Reviewerly/exHarmony huggingface dataset:
The Conventional Security of Cloud Computing and the Growing Threat to Quantum Computing

Cloud computing has turned into an important technology of our time. It has drawn attention due to its, availability, dynamicity, elasticity and pay as per use pricing mechanism this made multiple organizations to shift onto the cloud platform. It leverages the cloud to reduce administrative and backup overhead. Cloud computing offers a lot of versatility. Quantum technology, on the other hand, advances at a breakneck pace. Experts anticipate a positive outcome and predict that within the next decade, powerful quantum computers will be available. This has and will have a substantial impact on various sciences streams such as cryptography, medical research, and much more. Sourcing applications for business and informational data to the cloud, presents privacy and security concerns, which have become crucial in cloud installation and services adoption. To address the current security weaknesses, researchers and impacted organizations have offered several security techniques in the literature. The literature also gives a thorough examination of cloud computing security and privacy concerns.
Counterfactual Image Enhancement for Explanation of Face Swap Deepfakes

Highly realistic AI generated face swap facial imagery known as deepfakes can easily deceive human eyes and have drawn much attention. Deepfake detection models have obtained high accuracies and are still improving, but the explanation of their decisions receives little attention in current research. Explanations are important for the credibility of detection results, which is essential in serious applications like the court of law. The explanation is a hard problem, apart from the deep detection models are black boxes, the particular reason is that high-quality fakes often have no human-eye-sensitive artifacts. We call the artifacts that can be detected by models but are not human-eye-sensitive as subtle artifacts. In this work, we attempt to explain model detected face swap images to humans by proposing two simple automatic explanation methods. They enhance the original suspect image to generate its more real and more fake counterfactual versions. By visually contrasting the original suspect image with the counterfactual images, it may become easier for humans to notice some subtle artifacts. The two methods operate on pixel and color spaces respectively, they do not require extra training process and can be directly applied to any trained deepfake detection models. We also carefully design new subjective evaluation experiments to verify the effectiveness of proposed enhancement methods. Experiment results show that the color space enhancement method is more preferred by the tested subjects for explaining high-quality fake images, compared to the other pixel space method and a baseline attribution-based explanation method. The enhancement methods can be used as a toolset that helps human investigators to better notice artifacts in detected face swap images and to add weights on proofs.
NaturalCC

We present NaturalCC, an efficient and extensible open-source toolkit for machine-learning-based source code analysis (i.e., code intelligence). Using NaturalCC, researchers can conduct rapid prototyping, reproduce state-of-the-art models, and/or exercise their own algorithms. NaturalCC is built upon Fairseq and PyTorch, providing (1) a collection of code corpus with preprocessing scripts, (2) a modular and extensible framework that makes it easy to reproduce and implement a code intelligence model, and (3) a benchmark of state-of-the-art models. Furthermore, we demonstrate the usability of our toolkit over a variety of tasks (e.g., code summarization, code retrieval, and code completion) through a graphical user interface. The website of this project is http://xcodemind.github.io, where the source code and demonstration video can be found.
Semi-supervised Entity Alignment via Relation-based Adaptive Neighborhood Matching

Many recent studies of Entity Alignment (EA) use Graph Neural Networks (GNNs) to aggregate the neighborhood features of entities and achieve better performance. However, aligned entities in real Knowledge Graphs (KGs) usually have non-isomorphic neighborhood structures due to the different data sources of KGs. Therefore, it is insufficient to simply compare the global direct neighborhood of aligned entities, which may also become a variable for the EA judgment. In this paper, we propose a Relation-based Adaptive Neighborhood Matching method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RANM</i> ), which matches larger range and higher confidence neighborhoods for aligned entities based on relation matching instead of alignment seeds. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RANM</i> first uses alignment seeds to construct the best relation matching set, and then performs local direct neighborhood matching and feature aggregation on the candidate alignments. To obtain high-quality entity embeddings, we design a variant attention mechanism based on heterogeneous graphs, which considers the heterogeneity of relations in KGs. We also adopt a bi-directional iterative co-training to further improve the performance. Extensive experiments on three well-known datasets show our method significantly outperforms 14 state-of-the-art methods, and is 3.01-11.5% higher than the best-performing baselines in Hits@1. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RANM</i> also shows high performance on the long-tailed entities and the dataset with less alignment seeds.
Cooperation Among Groups of Agents in the Epistemic Logic L-DINF

