Reviewerly/exHarmony huggingface dataset:
Tile Embedding: A General Representation for Level Generation

In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human-annotated representations of game levels. Creating annotated datasets for games requires domain knowledge and is time-consuming. Hence, though a large number of video games exist, annotated datasets are curated only for a small handful. Thus current PLGML techniques have been explored in limited domains, with Super Mario Bros. as the most common example. To address this problem, we present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. To learn this embedding, we employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
Data privacy preservation in digital forensics investigation

As we know, in a digital forensic investigation, the investigators extract evidence from different types of digital media. However, the extraction method itself unlocks the way for digital forensic investigators to encroach the privacy of the individual. To detect fraud, an investigator collects and analyzes the digital evidence connected to the fraud that has taken place and presents it in court to prove convict. There are no legal or technical structures in place that can keep a check on the investigation procedures to prevent this breach of privacy. Privacy of personal data of a user is stated in the United Nations Declaration of Human Rights. In this ever-changing world, the increasing use of computing devices creates vast volumes of personal data, and correspondingly, in digital investigation, there is an increased presumption of accountability and protection for the rights of a user governing data privacy. So, in this research study, we have tried to respond to this concern by analyzing the perceptions about data privacy preservation and issues related to digital forensics investigation from three perspectives, i.e., user, forensic investigator, and technology, and put forward a framework that respects user privacy as well as ensures a forensically sound digital investigation.
3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos

We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for multimodal and multilingual semantic understanding on these unique videos by annotating them for concepts, affective states, media types, and audio language. We present a thorough analysis of 3MASSIV and highlight the variety and unique aspects of our dataset compared to other contemporary popular datasets with strong baselines. We also show how the social media content in 3MASSIV is dynamic and temporal in nature, which can be used for semantic understanding tasks and cross-lingual analysis.
MoNet: A Fast Payment Channel Network for Scriptless Cryptocurrency Monero

We propose MoNet, the first bi-directional payment channel network with unlimited lifetime for Monero. It is fully compatible with Monero without requiring any modification of the current Monero blockchain. MoNet preserves transaction fungibility, i.e., transactions over MoNet and Monero are indistinguishable, and guarantees anonymity of Monero and MoNet users by avoiding any potential privacy leakage introduced by the new payment channel network. We also propose a new crypto primitive, named Verifiable Consecutive One-way Function (VCOF). It allows one to generate a sequence of statement-witness pairs in a consecutive and verifiable way, and these statement-witness pairs are one-way, namely it is easy to compute a statement-witness pair by knowing any of the pre-generated pairs, but hard in an opposite flow. By using VCOF, a signer can produce a series of consecutive adaptor signatures CAS. We further propose the generic construction of consecutive adaptor signature as an important building block of MoNet. We develop a proof-of-concept implementation for MoNet, and our evaluation shows that MoNet can reach the same transaction throughput as Lightning Network, the payment channel network for Bitcoin. Moreover, we provide a security analysis of MoNet under the Universal Composable (UC) security framework.
VTONShoes: Virtual Try-on of Shoes in Augmented Reality on a Mobile Device

The virtual try-on (VTON) system in augmented reality (AR) has attracted significant research interest. This paper presents a novel real-time AR virtual shoe try-on system (VTONShoes). Users can see the virtual shoes with full degrees of freedom on the mobile device. In particular, we propose an efficient framework to detect, classify, and recover 6-DoF pose of shoes from the captured images and then accurately render the 3D shoe model on the screen in realtime and in full degrees of freedom. For accurate pose recovery, dense keypoints are designed on the 3D shoe model. An efficient joint 2D keypoint localization and leg silhouette segmentation module (KeyPointLoc) is designed to predict keypoint projections on 2D images and the shoe-leg occlusion relationship. In order to reduce jitter between frames, an optimization-based framework with the longest continuous invariant subarray constraints is proposed to minimize classification errors caused by model switching, and a smoothing module with Exponential Weights Decay is presented to post-process the rendered results. We also developed a large-scale dataset named Diverse-Shoes which contains images extracted from 80K videos, annotated with the shoe bounding box, transformation matrices, and silhouettes of legs. Our system has achieved a smooth and stable try-on effect on mainstream devices with a real-time speed of around 25 to 45 FPS on mainstream mobile phones, which significantly outperforms state-of-the-art methods for real-time performance and rendering accuracy.
Rectifying adversarial inputs using XAI techniques

