Reviewerly/exHarmony huggingface dataset:
Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning

Reinforcement learning (RL) is a framework to optimize a control policy using rewards that are revealed by the system as a response to a control action. In its standard form, RL involves a single agent that uses its policy to accomplish a specific task. These methods require large amounts of reward samples to achieve good performance, and may not generalize well when the task is modified, even if the new task is related. In this paper we are interested in a collaborative scheme in which multiple agents with different tasks optimize their policies jointly. To this end, we introduce cross-learning, in which agents tackling related tasks have their policies constrained to be close to one another. Two properties make our new approach attractive: (i) it produces a multi-task central policy that can be used as a starting point to adapt quickly to one of the tasks trained for, in a situation when the agent does not know which task is currently facing, and (ii) as in meta-learning, it adapts to environments related but different to those seen during training. We focus on continuous policies belonging to reproducing kernel Hilbert spaces for which we bound the distance between the task-specific policies and the cross-learned policy. To solve the resulting optimization problem, we resort to a projected policy gradient algorithm and prove that it converges to a near-optimal solution with high probability. We evaluate our methodology with a navigation example in which agents can move through environments with obstacles of multiple shapes and avoid obstacles not trained for.
Transparency order versus confusion coefficient: a case study of NIST lightweight cryptography S-Boxes

Abstract Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations. In most cases, non-linear components (e.g. S-Boxes) of cryptographic algorithms will be chosen as primary targets of side-channel attacks (SCAs). In order to measure side-channel resistance of S-Boxes, three theoretical metrics are proposed and they are reVisited transparency order (VTO), confusion coefficients variance (CCV), and minimum confusion coefficient (MCC), respectively. However, the practical effectiveness of these metrics remains still unclear. Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples, this paper takes a comprehensive study of the applicability of these metrics. First of all, we empirically investigate the relations among three metrics for targeted S-boxes, and find that CCV is almost linearly correlated with VTO, while MCC is inconsistent with the other two. Furthermore, in order to verify which metric is more effective in which scenarios, we perform simulated and practical experiments on nine 4-bit S-Boxes under the non-profiled attacks and profiled attacks, respectively. The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks, VTO and CCV are more reliable while MCC fails. We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs. Finally, we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes. Unfortunately, all of them are invalid in this scenario.
Optimizing Few-Shot Learning Based on Variational Autoencoders

Despite the importance of few-shot learning, the lack of labeled training data in the real world makes it extremely challenging for existing machine learning methods because this limited dataset does not well represent the data variance. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations on the Labeled Faces in the Wild (LFW) dataset. The purpose of our research is to increase the size of the training dataset using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training dataset, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. The face generation method based on VAEs with perceptual loss can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.
Digital Watermarking Properties, Classification and Techniques

The rise in the demand for multimedia digital products has led to significant copyright challenges, which concerns proof of ownership and copy control. Digital watermarking method provides a solution to the problems associated to copyright protection and control. Considerable quantities of multimedia content are printed, edited or distributed unlawfully without the legal consent of the owner. Digital piracy in the movie and music industry contributes to severe economic losses annually. The problem of digital piracy has led to the urgent need for digital watermarking as a method to counter the piracy. The protection of digital content is currently the main responsibility of the content owner since piracy is evident in all levels of multimedia industry. Therefore, for multimedia information, protection of content copyright has increasingly become the sole focus of the content owner. Digital watermarking is a vital technology, which is applicable in protecting the contemporary multimedia digital contents..
Investigating the variance increase of readout error mitigation through classical bit-flip correction on IBM and Rigetti quantum computers

Readout errors are among the most dominant errors on current noisy intermediate-scale quantum devices.Recently, an efficient and scaleable method for mitigating such errors has been developed, based on classical bit-flip correction [1,2].In this talk, we compare the performance of this method for IBM's and Rigetti's quantum devices, demonstrating how the method improves the noisy measurements of observables obtained on the quantum hardware.Moreover, we examine the variance amplification to the data after applying of our mitigation procedure, which is common to all mitigation strategies.We derive a new expression for the variance of the mitigated Pauli operators in terms of the corrected expectation values and the noisy variances.Our hardware results show good agreement with the theoretical prediction, and we demonstrate that the increase of the variance due to the mitigation procedure is only moderate.
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication

This paper models unsupervised learning of an identity-based pattern (or copying) in speech called reduplication from raw continuous data with deep convolutional neural networks. We use the ciwGAN architecture Begu\v{s} (2021a; arXiv:2006.02951) in which learning of meaningful representations in speech emerges from a requirement that the CNNs generate informative data. We propose a technique to wug-test CNNs trained on speech and, based on four generative tests, argue that the network learns to represent an identity-based pattern in its latent space. By manipulating only two categorical variables in the latent space, we can actively turn an unreduplicated form into a reduplicated form with no other substantial changes to the output in the majority of cases. We also argue that the network extends the identity-based pattern to unobserved data. Exploration of how meaningful representations of identity-based patterns emerge in CNNs and how the latent space variables outside of the training range correlate with identity-based patterns in the output has general implications for neural network interpretability.
Cross-lingual Sentence Embedding using Multi-Task Learning

