Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Framework for Secure Transaction in Mobile Banking Platform

Blockchain is the growing technical technology that has attracted interest from around the world. This technology makes the transfer of money possible and makes transactions electronically easier and safer than the conventional payment system. It's a typical awareness that Blockchain can make financial data security easier. Technology, however, also poses specific difficulties in fulfilling some financial data protection needs. Therefore, this paper recommends a framework founded on Blockchain to protect and secure financial data transfer on mobile. A multi-level authentication was used to secure the mobile banking and a two-factor authentication protocol to generate a time-based one-time password (TOTP) for secure funds transfer. The attained outcomes point to the sovereignty of the projected Framework when compared with existing work. The proposed system addresses the requirements of building a secure application for mobile banking because of the increased security and privacy of clients on mobile banking applications.
Accurate and Privacy-Preserving Task Allocation for Edge Computing Assisted Mobile Crowdsensing

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user’s location such as the user’s home address or the user’s trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user’s location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user’s rough location information but cannot obtain the user’s exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson–Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.
Multi-level Hyperedge Distillation for Social Linking Prediction on Sparsely Observed Networks

Social linking prediction is one of the most fundamental problems in online social networks and has attracted researchers’ persistent attention. Most of the existing works predict unobserved links using graph neural networks (GNNs) to learn node embeddings upon pair-wise relations. Despite promising results given enough observed links, these models are still challenging to achieve heart-stirring performance when observed links are extremely limited. The main reason is that they only focus on the smoothness of node representations on pair-wise relations. Unfortunately, this assumption may fall when the networks do not have enough observed links to support it. To this end, we go beyond pair-wise relations and propose a new and novel framework using hypergraph neural networks with multi-level hyperedge distillation strategies. To break through the limitations of sparsely observed links, we introduce the hypergraph to uncover higher-level relations, which is exceptionally crucial to deduce unobserved links. A hypergraph allows one edge to connect multiple nodes, making it easier to learn better higher-level relations for link prediction. To overcome the restrictions of manually designed hypergraphs, which is constant in most hypergraph researches, we propose a new method to learn high-quality hyperedges using three novel hyperedges distillation strategies automatically. The generated hyperedges are hierarchical and follow the power-law distribution, which can significantly improve the link prediction performance. To predict unobserved links, we present a novel hypergraph neural networks named HNN. HNN takes the multi-level hypergraphs as input and makes the node embeddings smooth on hyperedges instead of pair-wise links only. Extensive evaluations on four real-world datasets demonstrate our model’s superior performance over state-of-the-art baselines, especially when the observed links are extremely reduced.
An Approach to identify Captioning Keywords in an Image using LIME

Machine Learning models are being increasingly deployed to tackle real-world problems in various domains like healthcare, crime and education among many others. However, most of the models are practically "black-boxes": although they may provide accurate results, they are unable to provide any conclusive reasoning for those results. In order for these decisions to be trusted, they must be explainable. Explainable AI, or XAI refers to methods and techniques in the application of AI such that the results of the solution are understandable by human experts. This paper focuses on the task of Image Captioning, and tries to employ XAI techniques such as LIME (Local Interpretable Model-Agnostic Explanations) to explain the predictions of complex image captioning models. It visually depicts the part of the image corresponding to a particular word in the caption, thereby justifying why the model predicted that word.
Sequential targeting: A continual learning approach for data imbalance in text classification

Text classification has numerous use cases including sentiment analysis, spam detection, document classification, hate speech detection, etc. In realistic settings, classification on text data confronts imbalanced data conditions where classes of interest usually compose a minor fraction. Deep neural networks used for text classification, such as recurrent neural networks and transformer networks, suffer from a lack of efficient methods addressing imbalanced data. Traditional data-level methods attempting to mitigate distributional skew include oversampling and undersampling. The oversampling methods destruct the quality of original language representation of the sparse data coming from minority classes whereas the undersampling methods fail to fully utilize the rich context of majority classes. We address such issues in data-driven approaches by enforcing continual learning on imbalanced data by partitioning the training data distribution into mutually exclusive subsets and performing continual learning, treating the individual subsets as distinct tasks. We demonstrate the effectiveness of our method through experiments on the IMDB dataset and constructed datasets from real-world data. The experimental results show that the proposed method improves by 56.38 %p on the IMDB dataset and by 16.89 %p and 34.76 %p on the constructed datasets compared to the baseline method in terms of the F1-score metric.
Algorithmic Stability for Adaptive Data Analysis

