Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Verification Process for Smart Cities

Blockchain technology is widely used in many security-related Internet of Things (IoT) paradigms due to its dynamic and distributed architecture, which assures secure and private network access. Smart cities are one of these IoT applications in which security and privacy play a vital role. This paper proposes a blockchain-based verification process for smart nodes communication within the smart city, demonstrating the implementation details of the proposed security verification process and discussing potential security attacks.
A Library-Based Approach for Writing Design Assertions

Assertions are a crucial aspect of software development, serving as a way to validate conditions during code execution. They are expressed as Boolean expressions that must hold true for the code to run correctly and enforce design decisions and constraints. This paper introduces a new approach for creating assertions called design assertions. These assertions are generated from formally written design constraints and help to enforce important design decisions during code execution. The approach utilizes immutable library classes, specifically designed for writing design assertions, and converts design constraints written in the Object Constraint Language (OCL) into assert statements in the Dart language. The result is a more readable, maintainable, and reliable set of Dart assertions, providing a powerful tool for ensuring software design quality and integrity.
Evaluating the Performance of Containerized Webservers against web servers on Virtual Machines using Bombardment and Siege

Containerization is becoming an increasingly common aspect of DevOps. Adding a container layer increases the complexity and could impact system performance. This study explores the performance differences of the Apache and Nginx web servers on Virtual Machines (VMs) and Docker Containers with official web server images from Docker Hub. A sandbox environment was created with both containerized and non-containerized versions of the web servers, and their performance was analyzed using line graphs. The results showed differences in performance between VMs and Docker Containers, with some variation from previous research due to the virtualization being done locally rather than on the cloud. This study would be advantageous for organizations with on-premises infrastructure due to security or governing regulations.
Experimental Evaluation of Adversarial Attacks Against Natural Language Machine Learning Models

Machine learning models are being increasingly relied on for many natural language processing tasks. However, these models are vulnerable to adversarial attacks, i.e., inputs designed to target models into making a wrong prediction. Among different methods of attacking a model, it is important to understand what attacks are effective, so that we can design countermeasures to protect the models. In this paper, we design and implement six adversarial attacks against natural language machine learning models. Then, we evaluate the effectiveness of these attacks using a fine-tuned distilled BERT model and 5,000 sample sentences from the SST-2 dataset. Our results indicate that the Word-replace attack affected the model the most, which reduces the F1-score of the model by 34%. The Word-delete attack is the least effective, but still reduces the model’s accuracy by 17%. Based on the experimental results, we discuss our insights and provide our recommendations for building robust natural language machine learning models.
Examining Manual and Automatic MT Evaluation: A Grey Relational Analysis for Chinese-Portuguese Translation Quality

This study investigates the relationship between manual and automatic machine translation evaluation methodologies by employing Grey Correlation Analysis (GRA) to assess the correlation between Chinese-Portuguese machine translation outputs’ BLEU scores and human evaluation scores based on a proposed evaluation index system. The research aims to provide insights into the factors impacting machine translation quality and the most relevant linguistic dimensions in translation evaluation. The findings reveal that “usability” and “adequacy” exhibit the highest correlation with BLEU scores, while “Semantics” ranks highest among the ten manual evaluation indicators, when correlated with the aggregated human evaluation results, followed by “Correction” (correct information) and “Omission and/or Addition”. The findings contribute to the field of machine translation evaluation by illuminating the complex relationship between manual and automatic evaluation techniques and guiding future improvements in machine translation systems and evaluation methodologies.
Split and Federated Learning with Mobility in Vehicular Edge Computing

Vehicular edge computing (VEC) is a promising technology to support vehicular applications that leverage machine learning (ML) technology. Due to limited resources of the vehicle, the vehicle uses Split learning (SL) to split the computation of the ML model and offload it to the VEC server (VECS). Federated learning (FL) is also used for data privacy and parallel training of the vehicles. Therefore, SplitFed learning, which combines SL and FL, enables parallel processing, which is an advantage of FL, and reduces the computational burden on the vehicle through ML model split, which is an advantage of SL. However, the SplitFed learning does not consider the mobility of device/vehicle. Therefore, we propose a SplitFed learning with mobility method to minimize the training time of the model. SplitFed learning with mobility method is a migration method of the ML model when the vehicle moves from the current serving VECS to the target VECS. Through simulations, compared with conventional SplitFed learning where the vehicle travels after 50% and 80% of training is completed, the proposed method can reduce training time by about 19-33% for LeNet and by about 22-44% for VGG16, respectively, and does not degrade accuracy of model.
Multi-level Adversarial Training for Stock Sentiment Prediction

