Reviewerly/exHarmony huggingface dataset:
Perturbation-enabled Deep Federated Learning for Preserving Internet of Things-based Social Networks

Federated Learning (FL), as an emerging form of distributed machine learning (ML), can protect participants’ private data from being substantially disclosed to cyber adversaries. It has potential uses in many large-scale, data-rich environments, such as the Internet of Things (IoT), Industrial IoT, Social Media (SM), and the emerging SM 3.0. However, federated learning is susceptible to some forms of data leakage through model inversion attacks. Such attacks occur through the analysis of participants’ uploaded model updates. Model inversion attacks can reveal private data and potentially undermine some critical reasons for employing federated learning paradigms. This article proposes novel differential privacy (DP)-based deep federated learning framework. We theoretically prove that our framework can fulfill DP’s requirements under distinct privacy levels by appropriately adjusting scaled variances of Gaussian noise. We then develop a Differentially Private Data-Level Perturbation (DP-DLP) mechanism to conceal any single data point’s impact on the training phase. Experiments on real-world datasets, specifically the social media 3.0, Iris, and Human Activity Recognition (HAR) datasets, demonstrate that the proposed mechanism can offer high privacy, enhanced utility, and elevated efficiency. Consequently, it simplifies the development of various DP-based FL models with different tradeoff preferences on data utility and privacy levels.
Review of the state of the art in autonomous artificial intelligence

Abstract This article presents a new design for autonomous artificial intelligence (AI), based on the state-of-the-art algorithms, and describes a new autonomous AI system called ‘AutoAI’. The methodology is used to assemble the design founded on self-improved algorithms that use new and emerging sources of data (NEFD). The objective of the article is to conceptualise the design of a novel AutoAI algorithm. The conceptual approach is used to advance into building new and improved algorithms. The article integrates and consolidates the findings from existing literature and advances the AutoAI design into (1) using new and emerging sources of data for teaching and training AI algorithms and (2) enabling AI algorithms to use automated tools for training new and improved algorithms. This approach is going beyond the state-of-the-art in AI algorithms and suggests a design that enables autonomous algorithms to self-optimise and self-adapt, and on a higher level, be capable to self-procreate.
High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators: Symmetry Breaking and Floquet Protection

Although the Gaussian-noise assumption is widely adopted in the study of qubit decoherence, non-Gaussian noise sources, especially the strong discrete fluctuators, have been detected in many qubits. It remains an important task to further understand and mitigate the distinctive decoherence effect of the non-Gaussian noise. Here, we study the qubit dephasing caused by the non-Gaussian fluctuators, and predict a symmetry-breaking effect that is unique to the non-Gaussian noise. This broken symmetry results in an experimentally measurable mismatch between the extremum points of the dephasing rate and qubit frequency, which demands extra carefulness in characterizing the noise and locating the optimal working point. To further enhance the coherence time at the sweet spot, we propose to suppress the second-order derivative of the qubit frequency by the Floquet engineering. Our simulation with a heavy fluxonium shows an order of magnitude improvement of the dephasing time, even after including the noise introduced by the drive.
Unsupervised statistical concept drift detection for behaviour abnormality detection

Abstract Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F 1 -Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75 y). Our method was able to find abnormal days when a participant suffered from severe medical condition.
Human Psychological Disorder towards Cryptography: True Random Number Generator from EEG of Schizophrenics and Its Application in Block Encryption’s Substitution Box

Schizophrenia is a multifaceted chronic psychiatric disorder that affects the way a human thinks, feels, and behaves. Inevitably, natural randomness exists in the psychological perception of schizophrenic patients, which is our primary source of inspiration for this research because true randomness is the indubitably ultimate valuable resource for symmetric cryptography. Famous information theorist Claude Shannon gave two desirable properties that a strong encryption algorithm should have, which are confusion and diffusion in his fundamental article on the theoretical foundations of cryptography. Block encryption strength against various cryptanalysis attacks is purely dependent on its confusion property, which is gained through the confusion component. In the literature, chaos and algebraic techniques are extensively used to design the confusion component. Chaos- and algebraic-based techniques provide favorable features for the design of the confusion component; however, researchers have also identified potential attacks on these techniques. Instead of existing schemes, we introduce a novel methodology to construct cryptographic confusion component from the natural randomness, which are existing in the psychological perception of the schizophrenic patients, and as a result, cryptanalysis of chaos and algebraic techniques are not applicable on our proposed technique. The psychological perception of the brain regions was captured through the electroencephalogram (EEG) readings during the sensory task. The proposed design passed all the standard evaluation criteria and validation tests of the confusion component and the random number generators. One million true random bits are assessed through the NIST statistical test suite, and the results proved that the psychological perception of schizophrenic patients is a good source of true randomness. Furthermore, the proposed confusion component attains better or equal cryptographic strength as compared to state-of-the-art techniques (2020 to 2021). To the best of our knowledge, this nature of research is performed for the first time, in which psychiatric disorder is utilized for the design of information security primitive. This research opens up new avenues in cryptographic primitive design through the fusion of computing, neuroscience, and mathematics.
Incompatibility measures in multiparameter quantum estimation under hierarchical quantum measurements

