Reviewerly/exHarmony huggingface dataset:
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-identification

Generalizable person re-identification aims to learn a model with only several labeled source domains that can perform well on unseen domains. Without access to the unseen domain, the feature statistics of the batch normalization (BN) layer learned from a limited number of source domains is doubtlessly biased for unseen domain. This would mislead the feature representation learning for unseen domain and deteriorate the generalizaiton ability of the model. In this paper, we propose a novel Debiased Batch Normalization via Gaussian Process approach (GDNorm) for generalizable person re-identification, which models the feature statistic estimation from BN layers as a dynamically self-refining Gaussian process to alleviate the bias to unseen domain for improving the generalization. Specifically, we establish a lightweight model with multiple set of domain-specific BN layers to capture the discriminability of individual source domain, and learn the corresponding parameters of the domain-specific BN layers. These parameters of different source domains are employed to deduce a Gaussian process. We randomly sample several paths from this Gaussian process served as the BN estimations of potential new domains outside of existing source domains, which can further optimize these learned parameters from source domains, and estimate more accurate Gaussian process by them in return, tending to real data distribution. Even without a large number of source domains, GDNorm can still provide debiased BN estimation by using the mean path of the Gaussian process, while maintaining low computational cost during testing. Extensive experiments demonstrate that our GDNorm effectively improves the generalization ability of the model on unseen domain.
DeepHardMark: Towards Watermarking Neural Network Hardware

This paper presents a framework for embedding watermarks into DNN hardware accelerators. Unlike previous works that have looked at protecting the algorithmic intellectual properties of deep learning systems, this work proposes a methodology for defending deep learning hardware. Our methodology embeds modifications into the hardware accelerator's functional blocks that can be revealed with the rightful owner's key DNN and corresponding key sample, verifying the legitimate owner. We propose an Lp-box ADMM based algorithm to co-optimize watermark's hardware overhead and impact on the design's algorithmic functionality. We evaluate the performance of the hardware watermarking scheme on popular image classifier models using various accelerator designs. Our results demonstrate that the proposed methodology effectively embeds watermarks while preserving the original functionality of the hardware architecture. Specifically, we can successfully embed watermarks into the deep learning hardware and reliably execute a ResNet ImageNet classifiers with an accuracy degradation of only 0.009%
Malicious Use of Artificial Intelligence in Sub-Saharan Africa: Challenges for Pan-African Cybersecurity

For almost two decades, Sub-Saharan African countries have been making significant efforts to ensure the rapid development of industries related to information and communication technology (ICTs) in the region. At present, all leading nations are placing greater emphasis on the development of hybrid intelligent systems capable of solving extremely complicated tasks. This includes Sub-Saharan African countries, which consider the development of advanced technologies to be an effective instrument for ensuring sustainable social and economic growth and solving a great number of the continent’s problems. It has become evident, however, that all technological novelties that should simplify our lives can be used for malicious purposes. The present study examines existing practices and risks of malicious use of artificial intelligence (MUAI) in Sub-Saharan African countries. At the end of the study, the author comes to the conclusion that the problem of ensuring information, psychological, and cybersecurity is common to all African countries, which creates a serious obstacle for their further sustainable social and economic development. Over the past decade, Sub-Saharan Africa has made significant efforts to elaborate a joint vision for counteracting cybercrimes and the malicious use of advanced technologies. But all the attempts to establish effective supranational instruments that would regulate the fight against cyberattacks at the Pan-African level and take into account the interests of the vast majority of African countries in this area have failed. This demonstrates the presence of serious contradictions among African countries, which, taken together, prevent the establishment of mutually beneficial cooperation even in such an important field as cybersecurity. However, until such cooperation is established, it seems unlikely that African countries will even come close to solving this problem, which means that their information space will continue to be subjected to large-scale cyber-attacks that pose a serious threat not only to the security of individuals, but also to national and Pan-African security.
Correlation Field for Boosting 3D Object Detection in Structured Scenes

Data augmentation is an efficient way to elevate 3D object detection performance. In this paper, we propose a simple but effective online crop-and-paste data augmentation pipeline for structured 3D point cloud scenes, named CorrelaBoost. Observing that 3D objects should have reasonable relative positions in a structured scene because of the objects' functionalities and natural relationships, we express this correlation as a kind of interactive force. An energy field called Correlation Field can be calculated correspondingly across the whole 3D space. According to the Correlation Field, we propose two data augmentation strategies to explore highly congruent positions that a designated object may be pasted to: 1) Category Consistent Exchanging and 2) Energy Optimized Transformation. We conduct exhaustive experiments on various popular benchmarks with different detection frameworks and the results illustrate that our method brings huge free-lunch improvement and significantly outperforms state-of-the-art approaches in terms of data augmentation. It is worth noting that the performance of VoteNet with mAP@0.5 is improved by 7.7 on ScanNetV2 dataset and 5.0 on SUN RGB-D dataset. Our method is simple to implement and increases few computational overhead.
A Novel Algorithm to Secure Data in New Generation Health Care System from Cyber Attacks Using IoT

