Reviewerly/exHarmony huggingface dataset:
From Continuity to Editability: Inverting GANs with Consecutive Images

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them clearly cannot realize real image editing. In this paper, we resolve this paradox by introducing consecutive images (e.g., video frames or the same person with different poses) into the inversion process. The rationale behind our solution is that the continuity of consecutive images leads to inherent editable directions. This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted codes is semantically accessible from one of the other and fastened in an editable domain; 2) enforcing inter-image coherence, such that the fidelity of each inverted code can be maximized with the complement of other images. Extensive experiments demonstrate that our alternative significantly outperforms state-of-the-art methods in terms of reconstruction fidelity and editability on both the real image dataset and synthesis dataset. Furthermore, our method provides the first support of video-based GAN inversion and an interesting application of unsupervised semantic transfer from consecutive images. Source code can be found at: https://github.com/cnnlstm/InvertingGANs_with_ConsecutiveImgs.
Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices.These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable.This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks.An algorithm for detecting an attacker between a transmitter and receiver is proposed, with the side effect of interrupting the QKD process while detecting the attacker.Afterwards, Artificial neural network (ANN) and deep learning (DL) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process.An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers.In addition, an implementation scenario for securing IoT communications for sensors deployed in railroad networks is described.The results show that the proposed ML techniques can reach 99% accuracy in detecting attackers.
A study of university law students’ self-perceived digital competences

The concept of digital competences incorporates the effective use of constantly-changing digital tools and media for learning and performing digital tasks, digital behaviours (such as online communication, teamwork, ethical sharing of information), as well as digital mindsets that value lifelong digital learning and development. The current pandemic crisis has accelerated the need to diagnose and understand more systematically Higher Education students’ digital competences and the way in which they shape academic performance and outcomes. This empirical study explores the digital competences of students, studying in Law related courses, by means of a self-assessment survey tool, which has been previously tested with information and library science students, and was developed to study students’ technology mastery (i.e. the abilities, competences, capabilities and skills required for using digital technology, media and tools) and their digital citizenship mindsets (consisting of attitudes and behaviours necessary to develop as a critical, reflective and lifelong learners). The study found age demographic differences, which presented significant correlations pointing to the presence of diverse levels of competences in the student group. Correlation statistics of the survey data demonstrated that students’ prior everyday participation as a digital citizen was connected to a number of important academic skills, such as the ability to identify information in different contexts, students’ digital learning and development, their digital abilities to complete academic work, their information literacy skills and their skills around managing their digital wellbeing and identity. Focus groups data with academics revealed that they valued the development of students’ digital competences for the purposes of learning, while studying at university and placed less emphasis on digital citizenship skills. These academics also considered the value of digital platforms and tools (the focus on ‘ICT Proficiency’) to be more relevant for academic study than digital citizenship mindsets.
Learning Deep Local Features with Multiple Dynamic Attentions for Large-Scale Image Retrieval

In image retrieval, learning local features with deep convolutional networks has been demonstrated effective to improve the performance. To discriminate deep local features, some research efforts turn to attention learning. However, existing attention-based methods only generate a single attention map for each image, which limits the exploration of diverse visual patterns. To this end, we propose a novel deep local feature learning architecture to simultaneously focus on multiple discriminative local patterns in an image. In our framework, we first adaptively reorganize the channels of activation maps for multiple heads. For each head, a new dynamic attention module is designed to learn the potential attentions. The whole architecture is trained as metric learning of weighted-sum-pooled global image features, with only image-level relevance label. After the architecture training, for each database image, we select local features based on their multi-head dynamic attentions, which are further indexed for efficient retrieval. Extensive experiments show the proposed method outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets. Besides, it typically achieves competitive results even using local features with lower dimensions. Code will be released at https://github.com/CHANWH/MDA.
Ensemble Transfer Learning for Lung Cancer Detection

Lung cancer is the most leading cause of death. One of the significant screening problems is the difficulty in diagnosing it at an early stage. Consequently, this is a better way to study the ensemble of the transfer learning model to improve their accuracy and performance for lung cancer detection. This study has proposed the three CNN models for detecting lung cancer using VGG16, ResNet50V2, and DenseNet201 architecture based on transfer learning. The proposed models enhance to classify lung cancer into five different classes. The three transfer learning of CNN architectures were used to train, test, and validate based on the image dataset. The results reveal that the proposed models have performed the classification task for detecting lung cancer. The models achieved an accuracy level of VGG16, ResNet50V2, and DenseNet201 were 62%, 90%, and 89%, respectively. Finally, the ensemble of the three proposed CNN models is created and validated. The final proposed ensemble model achieved 91% validation accuracy that performed better than the other existing models.
On the Lattice Isomorphism Problem, Quadratic Forms, Remarkable Lattices, and Cryptography

