Reviewerly/exHarmony huggingface dataset:
The exploration of internet finance by using neural network

In order to find out the risks of Internet finance as much as possible, and to ensure the rapid and healthy development of Internet finance, random forest (RF), a common classification algorithm in machine learning, was applied to analyze the risk factors of Internet finance. Additionally, the results of traditional statistical methods were compared with those of RF and back propagation (BP) neural network methods and their performance was evaluated. Finally, some suggestions were given for these risk factors, especially for the problems with high risk. The results showed that the RF algorithm model had the best classification effect and could accurately analyze the risks of Internet finance in terms of market, law, credit, personal information, and professional knowledge. It was found that credit and personal information risk were the most important factors in the future development of Internet finance when BP neural network was used to evaluate these risks. To a certain extent, they would also hinder the use and development of Internet finance. At the same time, it proved that BP neural network had a good prediction effect. To sum up, using the RF algorithm and BP neural network method in machine learning to explore the problems of Internet finance is of great significance for risk prediction for other financial institutions.
Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition

This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters than 3D CNNs. We adopt transfer learning from pre-trained 2D CNNs for spatial extraction, followed by temporal encoding, before connecting to 3D convolution layers at the top of the architecture. We construct our fusion architecture, semi-CNN, based on three popular models: VGG-16, ResNets and DenseNets, and compare the performance with their corresponding 3D models. Our empirical results evaluated on the action recognition dataset UCF-101 demonstrate that our fusion of 1D, 2D and 3D convolutions outperforms its 3D model of the same depth, with fewer parameters and reduces overfitting. Our semi-CNN architecture achieved an average of 16–30% boost in the top-1 accuracy when evaluated on an input video of 16 frames.
A Low-Overhead, Confidentiality-Assured, and Authenticated Data Acquisition Framework for IoT

In the presence of several critical issues during data acquisition in industrial-informatics-based applications, like Internet of Things (IoT) and smart grid, this article proposes a novel framework based on compressive sensing (CS) and a cascade chaotic system (CCS). This framework can ensure low overhead, confidentiality, and authentication. Based on CS and the CCS, three technologies, including CCS-driven CS, CCS-driven local perturbation, and authentication mechanism, are introduced in the proposed data acquisition framework in this article. CCS-driven CS generates the measurement matrix with chaotic initial conditions and avoids the transmission of a large-size measurement matrix. CCS-driven local perturbation only perturbs a small number of elements in the original measurement matrix for each sampling and avoids the regeneration of the large-size measurement matrix. The authentication mechanism employs the authentication password and the access password to deal with the passive tampering attack and the active tampering attack, respectively. Moreover, the permutation-diffusion structure is used to encrypt the obtained measurements to enhance the security. Both theoretical and experimental analyses validate low overhead, confidentiality, and effective authentication of the proposed data acquisition framework for a number of industrial-informatics-based applications, such as IoT.
The Five Laws of SE for AI

It is time to talk about software engineering (SE) for artificial intelligence (AI). As shown in Figure 1, industry is becoming increasingly dependent on AI software. Clearly, AI is useful for SE. But what about the other way around? How important is SE for AI? Many thought leaders in the AI industry are asking how to better develop and maintain AI software (see Figure 2).
Blockchain of Finite-Lifetime Blocks With Applications to Edge-Based IoT

Edge computing is a promising approach for provisioning distributed cloud services to Internet of Things (IoT) systems. Many recent studies propose that edge nodes use blockchain for the decentralized management and access control of IoT data. However, due to the massive volume of data and related transactions, edge servers will eventually run out of space to store the full chain. We introduce scalable and lightweight architecture called LiTiChain, a blockchain of blocks with finite lifetime. In LiTiChain, outdated transactions and blocks, that is, the blocks whose lifetimes are expired, can be safely removed from the chain. Two graphs are merged into the structure of LiTiChain: 1) a tree representing the order of expiry of lifetimes and 2) a linear graph representing the order of block creation. We show that this construction not only ensures the connectivity of the chain after block deletions but also helps to maintain the block height of shortened chain. LiTiChain also supports transactions whose lifetime is unknown at the time of creation. It is possible that some expired blocks need to be retained in the chain, in case they are needed to validate remaining blocks, which incurs additional storage costs. A detailed analysis of such overhead in storage costs is presented for stochastic and worst case scenarios. Extensive simulation is performed on actual and synthetic IoT data so as to gain insights on the storage costs under various lifetime distributions. It is demonstrated that LiTiChain provides a simple yet effective solution to scalability problems in storing blockchains for the IoT ecosystems.
Building A Decentralized Application on the Ethereum Blockchain

Blockchain technology is being used in many areas, during last few years. Furthermore, different application opportunities are still been investigated. Blockchain relies on and permits to implement the concept of Decentralized Application (DApps). This makes the applications more transparent, distributed and flexible. The complexity of blockchain and its integration problems require expertise that differs from traditional application development approaches. Within this context, this paper presents our experience in building a DApp with one of the most popular blockchain based platforms called Ethereum.
Blockchain-Based Resource Allocation Model in Fog Computing

Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities.
Enhancing deep neural networks via multiple kernel learning

Deep neural networks and Multiple Kernel Learning are representation learning methodologies of widespread use and increasing success. While the former aims at learning representations through a hierarchy of features of increasing complexity, the latter provides a principled approach for the combination of base representations. In this paper, we introduce a general framework in which the internal representations computed by a deep neural network are optimally combined by means of Multiple Kernel Learning. The resulting ensemble methodology is instantiated for Multi-layer Perceptrons architectures (both fully trained and with random-weights), and for Convolutional Neural Networks. Experimental results on several benchmark datasets concretely show the advantages and potentialities of the proposed approach.
View Confusion Feature Learning for Person Re-Identification

Person re-identification is an important task in video surveillance that aims to associate people across camera views at different locations and time. View variability is always a challenging problem seriously degrading person re-identification performance. Most of the existing methods either focus on how to learn view invariant feature or how to combine viewwise features. In this paper, we mainly focus on how to learn view-independent features by getting rid of view specific information through a view confusion learning mechanism. Specifically, we propose an end-to-end trainable framework, called View Confusion Feature Learning (VCFL), for person Re-ID across cameras. To the best of our knowledge, VCFL is originally proposed to learn view-independent identity-wise features, and it's a kind of combination of view-generic and view-specific methods. Furthermore, we extract sift-guided features by using bag-of-words model to help supervise the training of deep networks and enhance the view invariance of features. In experiments, our approach is validated on three benchmark datasets including CUHK01, CUHK03, and MARKET1501, which show the superiority of the proposed method over several state-of-the-art approaches.
Generating High-Resolution Fashion Model Images Wearing Custom Outfits

Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determined set of outfits. In this paper, we broaden these visualizations by generating high-resolution images of fashion models wearing a custom outfit under an input body pose. We show that our approach can not only transfer the style and the pose of one generated outfit to another, but also create realistic images of human bodies and garments.
High-fidelity dual-image reversible data hiding via prediction-error shift

Dual-image reversible data hiding (RDH) technology has been an active research area and became an essential part of information security because of its unique advantages in security, embedding capacity and visual quality. For a dual-image RDH strategy, message data is embedded into a cover image to generate two marked images with similar visual quality. In this study, we propose an improved dual-image RDH algorithm based on a prediction-error shift strategy. As a more compact image feature, prediction error is exploited to make better use of the redundancy of image content with the maximum distortion of one. A bidirectional-shift strategy is used to extend the shiftable positions in the central zone of the allowable coordinates. Next, we design a new shiftable-position model for the prediction error corresponding to the peak value in the prediction-error histogram to embed more message bits without causing more distortion. The experimental results demonstrate the efficacy and high-fidelity of the proposed method, especially in relatively low embedding rate circumstances. The average peak signal-to-noise ratio gain of the proposed method on eight standard test images is at least 1.15 dB for the given embedding capacity of 50,000 bits compared with other state-of-the-art dual-image RDH methods.
A Recursive Regularization Based Feature Selection Framework for Hierarchical Classification

The sizes of datasets in terms of the number of samples, features, and classes have dramatically increased in recent years. In particular, there usually exists a hierarchical structure among class labels as hundreds of classes exist in a classification task. We call these tasks hierarchical classification, and hierarchical structures are helpful for dividing a very large task into a collection of relatively small subtasks. Various algorithms have been developed to select informative features for flat classification. However, these algorithms ignore the semantic hyponymy in the directory of hierarchical classes, and select a uniform subset of the features for all classes. In this paper, we propose a new feature selection framework with recursive regularization for hierarchical classification. This framework takes the hierarchical information of the class structure into account. In contrast to flat feature selection, we select different feature subsets for each node in a hierarchical tree structure with recursive regularization. The proposed framework uses parent-child, sibling, and family relationships for hierarchical regularization. By imposing <inline-formula><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> -norm regularization to different parts of the hierarchical classes, we can learn a sparse matrix for the feature ranking at each node. Extensive experiments on public datasets demonstrate the effectiveness and efficiency of the proposed algorithms.
Immutability and Decentralized Storage: An Analysis of Emerging Threats

The widespread adoption of the new generation of decentralised architectures, leveraged by blockchain and decentralised file storage (DFS) systems, enables a myriad of new applications and opportunities. Nevertheless, their remarkable features, namely auditability, availability and, among all, immutability, do not come without a cost. In this article, we examine blockchain and the most widely used DFS systems and discuss their main challenges and opportunities, with special regard to their immutability and its impact on their GDPR compliance. A description of current and prospective threats is also provided, along with an analysis of the features that each threat exploits. In addition, we discuss several measures to address the identified threats, and we provide a fertile common ground for further research.
Cyberbullying detection in social media text based on character‐level convolutional neural network with shortcuts

