Reviewerly/exHarmony huggingface dataset:
Differential Privacy Machine Learning Based on Attention Residual Networks

To address the problems that the differential privacy stochastic gradient descent algorithm DP-SGD does not apply to deep network models with more parameters and noise addition affects model performance, a differential privacy machine learning model based on attention residual networks is proposed, which solves the problem of differential privacy training on larger models by embedding the attention module, replacing the batch normalization with group normalization, and replacing the activation function with more efficient Experimental results on MNIST and CIFAR-10 datasets show that the classification accuracy of the model is improved by 3.99% and 2.43%, respectively, compared with the original model before the improvement, with the same privacy budget <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> .
Blockchain-based Distributed Identity Cryptography Key Management

The identity-based cryptography algorithm SM9 requires a trusted third-party key generation center KGC to generate the user's private key. This will bring the risks and problems of key escrow and single point failure. This paper proposes a distributed identity-based cryptography key management scheme. Participants generate partial identity private key and send to the user. The user locally generates its own identity private key. Meanwhile, the identity status data can be uploaded to the blockchain. This avoids the key escrowing problem, solves the single point failure and trust problem, and improves the system's availability and security. The proposed scheme has obvious advantages in terms of efficiency and bandwidth requirements.
Mobile Robot Path Planning using Multi-Objective Adaptive Ant Colony Optimization

Path planning for a mobile robot is a vital task for navigating in a complex environment. However, the path planning problem is challenging because of its non-deterministic polynomial-time (NP) character. In this research, the authors have proposed the Multi-objective Adaptive Ant Colony Optimization for path planning of mobile robot in a static object environment on the grid. The heuristic information function in the conventional ant colony optimization is modified according to the A* algorithm, which helps in mitigating the slow convergence of the traditional algorithm. It is because the ants will choose the nodes which are closer to the goal position. Additionally, an objective function is formulated as a multiple objective problem by incorporating (a) Path length, (b) Safety factor, and (c) Energy consumption. Simulation results show that the proposed modification helps to achieve the optimal path in a quicker time i.e., 1.3 times faster than the traditional counterpart.
Data Driven User Feature Construction and Requirement Generation in Mobile Applications

Mobile applications have been widely used in people's daily life, and lots of data is generated in use of these mobile applications. These data including various context elements and contains rich user information. How to construct user features and discover user requirements from these data is vital for developer to improve the mobile application and providing smartly service for users. Therefore, a user feature construction and requirements generation method is proposed in this study. The main constructions of the study including three parts. Firstly, a time smoothing strategy is proposed to assist the generation of users' time-related features. Secondly, a fuzzy logic based method is proposed to generate user requirements. Thirdly, a tool is developed to automate and standardize the method. Finally, an experiment is conducted, and the results show the method is effective.
Multi-Chain Model and Cross-Chain Communication Protocol for Financial Transactions

Aiming at the cross-chain problems faced by financial transactions, study the cross-chain communication protocols of the financial-oriented autonomous panda model and golden monkey model, and study the construction of a new scalable and credible multi-chain model that supports homogeneous blockchains and heterogeneous blockchains. The models and protocols that support financial transactions in the blockchain environment need to be able to meet the SSL or SET security protocols similar to traditional Internet transactions, and meet various requirements such as transaction integrity, reliability, and privacy protection.
A Vision of DevOps Requirements Change Management Standardization

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to the lack of frequent communication and coordination in GSD, the planning and managing of the requirements change process becomes a challenging task. As in DevOps, requirements are not only shaped by development feedback but also by the operations team. This means requirements affect development, development affects operations and operations affect requirements. However, DevOps in GSD still faces many challenges in terms of requirement management. The purpose of this research project is to develop a DevOps requirement change management and implementation maturity model (DevOps-RCMIMM) that could assist the GSD organizations in modifying and improving their requirement management process in the DevOps process. The development of DevOps-RCMIMM will be based on the existing DevOps and RCM literature, industrial empirical study, and understanding of factors that could impact the implementation of the DevOps requirement change management process in the domain of GSD. This vision study presents the initial results of a systematic literature review that will contribute to the development of maturity levels of the proposed DevOps-RCMIMM.
TrustAuction: A TEE based Privacy Protection Framework for Auction Contracts