With deep neural networks (DNNs) involved in more and more decision making processes, critical security problems can occur when DNNs give wrong predictions. This can be enforced with so-called adversarial attacks. These attacks modify the input in such a way that they are able to fool a neural network into a false classification, while the changes remain imperceptible to a human observer. Even for very specialized AI systems, adversarial attacks are still hardly detectable. The current state-of-the-art adversarial defenses can be classified into two categories: pro-active defense and passive defense, both unsuitable for quick rectifications: Pro-active defense methods aim to correct the input data to classify the adversarial samples correctly, while reducing the accuracy of ordinary samples. Passive defense methods, on the other hand, aim to filter out and discard the adversarial samples. Neither of the defense mechanisms is suitable for the setup of autonomous driving: when an input has to be classified, we can neither discard the input nor have the time to go for computationally expensive corrections. This motivates our method based on explainable artificial intelligence (XAI) for the correction of adversarial samples. We used two XAI interpretation methods to correct adversarial samples. We experimentally compared this approach with baseline methods. Our analysis shows that our proposed method outperforms the state-of-the-art approaches.
Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM

Audio recordings used as evidence have become increasingly important to litigation. Before their admissibility as evidence, an audio forensic expert is often required to help determine whether the submitted audio recordings are altered or authentic. Within this field, the copy-move forgery detection (CMFD), which focuses on finding possible forgeries that are derived from the same audio recording, has been an urgent problem in blind audio forensics. However, most of the existing methods require idealistic pre-segmentation and artificial threshold selection to calculate the similarity between segments, which may result in serious misleading and misjudgment especially on high frequency words. In this work, we present a robust method for detecting and locating an audio copy-move forgery on the basis of constant Q spectral sketches (CQSS) and the integration of a customised genetic algorithm (GA) and support vector machine (SVM). Specifically, the CQSS features are first extracted by averaging the logarithm of the squared-magnitude constant Q transform. Then, the CQSS feature set is automatically optimised by a customised GA combined with SVM to obtain the best feature subset and classification model at the same time. Finally, the integrated method, named CQSS-GA-SVM, is evaluated against the state-of-the-art approaches to blind detection of copy-move forgeries on real-world copy-move datasets with read English and Chinese corpus, respectively. The experimental results demonstrate that the proposed CQSS-GA-SVM exhibits significantly high robustness against post-processing based anti-forensics attacks and adaptability to the changes of the duplicated segment duration, the training set size, the recording length, and the forgery type, which may be beneficial to improving the work efficiency of audio forensic experts.
How to Evaluate Explainability? - A Case for Three Criteria

The increasing complexity of software systems and the influence of software-supported decisions in our society have sparked the need for software that is safe, reliable, and fair. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. However, in order to develop explainable systems, we need to understand when a system satisfies this NFR. To this end, appropriate evaluation methods are required. However, the field is crowded with evaluation methods, and there is no consensus on which are the "right" ones. Much less, there is not even agreement on which criteria should be evaluated. In this vision paper, we will provide a multidisciplinary motivation for three such quality criteria concerning the information that systems should provide: comprehensibility, fidelity, and assessability. Our aim is to to fuel the discussion regarding these criteria, such that adequate evaluation methods for them will be conceived.
Design and Simulation of an Autonomous Floor Cleaning Robot with Optional UV Sterilization

