Reviewerly/exHarmony huggingface dataset:
Secure Aggregation with Uncoded Groupwise Keys Against User Collusion

In this paper, we study the information theoretic secure aggregation problem, where the server node aims to aggregate K users' locally trained models, without revealing any other information about the users' local data. To ensure security, some keys are shared among the users, which is referred to as the key sharing phase. Uncoded groupwise keys are considered, where each key is shared by a subset of S users and is independent from other keys. After the key sharing phase, each user masks its trained model and sends to the server, which is referred to as the model aggregation phase. In the presence of users' dropouts (i.e., up to K – U user may drop during the model aggregation phase and the identity of the dropped users cannot be predicted), to guarantee the information theoretic security, two-round transmissions are necessary. Our objective is to characterize the capacity region of the transmission rates (i.e., the normalized numbers of two-round transmissions by each user) in the two rounds. When <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{S}\geq \mathsf{K}- \mathsf{U}+1$</tex> , the capacity region was recently characterized. In this paper, we additionally consider the potential effect of user collusion, where there may exist up to T users colluding with the server. With the presence of the colluding users, the security constraint becomes that, except the sum of trained models, the server cannot learn any information about the other users' local data even if it colludes with any set of up to T users. For this new problem, we propose two secure aggregation schemes, which work for the cases of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{S} = \mathsf{K}-\mathsf{U}+1$</tex> and of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{K}-\mathsf{U}+1\leq \mathsf{S}\leq \mathsf{K} - \mathsf{T}$</tex> , respectively. The first scheme is then proven to achieve the capacity region.
Migrating from Monolithic Applications to Cloud Native Applications

With the rapid development of cloud computing, more and more applications are developed explicitly for the cloud infrastructure. And there is an increasing demand to migrate monolithic applications to cloud computing environment. Because of the differences in infrastructures, the cloud native applications have different design principles with the monolithic applications. Without correct guidance when working on the migration, the migrated applications cannot take good advantage of the cloud computing environment. Even worse, it may cause a totally failure of the migration. That would be a waste of resource because migrating a monolithic application to cloud computing environment is usually a huge project. With the hope to provide a general guidance, this article introduces the design principles of cloud native applications and the technical aspects needed to be taken into consideration when migrating monolithic applications to cloud environment.
On the use of Stable Diffusion for creating realistic faces: from generation to detection

The mass adoption of diffusion models has shown that artificial intelligence (AI) systems can be used to easily generate realistic images. The spread of these technologies paves the way to previously unimaginable creative uses while also raising the possibility of malicious applications. In this work, we propose a critical analysis of the overall pipeline, i.e., from creating realistic human faces with Stable Diffusion v1.5 [1] to recognizing fake ones. We first propose an analysis of the prompts that allow the generation of extremely realistic faces with a human-in-the-loop approach. Our objective is to identify the text prompts that drive the image generation process to obtain realistic photos that resemble everyday portraits captured with any camera. Next, we study how complex it is to recognize these fake contents for both AI-based models and non-expert humans. We conclude that similar to other deepzfake creation techniques, despite some limitations in generalization across different datasets, it is possible to use AI to recognize these contents more accurately than non-expert humans would.
Object Detection for Rare Birds on the Plateau

Object detection has always been a hot research direction in the field of computer vision. At present, most methods are supervised learning methods, but this algorithm requires a large amount of image labeled data, which not only takes time to manually label, but also takes a lot of time when training data. In this paper, the improved object detection network based on yolov3 network is studied, due to the fast inference speed of yolov3, high cost performance and strong versatility, the improved object detection network can identify and locate specific class objects by extracting features by algorithms. In order to improve the performance of detection, before training, the labeled pictures of rare birds on the plateau were augmented to expand the data, and attention mechanisms were added to the last three effective output layers of the backbone network. Finally, the experimental results show that the obtained model has a certain improvement in the picture detection effect of rare birds on the plateau.
An Efficient Byzantine Fault-Tolerant Consensus Mechanism Based on Aggregate Threshold Ring Signature