The rise of digital technology has essentially enhanced the overall communication and data management system, facilitating essential medical care services. Considering this aspect, the healthcare system successfully managed patient requirements through online services and facilitated patient experience. However, the lack of adequate data security and increased digital activities during Covid-19 made the healthcare system a soft target for hackers to gain unauthorized access and steal crucial and sensitive information. Countries such as the UK and the US recently received such challenges, highlighting the need for effective data maintenance. IoT emerged as one of the critical solutions for data management systems in terms of addressing data security which certainly can enhance overall data collection, storage, maintenance, prediction of potential data security breaches and taking appropriate measurements. The concerned research considers a secondary data collection process where necessary data is collected from original scholarly articles, books and journals. Apart from that, a positivism research philosophy, a deductive research approach and a descriptive research design have been considered for this study. Qualitative data analysis techniques have also been incorporated into this research. Upon viewing the pros and cons of IoT algorithms, DES, AES, triple data encryption standards, and RSA encryption can be used in the healthcare system to facilitate data protection.
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.
Enhancing data mobilisation through a centralised data repository for Atlantic salmon (Salmo salar L.): Providing the resources to promote an ecosystem-based management framework.

Data and knowledge mobilisation are significant challenges in ecology and resource management, with the journey from data collection through to management action often left incomplete due to difficulties sharing information across diverse and dispersed communities. This disconnect between science and management must be resolved if we are to successfully tackle the increasing impact of human activity on our ecosystems. Across their North Atlantic range, Atlantic salmon (Salmo salar L.) populations are in steep decline in many areas and urgent actions are required to curb this decline. Being commercially important this species has been subject to intense research, but management action often suffers from both a lack of access to this knowledge resource and support for its integration into effective management strategies. To respond to this challenge, the science and management communities must place higher priority on mobilising existing and emerging knowledge sources to inform current and future resource use and mitigation strategies. This approach requires a more complete picture of the current salmon ecology data and knowledge landscape, new mechanisms to enable data mobilisation and re-use, and new research to describe and parameterise the responses of wild populations to habitat changes. Here we present a unique interface for registering and linking data resources relevant to the Atlantic salmon life cycle that can address the data mobilisation aspect of these challenges. The Salmon Ecosystem Data Hub is a salmon-specific metadata catalogue, natively interoperable with many existing data portals, which creates a low resistance pathway to maximise visibility of data relevant to Atlantic salmon. This includes the capacity to annotate datasets with life-stage domains and variable classes, thereby permitting dispersed data to be formally contextualised and integrated to support hypotheses specific to scenario-based modelling and decision-making. The alignment and mobilisation of data within the Salmon Ecosystem Data Hub will help advance the development of appropriate environmentally driven forecast models and an ecosystem-based management approach for Atlantic salmon that optimises future management strategies.
An improved recommendation based on graph convolutional network

Graph convolutional network is a recently developed artificial neural network method commonly used in recommendation system research. This paper points out three shortcomings of existing recommendation systems based on the graph convolutional network. 1. Existing models that take the one-hot encoding based on node ordinal numbers in the graph or encoding based on original entity attributes as input may not fully utilize the information carried by the attribute interactions. 2. Previous models update the node embeddings only by the first-order neighbors in the graph convolution layer, which is easily affected by noise. 3. Existing models do not take into account differences in user opinions. We propose an improved graph convolutional network-based collaborative filtering model to address these drawbacks. We identify inner and cross interaction between user attributes and item attributes, and then we take the vector representations of aggregated attributes graph as input. In the convolutional layer, we aggregate the second-order collaborative signals and incorporate the different user opinions. The experiments on three public datasets show that our model outperforms state-of-the-art models.
Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Background: Artificial intelligence techniques are widely used in solving medical problems.Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease.But these techniques suffer from overfitting and hyper-parameters tuning problems.Methods: In this paper, an artificial intelligence-based cyber-physical system (CPS) is proposed for the severity classification of Chikungunya disease.In CPS system, the physical components are integrated with computational algorithms to provide better results.Random forest (RF) is used to design the severity classification model for Chikungunya disease.However, RF suffers from overfitting and poor computational speed problems due to complex architectures and large amounts of connection weights.Therefore, an evolving RF model is proposed using the adaptive crossover-based genetic algorithm (ACGA).Results: ACGA can efficiently optimize the architecture of RF to achieve better results with better computational speed.Extensive experiments are performed by utilizing the Chikungunya disease dataset.Conclusion: Performance analysis demonstrates that ACGA-RF achieves higher performance as compared to the competitive models in terms of F-measure, accuracy, sensitivity, and specificity.The proposed CPS system can prevent users from visiting hospitals and can render services to patients living far away from hospitals.
Horizon: A Trajectory Optimization Framework for Robotic Systems

