Reviewerly/exHarmony huggingface dataset:
Unsupervised learning for financial statement fraud detection using manta ray foraging based convolutional neural network

Summary Financial statement fraud detection is a significant topic and a challenging task related to countless applications such as financial sectors, insurance, government agencies, law enforcement and so on. This article presents an unsupervised learning model to identify the financial fraud statements using the hybrid convolutional neural network. The fraud detection scheme can be performed using a few stages: data preprocessing, sampling, feature extraction, feature selection, clustering, and classification methods. The preprocessing and sampling processes are used to clean and recognize duplicate data. Then, the proposed model analyzes both text features and financial variables to provide the best classification of financial statement fraud. In this article, a new fuzzy red deer's algorithm is proposed for selecting the optimal feature set, which can improve the classification accuracy of the fraud prediction model. Subsequently, an adaptive density based clustering (ADBC) approach is introduced for labeling the selected features through the clustering process. Finally, the fraud statements are predicted by proposing a hybrid CNN‐MRFO model wherein the hyperparameters of the CNN model are optimized through the MRFO learning algorithm. The simulation results prove the effectiveness of the proposed model in terms of accuracy (98.85%), precision (98.62%) and F‐measure (99.32%).
Target tracking method of Siamese networks based on the broad learning system

Abstract Target tracking has a wide range of applications in intelligent transportation, real‐time monitoring, human‐computer interaction and other aspects. However, in the tracking process, the target is prone to deformation, occlusion, loss, scale variation, background clutter, illumination variation, etc., which bring great challenges to realize accurate and real‐time tracking. Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking, ensuring both accuracy and real‐time performance. However, due to its offline training, it is difficult to deal with the fast motion, serious occlusion, loss and deformation of the target during tracking. Therefore, it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online. The broad learning system (BLS) has a simple network structure, high learning efficiency, and strong feature learning ability. Aiming at the problems of Siamese networks and the characteristics of BLS, a target tracking method based on BLS is proposed. The method combines offline training with fast online learning of new features, which not only adopts the powerful feature representation ability of deep learning, but also skillfully uses the BLS for re‐learning and re‐detection. The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on, so as to change the selection of the Siamese networks search area, solve the problem that the search range cannot meet the fast motion of the target, and improve the adaptability. Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.
A controllable face forgery framework to enrich face-privacy-protection datasets

Deep learning not only brings convenience to people, but also promotes the development of facial forgery technology. Considering the current personal portrait security issues, the tampering and forgery of facial data has attracted more and more attention. In order to solve the above issues, we try to implement from another novel angle, that is, enrich the face-privacy-protection dataset to improve the detection ability of forgery faces. Therefore, we propose a controllable face forgery framework. In this work, we firstly analyze the identity information in the latent features and construct an identity latent space based on StyleGAN’s w+ latent space. Then, we propose an adaptive identity mapping network to edit the latent codes of the image through encoder and realize the identity transform. Finally, we further enhance the authenticity of the image through post-processing. In order to verify the superiority of our proposed method, we design extensive experiments. Experiments show the effectiveness of identity latent space and the controllability of our model. At the same time, it also shows that our proposed network can generate photo-level results and achieve excellent results in the comparison of other face swapping methods.
ProvNet-IoT: Provenance based network layer forensics in Internet of Things

Internet of Things is rapidly changing the human lives to bring convenience in domestic, public and industrial environments spanning across multiple application domains. At the same time, increasing security attacks on these networks raised alarms for timely response by forensic investigators to avoid severe consequences of the attacks. Major network forensic approaches proposed so far for IoT are based on recording and analyzing the network traffic to produce suitable evidences. One of the greatest challenges in this process is the identification and correlation of suitable artifacts among volumes of network packets to reconstruct the attack scenarios during forensic investigation. To address this challenge, we propose ProvNet-IoT, a novel provenance based forensic model for investigating network level attacks in IoT environment. The interactions between different nodes at network layer are depicted using information, functional, and event modeling techniques. We use progressive network provenance to explain different events pertaining to various attack scenarios and to provide forensically sound evidences. ProvNet-IoT is validated using two publicly available labeled IoT datasets with a corpus of different attacks. Experimental results showed the benchmark performance of ProvNet-IoT in identifying selective artifacts to produce reliable evidences during forensic investigation.
Perturbation of deep autoencoder weights for model compression and classification of tabular data

Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, in tabular data classification, DNNs are challenged by the often superior performance of traditional machine learning models. This paper proposes periodic perturbations (prune and regrow) of DNN weights, especially at the self-supervised pre-training stage of deep autoencoders. The proposed weight perturbation strategy outperforms dropout learning or weight regularization (L1 or L2) for four out of six tabular data sets in downstream classification tasks. Unlike dropout learning, the proposed weight perturbation routine additionally achieves 15% to 40% sparsity across six tabular data sets, resulting in compressed pretrained models. The proposed pretrained model compression improves the accuracy of downstream classification, unlike traditional weight pruning methods that trade off performance for model compression. Our experiments reveal that a pretrained deep autoencoder with weight perturbation can outperform traditional machine learning in tabular data classification, whereas baseline fully-connected DNNs yield the worst classification accuracy. However, traditional machine learning models are superior to any deep model when a tabular data set contains uncorrelated variables. Therefore, the performance of deep models with tabular data is contingent on the types and statistics of constituent variables.
Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles

The growing popularity of digital libraries as a medium for communicating scientific discoveries has made a large variety of research articles easily accessible. However, this constitutes a putative issue of information overloading with recommendation engines providing a compelling solution to the problem. Scientific Recommender Systems alleviate this problem by suggesting potential papers of interest to a user. For any researcher seeking developments in their field, it is important that the recommended papers are of high quality, recent and related to the field of interest, which has been largely overlooked in prior approaches. This study thus proposes a graph-based hybrid recommendation technique, SPACE-R, that amalgamates quality, semantic similarity and community structure of nodes in a citation network. The creation of a popularity network, a derivative of a citation network, in combination with a two-stage candidate selection process involving community detection and neighbourhood network identification, contributes to an improvement in the accuracy and scalability of the proposed model. The incorporation of semantic similarity achieves the necessary diversity in recommendations. Experimental evaluations on four large datasets against five baselines reveal that SPACE-R achieves an improvement of up to 45.53%, 56.76%, 49.39%, 46.84% and 78.18% in recall, precision, MRR, mAP, and response time, respectively.
Soft Error Resilient Deep Learning Systems Using Neuron Gradient Statistics

Deep learning techniques have been widely adopted in daily life with applications ranging from face recognition to recommender systems. The substantial overhead of conventional error tolerance techniques precludes their widespread use, while approaches involving median filtering and invariant generation rely on alterations to DNN training that may be difficult to achieve for larger networks on larger datasets. To address this issue, this paper presents a novel approach taking advantage of the statistics of neuron output gradients to identify and suppress erroneous neuron values. By using the statistics of neurons’ gradients with respect to their neighbors, tighter statistical thresholds are obtained compared to the use of neuron output values alone. This approach is modular and is combined with accurate, low-overhead error detection methods to ensure it is used only when needed, further reducing its cost. Deep learning models can be trained using standard methods and our error correction module is fit to a trained DNN, achieving comparable or superior performance compared to baseline error correction methods while incurring comparable hardware overhead without needing to modify DNN training or utilize specialized hardware architectures.
Microcontroller Performance Screening: Optimizing the Characterization in the Presence of Anomalous and Noisy Data