To address the problems of existing consensus mechanism in blockchain such as too much information interactions among nodes, and simple election strategy of master node, this paper firstly proposes an Aggregate Threshold Ring Signature (ATRS) algorithm, which is efficient, anonymous and takes up less space. Then we apply it to the consensus mechanism of blockchain, and proposes a consensus mechanism based on ATRS algorithm, named Ring Signature-Practical Byzantine Fault Tolerance (ATRS-PBFT) consensus mechanism. This consensus mechanism reduces the number of signatures and verifications and improves the efficiency of the mechanism. In order to increase the security, the consensus proposed in this paper also adopts a credit hierarchy in the master node election strategy, divides the nodes' credit rating before the master node election, and selects the nodes with high credit rating as the master nodes, which reduces the probability of malicious nodes becoming the master nodes. The optimized consensus mechanism can both achieve privacy protection for nodes using ring signature and reduce the data computation in the signature process to improve the efficiency of the mechanism. From the simulation experiments, it can be seen that ATRS-PBFT consensus has the advantages of increased throughput, reduced latency, short block-out time, and high efficiency compared with the traditional PBFT consensus mechanism.
On the Use of Cross-module Attention Statistics Pooling for Speaker Verification

In deep learning-based speaker verification frameworks, extraction of a speaker embedding vector plays a key role. In this contribution, we propose a hybrid neural network that employs a cross-module attention pooling mechanism for the extraction of speaker discriminant utterance-level embeddings. In particular, the proposed system incorporates a 2D-Convolution Neural Network (CNN)-based feature extraction module in cascade with a frame-level network, which is composed of a fully Time Delay Neural Network (TDNN) network and a TDNN-Long Short Term Memory (TDNN-LSTM) hybrid network in a parallel manner. The proposed system also employs cross-module attention statistics pooling for aggregating the speaker information within an utterance-level context by capturing the complementarity between two parallelly connected modules. We conduct a set of experiments on the Voxceleb corpus for evaluating the performance of the proposed system and the proposed hybrid network is able to provide better results than the conventional approaches trained on the same dataset.
Design and Research of the Whole Process Non-blocking Technology in High Concurrency Scenario

Under high concurrency scenarios, software applications can suffer from low access efficiency and high exception rate due to I/O bottlenecks and database connection number limitations. Without changing the hardware conditions, a fullprocess non-blocking technology is employed to alleviate the performance bottleneck of software applications under high concurrency scenarios from the perspective of optimizing access request reception and data read/write rates. Firstly, to mitigate the huge concurrency pressure on database caused by a large number of non-essential operations, a Bloom filter is selected to filter invalid access requests. Then, a concurrent data processing caching strategy is applied to hand with the performance bottleneck problem caused by the limitation of database connections in the high-concurrency scenario. Furthermore, to address the issue that synchronous I/O cannot take advantage of the responsive framework and combine with the new achievements in database technology development, an asynchronous non-blocking database access middleware is designed and developed to achieve a completely asynchronous non-blocking and streaming data processing mode. After testing and application practice, the effectiveness and efficiency of the proposed methods are confirmed, and it also has certain wide applicability.
On the Quality and Diversity of Synthetic Face Data and its Relation to the Generator Training Data

In recent years, advances in deep learning techniques and large-scale identity-labeled datasets have enabled facial recognition algorithms to rapidly gain performance. However, due to privacy issues, ethical concerns, and regulations governing the processing, transmission, and storage of biometric samples, several publicly available face image datasets are being withdrawn by their creators. The reason is that these datasets are mostly crawled from the web with the possibility that not all users had properly consented to processing their biometric data. To mitigate this problem, synthetic face images from generative approaches are motivated to substitute the need for authentic face images to train and test face recognition. In this work, we investigate both the relation between synthetic face image data and the generator authentic training data and the relation between the authentic data and the synthetic data in general under two aspects, i.e. the general image quality and face image quality. The first term refers to perceived image quality and the second measures the utility of a face image for automatic face recognition algorithms. To further quantify these relations, we build the analyses under two terms denoted as the dissimilarity in quality values expressing the general difference in quality distributions and the dissimilarity in quality diversity expressing the diversity in the quality values.
An Interactive Multi-task Learning Model for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to analyze and understand the sentiment attitude of reviews at the aspect level, and it consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). How-ever, most of the current work only focuses on the sub task of aspect polarity classification, without considering both sub tasks in combination. Besides, existing studies have paid less attention to the Chinese-oriented ABSA task. To address these challenges, we present a complete solution for ABSA by jointly training two subtasks. Compared with the former methods, our model can extract aspect terms and predict aspect polarity simultaneously; moreover, the model has the capacity to process both English and Chinese data. In this paper, we also propose a novel Local Context Focus framework for the APC subtask, which can effectively capture the contextual information of the aspect and improve the accuracy of polarity prediction. By integrating a parameter-sharing RoBERTa layer, our model accomplished two tasks perfectly, and we named it as LCF-RoBERTa. Experimental results on three English benchmark datasets and four Chinese datasets demonstrated the effectiveness of our method, the LCF-RoBERTa approached the state-of-the-art results.
Dance Gestures Recognition for Wheelchair Control