Auctions are particularly favored as a means of financial transaction for high-value commodities. The Turing-complete script provided by the Ethereum platform facilitates different types of auctions. However, the transparency of Ethereum may lead to threats to the privacy of financial transactions, which is also the biggest obstacle to the practical application of blockchain. This paper proposes an auction contract privacy protection method named TrustAuction, which protects the transaction data privacy and user identity privacy in the auction process through the Trusted Execution Environment(TEE). Specifically, TEE is used to generate new identities for users in the auction process, and the bids submitted by users using the new identities exist are encrypted outside the TEE.
A Light-Weight Convolutional Neural Network for Facial Expression Recognition using Mini-Xception Neural Networks

This paper builds a convolutional neural network model based on Xception; we delete the fully connected layer in the traditional convolutional neural network model and use four depthwise separable convolutions to replace the convolution layer in convolutional neural network; we use batch normalization to process the output data after each convolution operation, and use the ReLU activation function to add nonlinear factors to the output data, and finally use the SoftMax function for final result classification. Our model achieved an accuracy rate of 73% on the FER2013 dataset, which is a particular improvement compared to the original model Xception. We design and implement a facial recognition system that can be used for static images and real-time recognition, which can quickly and accurately recognize authentic facial expressions.
A Survey on Deepfake Video Detection Techniques Using Deep Learning

Deep learning is an effective technology that has been widely used in different ways. ‘DeepFake’ videos are generated using deep learning technology called generative adversarial network where the videos are created with swaped faces in a video, altered facial expressions, change of gender, creation of fake video content and altered facial features. Fake videos are used in the situations like extortion of money, terrorism events or create political agitation. The deepfake technologies are used for positive purposes, such as film-making and virtual reality. The good quality results from deepfakes are very hard to recognised with people eyes. Many deep learning techniques are built to detect deepfake content in images and videos. A comparative study of the performance of various deepfake videos detection models in the deep learning is preseted in this paper. Convolutional Neural Network (CNN) models include ResNet, VGG16, Efficientnet etc.. Recurrent Neural Network (RNN) model includes Long Short-Term Memory LSTM.
Video Inter-frame Tampering Detection Based on SN-VGG+BiLSTM-AE Composite Model

Video inter-frame tampering detection is the most common type of forensics in video forensics. The traditional detection method is to detect tampering by extracting digital image features of video frames, such as SIFT, HOG, and ORB. The accuracy of frame discrimination and localization is limited. This paper introduces deep learning into the problem of tampering detection, and proposes a composite network model structure using the Siamese network Siamese and the bidirectional long short-term memory network autoencoder BiLSTM AutoEncoder to detect tampered frames. Among them, Siamese calculates the inter-frame distance by calculating the depth features of the frames extracted by VGG-16, and inputs them into BiLSTM AutoEncoder for frame sequence anomaly detection and localization. The model is experimented on two different datasets with good results, validating the model generalization performance. Compared with the classical method, this model obtains higher precision(93.7%) of tamper points, which verifies the superiority of this deep learning model.
Speaker Diarization Using BiLSTM and BiGRU with Self-Attention

Speaker diarization is splitting an audio stream into segments based on speaker turns and clustering the segments according to speaker identity by using the speaker characteristics. This paper aims to devise techniques based on bidirectional Long Short Term Memory Units and Gated-Recurrent Units with self-attention for speaker segmentation in a speaker diarization system. Self-attention, also called intra-attention, is an attention mechanism relating different positions of a single sequence to compute a representation of the same sequence. Experiments were conducted on the Augmented Multi-party Interaction (AMI) Meeting Corpus, a popular speech recognition dataset. The results show the potential of BiLSTM sequential processing with attention to the speaker diarization process.
Detection of Breast Cancer from Histopathological Images