Stock sentiment prediction is a task to evaluate whether the investors are expecting or gaining a positive or negative return from a stock, which has a high correlation with investors’ sentiments towards the business. However, as the nature of social media, the textual information posted by ordinary people is usually noisy, inconsistent, and even grammatically incorrect, leading the model to generate unsatisfied predictions. In this paper, we improve the performance of stock sentiment prediction by applying and comparing adversarial training at multiple levels, including character, word, and sentence levels, with the utilization of three novel adversarial attack models: DeepWordBug, BAE, and Generative Adversarial Network (GAN). We also propose an effective pre-processing technique and a novel adversarial examples incorporation method to improve the prediction results. To make an objective evaluation, we select three backbone models: Embedding Bag, BERT, and RoBERTa-Twitter, and validate the models before and after adversarial training on the TweetFinSent dataset. Experimental results demonstrate remarkable improvements in the models after adversarial training, and the RoBERTa-Twitter model with word-level adversarial training performs optimally among the experimented models. We conclude that sentence-level and word-level adversarial training are the most appropriate for deep learning and pre-trained language models, respectively, and we further conduct ablation studies to highlight the usefulness of our data pre-processing and adversarial examples incorporation approaches and a case study to display the adversarial examples generated by the proposed adversarial attack models.
Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors

Smartphones are equipped with a wide variety of sensors, which can pose significant security and privacy risks if not properly protected. To assess the privacy and security risks of smartphone sensors, we first systematically reviewed 55 research papers. Driven by the findings of the systematic review, we carried out a follow-up questionnaire-based survey on 23 human end-users. The results reflect that the participants have a varying level of familiarity with smartphone sensors, and there is a noticeable dearth of awareness about the potential threats and preventive measures associated with these sensors. The findings from this study will inform the development of effective solutions for addressing security and privacy in mobile devices and beyond.
Sum-of-Squares Collision Detection for Curved Shapes and Paths

Sum-of-Squares Programming (SOSP) has recently been introduced to graphics as a unified way to address a large set of difficult problems involving higher order primitives. Unfortunately, a challenging aspect of this approach is the computational cost—especially for problems involving multiple geometries like collision detection. In this paper, we present techniques to reduce the cost of SOSP significantly. We use these improvements to speed up difficult problems like collision detection between Bézier triangles by as much as 300 ×. In addition, motivated by hair bundle simulation, we present SOSP based collision detection on the tapered cubic cylinder. We also present an algebraic formulation of rigid body motion enabling SOSP based collision detection for curved geometries and trajectories simultaneously. While these new formulations are complex, our speedups make them feasible. These advances improve the applicability of SOSP based collision detection and enable the continued progress of higher-order geometry processing.
DARAM: Dynamic Avatar-Human Motion Remapping Technique for Realistic Virtual Stair Ascending Motions

This paper introduces DARAM, a dynamic avatar-human motion remapping technique that enables VR users to ascend virtual stairs. The primary design goal is to provide a realistic sensation of virtual stair walking while accounting for discrepancies between the user’s real body motion and the avatar’s motion, arising due to the virtual stairs present only in the virtual environment. Another design goal is to make DARAM applicable to dynamic multi-user environments. To this end, DARAM is designed to achieve motion remapping dynamically without requiring prior information about virtual stairs or environments, simplifying implementation in diverse VR applications. Furthermore, DARAM aims to synthesize avatar motion that delivers not only a realistic first-person experience but also a believable third-person experience for surrounding observers, making it applicable to multi-user VR applications. Two user studies demonstrate that the proposed technique successfully serves our design goals.
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild

We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild. Our approach, called Neural Face Rigging (NFR), holds three key properties: (i) NFR’s expression space maintains human-interpretable editing parameters for artistic controls; (ii) NFR is readily applicable to arbitrary facial meshes with different connectivity and expressions; (iii) NFR can encode and produce fine-grained details of complex expressions performed by arbitrary subjects. To the best of our knowledge, NFR is the first approach to provide realistic and controllable deformations of in-the-wild facial meshes, without the manual creation of blendshapes or correspondence. We design a deformation autoencoder and train it through a multi-dataset training scheme, which benefits from the unique advantages of two data sources: a linear 3DMM with interpretable control parameters as in FACS and 4D captures of real faces with fine-grained details. Through various experiments, we show NFR’s ability to automatically produce realistic and accurate facial deformations across a wide range of existing datasets and noisy facial scans in-the-wild, while providing artist-controlled, editable parameters.
DJuggling: Sonification of expressive movement performance