Adaptivity is an important feature of data analysis---the choice of questions to ask about a dataset often depends on previous interactions with the same dataset. However, statistical validity is typically studied in a nonadaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. [Proceedings of STOC, ACM, 2015, pp.117--126] and Hardt and Ullman [Proceedings of FOCS, IEEE, 2014, pp. 454--463] initiated the formal study of this problem and gave the first upper and lower bounds on the achievable generalization error for adaptive data analysis. Specifically, suppose there is an unknown distribution ${P}$ and a set of $n$ independent samples ${x}$ is drawn from ${P}$. We seek an algorithm that, given ${x}$ as input, accurately answers a sequence of adaptively chosen “queries” about the unknown distribution ${P}$. How many samples $n$ must we draw from the distribution, as a function of the type of queries, the number of queries, and the desired level of accuracy? In this work we make two new contributions toward resolving this question: 1. We give upper bounds on the number of samples $n$ that are needed to answer statistical queries. The bounds improve and simplify the work of Dwork et al. and have been applied in subsequent work by those authors [Science, 349 (2015), pp. 636--638; Proceedings of NIPS, 2015, pp. 2350--2358]. 2. We prove the first upper bounds on the number of samples required to answer more general families of queries. These include arbitrary low-sensitivity queries and an important class of optimization queries (alternatively, risk minimization queries). As in Dwork et al., our algorithms are based on a connection with algorithmic stability in the form of differential privacy. We extend their work by giving a quantitatively optimal, more general, and simpler proof of their main theorem that stable algorithms of the kind guaranteed by differential privacy imply low generalization error. We also show that weaker stability guarantees such as bounded Kullback--Leibler divergence and total variation distance lead to correspondingly weaker generalization guarantees.
User-defined walking-in-place gestures for VR locomotion

Locomotion is one of the fundamental interactions in VR. As a relatively easy and simple method to implement VR locomotion, walking-in-place (WIP) techniques have been actively developed, which have shown advantages in terms of spatial constraints and immersion compared to real walking or controller-based interaction. However, existing WIP gestures have been largely adapted or designed from the perspective of developers, not the users, which may result in not only higher cognitive load to learn and memorize, but also worse presence and increased sensory conflict in VR. Therefore, this study aims to elicit and evaluate WIP gestures for different walking directions (forward, sideways, and backward) from users so that a complete user-defined WIP gesture set for VR locomotion can be generated. Two sequential user studies were conducted. In Experiment 1, 20 participants experienced the movement while wearing the VR headset and elicited the gesture for each of 8 walking directions. The grouping analysis revealed that Turn body + Stepping-in-place (SIP) and Step one foot + SIP/Rock/Stay were promising WIP gesture sets for VR locomotion. In the follow-up experiment (Experiment 2), 21 new participants experienced and compared the generated gesture sets and existing ones. The results showed that SIP performed the best for forward walking, while Step one foot + SIP/Rock/Stay were promising for other walking directions, which depended on application scenarios and the tradeoff between efficiency and naturalness. The generated WIP gesture sets from users can be used in VR applications to provide a better user experience and greater movement options in VR locomotion.
An Optimization-Based Receding Horizon Trajectory Planning Algorithm

This paper presents an optimization-based receding horizon trajectory planning algorithm for dynamical systems operating in unstructured and cluttered environments. The proposed approach is a two-step procedure that uses a motion planning algorithm in a first step to efficiently find a feasible, but possibly suboptimal, nominal solution to the trajectory planning problem where in particular the combinatorial aspects of the problem are solved. The resulting nominal trajectory is then improved in a second optimization-based receding horizon planning step which performs local trajectory refinement over a sliding time window. In the second step, the nominal trajectory is used in a novel way to both represent a terminal manifold and obtain an upper bound on the cost-to-go online. This enables the possibility to provide theoretical guarantees in terms of recursive feasibility, objective function value, and convergence to the desired terminal state. The established theoretical guarantees and the performance of the proposed algorithm are verified in a set of challenging trajectory planning scenarios for a truck and trailer system.
Feature-Based Twitter Sentiment Analysis With Improved Negation Handling