The paper mainly describes the design and stimulation of an autonomous robot for floor vacuum cleaning, wet mopping and an optional UV sterilization in the absence of the patients in hospital rooms. There are many floor cleaning robots available in market such as Roomba robot, Scooba robot etc. which are very expensive this robot is designed in such away it fits the pocket of a common man but at the same time serves all the purpose of a cleaning robot. The robot contains ultrasonic sensors and IR sensors to detect any obstacle in front of the robot and a LIDAR sensor, vacuum cleaner to remove dust on the floor and cleaning mops. The robot contains a removable UV sterilization device to sterilize the hospital room after the patients have left the room. The robot uses ball and standard wheels for locomotion. The main computer on board will be an Arduino micro controller. The 3D design of the robot is made using solid works software and the stimulation of the robot being done on ROS 2 software platform. The path planning and simultaneous localization and mapping (SLAM) will be done on ROS2 using the ROS2nav2 platform. The path planning algorithm being Dijkstra’s, Behaviour tree (BT)algorithm as it achieves more accurate path planning with shortest time and obstacle avoidance algorithm is also included. The visualization of the robot is achieved using Rviz visualization tool.
Visual Servoing with Geometrically Interpretable Neural Perception

An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.
On the Optimisation of the GSACA Suffix Array Construction Algorithm

The suffix array is arguably one of the most important data structures in sequence analysis and consequently there is a multitude of suffix sorting algorithms. However, to this date the GSACA algorithm introduced in 2015 is the only known non-recursive linear-time suffix array construction algorithm (SACA). Despite its interesting theoretical properties, there has been little effort in improving the algorithm’s subpar real-world performance. There is a super-linear algorithm DSH which relies on the same sorting principle and is faster than DivSufSort, the fastest SACA for over a decade. This paper is concerned with analysing the sorting principle used in GSACA and DSH and exploiting its properties in order to give an optimised linear-time algorithm. Our algorithm is not only significantly faster than GSACA but also outperforms DivSufSort and DSH.
Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization

In this paper, we study the problem of distributed optimization using an arbitrary network of lightweight computing nodes, where each node can only send/receive information to/from its direct neighbors. Decentralized stochastic gradient descent (SGD) has been shown to be an effective method to train machine learning models in this setting. Although decentralized SGD has been extensively studied, most prior works focus on the error-versus-iterations convergence, without taking into account how the topology affects the communication delay per iteration. For example, a denser (sparser) network topology results in faster (slower) error convergence in terms of iterations, but it incurs more (less) communication time per iteration. We propose MATCHA, an algorithm that can achieve a win-win in this error-runtime trade-off for any arbitrary network topology. The main idea of MATCHA is to communicate more frequently over connectivity-critical links in order to ensure fast convergence, and at the same time minimize the communication delay per iteration by using other links less frequently. It strikes this balance by decomposing the topology into matchings and then optimizing the set of matchings that are activated in each iteration. Experiments on a suite of datasets and deep neural networks validate the theoretical analyses and demonstrate that MATCHA takes up to 5x less time than vanilla decentralized SGD to reach the same training loss. The idea of MATCHA can be applied to any decentralized algorithm that involves a communication step with neighbors in a graph. It is also complementary to and can be used in conjunction with existing gradient compression, asynchronous aggregation, and acceleration methods for distributed optimization.
ATLAS-MVSNet: Attention Layers for Feature Extraction and Cost Volume Regularization in Multi-View Stereo

We present ATLAS-MVSNet, an end-to-end deep learning architecture relying on local attention layers for depth map inference from multi-view images. Distinct from existing works, we introduce a novel module design for neural networks, which we termed hybrid attention block, that utilizes the latest insights into attention in vision models. We are able to reap the benefits of attention in both, the carefully designed multi-stage feature extraction network and the cost volume regularization network. Our new approach displays significant improvement over its counterpart based purely on convolutions. While many state-of-the-art methods need multiple high-end GPUs in the training phase, we are able to train our network on a single consumer grade GPU. ATLAS-MVSNet exhibits excellent performance, especially in terms of accuracy, on the DTU dataset. Furthermore, ATLAS-MVSNet ranks amongst the top published methods on the online Tanks and Temples benchmark.
A Survey on Knowledge Graph-Based Methods for Automated Driving

Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD) in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. Knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The complexity of graph-structured data with complex relationships and inter-dependencies between objects has posed significant challenges to existing machine learning algorithms. However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data. Therefore, we motivate and discuss the benefit of KGs applied to AD. Then, we survey, analyze and categorize ontologies and KG-based approaches for AD. We discuss current research challenges and propose promising future research directions for KG-based solutions for AD.
Automated Progressive Learning for Efficient Training of Vision Transformers

Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, highlighting the urgent need to develop efficient training methods for ViTs. Progressive learning, a training scheme where the model capacity grows progressively during training, has started showing its ability in efficient training. In this paper, we take a practical step towards efficient training of ViTs by customizing and automating progressive learning. First, we develop a strong manual baseline for progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge the gap brought by model growth. Then, we propose automated progressive learning (AutoProg), an efficient training scheme that aims to achieve lossless acceleration by automatically increasing the training overload on-the-fly; this is achieved by adaptively deciding whether, where and how much should the model grow during progressive learning. Specifically, we first relax the optimization of the growth schedule to sub-network architecture optimization problem, then propose one-shot estimation of the sub-network performance via an elastic supernet. The searching overhead is reduced to minimal by recycling the parameters of the supernet. Extensive experiments of efficient training on ImageNet with two representative ViT models, DeiT and VOLO, demonstrate that AutoProg can accelerate ViTs training by up to 85.1% with no performance drop. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code:https://github.com/changlin31/AutoProg.
Multi-Scale Detail Enhancement Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) are widely used in single image super-resolution (SISR) and provide remarkable performance. However, most existing CNN-based super-resolution (SR) models focus mainly on designing deep or wide architecture and neglect intended detail enhancement, thereby hindering the CNN representational capacity. To resolve this problem, we propose a multi-scale detail enhancement network (MS-DEN) for SISR. Specifically, we introduce a multi-scale detail extraction module (MS-DEM), which first converts features into a 3-channel simulation image, and then, directly extracts detail information from the simulation image space. Furthermore, we concatenate the 3-channel image and extracted detail image to generate detail-guided features. Subsequently, we propose a multi-context channel attention module (MC-CAM) to relatively better fuse local and global features, and enhance features containing discontinuous detail information. With detail enhancement, MS-DEN can restore highly accurate details and lead to performance improvement. Numerous experiments show that our MS-DEN achieves competitive performance against the state-of-the-art methods.
The Competing Genes Evolutionary Algorithm: Avoiding Genetic Drift Through Competition, Local Search, and Majority Voting

An estimation-of-distribution algorithm is an evolutionary algorithm that uses a probabilistic model to represent its population. It iteratively draws solutions from its model and, considering their fitnesses, uses these solutions to update the model’s parameters. However, there is a risk of reinforcing random noise from sampling back into the model—a problem termed genetic drift. We propose the competing genes evolutionary algorithm that optimizes a fitness function over a binary search space and avoids this problem by updating only one model parameter at a time. The algorithm uses the model not only to sample solutions but also to select a parameter in each iteration that it pins to a value for the rest of the search process. We obtain upper bounds on the number of fitness evaluations the algorithm needs to solve well-known benchmark functions as a function of its population size and observe better or comparable results to other evolutionary algorithms. We attribute these favorable results to the algorithm’s efficient use of its samples to explore its dwindling search space. We also introduce two variants of the algorithm. The first version eliminates the need to preset the number of solutions to sample per iteration, and the second seeks to escape local optima. We provide evidence that these variants achieve their goals.
A Fast Accurate Deep Learning Framework for Prediction of All Cancer Types

The mortality rate of cancer is among the highest in the world.One death occurs every six in the world.Both machine learning (ML) and deep learning (DL) have been used by scientists to predict cancer.In addition, DL can analyze a huge amount of healthcare data in a short period of time to study the chances of recurrence, progression and patient survival.An accurate and quick framework for improving cancer prognosis prediction is presented in this study.A fast and accurate optimizer is necessary to predict both critical and non-critical cases, so a modified binary version of the Whale Optimization Algorithm (WOA) is proposed.Based on sigmoid transfer functions, this version identifies the subset of features that is minimally optimal while maximizing classification accuracy.This framework is composed of an optimized parameter Long-Short Term Memory (LSTM) Neural Network, with the input being the optimal set of feature selection layer.The proposed framework performs better than previous frameworks having an average accuracy of 100% and an execution time of 4113 seconds.