A natural and recurring idea in the knapsack/lattice cryptography literature is to start from a lattice with remarkable decoding capability as your private key, and hide it somehow to make a public key. This is also how the code-based encryption scheme of McEliece (1978) proceeds. This idea has never worked out very well for lattices: ad-hoc approaches have been proposed, but they have been subject to ad-hoc attacks, using tricks beyond lattice reduction algorithms. On the other hand the framework offered by the Short Integer Solution (SIS) and Learning With Errors (LWE) problems, while convenient and well founded, remains frustrating from a coding perspective: the underlying decoding algorithms are rather trivial, with poor decoding performance. In this work, we provide generic realizations of this natural idea (independently of the chosen remarkable lattice) by basing cryptography on the lattice isomorphism problem (LIP). More specifically, we provide: The purpose of this approach is for remarkable lattices to improve the security and performance of lattice-based cryptography. For example, decoding within poly-logarithmic factor from Minkowski's bound in a remarkable lattice would lead to a KEM resisting lattice attacks down to poly-logarithmic approximation factor, provided that the dual lattice is also close to Minkowski's bound. Recent works have indeed reached such decoders for certain lattices (Chor-Rivest, Barnes-Sloan), but these do not perfectly fit our need as their duals have poor minimal distance.
A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection

Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data. FL promises the privacy of clients and its security can be strengthened by cryptographic methods such as additively homomorphic encryption (HE). However, the efficiency of FL could seriously suffer from the statistical heterogeneity in both the data distribution discrepancy among clients and the global distribution skewness. We mathematically demonstrate the cause of performance degradation in FL and examine the performance of FL over various datasets. To tackle the statistical heterogeneity problem, we propose a pluggable system-level client selection method named Dubhe, which allows clients to proactively participate in training, meanwhile preserving their privacy with the assistance of HE. Experimental results show that Dubhe is comparable with the optimal greedy method on the classification accuracy, with negligible encryption and communication overhead.
Theorizing the Economy of Traces: From Audit Society to Surveillance Capitalism

This essay is a conversation between Shoshana Zuboff’s theory of surveillance capitalism, Mikkel Flyverbom’s conceptualization of the hyper-visibility afforded by digital architectures, and my own ‘analog’ theory of accounting dynamics in the ‘audit society’. Drawing upon trends in accounting practice and research I develop a number of inflection points which define theoretical tensions between the concepts of audit society and surveillance capitalism. These tensions suggest that theoretical innovation is required in the face of: the accelerating constitution of organizations by platforms and their processes – ‘platformization’; the constitution of human agents as data-driven subjects of these data architectures – ‘cyborgization’; and the reconstruction of the social sciences by a pervasive data positivism in which accounting becomes ‘accountics’. The exploration of these three inflection points reveals the deep operational logic of surveillance capitalism as an ‘economy of traces’ and traceability. Zuboff’s challenge of a political dystopia governed by technology giants and Flyverbom’s image of a society ‘overlit’ by digital architectures necessitate a re-specification of the audit society dynamics that I have previously theorized. The re-specification that I propose in this essay is a form of a critical ‘traceology’ which takes as its focus the ongoing production of all manner of traces and how they make up organizations, people and forms of knowledge.
Chess AI: Competing Paradigms for Machine Intelligence

Endgame studies have long served as a tool for testing human creativity and intelligence. We find that they can serve as a tool for testing machine ability as well. Two of the leading chess engines, Stockfish and Leela Chess Zero (LCZero), employ significantly different methods during play. We use Plaskett's Puzzle, a famous endgame study from the late 1970s, to compare the two engines. Our experiments show that Stockfish outperforms LCZero on the puzzle. We examine the algorithmic differences between the engines and use our observations as a basis for carefully interpreting the test results. Drawing inspiration from how humans solve chess problems, we ask whether machines can possess a form of imagination. On the theoretical side, we describe how Bellman's equation may be applied to optimize the probability of winning. To conclude, we discuss the implications of our work on artificial intelligence (AI) and artificial general intelligence (AGI), suggesting possible avenues for future research.
Movie Recommendation System using Cosine Similarity with Sentiment Analysis

Multimedia is considered as one of the best sources of entertainment. People of all age groups love to watch movies. Movie Recommender System is essential in our social lives as it enhances the field of entertainment. The proposed system on Movie Recommendation System caters the requirements of the user. The major aim is to provide crisp relevant content to the end-users out of semi-structured content on the internet. The main purpose is to generate accurate, efficient and personalized recommendations to the user. Various building blocks of the paper like Introduction, Literature Survey, Proposed System, Implementation & Result, Comparative Analysis, Conclusion and Future Work are discussed in detail. The proposed machine learning model is trained, tested, and a sentiment classifier is generated which classify the sentiments as a good or a bad sentiment. The recommender system is generated by applying Cosine similarity and making API Calls. As a result, the live working of the system generates accurate and personalized recommendations along with the analysis of sentiments for the end users. It is also concluded that Cosine Similarity provides better and efficient results for a recommender system.
Unitary Selective Coupled-Cluster Method