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of $F_{\max}$, that is, the maximum operating frequency. It has been demonstrated that data extracted from on-chip speed monitors can model the $F_{\max}$ of integrated circuits by means of machine learning models, and that those models are suitable for the performance screening process. However, while acquiring data from these monitors is quite an accurate process, the labelling is time-consuming, costly, and may be subject to different measurements errors, impairing the final quality. This paper presents a methodology to cope with anomalous and noisy data in the context of the multi-label regression problem of microcontroller performance screening. We used outlier detection based on Inter Quartile Range (IQR) and Z-score and imputation techniques to detect errors in the labels and to avoid to drop incomplete samples, building higher-quality training set for our models, optimizing the devices characterization phase. Experiments showed that the proposed methodology increases the performance of existing models, making them more robust. These techniques permitted us to use a significantly smaller number of samples (about one third of the devices available for characterization), thus making the costly data acquisition process more efficient.
Active projection nonlinear <i>γ</i> correction method for fringe projection profilometry

In fringe projection profilometry (FPP), the luminance nonlinearity generated by the superimposed γ effect of the projector and camera can lead to distortion of the intensity of the sinusoidal phase-shift fringe, resulting in a reduction of measurement precision and resolution. Traditional phase error compensation and γ-correction methods need to focus on the projector's optimal performance. However, commercial projectors often have huge apertures and are, therefore, unable to project a perfectly focused sinusoidal fringe image. This paper proposes an easy-to-implement active projection error correction method with high precision that is insensitive to projector defocus. After calibrating the projector to establish the nonlinear γ-response model of the optical measurement system, inverse γ compensation is performed. By generating and projecting a set of precorrected sinusoidal fringes, the camera can capture the high-quality sinusoidal fringe image and decrease the phase measurement error caused by the nonlinear γ effect of the FPP system. Computer simulations and experiments verify the effectiveness and feasibility of the proposed method for estimating and correcting the nonlinear γ distortion of the FPP system. The experimental results show that using the proposed active projection method to compensate for the error of the three-step phase-shift algorithm can achieve a high-precision measurement, and the influence of the system's nonlinear γ effect on the measurement accuracy is significantly suppressed.
Signed (Group) Diffie–Hellman Key Exchange with Tight Security

Abstract We propose the first tight security proof for the ordinary two-message signed Diffie–Hellman key exchange protocol in the random oracle model. Our proof is based on the strong computational Diffie–Hellman assumption and the multiuser security of a digital signature scheme. With our security proof, the signed DH protocol can be deployed with optimal parameters, independent of the number of users or sessions, without the need to compensate any security loss. We abstract our approach with a new notion called verifiable key exchange. In contrast to a known tight three-message variant of the signed Diffie–Hellman protocol (Gjøsteen and Jager, in: Shacham, Boldyreva (eds) CRYPTO 2018, Part II. LNCS, Springer, Heidelberg, 2018), we do not require any modification to the original protocol, and our tightness result is proven in the “Single-Bit-Guess” model which we know can be tightly composed with symmetric cryptographic primitives to establish a secure channel. Finally, we extend our approach to the group setting and construct the first tightly secure group authenticated key exchange protocol.
Usage of different Chroma Subsampling Modes in Image Compression by BPG Coder

A BPG (better portable graphics) coder is a novel approach that aims to replace common standards of compression such as JPEG, JPEG2000 and so on. That is why, the BPG coder needs a detailed analysis of its basic characteristics from the viewpoint of visual quality and compression ratio. The BPG coder can use different modes of chroma subsampling for color and three-channel images and it is worth analyzing and comparing them. In practice, images to be compressed are often noisy. Then, lossy compression of such images has a specific noise filtering effect. In particular, optimal operation point (OOP) might exist where compressed image quality is closer to the corresponding noise-free (true) image than uncompressed (original, noisy) image quality according to certain criterion (metric). It is also needed to analyze the coder performance from compression ratio point of view. In this paper, we pay attention on impact of different chroma subsampling modes on image quality and compression ratio. Based on simulation results obtained for a set of color images, the best possible ways of compression are recommended.
Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism

Falls are one of the significant causes of accidental injuries to the elderly. With the rapid growth of the elderly population, fall detection has become a critical issue in the medical and healthcare fields. In this paper, we propose a model based on an improved attention mechanism, CBAM-IAM-CNN-BiLSTM, to detect falls of the elderly accurately and in time. The model includes a convolution layer, bidirectional LSTM layer, sampling layer and dense layer, and incorporates the improved convolutional attention block module (CBAM) into the network structure so that the one-dimensional convolution layer replaces the dense layer to aggregate the information from channels, which allows the model to accurately extract different behavior characteristics. The acceleration and angular velocity data of the human body, collected by wearable sensors, are respectively input into the convolution layer and bidirectional LSTM layer of the model and then classified and identified by softmax after feature fusion. Based on comparison with models such as CNN and CNN-BiLSTM, as well as with different attention mechanisms such as squeeze-and-excitation (SE), efficient channel attention (ECA) and the convolutional block attention module (CBAM), this model improves the accuracy, sensitivity and specificity to varying degrees. The experimental results showed that the accuracy, sensitivity and specificity of the CBAM-IAM-CNN-BiLSTM model proposed in this paper were 97.37%, 97.29% and 99.56%, respectively, which proves that the model has good practicability and strong generalization ability.
Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network

The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models.
An integration of autonomic computing with multicore systems for performance optimization in Industrial Internet of Things

Abstract The goal of this work is to investigate how the self‐awareness characteristic of autonomic computing, paired with existing performance optimization rules, may be used in applications to minimise multi‐core processor performance concerns. The suggested self‐awareness technique can assist applications in self‐execution while also assisting other applications in executing in the system with optimal resource usage and reducing conflicts in a collaborative manner. It means self‐awareness is created in the application to get resources, schedules itself, running autonomically with respect to runtime variations in the applications and system parameters. Further, self‐aware applications would help collaboratively resolving the performance issues like; contention, bandwidth bottleneck, efficient task to core mapping, and performance monitoring through analysis in a dynamic collaborative multi‐core environment. To show the usefulness of this research, “Autonomic Computing Interface” (ACI) for is proposed Multicore systems. The proposed firmware as an interface between applications and the multi‐core system can dynamically handle performance issues with coordination of applications in an autonomic way. Also, in a highly dynamic execution environment the proposed runtime parameter tuning, existing performance improvement policies and self‐awareness approach in totality, for monitoring, analysis, and possibility for performance improvement would be effective as compared to the existing approaches which work in isolation.
Tutorial on UCD Sprint: Inclusive Process for Concept Design

Integrating User-Centred Design (UCD) methods into the first phases of software development projects has its challenges. A new process called the User-Centred Design Sprint process, UCD Sprint for short, has been suggested to support the project team during concept design. The tutorial introduces the UCD Sprint process, and participants practice two less-known methods that are part of the sprint: the User Group Analysis method and stating user experience goals. By the end of the tutorial, participants know why, when, and how to use the UCD Sprint process. This tutorial appeals to researchers and developers working in the concept design phase of designing software products, focusing on users that might be different from the developers.
TCA-PEKS: Trusted certificateless authentication public-key encryption with keyword search scheme in cloud storage

Public key encryption with keyword search (PEKS) technology is capable of achieving accurate ciphertext retrieval while protecting data privacy. However, curious or malicious semi-trusted cloud servers can cause privacy breaches, which then creates a trust problem for ciphertext management and searching. To address this problem, we propose a trusted certificateless authentication public-key encryption with a keyword search scheme in cloud storage (TCA-PEKS), which ensuring trusted retrieval, simultaneously resolves the problems of key escrow and certificate management that exist in PEKS. In the scheme, the security of the ciphertext storage and verifications are strengthened based on blockchain non-tampering features, which assists users in verifying the correctness of a file. In particular, we construct an open and transparent smart contract to limit the malicious behaviour of cloud servers, in which the user's complete private key splits the secret value and the partial private key, further guaranteeing the correctness of the retrieval process. Finally, the scheme is proven to satisfy ciphertext and trapdoor indistinguishability under the random oracle model, and the performance evaluation results show that the scheme is highly efficient.