Summary As people spend increasingly more time on social networks, cyberbullying has become a social problem that needs to be solved by machine learning methods. Our research focuses on textual cyberbullying detection because text is the most common form of social media. However, the content information in social media is short, noisy, and unstructured with incorrect spellings and symbols, and this impacts the performance of some traditional machine learning methods based on vocabulary knowledge. For this reason, we propose a Char‐CNNS ( Char acter‐level C onvolutional N eural N etwork with S hortcuts) model to identify whether the text in social media contains cyberbullying. We use characters as the smallest unit of learning, enabling the model to overcome spelling errors and intentional obfuscation in real‐world corpora. Shortcuts are utilized to stitch different levels of features to learn more granular bullying signals, and a focal loss function is adopted to overcome the class imbalance problem. We also provide a new Chinese Weibo comment dataset specifically for cyberbullying detection, and experiments are performed on both the Chinese Weibo dataset and the English Tweet dataset. The experimental results show that our approach is competitive with state‐of‐the‐art techniques on cyberbullying detection task.
Creating speaker independent ASR system through prosody modification based data augmentation

In this paper, the effect of prosody-modification-based data augmentation is explored in the context of automatic speech recognition (ASR). The primary motive is to develop ASR systems that are less affected by speaker-dependent acoustic variations. Two factors contributing towards inter-speaker variability that are focused on in this paper are pitch and speaking-rate variations. In order to simulate such an ASR task, we have trained an ASR system on adults’ speech and tested it using speech data from adult as well as child speakers. Compared to adults’ speech test case, the recognition rates are noted to be extremely degraded when the test speech is from child speakers. The observed degradation is basically due to large differences in pitch and speaking-rate between adults’ and children’s speech. To overcome this problem, pitch and speaking-rate of the training speech are modified to create new versions of the data. The original and the modified versions are then pooled together in order to capture greater acoustic variability. The ASR system trained on augmented data is noted to be more robust towards speaker-dependent variations. Relative improvements of 11.5% and 27.0% over the baseline are obtained on decoding adults’ and children’s speech test sets, respectively.
A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.
Effect of Avatar Appearance on Detection Thresholds for Remapped Hand Movements

Hand interaction techniques in virtual reality often exploit visual dominance over proprioception to remap physical hand movements onto different virtual movements. However, when the offset between virtual and physical hands increases, the remapped virtual hand movements are hardly self-attributed, and the users become aware of the remapping. Interestingly, the sense of self-attribution of a body is called the sense of body ownership (SoBO) in the field of psychology, and the realistic the avatar, the stronger is the SoBO. Hence, we hypothesized that realistic avatars (i.e., human hands) can foster self-attribution of the remapped movements better than abstract avatars (i.e., spherical pointers), thus making the remapping less noticeable. In this article, we present an experiment in which participants repeatedly executed reaching movements with their right hand while different amounts of horizontal shifts were applied. We measured the remapping detection thresholds for each combination of shift directions (left or right) and avatar appearances (realistic or abstract). The results show that realistic avatars increased the detection threshold (i.e., lowered sensitivity) by 31.3 percent than the abstract avatars when the leftward shift was applied (i.e., when the hand moved in the direction away from the body-midline). In addition, the proprioceptive drift (i.e., the displacement of self-localization toward an avatar) was larger with realistic avatars for leftward shifts, indicating that visual information was given greater preference during visuo-proprioceptive integration in realistic avatars. Our findings quantifiably show that realistic avatars can make remapping less noticeable for larger mismatches between virtual and physical movements and can potentially improve a wide variety of hand-remapping techniques without changing the mapping itself.
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet

Adversarial attacks on deep neural networks (DNNs) have been found for several years. However, the existing adversarial attacks have high success rates only when the information of the victim DNN is well-known or could be estimated by the structure similarity or massive queries. In this paper, we propose to Attack on Attention (AoA), a semantic property commonly shared by DNNs. AoA enjoys a significant increase in transferability when the traditional cross entropy loss is replaced with the attention loss. Since AoA alters the loss function only, it could be easily combined with other transferability-enhancement techniques and then achieve SOTA performance. We apply AoA to generate 50000 adversarial samples from ImageNet validation set to defeat many neural networks, and thus name the dataset as DAmageNet. 13 well-trained DNNs are tested on DAmageNet, and all of them have an error rate over 85 percent. Even with defenses or adversarial training, most models still maintain an error rate over 70 percent on DAmageNet. DAmageNet is the first universal adversarial dataset. It could be downloaded freely and serve as a benchmark for robustness testing and adversarial training.
Crowding reveals fundamental differences in local vs. global processing in humans and machines

Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have suggested that current ffCNNs do not make use of global shape information. However, it is currently unclear whether this reflects fundamental differences between ffCNN and human processing or is merely an artefact of how ffCNNs are trained. Here, we use visual crowding as a well-controlled, specific probe to test global shape computations. Our results provide evidence that ffCNNs cannot produce human-like global shape computations for principled architectural reasons. We lay out approaches that may address shortcomings of ffCNNs to provide better models of the human visual system.