Simulating molecules using the Variational Quantum Eigensolver method is one of the promising applications for NISQ-era quantum computers. Designing an efficient ansatz to represent the electronic wave function is crucial in such simulations. Standard unitary coupled-cluster with singles and doubles (UCCSD) ansatz tends to have a large number of insignificant terms that do not lower the energy of the system. In this work, we present a unitary selective coupled-cluster method, a way to construct a unitary coupled-cluster ansatz iteratively using a selection procedure with excitations up to fourth order. This approach uses the electronic Hamiltonian matrix elements and the amplitudes for excitations already present in the ansatz to find the important excitations of higher order and to add them to the ansatz. The important feature of the method is that it systematically reduces the energy error with increasing ansatz size for a set of test molecules. The main advantage of the proposed method is that the effort to increase the ansatz does not require any additional measurements on a quantum computer.
GeometryMotion-Net: A Strong Two-Stream Baseline for 3D Action Recognition

In this work, we propose a strong two-stream baseline method referred to as GeometryMotion-Net for 3D action recognition. For efficient 3D action recognition, we first represent each point cloud sequence as a limited number of randomly sampled frames with each frame consisting of a sparse set of points. After that, we propose a new two-stream framework for effective 3D action recognition. For the geometry stream, we propose a new module to produce a virtual overall geometry point cloud by first merging all 3D points from these selected frames, and then we exploit local neighborhood information of each point in the feature space. In the motion stream, for any two neighboring point cloud frames, we also propose a new module to generate one virtual forward motion point cloud and one virtual backward motion point cloud. Specifically, for each point in the current frame, we first produce a set of 3D offset features relative to the neighboring points in the reference frame ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the previous/subsequent frame) and then exploit local neighborhood information of this point in the offset feature space. Based on the newly generated virtual overall geometry point cloud and multiple virtual forward/backward motion point clouds, any existing point cloud analysis methods ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , PointNet) can be readily adopted for extracting discriminant geometry and bidirectional motion features in the geometry and motion streams, respectively, which are further aggregated to make our two-stream network trainable in an end-to-end fashion. Comprehensive experiments on both large-scale datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> NTU RGB+D 60 and NTU RGB+D 120) and small-scale datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , N-UCLA and UWA3DII) demonstrate the effectiveness and efficiency of our two-stream network for 3D action recognition.
3D Human Texture Estimation from a Single Image with Transformers

We propose a Transformer-based framework for 3D human texture estimation from a single image. The proposed Transformer is able to effectively exploit the global information of the input image, overcoming the limitations of existing methods that are solely based on convolutional neural networks. In addition, we also propose a mask-fusion strategy to combine the advantages of the RGB-based and texture-flow-based models. We further introduce a part-style loss to help reconstruct high-fidelity colors without introducing unpleasant artifacts. Extensive experiments demonstrate the effectiveness of the proposed method against state-of-the-art 3D human texture estimation approaches both quantitatively and qualitatively. The project page is at https://www.mmlab-ntu.com/project/texformer.
Unpaired Learning for Deep Image Deraining with Rain Direction Regularizer

We present a simple yet effective unpaired learning based image rain removal method from an unpaired set of synthetic images and real rainy images by exploring the properties of rain maps. The proposed algorithm mainly consists of a semi-supervised learning part and a knowledge distillation part. The semi-supervised part estimates the rain map and reconstructs the derained image based on the well-established layer separation principle. To facilitate rain removal, we develop a rain direction regularizer to constrain the rain estimation network in the semi-supervised learning part. With the estimated rain maps from the semi-supervised learning part, we first synthesize a new paired set by adding to rain-free images based on the superimposition model. The real rainy images and the derained results constitute another paired set. Then we develop an effective knowledge distillation method to explore such two paired sets so that the deraining model in the semi-supervised learning part is distilled. We propose two new rainy datasets, named RainDirection and Real3000, to validate the effectiveness of the proposed method. Both quantitative and qualitative experimental results demonstrate that the proposed method achieves favorable results against state-of-the-art methods in benchmark datasets and real-world images.
Federated synthetic data generation with differential privacy

Distributed machine learning has attracted much attention in the last decade with the widespread use of the Internet of Things. As a generative model, Generative Adversarial Network (GAN) has excellent empirical performance. However, the distributed storage of data and the fact that data cannot be shared for privacy reasons in a federated learning setting bring new challenges to training GAN. To address this issue, we propose private FL-GAN, a differentially private GAN based on federated learning. By strategically combining the Lipschitz condition with differential privacy sensitivity, our model can generate high-quality synthetic data without sacrificing the training data’s privacy. When communication between clients becomes the main bottleneck for federated learning, we propose to use a serialized model-training paradigm, which significantly reduces communication costs. Considering the distributed data is often non-IID in reality, which poses challenges to modeling, we further propose universal private FL-GAN to approach this problem. We not only theoretically prove that our algorithms can provide strict privacy guarantees with differential privacy, but also experimentally demonstrate that our models can generate satisfactory data while protecting the privacy of the training data, even if the data is non-IID.