The Logic of “Inferable” L-DINF has been recently proposed as a declarative framework to formally model via epistemic logic (aspects of) the group dynamics of cooperative agents. The framework permits to model groups of cooperative agents that can jointly perform actions. Various aspects of Multi-Agent Systems can be formalized in L-DINF, such as costs of actions, agents’ preferences, and roles of agents within groups. In this paper we extend the framework by introducing the possibility of cooperation among different groups: if a group has not an agent with the right role for performing an action, the group can ask another group in order to have the action performed.
Implementation of a Pitch Enhancement Technique: Punjabi Automatic Speech Recognition (PASR)

Automatic speech recognition (ASR) has been an active area of research for decades. As demand for ASR systems has been increased in the last few years due to their utility in a wide range of applications. The proposed work intends to develop a Punjabi ASR-based speech classification system using pitch enhancement corps optimization technique and Mel frequency cepstral coefficient (MFCC) feature extraction method. The complete experiment has been conducted on a total of 80 speakers containing comprising 6 hours of speech voice recording corpus. The classification language model resulted in the minimum word error rate (WER) of 8.2% in the case of the tri-gram model with 80 speakers. In addition to this, a comparison of the resulting outcomes has been conducted which present the outperformance of the tri-gram language classification model in the case of all set of speakers.
Review on security issues and solutions in cloud computing

AbstractAbstractCloud computing is the on-demand availability of a group of networked elements providing services, especially data storage and computing power, without actual management and addressing by the user. In lieu, the entire suite of hardware and software is managed by a provider and have functions dispersed over various location, each location being a data center. Since cloud computing users rely on sharing resources and may request multiple cloud services concurrently, a dynamic provision must be there so that all the resources are available to the user in a competent manner. However, despite abundantly available benefits, many organizations are still skeptical about using the cloud computing service model because of security concerns associated with the service. The paper also discusses different types of cloud security, controls of cloud security and algorithms used for emanating the privacy of the client's data. In this paper, a CNN and SVM model is also applied for detection Anti-Spoofing images for identification on images on cloud.Subject Classification: 68M25Keywords: Cloud securitySVMCNN
Meta-learning and Personalization Layer in Federated Learning

Federated learning systems are confronted with two challenges: systemic and statistical. Non-IID data is acknowledged to be a primary component in causing statistical challenges. To address the federated learning system's substantial performance loss on non-IID data, we offer the algorithm (which combines meta-learning methods and personalization layer approaches into a federated learning system). In terms of performance and personalization, has been shown in experiments to outperform typical federated learning algorithm, algorithms using personalization layer techniques and algorithms using meta-learning in system optimization.
PeC-HiCA: A Perception Centric Human-in-loop Cognitive Architecture

A layered model of the mammalian cortex is an accepted one. One of the major challenges of using this architecture in real-life use cases is the lack of reliable implementations of the symbolic and cognitive layers. On another parallel stream, the adoption of cognitive and artificial intelligence (AI) solutions in industries has been slow. One of the reasons for this is the fact that the current generation of AI architectures does not sufficiently take care of the perception levels of the human operator. This paper presents an architecture that solves both of the above challenges. We propose a cognitive architecture where a natural-human in loop replaces most of the symbolic and cognitive layers. At the same time, we propose the use of instrumentation to measure the perceptual level of the human in real-time. This enables us to bridge the symbolic non-symbolic gap to some extent as well. Lastly, the perception-centric block also adds a novel attribute to the existing AI architectures. The current generation of AI algorithms does not incorporate the users’ perception as an integral part of the training and operation of the algorithm. To our knowledge, this is the first time user perception is an essential part of the algorithm. We call this architecture Perception-Centric Human-in-loop Cognitive Architecture (PeC-HiCA). PeC-HiCA is not only a more implementable cognitive architecture, it will also be more successful in industrial applications.
Inter-Intra Cross-Modality Self-Supervised Video Representation Learning by Contrastive Clustering

This paper introduces an online self-supervised method that leverages inter- and intra-level variance for video representation learning. Most existing methods tend to focus on instance-level or inter-variance encoding but ignore the intra-variance existing in clips. The key observation to solving this problem is the underlying correlation between visual and audio, in which the distribution of flow patterns in feature space is diverse, but expresses complementary similar semantics. And in the semantic feature space, the horizontal dimension of the feature matrix could be regarded as cluster labels. These cluster labels should be consistent for different modalities of the same video clip. Based on this idea, we propose an end-to-end inter-intra cross-modality contrastive clustering scheme to simultaneously optimize the inter- and intra-level contrastive loss. Experiments show that our proposed approach is able to considerably outperform previous methods for self-supervised learning on HMDB51 and UCF101 when applied to video retrieval and action recognition tasks.
Design and Use of a Chatbot for Learning Selected Topics of Physics