Multilingual sentence embeddings capture rich semantic information not only for measuring similarity between texts but also for catering to a broad range of downstream crosslingual NLP tasks.State-of-the-art multilingual sentence embedding models require large parallel corpora to learn efficiently, which confines the scope of these models.In this paper, we propose a novel sentence embedding framework based on an unsupervised loss function for generating effective multilingual sentence embeddings, eliminating the need for parallel corpora.We capture semantic similarity and relatedness between sentences using a multitask loss function for training a dual encoder model mapping different languages onto the same vector space.We demonstrate the efficacy of an unsupervised as well as a weakly supervised variant of our framework on STS, BUCC and Tatoeba benchmark tasks.The proposed unsupervised sentence embedding framework outperforms even supervised stateof-the-art methods for certain under-resourced languages on the Tatoeba dataset and on a monolingual benchmark.Further, we show enhanced zero-shot learning capabilities for more than 30 languages, with the model being trained on only 13 languages.Our model can be extended to a wide range of languages from any language family, as it overcomes the requirement of parallel corpora for training.
Integration of Linked Open Data in Collaborative Group Recommender Systems

A group recommender system (GRS) is a system that collectively recommends items to a group of users based on their preferences. The GRS and the individual RS challenge lies in a very small and incompleteness of user-item ratings. Such incompleteness resulted in the data sparsity problem. The issues of data sparsity in a group negatively affect the quality of recommendations to the group. It occurs due to the inefficient formation of groups, which usually involves individuals with sparse data in their user profiles. Most of the current studies focus on this issue after the formation of groups. However, this study focused before the group formation, based on the intuition that it will be more efficient if the data sparsity at the individual level is addressed before the group formation process takes place. Therefore, applying the approach through Linked Open Data (LOD) technology is proposed to ensure that the data sparsity issues can be overcome before the group formation process is implemented. We proposed a GRS-LOD model. The experimental evaluations relating to the prediction accuracy and recommendation relevancy of the proposed model were implemented on three aspects: comparison with the basic approach or baselines; comparison with the current approaches, and comparison in terms of group size and aggregation strategies. The aggregation strategies used were the Average (AV), Most Pleasure (MP), Average without Misery (AVM), and Least Misery (LM). The metrics for prediction accuracy were based on the RMSE and MAE, whereas for relevancy, precision, recall, and F1-score were considered. The results show that the prediction accuracy and relevancy of the developed model’s recommendations is better than the baseline study by adapting the Average (AV) strategy with the individual profile aggregation approach. Meanwhile, for the evaluation in terms of group size, the results show larger group size exhibits better prediction accuracy for the four used aggregation strategies. On the other hand, in terms of recommendation relevancy, the result shows that relevancy decreases with the increase in group size for the MP, AV and AVM strategies.
Sentiment Analysis of Fashion Related Posts in Social Media

The role of social media in fashion industry has been blooming as the years have continued on. In this work, we investigate sentiment analysis for fashion related posts in social media platforms. There are two main challenges of this task. On the first place, information of different modalities must be jointly considered to make the final predictions. On the second place, some unique fashion related attributes should be taken into account. While most existing works focus on traditional multimodal sentiment analysis, they always fail to exploit the fashion related attributes in this task. We propose a novel framework that jointly leverages the image vision, post text, as well as fashion attribute modality to determine the sentiment category. One characteristic of our model is that it extracts fashion attributes and integrates them with the image vision information for effective representation. Furthermore, it exploits the mutual relationship between the fashion attributes and the post texts via a mutual attention mechanism. Since there is no existing dataset suitable for this task, we prepare a large-scale sentiment analysis dataset of over 12k fashion related social media posts. Extensive experiments are conducted to demonstrate the effectiveness of our model.
Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.
An Improved Procedure for the Determination of Trace Elements in Silicate Rocks Using NH<sub>4</sub>HF<sub>2</sub> Digestion

Knowledge of the elemental composition of geological samples is of high significance to research in Earth science. For whole‐rock elemental determination, complete sample digestion is essential to obtain bias‐free measurement results. In recent years, NH 4 HF 2 digestion has been proposed for geological sample dissolution; however, this requires 3 h of sample evaporation and 6 h of re‐dissolution of residues. In this study a PTFE beaker, which can withstand 300 °C, was designed to increase the evaporation temperature, reducing the evaporation time to 15 min. Further investigations found that the insoluble fluoride residues formed during the high‐temperature evaporation stage significantly influenced the recoveries of Sc, Y, rare earth elements and Th. Among the tested strategies to suppress the formation of fluorides, the microwave heating method was the most efficient and robust. The optimised NH 4 HF 2 digestion shortened the sample evaporation and re‐dissolution times from 9 h to 21 min, significantly reducing the time consumed for sample evaporation by a factor of ~ 26. A suite of silicate rock reference materials was analysed. The results for most elements were in agreement with reference values with a measurement bias of &lt; 10%.
Path Planning of Arbitrary Shaped Mobile Robots With Safety Consideration

