high-quality true random sequences, which are easy to build, low in circuit cost, and small in size. The experimental results show that the method is low-cost, highly reliable and easy to integrate, providing an effective solution for the generation of true random numbers in electronic systems.
MSStyleTTS: Multi-Scale Style Modeling With Hierarchical Context Information for Expressive Speech Synthesis

Expressive speech synthesis is crucial for many human-computer interaction scenarios, such as audiobooks, podcasts, and voice assistants. Previous works focus on predicting the style embeddings at one single scale from the information within the current sentence. Whereas, context information in neighboring sentences and multi-scale nature of style in human speech are neglected, making it challenging to convert multi-sentence text into natural and expressive speech. In this paper, we propose MSStyleTTS, a style modeling method for expressive speech synthesis, to capture and predict styles at different levels from a wider range of context rather than a sentence. Two sub-modules, including multi-scale style extractor and multi-scale style predictor, are trained together with a FastSpeech 2 based acoustic model. The predictor is designed to explore the hierarchical context information by considering structural relationships in context and predict style embeddings at global-level, sentence-level and subword-level. The extractor extracts multi-scale style embedding from the ground-truth speech and explicitly guides the style prediction. Evaluations on both in-domain and out-of-domain audiobook datasets demonstrate that the proposed method significantly outperforms the three baselines. In addition, we conduct the analysis of the context information and multi-scale style representations that have never been discussed before.
Stacking

We briefly introduce stacking as a way of combining predictions from several learning algorithms. Winning entries in Kaggle competitions often use some form of stacking.
Neural Network-Based Moving Window Iterative Nonlinear System Identification

The finite memory estimation based learning algorithm (FME-LA 1) is a newly developed technique for identifying unknown nonlinear systems that demonstrates remarkable accuracy and robustness against disturbances and error accumulation by utilizing a finite number of measurements on a moving window. However, there are several drawbacks to FME-LA 1. First, the weights of higher-order or complex systems cannot be learned because it only learns the output layer's weights. Second, it does not consider noise statistics, resulting in significant performance degradation in complex environments. Finally, it is not suitable for real-time implementation because the computation increases rapidly with the size of the moving window. To address these limitations, we propose the moving window iterative nonlinear system identification (MWI-NSID) scheme, which guarantees robustness and accuracy with a significant reduction in computation. In simulation results on a complex nonlinear system, the proposed MWI-NSID scheme demonstrated robustness, accuracy, and significantly reduced computation.
Towards a Cash-on-Delivery System Based on Blockchain Technology for Developing Countries: A Case Study in Vietnam

Cargo transport patterns have played an extremely important role in the economic development of a region or a country. For developing countries, this model faces many risks affecting the interests of sellers and buyers. In Vietnam (i.e., the case study model in this study), traditional freight transport models are based on three methods, i.e., postal system, shipping company, or commercial platform electronic. However, the above models are all dependent on the shipping company (i.e., third party). If there is a conflict (e.g., loss of goods), it is difficult to determine who is responsible (i.e., seller, buyer, or carrier). Because of these risks, developing countries need to adopt a model of freight transport that ensures the interests of the parties involved (i.e., the seller and the buyer). In this article, we aim at a freight model that applies Blockchain technology and Smart contracts to eliminate risks from traditional models. The main contribution of this paper consists of three aspects: (a) proposes a model of cargo management and transportation based on Blockchain technology and Smart contract applied to Vietnam to replace the traditional shipping model; (b) implement the proposed model (i.e., proof-of-concept) on the Hyperledger Fabric platform; (c) evaluate the proposed model against the Hyperledger Caliper platform.
A Knowledge Graph Approach to Spectrum Explainability