The recent use of chatbots in education opens up the need to investigate how to use them and their effects on learning processes. This research aims to measure the effects of integrating a Physics chatbot with an active learning sequence for first-year physics students. The chatbot’s purpose was to help the student understand the concepts in the different resources it contained. The study was conducted in a private higher education system, where the sequence was implemented to a population of 145 freshmen engineering students in five different groups (classes) of introductory physics courses. The data analysis of this quantitative exploratory research was conducted using descriptive and parametric statistical methods. We implemented the Half Force Concept Inventory (HFCI) as pre- and post-tests to measure the learning gain of the conceptual understanding of Newton's laws. This research contributes to understanding the impact of the chatbot on the learning of first-year university engineering students. It outlines the best practices for providing empirical evidence on using chatbots as digital learning resources. The research results indicated a heterogeneous change in the students’ conceptual understanding, attaining a positive gain among half of the freshmen. Also, the ANOVA statistical analysis showed equivalent behaviour between each group of students, where Hake's gain was significantly equivalent.
Outlier Detection based on Transformations for Astronomical Time Series

In this work we propose an outlier detection method for astronomical light curves, based on time series transformations as temporal shift, masking, warping, etc. It is assumed that the outliers are unknown, and that we have access only to a set of inliers. A neural network encoder is used to learn a representation of a light curve minimizing the distance between objects of the same class and maximizing it otherwise. Each light curve is encoded as a single vector. An outlier score is computed based on the distance to the nearest class centroid. The model is applied to datasets from the Zwicky Transient Facility (ZTF), All Sky Automated Survey (ASAS) and Lincoln Near-Earth Asteroid Research (LINEAR) surveys. For model selection, surrogate metrics are estimated with the validation set. The metrics under test are the average hit ratio of the k-nearest neighbors of each light curve in the representation space, silhouette coefficient, Calinski-Harabasz index and Davies-Bouldin index. The results show that the proposed model outperforms state-of-the-art methods based on time series features and neural network approaches, reaching an average AUCPR of 0.89 for detecting outliers in the three datasets.
Video Object Detection with MeanShift Tracking

Video object detection, a basic task in the computer vision, is rapidly evolving and widely used in various real-world applications. Recently, with the success of deep learning, deep video object detection has become an important research direction. Although existing deep video object detection methods have achieved excellent results compared with those of traditional methods, they ignore the motion laws of objects and are hard to improve the detection performance of the fast moving objects suffering from deteriorated problems such as the motion blur, video defocus, object occlusion and rare poses. To address this limitation, we add the object trajectory information into the process of the video object detection and devise a novel deep video object detection method which utilizes the MeanShift algorithm to guide the deep neural networks to enhance the video object detection performance. The experiments on ImageNet VID dataset validate that the proposed method can improve the recognition performance of fast moving objects with taking into account the motion laws of objects.
Minimal Euler State Networks

The Euler State Network (EuSN) is a recently proposed Reservoir Computing (RC) model where the fixed state dynamics are obtained by discretizing an ordinary differential equation under stability and non-dissipative conditions. As a result, the model is able to effectively propagate input information over time, hugely improving the performance of RC models in tasks requiring long-term memory. Aiming at both reducing the complexity of the reservoir structure and further improving its efficiency, in this paper we propose a minimalistic EuSN architecture where the reservoir is constrained to a fixed bi-directional chain structure. We explore progressive simplifications where the recurrent and the input connections of the reservoir are fully described by a single weight value. While reducing the complexity of the base EuSN, the proposed minimal EuSN approach shows comparable performance on several tasks on time-series classification, thus offering considerable potential advantages, especially in embedded applications and physical implementations.
Self-supervised Human Mesh Recovery with Cross-Representation Alignment