Breast cancer is the second leading cause of death after lung cancer, and breast cancer accounts for around 30 per cent of newly diagnosed cancer cases. Since manually detecting a cancer cell is time-consuming and prone to error, computer-assisted processes are used to get better results than manual pathological detection systems. Breast cancer is detected via Mammography, MRI scans, CT scans, ultrasounds, and nuclear imaging, but none of these guarantee a 100 per cent accurate diagnosis. For tumor detection, a histopathology test is performed to detect invasive cancer cells using HE stained tissues. However, intra-observer variation, numerous appearances of cancer cells or tissues, and the same hyperchromatic highlighted cells make identification challenging. Deep learning algorithms can be used to solve these issues. Deep learning uses a Convolutional Neural Network (CNN) to extract information before classifying them with a fully connected network. Deep learning is widely used in the field of medical imaging, and it does not require prior knowledge in a related discipline. The purpose of this automatic retrieval approach appears to have practical utility in the field of imaging for diagnostic imaging professionals in determining whether a tumour is benign or malignant in nature. The objectives of the project include the following: 1. Learn about different deep learning methods used in analysis of histopathological images for medical diagnosis. 2. Detect breast cancer from histopathological images using deep learning. 3. Classify and identify different types of cancerous cell using the algorithm. 4. Comparing the method with other existing methods and analyse the performance.
A Software Defect Prediction Classifier based on Three Minimum Support Threshold Association Rule Mining

With the increasing complexity of software system, the cost of software maintenance is increasing. In this case, software reliability is difficult to guarantee. To address this problem, software defect prediction technology based on machine learning has been attached great importance by a large number of scholars. Because of the strong interpretability of association rules, association rule algorithms are often used in classification tasks. However, the class imbalance problem seriously impacts the performance of traditional software defect classifiers based on association rule mining, therefore, it is necessary to use association rule algorithm that can be used to handle class imbalance data to deal with this problem. In this paper, a software defect prediction classifier based on three minimum support threshold association rule mining is proposed, which aims to improve the quality of these three frequent item-sets by considering the support of frequent item-sets containing defect labels, including non-defect labels and only including software metrics. The algorithm is compared with other four machine learning algorithms, and the results show that the algorithm is effective.
OpenVenus: An Open Service Interface for HPC Environment Based on SLURM

With the emergence of more and more “AI + Field + HPC” applications, it is urgent to solve the problem of scheduling and management of High-Performance Computing (HPC) resources, as well as the fast and efficient “cloud service” of HPC applications. This engineering problem is particularly critical because it affects the progress of scientific research, the development period of the research platform, and the learning cost of scientists. To solve the problem, a set of reusable life cycle processes for HPC resources are designed. Based on the life cycle, we propose an open service interface based on HPC, which reduces the startup time under multiple refreshes and abnormal retries by using the mode of contention lock. The active interruption of users is a typical scenario in the startup phase. Furthermore, a read-write strategy with an overlay based on Singularity is implemented to save storage space and improve running speed. In order to evaluate the serviceability and performance of the proposed interface, we deploy the service on the Venus platform and make a startup comparison experiment. In addition, the reduction of storage for 100 users is also tested. The experimental results show that under the HPC environment with SLURM, the proposed open-service interface can effectively shorten 46% startup time of applications and services and reduce 25% storage at least for each user of the Venus platform.
People@Places and ToDY: Two Datasets for Scene Classification in Media Production and Archiving

In order to support common annotation tasks in visual media production and archiving, we propose two datasets which cover the annotation of the bustle of a scene (i.e., populated to unpopulated), the cinematographic type of a shot as well as the time of day and season of a shot. The dataset for bustle and shot type, called People@Places, adds annotations to the Places365 dataset, and the ToDY (time of day/year) dataset adds annotations to the SkyFinder dataset. For both datasets, we provide a toolchain to create automatic annotations, which have been manually verified and corrected for parts of the two datasets. We provide baseline results for these tasks using the EfficientNet-B3 model, pretrained on the Places365 dataset.
Fourier Channel Attention Powered Lightweight Network for Image Segmentation

The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.
Quantum Programming on Azure Quantum—An Open Source Tool for Quantum Developers