The incompatibility of the measurements constraints the achievable precisions in multi-parameter quantum estimation. Understanding the tradeoff induced by such incompatibility is a central topic in quantum metrology. Here we provide an approach to study the incompatibility under general $p$-local measurements, which are the measurements that can be performed collectively on at most $p$ copies of quantum states. We demonstrate the power of the approach by presenting a hierarchy of analytical bounds on the tradeoff among the precision limits of different parameters. These bounds lead to a necessary condition for the saturation of the quantum Cram\'er-Rao bound under $p$-local measurements, which recovers the partial commutative condition at p=1 and the weak commutative condition at $p=\infty$. As a further demonstration of the power of the framework, we present another set of tradeoff relations with the right logarithmic operators(RLD).
Improving Bayesian Neural Networks by Adversarial Sampling

Bayesian neural networks (BNNs) have drawn extensive interest due to the unique probabilistic representation framework. However, Bayesian neural networks have limited publicized deployments because of the relatively poor model performance in real-world applications. In this paper, we argue that the randomness of sampling in Bayesian neural networks causes errors in the updating of model parameters during training and some sampled models with poor performance in testing. To solve this, we propose to train Bayesian neural networks with Adversarial Distribution as a theoretical solution. To avoid the difficulty of calculating Adversarial Distribution analytically, we further present the Adversarial Sampling method as an approximation in practice. We conduct extensive experiments with multiple network structures on different datasets, e.g., CIFAR-10 and CIFAR-100. Experimental results validate the correctness of the theoretical analysis and the effectiveness of the Adversarial Sampling on improving model performance. Additionally, models trained with Adversarial Sampling still keep their ability to model uncertainties and perform better when predictions are retained according to the uncertainties, which further verifies the generality of the Adversarial Sampling approach.
Users and Contemporary SERPs

The Search Engine Results Page (SERP) has evolved significantly over the last two decades, moving away from the simple ten blue links paradigm to considerably more complex presentations that contain results from multiple verticals and granularities of textual information. Prior works have investigated how user interactions on the SERP are influenced by the presence or absence of heterogeneous content (e.g., images, videos, or news content), the layout of the SERP (list vs. grid layout), and task complexity. In this paper, we reproduce the user studies conducted in prior works-specifically those of Arguello et al. [4] and Siu and Chaparro [29]-to explore to what extent the findings from research conducted five to ten years ago still hold today as the average web user has become accustomed to SERPs with ever-increasing presentational complexity. To this end, we designed and ran a user study with four different SERP interfaces: (i) a heterogeneous grid; (ii) a heterogeneous list; (iii) a simple grid; and (iv) a simple list. We collected the interactions of 41 study participants over 12 search tasks for our analyses. We observed that SERP types and task complexity affect user interactions with search results. We also find evidence to support most (6 out of 8) observations from [4 , 29] indicating that user interactions with different interfaces and to solve tasks of different complexity have remained mostly similar over time.
Blockchain Based Big Data Solutions for Internet of Things (IoT) and Smart Cities

The popularity of both the blockchain and Big Data (BD) with its extensive applications, with the most ongoing research in various scientific and practical areas. Although both of these technologies are still in their initial and experimenting phase, Internet of Things (IoT) complements the data collection for BD. Moreover, Blockchain has been seen as a revolutionary solution towards the major problems in the field of BD like data-driven decisions, data ownership, identity, trust, and decentralization. In the modern era, the world has seen a huge expansion in the diversity and quantity of data generated by machines and users. Lacking the best possible way to process, organize and store BD, the Blockchain technology is here for the rescue. Blockchain's proposed solution towards decentralized management of the public institution, IoT communication, digital property resolution, and private data has created a deep impact on the evolution of BD. This paper proposes a novel solution 89towards the gap of the BD technology with the help of Blockchain technology.
Social Distance Monitoring and Face Mask Detection Using Deep Learning