Increased access to the radio frequency spectrum is critical to continued wireless innovation, yet there is limited understanding about how this valuable natural resource is being used. Measurements play a key role in understanding spectrum use and can be used to build sophisticated machine learning models for spectrum access and management. Although there have been many different spectrum measurement campaigns, technical and regulatory communities still struggle to extract the information they need from the existing studies and datasets. Spectrum measurements are highly complex spatiotemporal data sets requiring specialized domain knowledge to collect, analyze and interpret. A variety of contextual information ranging from the specific configuration of the spectrum sensor and potential emitters to drivers of spectrum use is needed to understand and document the coverage and limitations of spectrum measurements. This paper presents a knowledge graph approach to spectrum explainability. The knowledge graph unifies relevant contextual information from a variety of sources with spectrum measurement summaries. A prototype knowledge graph that includes a variety of contextual information along with over 4 years of multiband measurement summaries has been implemented on the Neo4j graph data platform. The knowledge graph can be queried to extract a wide variety of insights thus making spectrum knowledge more widely accessible to a variety of stakeholders from researchers to policy makers. Several examples of insights gained through queries are provided.
The Social and Technological Incentives for Cybercriminals to Engage in Ransomware Activities

Ransomware attacks and the use of the dark web forums are two serious contemporary cyber-problems. These two areas have been investigated separately in the past, but there is currently a gap in our understanding with regard to the interactions between them – i.e., dark web forums that can potentially lead to ransomware activities. The rise of Ransomware-as-a-Service (RaaS) exacerbates these problems even further. The aim of this paper is therefore to investigate the social and technological discourse within the dark web forums that may foster or initiate some of the users’ pathway towards ransomware-related criminal activities. To this aim, we carried out data collection (crawling) of pertinent posts from the “Dread” dark web forum, based on sixteen keywords commonly associated with ransomware. Our data collection and manual screening processes resulted in the identification of 1,279 posts related to ransomware, with the posting dates between 25 March 2018 and 30 September 2022. Our dataset confirms that ransomware-related posts exist on the Dread dark web forum. We found that these posts can generally be grouped into eight categories: Hacker, Potential Hacker, RaaS Provider, Education, Information, News, Debate and Other. Furthermore, the contents of these posts shed some light on the social and technological incentives that may encourage some actors to get involved in ransomware crimes. In conclusion, such posts pose a threat to cyber security, because they might provide a pathway for wannabe ransomware operators to get in on the act. The findings from our research can serve as a starting point for devising practical countermeasures, for instance by considering how such posts should be handled in the future, or how some follow-up intervention actions can be prepared in anticipation of certain actors getting involved in ransomware as a result of reading posts in such forums.
A Comparison of YOLOv5 and YOLOv8 in the Context of Mobile UI Detection

With ever increasing technological capabilities, nowadays artificially intelligent systems solve mathematical equations, write poems and songs. However, to this day, many companies who build native mobile applications replicate their work and increase their workload by implementing the same user interface for each one of their target mobile platforms. Accordingly, this study aims to design, train and test a GUI element recognition model by utilizing the latest, state-of-the-art YOLOv8 and Roboflow Object Detection (Fast) algorithm, which then can be used to implement a multi-platform user interface generator. For evaluation purposes, a study in the same domain is set as a benchmark so that the newly obtained results can be interpreted meaningfully and put into a perspective. Accordingly, the results showed that the newly proposed YOLOv8s and YOLOv8n models have performed with a 3.32% and 1.62% better mAP respectively, than the benchmark study. On the other hand, the Roboflow Object Detection (Fast) model performed with a 1.08% lower mAP than the YOLOv5s benchmark study.
Data Reconstruction Attack Against Principal Component Analysis

Abstract Attacking machine learning models is one of the many ways to measure the privacy of machine learning models. Therefore, studying the performance of attacks against machine learning techniques is essential to know whether somebody can share information about machine learning models, and if shared, how much can be shared? In this work, we investigate one of the widely used dimensionality reduction techniques Principal Component Analysis (PCA). We refer to a recent paper that shows how to attack PCA using a Membership Inference Attack (MIA). When using membership inference attacks against PCA, the adversary gets access to some of the principal components and wants to determine if a particular record was used to compute those principal components. We assume that the adversary knows the distribution of training data, which is a reasonable and useful assumption for a membership inference attack. With this assumption, we show that the adversary can make a data reconstruction attack, which is a more severe attack than the membership attack. For a protection mechanism, we propose that the data guardian first generate synthetic data and then compute the principal components. We also compare our proposed approach with Differentially Private Principal Component Analysis (DPPCA). The experimental findings show the degree to which the adversary successfully attempted to recover the users’ original data. We obtained comparable results with DPPCA. The number of principal components the attacker intercepted affects the attack’s outcome. Therefore, our work aims to answer how much information about machine learning models is safe to disclose while protecting users’ privacy.
Path Planning for Electric Power Inspection Robot Based on the Fusion of Improved A* and DWA Algorithm