There is remarkable progress in the research of Twitter sentiment analysis (TSA) which is a technique of extracting opinion by automatically processing digital data. In this article, we propose a feature-based TSA system in conjunction with improved negation accounting by leveraging different types of features such as lexicon-based, morphological, POS-based, n-gram features, and many more, which would be used for classifier training and have the strong impact on polarity determination. We use three different state-of-the-art classifiers such as support vector machine (SVM), Naive Bayesian, and decision tree, and the series of experiments are conducted to determine which classifier works well with which feature group. In addition, this work focuses on investigating a significant linguistic phenomenon called negation which can either change polarity or strength of polarity of opinionated words. To enhance the classification performance, an algorithm is also developed to handle those negation tweets in which the presence of negation does not necessarily mean negation. The proposed feature-based Twitter system with negation accounting is evaluated on the benchmark Twitter data set SemEval-2013 Task 2. The experimental results demonstrate that the SVM classifier outperforms the other classifiers and the state-of-the-art TSA system developed by the NRC Canada winning team of SemEval-2013 Task 2. In addition, extensive experiments are also conducted to demonstrate that the proposed negation strategy with incorporated negation exception rules provides a substantial improvement by preventing misclassification of tweets. Finally, impact of each preprocessing module on classification performance is presented.
Crowd counting based on attention-guided multi-scale fusion networks

• A novel framework is proposed to deal with crowd counting in dense scenarios. • We adopt a multi-scale fusion strategy, which is built upon dilated convolution. • An attention mechanism is introduced to concentrate on crowd regions. In this paper, we propose an attention-guided multi-scale fusion network (named as AMS-Net) for crowd counting in dense scenarios. The overall model is mainly comprised by the density and the attention networks. The density network is able to provide a coarse prediction of the crowd distribution (density map), while the attention network helps to distinguish crowded regions from backgrounds. The output of the attention network serves as a mask of the coarse density map. The number of persons in the scene is finally estimated by applying integration on the refined density map. In order to deal with persons of varied resolutions, we introduce a multi-scale fusion strategy which is built upon dilated convolution. Experiments are carried out on the standard benchmark datasets, covering varied over-crowded scenarios. Experimental results demonstrate the effectiveness of the proposed approach.
Tensor Krylov subspace methods with an invertible linear transform product applied to image processing

This paper discusses several transform-based methods for solving linear discrete ill-posed problems for third order tensor equations based on a tensor-tensor product defined by an invertible linear transform. Linear transform-based tensor-tensor products were first introduced in Kernfeld et al. (2015) [16]. These tensor-tensor products are applied to derive Tikhonov regularization methods based on Golub-Kahan-type bidiagonalization and Arnoldi-type processes. GMRES-type solution methods based on the latter process also are described. By applying only a fairly small number of steps of these processes, large-scale problems are reduced to problems of small size. The number of steps required by these processes and the regularization parameter are determined by the discrepancy principle. The data tensor is a general third order tensor or a tensor defined by a laterally oriented matrix. A quite general regularization tensor can be applied in Tikhonov regularization. Applications to color image and video restorations illustrate the effectiveness of the proposed methods.
Toward a Better Understanding of “Cybersecurity”

The term “cybersecurity” has gained widespread popularity but has not been defined properly. The term is used by many different people to mean different things in different contexts. A better understanding of “cybersecurity” will allow us a better understanding of what it means to be “cybersecure.” This in turn will allow us to take more appropriate measures to ensure actual cybersecurity.
Hierarchical feature selection with multi-granularity clustering structure