As there is no evidence of COVID-19 slowing down in several components of the world, maintaining “social distancing” (also mentioned as physical distance) between indoor and outside people is more vital than ever. It is counseled that two people keep a distance of 1.8 m (approximately, vi feet) apart. Python can be used to capture people and monitor social distancing. Deep learning, TensorFlow, Keras, and OpenCV are used to acknowledge masks; it uses a good computer vision-based technique that focuses on the automatic period observance of individuals to sight safe social distance twenty-four hours a day, seven days every week, for inspectors in public places, commercial centers, and different locations.
Real-Time Sign Language Translator

The main objective of this work is to build a real-time sign language translator to translate sign language to text using TensorFlow object detection and Python. It would provide an easy way for people to communicate with others using sign language further leading to the elimination of the middle person who generally acts as a medium of translation and would also provide an easy-to-use environment for the users by providing text output for the input sign gesture. Here, the sign language is to be fed on a real-time basis using a webcam and the input fed would be compared with the trained model to recognize the sign and those who do not know the sign language could use it to understand the sign language and get a text output for the same with accuracy. Currently, the model was trained for Indian Sign Language (ISL) consisting of alphabets from A to Z and digits from 1 to 9 counting to a total of 35 signs and we obtained a loss of 0.227 on the trained model and the model predicted the signs with nearly an accuracy of 50–80% in our case.
Health-X dataLOFT: A Sovereign Federated Cloud for Personalized Health Care Services

Future preventative health care will rely more and more on our personal health data that comes both from clinical sources, but also from many wearable and mobile health devices and apps. However, data from such devices is distributed and partly locked in different data stores, impeding the use for advanced health and care services. To unlock the large potential of such data for future personalized health care, it must be unlocked and released and made accessible for joint use. Health-X is an interdisciplinary scientific research project that will develop and offer a federated cloud infrastructure which puts the individual at the center of the access and control of future health care services.
Prior Knowledge and Memory Enriched Transformer for Sign Language Translation

This paper attacks the challenging problem of sign language translation (SLT), which involves not only visual and textual understanding but also additional prior knowledge learning (i.e.performing style, syntax).However, the majority of existing methods with vanilla encoder-decoder structures fail to sufficiently explore all of them.Based on this concern, we propose a novel method called Prior knowledge and memory Enriched Transformer (PET) for SLT, which incorporates the auxiliary information into vanilla transformer.Concretely, we develop gated interactive multi-head attention which associates the multimodal representation and global signing style with adaptive gated functions.One Part-of-Speech (POS) sequence generator relies on the associated information to predict the global syntactic structure, which is thereafter leveraged to guide the sentence generation.Besides, considering that the visualtextual context information, and additional auxiliary knowledge of a word may appear in more than one video, we design a multi-stream memory structure to obtain higher-quality translations, which stores the detailed correspondence between a word and its various relevant information, leading to a more comprehensive understanding for each word.We conduct extensive empirical studies on RWTH-PHOENIX-Weather-2014T dataset with both signer-dependent and signer-independent conditions.The quantitative and qualitative experimental results comprehensively reveal the effectiveness of PET.
Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events

Although various weakly supervised anomaly detection methods have been proposed in recent years, generalization of anomaly detection is still not well-explored. Existing weakly supervised methods usually use normal and abnormal events to pose anomaly detection as a regression problem. However, defining concepts that encompass all possible normal and abnormal event patterns is nearly unrealistic, so the anomaly detection model is likely to face both open normal and abnormal events in practical applications. We find some weakly supervised anomaly detection methods suffer from performance degradation when faced with open events due to their poor generalization. To tackle this issue, we propose a two-branch weakly supervised approach, which can improve the anomaly detection performance of open events without affecting the performance of the seen events. Specifically, considering that the pattern of open events is different from that of seen events, we design a Test Data Analyzer (TDA) that determines whether the test video features belong to seen or open data and argue for separate treatment for them. For the seen data, a classifier trained by multiple instance learning is used to predict anomaly scores. For the open data, we design an anomaly detection model via meta-learning named Meta-Learning Anomaly Detection (MLAD), which can directly determine whether open data is abnormal without updating model parameters. In detail, MLAD synthesizes pseudo-seen data and pseudo-open data so that the model can learn to detect anomalies in open data by transferring the knowledge of seen data. Experimental results validate the effectiveness of our proposed method.
Refined Cryptanalysis of the GPRS Ciphers GEA-1 and GEA-2