This paper presents a neural network-based approach for the path planning of arbitrary shaped mobile robots in complex environments, with the consideration of safety. A 2D workspace is discretized to a topologically organized map using a biological neural network, in which the dynamic neural activity landscape represents the environmental information. A set of kernel matrices are established to describe the shape and orientation features of the robot. Taking the safety factor into consideration, the translation and rotation performances of the robot on each neuron node of the workspace are determined using a convolutional neural network (CNN). Then, from the initial state of the robot to the target state, a node rooted tree is constructed by searching the adjacent neurons, and the moving path of the robot is generated by backward searching the node rooted tree. By changing the bias coefficient in the convolutional calculation, the clearance between the planned path and the obstacles can be conveniently adjusted. The effectiveness of the proposed method is demonstrated through several simulations conducted in both static and dynamic environments. The results show that the method can effectively solve the “path blocked” issue caused by small densely scattered obstacles, and also solve the “too close” and “too far” path planning problems.
Making Things Explainable vs Explaining: Requirements and Challenges Under the GDPR

The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit is the belief that making things explainable alone is enough to have pragmatic explanations. Thus, insisting on a clear separation between explainabilty (something that can be explained) and explanations, we point to explanatorY AI (YAI) as an alternative and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI with the goal to collect and organize explainable information, articulating it into something we called user-centred explanatory discourses. Through the use of explanatory discourses/narratives we represent the problem of generating explanations for Automated Decision-Making systems (ADMs) into the identification of an appropriate path over an explanatory space, allowing explainees to interactively explore it and produce the explanation best suited to their needs.
Obstacle Recognition and Path Planning of UAV Flight

Aiming at the problem of classification and detection of obstacles and flight path planning in the process of UAV obstacle avoidance, YOLO is used the algorithm detects the category information of obstacles. Based on the construction of indoor 3D maps, the TSP algorithm is used to detect obstacles. Research on the path planning of man-machines, establish the control mathematical model of obstacle avoidance flight under hypothetical scenarios, and obtain the best flight path through simulation analysis and comparison.
Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN

In the rapid development of network technology, with the improvement of the quality and quantity of network users’ demands, more and more network information technology and excessive network traffic also raise people’s attention to the internal network security. Especially for the classification and resource allocation of encrypted network traffic, the research of related technologies has become the main research direction of the development of network technology. The extensive application of deep learning provides a new idea for the study of traffic classification. Therefore, on the basis of understanding the current situation, the improved convolutional neural network is selected to conduct an in-depth discussion on traffic classification and resource allocation of encrypted networks based on deep learning. The performance of the system is verified from the perspective of practical application.
Comparison between common virtualization solutions: VMware Workstation, Hyper-V and Docker

This work aims to present the variations between three common virtualization platforms: VMware Workstation, Hyper-V and Docker in the respect of hardware efficiency with different operating systems (Ubuntu20.04 and Windows 10). Firstly, the differences in the theoretical architecture of these three virtualization solutions are discussed in order to provide a brief reference for the testing of operational efficiency in the following part. Subsequently, some common test applications (sysbench, fio, mbw, Fritz Chess Benchmark and Crystal Disk Mark) were run to test their efficiency in CPU, hard disk and memory. Eventually, these statistics are collected in the form of graphs for comparison. The results of the comparison clearly reveal the advantages of VMware workstation in the efficiency of virtual machine hardware operation, while Hyper-V, supported by Microsoft, is inferior to the former in both ubuntu and Windows 10 environments. Meanwhile, Docker, as a representative of an emerging container virtualization technology, has demonstrated a superior performance closer to physical machine performance than traditional virtual machine technologies because of its unique architecture.
The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

Following the popularisation of media streaming, a number of video streaming services are continuously buying new video content to mine the potential profit from them. As such, the newly added content has to be handled well to be recommended to suitable users. In this paper, we address the new item cold-start problem by exploring the potential of various deep learning features to provide video recommendations. The deep learning features investigated include features that capture the visual-appearance, audio and motion information from video content. We also explore different fusion methods to evaluate how well these feature modalities can be combined to fully exploit the complementary information captured by them. Experiments on a real-world video dataset for movie recommendations show that deep learning features outperform hand-crafted features. In particular, recommendations generated with deep learning audio features and action-centric deep learning features are superior to MFCC and state-of-the-art iDT features. In addition, the combination of various deep learning features with hand-crafted features and textual metadata yields significant improvement in recommendations compared to combining only the former.