Quantum computing has become a new buzzword in recent years. Although quantum computing techniques have been available in the literature for the past 40 or more years, the desire for real-time implementation of such quantum computing techniques has become possible due to the ongoing superspeed development of quantum computers by multinational corporations. Albeit, only 40 qubits quantum computer has been developed to date. Still, the pathway of big corporations reveals that by the end of this decade, a full-fledged quantum computer will be available in the market for everyone. Quantum computing uses quantum key distribution for quantum communication. It is expected that quantum computing and quantum communication will completely change the workflow of many industries. Studies are also predicting that the market demand for the quantum computing industry will be in multi-trillion dollars as early as 2030. Besides, the perspective of researchers has been drastically changing due to the plethora of opportunities brought forth by quantum computing for data processing and data encryption tasks. The quantum computer is deep-rooted in uncertainty principle, and probability theories thereby prohibit the copying and replicating of quantum information. Consequently, the guarantee of unconditional security for transmitted data is ensured, otherwise impossible. Generally, transmitted data are hacked due to attackers’ generation of keys replica. We may note that despite quantum computing being in a nascent stage, it possesses the potential to change internet activities by speeding up many tasks. Many day-to-day activities of many industries like finance, healthcare, and security will unseal imperceptible abilities. Furthermore, many big corporations invest in developing quantum computers and open-source tools to enable the development of quantum programs running on quantum computers. Also, community-driven activities are accomplished to upgrade the skills of current software developers to make them ready with appropriate skills for the development of future quantum software, which will run on large bits quantum computers. In this direction, Microsoft Incorporation has not only developed a quantum development kit (QDK) but also provides cloud-based quantum computing as a service, namely Azure Quantum, for developing and testing new quantum programs for the community. The newly designed quantum programs can now be simulated locally or run on the real quantum computer through Azure Quantum. Consequently, this chapter introduces the what, when, and why’s of quantum computing. Also, this chapter presents all necessary tools (with detailed installation and execution steps) required by the quantum developer for the possible development of a quantum program.
A Semantic-Preserving Scheme to Trajectory Synthesis Using Differential Privacy

With the ubiquity of Internet of Things, location-based service (LBS) providers have collected huge volumes of individuals’ trajectories, which are valuable for some applications, e.g., store location choosing for merchants. However, directly publishing raw trajectories to applications may violate individuals’ data privacy and lead to unexpected loss. Although many trajectory synthesis methods under differential privacy have been proposed to privately publish trajectories data, they cannot sufficiently preserve the semantic information of trajectories. Aiming at this issue, in this article, we introduce a semantic-preserving scheme to synthesize trajectories for publishing under differential privacy. Specifically, we first design a hierarchical graphical model (HGM) to capture the semantic feature of trajectories. Then, we propose a metric, named the correlation score, to measure the relationship between two locations, which can well capture the geographic feature of trajectories. After that, we propose a private trajectory synthesis algorithm by first adding Laplace noises to the extracted features and then synthesizing trajectories based on the noisy features and the Markov chain theory. Privacy analysis demonstrates that our scheme can protect the privacy of trajectories. In addition, performance evaluation illustrates that our synthetic trajectories maintain good utility semantically and geographically.
Deep Learning With Fault Prior for 3-D Seismic Data Super-Resolution

Seismic data are always in low-resolution due to the limitations of seismic acquisition and processing technology, which bring challenges to subsequent seismic interpretation. Deep learning has been successfully applied to the seismic data super-resolution, all methods tend to directly learn the mapping relationship between low-resolution seismic images and super-resolution seismic images through some complex convolutional neural networks. But blindly increasing the depth of the network brings limited improvement to the super-resolution work. We propose a novel geophysical prior guided framework for seismic data super-resolution to solve this problem. Specifically, we select fault prior to guide the training of deep learning model: we use knowledge distillation technology to progressively propagate the fault prior from the teacher network (trained with the low-resolution synthetic seismic data/high-resolution fault prior and high-resolution synthetic seismic data pairs) to the student net-work (trained with the low-resolution synthetic seismic data and high-resolution synthetic seismic data pairs). To better propagate fault priors, we use feature space loss and soft ground truth loss in student network training. Finally, we use the trained student network to complete the super-resolution of synthetic validation seismic data and real seismic data. In addition, in our super-resolution framework, we directly process 3D seismic data instead of 2D seismic images, which further improves the effects of super-resolution and the subsequent seismic interpretation. Compared with the state-of-the-art seismic super-resolution method, the experimental results show that the super-resolution results of our method can depict the faults more clearly.