At EUROCRYPT 2021, Beierle et al. presented the first public analysis of the GPRS ciphers GEA-1 and GEA-2. They showed that although GEA-1 uses a 64-bit session key, it can be recovered with the knowledge of only 65 bits of keystream in time $$2^{40}$$ using 44 GiB of memory. The attack exploits a weakness in the initialization process of the cipher that was presumably hidden intentionally by the designers to reduce its security. While no such weakness was found for GEA-2, the authors presented an attack on this cipher with time complexity of about $$2^{45}$$ . The main practical obstacle is the required knowledge of 12800 bits of keystream used to encrypt a full GPRS frame. Variants of the attack are applicable (but more expensive) when given less consecutive keystream bits, or when the available keystream is fragmented (it contains no long consecutive block). In this paper, we improve and complement the previous analysis of GEA-1 and GEA-2. For GEA-1, we devise an attack in which the memory complexity is reduced by a factor of about $$2^{13} = 8192$$ from 44 GiB to about 4 MiB, while the time complexity remains $$2^{40}$$ . Our implementation recovers the GEA-1 session key in average time of 2.5 h on a modern laptop. For GEA-2, we describe two attacks that complement the analysis of Beierle et al. The first attack obtains a linear tradeoff between the number of consecutive keystream bits available to the attacker (denoted by $$\ell $$ ) and the time complexity. It improves upon the previous attack in the range of (roughly) $$\ell \le 7000$$ . Specifically, for $$\ell = 1100$$ the complexity of our attack is about $$2^{54}$$ , while the previous one is not faster than the $$2^{64}$$ brute force complexity. In case the available keystream is fragmented, our second attack reduces the memory complexity of the previous attack by a factor of 512 from 32 GiB to 64 MiB with no time complexity penalty. Our attacks are based on new combinations of stream cipher cryptanalytic techniques and algorithmic techniques used in other contexts (such as solving the k-XOR problem).
Long-term Leap Attention, Short-term Periodic Shift for Video Classification

Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes $T$ times longer sequence than the latter under the current attention of quadratic complexity $(T^2N^2)$. The existing works treat the temporal axis as a simple extension of spatial axes, focusing on shortening the spatio-temporal sequence by either generic pooling or local windowing without utilizing temporal redundancy. However, videos naturally contain redundant information between neighboring frames; thereby, we could potentially suppress attention on visually similar frames in a dilated manner. Based on this hypothesis, we propose the LAPS, a long-term ``\textbf{\textit{Leap Attention}}'' (LA), short-term ``\textbf{\textit{Periodic Shift}}'' (\textit{P}-Shift) module for video transformers, with $(2TN^2)$ complexity. Specifically, the ``LA'' groups long-term frames into pairs, then refactors each discrete pair via attention. The ``\textit{P}-Shift'' exchanges features between temporal neighbors to confront the loss of short-term dynamics. By replacing a vanilla 2D attention with the LAPS, we could adapt a static transformer into a video one, with zero extra parameters and neglectable computation overhead ($\sim$2.6\%). Experiments on the standard Kinetics-400 benchmark demonstrate that our LAPS transformer could achieve competitive performances in terms of accuracy, FLOPs, and Params among CNN and transformer SOTAs. We open-source our project in \sloppy \href{https://github.com/VideoNetworks/LAPS-transformer}{\textit{\color{magenta}{https://github.com/VideoNetworks/LAPS-transformer}}} .
Learnability Enhancement for Low-light Raw Denoising

Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limited data volume and complicated noise distribution have constituted a learnability bottleneck for paired real data, which limits the denoising performance of learning-based methods. To address this issue, we present a learnability enhancement strategy to reform paired real data according to noise modeling. Our strategy consists of two efficient techniques: shot noise augmentation (SNA) and dark shading correction (DSC). Through noise model decoupling, SNA improves the precision of data mapping by increasing the data volume and DSC reduces the complexity of data mapping by reducing the noise complexity. Extensive results on the public datasets and real imaging scenarios collectively demonstrate the state-of-the-art performance of our method.
Improved reference-frame-independent quantum key distribution

Reference-frame-independent quantum key distribution (RFI-QKD) can generate secure keys between two remote peers with an unknown and slowly drifted reference frame. It adopts a statistical quantity C independent of the rotation of the reference frame to estimate the eavesdropper’s knowledge. However, in practical scenarios, the estimation of C should be completed with data collected in a short period of time, since the variation of the reference frame during the run will smear its correlations. In this paper, we propose to improve the performance of RFI-QKD with a new, to the best of our knowledge, statistical quantity R , which is less sensitive to statistical fluctuations and more robust to the variation of the reference frame, and obtain an analytical bound to estimate the eavesdropper’s knowledge. Simulation results show that the performance of RFI-QKD in the non-asymptotic case can be significantly promoted, which is very promising in practical QKD systems. Furthermore, we apply the new statistical quantity R to the RFI measurement-device-independent QKD (RFI-MDI-QKD) protocol and demonstrate that significant improvement can also be obtained in RFI-MDI-QKD.