Wheelchair dance is an inclusive activity that gives more and more people with disabilities the opportunity to express themselves, exercise and improve their quality of life. In this article we present the development of a wearable sensor system capable of detecting dance gestures to command Voting, an electric wheelchair developed by the authors for dance purposes. Thus, with the support of the professional wheelchair dance teacher Gladys Foggea and the choreographer Maflohé Passedouet, thirteen dance gestures were defined, consisting of 7 simple gestures and 6 complex gestures. These gestures were used to train the algorithm of the proposed system. In order to find the appropriate algorithm and parameters for the present application, three classifiers were evaluated for their accuracy: SVM, KNN and Random Forest. Then, the most suitable parameterisation was determined by iterating each parameter for each classifier. As a result of this evaluation, it was found that the most suitable classifier was Random Forest, which achieved an accuracy of 97.7%• In addition, no difference in accuracy was observed between the detection of simple and complex gestures. Finally, the authors consider the result to be suitable to control Volting dance wheelchair, the implementation of which will be carried out in the next stage of the research.
Cross-Sensor Micro-Texture Material Classification and Smartphone Acquisition do not go well together

Intrinsic, non-invasive product authentication is still an important topic as it does not generate additional costs during the production process. This topic is of specific interest for medical products as non-genuine products can directly effect the patients’ health. This work investigates micro-texture classification as a mean of proving the authenticity of zircon oxide blocks (for dental implants). Samples of three different manufacturers were acquired using four smartphone devices with a clip-on macro lens. In addition, an existing drug packaging material database was utilized. While the intra-sensor microtexture classification worked well, the cross-sensor classification results were less promising. In an attempt to track down the limiting factors, intrinsic sensor features usually used in device identification were investigated as well.
Video Summarization Generation Model Based on Transformer and Deep Reinforcement Learning

Video summarization technology is to extract the key frames or clips of the original video to generate a summarization. Most of the existing methods are only improved based on image features, ignoring the temporal sequence between images and the lack of autonomous learning ability of models. We propose a video summarization network based on Transformer and deep reinforcement learning. The network takes Transformer's encoder-decoder as its main structure. The encoder is composed of self-attention and feed forward neural network modules in Transformer. BiLSTM and reinforcement learning are used to replace the decoder in Transformer. Experiments are carried out on two public standard data sets of video abstracts, and the experimental results prove the effectiveness of the proposed method.
Lightweight CNN-Based RF Fingerprint Recognition Method

Large network models, numerous parameters, and computational complexity are issues that the current deep learning RF fingerprint recognition approach must deal with since mobile terminals have a finite amount of storage for computations. Therefore, a lightweight convolutional neural network structure is suggested in this article (IQCNN). To decrease the platform's additional signal transformation processing, the IQCNN model uses the original IQ signal as input. It then constructs a convolutional layer in the signal IQ to extract IQ-related features. The IQ-related features are created by building convolutional layers in the signal's IQ direction, reducing the two-dimensional data into one dimension, extracting the signal's time-domain features using a maximum pooling layer, dimensionality-reducing with a fully-connected layer, and then achieving classification recognition. The IQCNN achieves an average recognition rate of 84.82% on a publicly available dataset with 16 USRP devices and signal-to-noise ratios of -10 to 20 dB, which is better than other deep learning methods and conventional SVM methods and has a lightweight feature for better engineering applications.
A Novel Ensemble Classifier Framework to Preprocess, Learn and Predict Imbalanced Heterogeneous Drifted Data Stream

Organizations nowadays are producing huge size of data stream, whose velocity is extra-ordinary, at a rapid speed. Mining data on stream data is a topic of great interest to the research community and has a great effect of a different types of applications. These applications include banking, communication technologies, field of learning, estimation of forthcoming weather, sensors, social media, and so on. However, Internet of Things (IoT) and smart devices generates enormous volume of data streams that are entirely heterogeneous in nature. The size, rapidity, pattern and category of these data streams are all non-stationary and highly vulnerable to concept drift. It is a significant problem, considering the realm of data mining in stream data. However, one significant and challenging issue in stream data mining is how to deal with the concept drifts, to the same degree, sudden, gradual and incremental drifts, which happens explosively. Additionally, the problem of streaming data classification of nonlinear data stream can only be partially solved by the current methodologies. Most of the works consider fixed size blocks, chunks or windows of homogeneous data stream to address the issues of the concept drift. Thus, a novel ensemble classifier is therefore necessary in order to accurately classify heterogeneous stream of data while swiftly changing to different sorts of concept drifts. This works proposes a novel method, Ensemble Classifier Framework for the detection and learning of concept-drift in addition to the classification in heterogeneous data stream. This work further presents a solution to deal with dynamic occurrence of concept drift.
A Video Image Processing Method for Continuous Object Detection