In the real-time demo, we demonstrate how to create a musical performance using juggling movement as an instrument. We have equipped juggling balls with accelerometers, gyroscopes, and WiFi sensors. The system measures acceleration and rotation in a small HW footprint, allowing us to map various events to music. We provide hardware and software platforms to ease the creation of real-time live performances for artists and researchers alike.
Real-time Stage Modelling and Visual Effects for Live Performances

We present a novel live platform enhancing stage performances with real-time visual effects. Our demo showcases real-time 3D modeling, rendering and blending of assets, and interaction between real and virtual performers. We demonstrate our platform's capabilities with a mixed reality performance featuring virtual and real actors engaged with in-person audiences.
Intermediated Reality with an AI 3D Printed Character

We introduce live character conversational interactions in Intermediated Reality to bring real-world objects to life in Augmented Reality (AR) and Artificial Intelligence (AI). The AI recognizes live speech and generates short character responses, syncing the character’s facial expressions with speech audio. The Intermediated Reality AR warping technique allows for a high degree of realism in the animated facial expressions and movements reusing live video optical appearance direct from the device’s embedded camera. The proposed applications of Intermediated Reality with AI are exemplified through the captivating fusion of these technologies in toy interactive storytelling broadcasts and social telepresence scenarios. This innovative combination allows individuals to engage with AI characters in a natural and intuitive manner, creating new opportunities for social engagement and entertainment.
Real-time Collision using AI

Traditional narrow collision pipelines for rigid body dynamics typically consist of calculation of collisions between primitives with a dedicated deepest point function for each primitive pair and a much slower general collision calculation between a pair of convex shapes. The mixture of different collision functions makes these methods not CPU-cache friendly and unsuitable for modern GPU hardware. For many reinforcement learning applications, data transfers become the bottle-neck of the whole pipeline.
A systematic empirical study on word embedding based methods in discovering Chinese black keywords

With the development of online transactions, the Chinese cyber black market is proliferating and facilitates many cybercrimes. It is difficult to understand the cyber black market due to the confusing jargon (called black keywords in this paper) used by criminals to conceal underground transactions. To discover black keywords automatically, some natural language processing based methods have been proposed by comparing the similarity of word vectors generated by word embedding models. Therefore, the quality of word vectors generated has a significant impact on black keyword discovery and it is necessary to evaluate different word embedding models in discovering black keywords. To this end, we design a Chinese black keyword discovery framework and conduct a systematic empirical study on six existing word embedding models including both static and dynamic types in discovering Chinese black keywords. In specific, we classify Chinese black keywords in four types: domain specific words (DSWs), new meaning words (NMWs), similar pronunciation words (SPWs), and similar glyph words (SGWs). We experimentally find that different word embedding models vary greatly in performance when discovering black keywords, e.g., dynamic models perform well in discovering DSWs and NMWs, static ones perform poorly in discovering NMWs. We improve the static word embedding model based NMW discovery algorithm by additionally comparing the differences in cross-corpus word nearest-neighbors before and after domain incremental training. For effectively discovering variant words like SPWs and SGWs, we additionally introduce Chinese pronunciation and glyph features. The experimental results demonstrate the effectiveness of the proposed Chinese black keyword discovery framework, with detection accuracies of over 90% for DSWs, 80% for NWMs, 90% for SPWs, and 61% for SGWs.
Strict optimal rubbling of graphs

In 2009, Belford and Sieben introduced the concept of graph rubbling, which added the rubbling move to the well-known graph pebbling game. While a pebbling move removes two pebbles from one vertex and places a new pebble on a neighboring vertex, a rubbling move takes one pebble from each of two distinct neighbors of some vertex v and places one new pebble on v. The optimal rubbling number of a graph is the minimum number of pebbles needed to reach each vertex using some combination of pebbling and rubbling moves. We introduce the strict optimal rubbling number ρstr(G), which we define to be the minimum number of pebbles needed to reach each vertex using only rubbling moves. We then determine the value of ρstr for and handful of families of graphs, highlighting its similarities and differences to the optimal rubbling number.
On a generalization of Tupper's formula for m colors and n dimensions