Based on the real-time path planning needs of electric power inspection robot in substation environments, we propose a path planning algorithm using an improved A* and dynamic window approach (DWA). This addresses the issues of low search efficiency and numerous turning points associated with traditional A* algorithms. Firstly, we propose an adaptive dynamic weighting factor to enhance the evaluation function of the A* algorithm. Secondly, we integrate the dynamic window approach with the improved A* algorithm to achieve optimal path planning. The simulation results indicate that the fusion algorithm produces a smoother and more efficient path plan in comparison to the traditional A* algorithm. Moreover, field experiments demonstrate that the fusion algorithm is capable of generating smooth paths at corners while maintaining a safe distance from obstacles. The electric power inspection robot effectively navigates through obstacles during practical operations, thereby satisfying the imperative need for safe and efficient inspection practices in the electric power industry.
Trees

Decision trees divide the x-space with nested if-then rules into hyper-rectangles. The if-then decision rules can be depicted graphically to look like an upside-down tree: starting with the root node, an if-then rule splits into two branches, and each of the branches splits again, and so forth. Each terminal node represents one hyper-rectangle. For prediction, all observations in the same hyper-rectangle are assigned the same constant value. Decision trees are interpretable, but they are also highly unstable and predict only moderately well. However, decision trees are important building blocks for random forests and boosting. A case study classifies Shakespeare plays based on the social status of the first two speakers in the play as a comedy, history, or tragedy.
Layer-wise partitioning and merging for efficient and scalable deep learning

Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of 1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process; 2) a forward pass and backward pass parallelisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed; and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach.
FedHM: Practical federated learning for heterogeneous model deployments

In this paper, we propose a novel federated learning framework named FedHM that aims to address the challenge of training models on heterogeneous devices with varying architectures. Our approach enables the collaborative training of diverse local models by sharing a fully convolutional network (FCN) architecture that effectively extracts the local-to-global representations. By leveraging the weights with respect to this abstraction as common information across different DNN architectures, FedHM achieves efficient federated learning with minimal computational and communication overhead. We compare FedHM with three federated learning frameworks using two datasets for image classification tasks. Our results show that FedHM achieves high accuracy with considerably lower computational and communication costs compared to the other frameworks.
Effective &amp; near real-time track-to-track association for large sensor data in Maritime Tactical Data System

The Maritime Tactical Data System is a software system that collects track data from maritime sensors to compile and show on a sea map to provide maritime patrol vessels or maritime surveillance stations with situational awareness. Speed and precision in tracking multiple targets are crucial in achieving situational awareness. A multi-target tracking problem is NP-hard if it involves more than two sensors and a large amount of data since it generates many potential solutions that must be evaluated. Previous research has demonstrated that the Density-based Spatial Clustering of Applications with Noise (DBSCAN) method can perform track-to-track association with pretty good results; nevertheless, the density-reachable concept of DBSCAN poses a problem when two targets are within a distance less than the threshold. Another limitation is the inability of DBSCAN to associate tracks as soon as sensor track data is received. DBSCAN must run after all data has been collected in a database. In this paper, a novel track-to-track association method called Neighborhood Clustering Track Association and Fusion (NCTAF) is proposed to address the limitations of DBSCAN. According to the experiment results, NCTAF overcame the inaccurate cluster form generated by DBSCAN. The most remarkable result is that NCTAF performs track associations in an average of one second after receiving sensor track data involving three sensors, 4000 track data per sensor, and an update rate of 5-12 s per sensor. In contrast, DBSCAN required more than 10 min for the same scenario.