Post-processing of video images is essential to the whole video image detection, especially for continuous objects. Continuous objects refer to objects with continuity, integrity, and consistency at the level of physical media or data application, including tracks, cables, lane lines, chain structures, etc. Usually, objects are composed of a series of small homogeneous units, such as a piece of track, a piece of cable, a color ring, etc. The rapid development of artificial intelligence technology and 5G communication technology has driven the gradual maturity of deep learning and autonomous driving technology. At the same time, the standard for the detection of continuous objects is growing higher and higher. Therefore, how to detect objects in different business scenarios and scene-based optimization have become a top priority. The existing problems include: (1) Small units of multiple continuous objects in complex scenes interfere with each other, hindering recognition; (2) Various negative factors (insufficient brightness, steam, smoke occlusion, etc.) cause poor image quality. Consequently, the recognition performance is negatively influenced, and the model cannot adjust parameters adaptively; (3) The skew and distortion of image lower the object recognition performance. Aiming at these problems, this paper proposes a video image post-processing optimization algorithm for object detection of continuous objects, including an object integration search algorithm, threshold adaptive adjustment algorithm, rotation perspective correction algorithm, etc. Prior knowledge is utilized to improve the accuracy and efficiency of detection.
Face Feature Visualisation of Single Morphing Attack Detection

This paper proposes an explainable visualisation of different face feature extraction algorithms that enable the detection of bona fide and morphing images for single morphing attack detection. The feature extraction is based on raw image, shape, texture, frequency and compression. This visualisation may help to develop a Graphical User Interface for border policies and specifically for border guard personnel that have to investigate details of suspect images. A Random forest classifier was trained in a leave-one-out protocol on three landmarks-based face morphing methods and a StyleGAN-based morphing method for which morphed images are available in the FRLL database. For morphing attack detection, the Discrete Cosine-Transformation-based method obtained the best results for synthetic images and BSIF for landmark-based image features.
Detecting Deepfakes in Alternative Color Spaces to Withstand Unseen Corruptions

The adverse impact of deepfakes has recently raised world-wide concerns. Many ways of deepfake detection are published in the literature. The reported results of existing methods are generally good under known settings. However, the robustness challenge in deepfake detection is not well addressed. Most detectors fail to identify deepfakes that have undergone post-processing. Observing that the conventionally adopted RGB space does not guarantee the best performance, we propose other color spaces that prove to be more effective in detecting corrupted deepfake videos. We design a robust detection approach that leverages an adaptive manipulation trace extraction network to reveal artifacts from two color spaces. To mimic practical scenarios, we conduct experiments to detect images with post-processings that are not seen in the training stage. The results demonstrate that our approach outperforms state-of-the-art methods, boosting the average detection accuracy by 7% ~ 17%.
Unsupervised Face Synthesis Based on Human Traits

This paper presents a strategy to synthesize face images based on human traits. Specifically, the strategy allows synthesizing face images with similar age, gender, and ethnicity, after discovering groups of people with similar facial features. Our synthesizer is based on unsupervised learning and is capable to generate realistic faces. Our experiments reveal that grouping the training samples according to their similarity can lead to more realistic face images while having semantic control over the synthesis. The proposed strategy achieves competitive performance compared to the state-of-the-art and outperforms the baseline in terms of the Frechet Inception Distance.
A Hybrid Bayesian Network Structure Learning Algorithm in Equivalence Class Space

Greedy equivalence search (GES) is a well-known Bayesian network structure learning algorithm in equivalence class space (E-space). However, the extensive search space limits the efficiency of GES. In this paper, we propose a hybrid method to improve GES. We use mutual information to determine the strongly connected components (SCCs) graph. The SCCs graph is converted to E-space, and we take it as the initial graph of GES. The experiments reveal that our proposed approach significantly prunes the search space of GES and improves the efficiency of GES. Compared with the state-of-the-art methods, our method also has excellent accuracy.