Tupper's formula 12<⌊mod(⌊y17⌋2−17⌊x⌋−mod(⌊y⌋,17),2)⌋ has an interesting property: that for any monochrome image that can be represented using a 106×17 array of two-dimensional pixels, there exists a natural number k such that the graph of the inequality over the range 0≤x<106 and k≤y<k+17 is that image. In this paper, we give a generalization for m colors and n dimensions. In this paper, by “natural numbers” we mean non-negative integers. We give m formulae employing n free variables with the property that, for any n-dimensional object of m colors C1,…,Cm that can be represented by hypervoxels (hypervoxels being the multidimensional analogue of pixels) in an n-dimensional array of dimensions A1×⋯×An, there exists a natural number k such that, when the first formula is graphed using color C1, the second formula is graphed using color C2, …, and the mth formula is graphed using color Cm over 0≤x1<A1, 0≤x2<A2,…,0≤xn−1<An−1, and k≤xn<k+An, the union of all colored graphs is that n-dimensional object.
Smart contracts vulnerability detection model based on adversarial multi-task learning

Vulnerability detection is important for smart contracts because of their immutable and irreversible features. In this work, a new detection method based on adversarial multi-task learning is proposed to improve the accuracy of existing vulnerability detection methods, which is based on the multi-task learning framework, including a shared part and a task-specific part. We optimize the multi-task learning frameworks and propose the mixed parameter sharing method to make each task not only maintain its uniqueness, but also share features with other tasks, which helps solve the problem that the hard parameter sharing method cannot constrain the underlying shared layer and improve the quality of extracted features. In addition, we introduce adversarial transfer learning to reduce noise pollution caused by the private feature and interference between the general feature and the private feature. We experimented on datasets obtained from our previous work, and the experimental results prove that our proposed model can judge whether there are vulnerabilities in smart contracts and then identify their types. Additionally, the results also show that our model effectively improves detection accuracy and has an advantage in performance over representative methods.
An efficient ensemble-based Machine Learning for breast cancer detection

Breast cancer is a very severe type of cancer that often develops in breast cells. Attempting to develop an effective predictive model for breast cancer prognosis prediction is urgently needed despite substantial advancements in the management of symptomatic breast cancer over the past ten years. The precise prediction will offer numerous advantages, including the ability to diagnose cancer at an early stage and protect patients from needless medical care and related costs. In the medical field, recall is just as important as model accuracy. Even more crucially in the medical area, a model is not very good if its accuracy is high but its recall is low. To boost accuracy while still assigning equal weight to recall, we proposed a model that ensembles Feature Selection (FS), Feature Extraction (FE), and 5 Machine Learning (ML) models. There are three steps in our proposed model. The Correlation Coefficient (CC) and Anova (Anv) feature selection methodologies to choose the features in the first stage. Applying Uniform Manifold Approximation and Projection (UMAP), t-distributed Stochastic Neighbour Embedding (t-SNE), and Principal Component Analysis (PCA) to extract the features in the second stage without compromising the crucial information. With 5 ML models and ensemble models such as Voting Classifier (VC) and Stacking Classifier (SC) after selecting and extracting features from the dataset to predict the disease will be the last stage. The results show that the proposed model CC-Anv with PCA using a SC outperformed all the existing methodologies with 100% accuracy, precision, recall, and f1-score.
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering

Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal inference to mitigate this problem. However, they solely employ pointwise or pairwise loss functions and neglect to adopt a contrastive loss function for learning meaningful user and item representations. In this paper, we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing alignment and uniformity functions derived from the InfoNCE loss function for CF models. Specifically, we formulate an unbiased alignment function used in uCTRL. We also devise a novel IPW estimation method that removes the bias of both users and items. Despite its simplicity, uCTRL equipped with existing CF models consistently outperforms state-of-the-art unbiased recommender models, up to 12.22% for Recall@20 and 16.33% for NDCG@20 gains, on four benchmark datasets.
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence

Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be faithful (reflecting the model's decision-making process in claim verification) and plausible (convincing to humans), and can improve the accuracy of verification task. Although existing approaches leverage the similarity measure of semantic or surface form between claims and documents to retrieve evidence, they all rely on certain heuristics that prevent them from satisfying all three requirements. In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i.e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy. The proposed system is able to achieve significant improvements upon best-reported models under different settings.