Hierarchical feature selection addresses the issues caused by the presence of high-dimensional features in multi-category classification systems with hierarchical structures. Granular calculations are made to analyze the hierarchical relationships among categories when selecting the optimal feature subset. However, semantic hierarchy-based feature selection methods are prone to the semantic gap problem, which affects classification accuracy. In this paper, we propose a hierarchical feature selection method with a multi-granularity clustering structure that can effectively alleviate the semantic gap problem. Firstly, a hierarchical structure is constructed via bottom-up multi-granularity clustering based on feature similarities rather than semantic categories. This clustering hierarchy is conducive to solving semantic gap problems in the existing hierarchy. Secondly, the optimal feature subset is selected using the ℓ1,2-norms in each hierarchy’s granularity layer. This joint minimization approach can retain both the granularity layers’ shared features and granularity-specific features. Finally, we execute hierarchical classification according to the granular structure in a coarse to fine sequence. Extensive experiments demonstrate that the proposed method outperforms several state-of-the-art hierarchical feature selection approaches.
Source Region Geochemistry From Unmixing Downstream Sedimentary Elemental Compositions

Abstract The geochemistry of river sediments is routinely used to obtain information about geologic and environmental processes occurring upstream. For example, downstream samples are used to constrain chemical weathering and physical erosion rates upstream, as well as the locations of mineral deposits or contaminant sources. Previous work has shown that, by assuming conservative mixing, the geochemistry of downstream samples can be reliably predicted given a known source region geochemistry. In this study, we tackle the inverse problem and “unmix” the composition of downstream river sediments to produce geochemical maps of drainage basins (i.e., source regions). The scheme is tested in a case study of rivers draining the Cairngorms, UK. The elemental geochemistry of the μm fraction of 67 samples gathered from the beds of channels in this region is used to invert for concentrations of major and trace elements upstream. A smoothed inverse problem is solved using the Nelder‐Mead optimization algorithm. Predictions of source region geochemistry are assessed by comparing the spatial distribution of 22 elements of different affinities (e.g., Be, Li, Mg, Ca, Rb, U, V) using independent geochemical survey data. The inverse approach makes reliable predictions of the major and trace element concentration in first order river sediments. We suggest this scheme could be a novel means to generate geochemical baselines across drainage basins and within river channels.
PPMCK: Privacy-preserving multi-party computing for K-means clustering

The powerful resource advantage of the cloud provides a suitable computing environment for data processing. By transferring local computing to the cloud, the efficiency of data processing can be improved. However, the open cloud environment has defects in data privacy-preserving. In order to strengthen the protection of data privacy and ensure the security of multi-party interaction, we propose a privacy-preserving multi-party computing scheme for K-means clustering (PPMCK). PPMCK can preserve data privacy in the cloud and in the local side for each party from multi-party computing. In addition, PPMCK uses homomorphic encryption to protect data privacy. To support the division operation and ciphertext value size comparison with which homomorphic encryption cannot handle, the corresponding measurements are adopted, which make homomorphic encryption work smoothly. The experimental results demonstrate that PPMCK is effective in both data processing and privacy-preserving.
Speech To Indian Sign Language (ISL) Translation System

The deaf and dumb population across the Indian subcontinent predominantly communicate with each other and with other people using the Indian Sign Language (ISL). ISL employs signs consisting of hand gestures, facial expressions or body postures to convey the desired message and emotion. It is a full-fledged natural language with its own grammar and lexicon. In order to lessen this significant communication gap between the hearing and speaking impaired community and the normal population, there is a need for translation systems. We propose an end-to-end human interface framework that is capable of recognizing and interpreting spoken language and then act out the corresponding ISL gestures to facilitate a very convenient, real time form of conversation between the disabled community and the rest of the population. We made use of the Microsoft Xbox Kinect 360s depth sensing and motion capturing abilities to capture motion data for all the different ISL gestures and then used Unity3D to set up all the animations and then finally bundle everything into an Android application.
EfficientNets for DeepFake Detection: Comparison of Pretrained Models

Rapid advances in media generation techniques have made the creation of AI-generated fake face videos more accessible than ever before. In order to accelerate the development of new ways to expose forged videos, Facebook created Deep Fake Detection Challenge (DFDC), which demonstrated multiple approaches to solve this problem. Analysis of top-performing solutions revealed that all winners used pre-trained EfficientNet networks, which was finetuned on videos containing face manipulations. Because of this observation, we decide to compare the performance of EfficientNets models within the task of detecting fake videos. For comparison, we use models, based on the highest-performing entrant of DFDC, entered by Selim Seferbekov, and the DFDC dataset as training data. Our experiments show that there is no strong correlation between model performance and its size. The best accuracy was achieved by B4 and B5 models.