Fully supervised human mesh recovery methods are data-hungry and have poor generalizability due to the limited availability and diversity of 3D-annotated benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained from synthetic paired 2D representation (e.g., 2D keypoints and segmentation masks) and 3D mesh. However, on synthetic dense correspondence maps (i.e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation. To alleviate this domain gap on IUV, we propose cross-representation alignment utilizing the complementary information from the robust but sparse representation (2D keypoints). Specifically, the alignment errors between initial mesh estimation and both 2D representations are forwarded into regressor and dynamically corrected in the following mesh regression. This adaptive cross-representation alignment explicitly learns from the deviations and captures complementary information: robustness from sparse representation and richness from dense representation. We conduct extensive experiments on multiple standard benchmark datasets and demonstrate competitive results, helping take a step towards reducing the annotation effort needed to produce state-of-the-art models in human mesh estimation.
Adaptive Graph Convolutional Networks based on Decouple and Residuals to relieve Over-Smoothing

For the node classification problem, the traditional graph convolutional network (GCN) and many of its variants achieve the best results at shallow layers, especially for sparse graphs, which do not take full advantage of the higher-order neighbor information of the nodes in the graph. However, multiple stacked graph convolutional networks will suffer from the over-smoothing problem, resulting in ineffective differentiation between different classes of nodes in the graph. To address this problem, we propose an adaptive graph convolutional network based on decouple and residuals (ADR-GCN) to alleviate the over-smoothing problem. The model first obtains the initial representation of nodes using autoencoder and then decouples the representation transformation of nodes and feature propagation. In addition, we add initial residuals to the feature propagation of nodes and adaptively selects the appropriate local and global information of each node to obtain node representations containing rich information. Finally, we use a softmax classifier to generate the final node prediction. The experimental results on seven datasets show that the classification accuracy of ADR-GCN improves 1.3%-4.1% compared with GCN, which shows that the model can better alleviate over-smoothing,
An outline of geochemical proxies used on marine sediments deposited during the Quaternary Period

The sediments have been a ubiquitous archive for paleoclimate reconstruction while the marine sediments are considered to be serene and mostly undisturbed from anthropogenic encroachments. Unlike continental records, the deep-sea sediments elucidated the evidence of at least 50 glacial and interglacial stages during the Quaternary period and thus proved its applicability in quaternary climate reconstruction. The geochemical variations in the marine sediments are often used as a proxy to decode the past productivity, redox, weathering and provenance changes as a function of past climate and oceanographic perturbations. The geochemical behavior of the elements in the sedimentary environment primarily relies on ionic potential along with redox potential and pH which leads to mobility and enrichment of selected elements and thus acting as potential evidence for ambient temporal changes. The present chapter aims to provide an overview of frequently used geochemical proxies and their applicability. Further, the chapter also tries to provide the significance of statistical and machine learning approaches to the geochemical datasets in understanding the climatic processes that led to changes in the geochemical variability.
Label Correlation Based Graph Convolutional Network for Multi-label Text Classification

Multi-label text classification aims to assign a set of most relevant labels to a given document. To build such a classifier, apart from demanding an efficient document representation, capturing label information for classification performance improvement is still challenging. In this paper, we propose a novel model based on a graph convolutional network to model label correlation. To do that, we design a correlation matrix from labels in a data-driven way. The learned label correlations are then fused with fine-grained document information extracted by a RoBERTa-based subnet for classification. Furthermore, we introduce a simple mechanism to make the label correlation matrix more effective in propagating information among label nodes. We first normalize the correlation matrix to deal with the highly skewed problem and then filter noisy edges to alleviate the long-tailed distribution problem. Evaluation results show that our model achieves competitive results compared to existing state-of-the-art methods. Ablation studies are also conducted to explore the proposed model's behaviors.
Safety Guided Policy Optimization

In reinforcement learning (RL), exploration is essential to achieve a globally optimal policy but unconstrained exploration can cause damages to robots and nearby people. To handle this safety issue in exploration, safe RL has been proposed to keep the agent under the specified safety constraints while maximizing cumulative rewards. This paper introduces a new safe RL method which can be applied to robots to operate under the safety constraints while learning. The key component of the proposed method is the safeguard module. The safeguard predicts the constraints in the near future and corrects actions such that the predicted constraints are not violated. Since actions are safely modified by the safeguard during exploration and policies are trained to imitate the corrected actions, the agent can safely explore. Additionally, the safeguard is sample efficient as it does not require long horizontal trajectories for training, so constraints can be satisfied within short time steps. The proposed method is extensively evaluated in simulation and experiments using a real robot. The results show that the proposed method achieves the best performance while satisfying safety constraints with minimal interaction with environments in all experiments.