This paper presents Horizon , an open-source framework for trajectory optimization tailored to robotic systems that implements a set of tools to simplify the process of dynamic motion generation. Its user-friendly Python-based API allows designing the most complex robot motions using a simple and intuitive syntax. At the same time, the modular structure of Horizon allows for easy customization on many levels, providing several recipes to handle fixed and floating-base systems, contact switching, variable time nodes, multiple transcriptions, integrators and solvers to guarantee flexibility towards diverse tasks. The proposed framework relies on direct simultaneous methods to transcribe the optimal problem into a nonlinear programming problem that can be solved by state-of-the-art solvers. In particular, it provides several off-the-shelf solvers, as well as two custom-implemented solvers, i.e. GN-SQP and Iterative Linear-Quadratic Regulator. Solutions of optimized problems can be stored for warm-starting, and re-sampled at a different frequency while enforcing dynamic feasibility. The proposed framework is validated through a number of use-case scenarios involving several robotic platforms. Finally, an in-depth analysis of a specific case study is carried out, where a highly dynamic motion (i.e., a twisting jump using the quadruped robot Spot ® from BostonDynamics 1 ) is generated, in order to highlight the main features of the framework and demonstrate its capabilities.
Preventing Price Manipulation Attack by Front-Running

Decentralized finance (DeFi), as one of the fastest growing directions in the field of decentralized applications based on blockchain and smart contract in the past two years, has attracted many participants and institutional investments, and represents a series of decentralized financial projects in the blockchain ecosystem behind it. The most noteworthy one is the Flash-loan based Price Manipulation Attack, which uses a series of new features of DeFi such as flash loan and liquidity pool, and the attack is complex and diverse, leading to the inability of traditional smart contract vulnerability mining means to effectively. In this work, we focus on detecting flash loan-based price manipulation attacks on DeFi applications, which exploit the DeFi project's simple inquiry mechanism to gain large profits by manipulating users' crypto asset prices. To this end, we propose a detector embedded in the blockchain client to detect whether the user's crypto asset price is manipulated by monitoring the transaction execution process in real time, and innovatively propose the use of a front-running attack for remediation based on the amount of profit made by the attacker, solving the problem that the relevant attack can only be detected but not defended against, and reducing the possible financial loss to the DeFi project.
Federated-Learning-Driven Radio Access Networks

In this work, we present federated learning (FL) driven radio access mechanisms, which can be employed at the future radio access networks (RANs) to achieve high accuracy rather than data rate that was the objective in RANs without FL application so far. We first present low feedback FL techniques based on local aggregation updates such as sparsification and quantization, which enable a communication efficient FL application in RANs. Next, the concept of FL-driven radio access mechanisms is presented, which is considered the joint design/optimization of FL with beamforming, retransmission, resource allocation and scheduling respectively. The aim of each joint optimization is to achieve the highest accuracy of the considered training model on data originated from the connected devices. Finally, a simulation setup is developed to obtain simulation results, which show the advantage of using the proposed radio access mechanisms comparing with FL without joint design with RAN in terms of accuracy, feedback overhead and communication rounds.
Automatic Identification and Classification of Bragging in Social Media

Bragging is a speech act employed with the goal of constructing a favorable self-image through positive statements about oneself.It is widespread in daily communication and especially popular in social media, where users aim to build a positive image of their persona directly or indirectly.In this paper, we present the first large scale study of bragging in computational linguistics, building on previous research in linguistics and pragmatics.To facilitate this, we introduce a new publicly available data set of tweets annotated for bragging and their types.We empirically evaluate different transformerbased models injected with linguistic information in (a) binary bragging classification, i.e., if tweets contain bragging statements or not; and (b) multi-class bragging type prediction including not bragging.Our results show that our models can predict bragging with macro F1 up to 72.42 and 35.95 in the binary and multi-class classification tasks respectively.Finally, we present an extensive linguistic and error analysis of bragging prediction to guide future research on this topic. 1
Edge-Assisted Intelligent Video Compression for Live Aerial Streaming

The ultra-low latency transmission, high speed broadband and ubiquitous access points in modern cities have greatly alleviated the last mile problem of live streaming services. Yet, the sophisticated video compression of high definition videos is still inevitable before transmission, which leads to overwhelming workloads in the light-weighted video collectors. In this paper, we are motivated to explore the computation offloading of live streaming video compression for UAVs, which are featured by limited capacity and fixed trajectory. In particular, the global motion model is utilized for inter frame residual coding instead of motion vector estimation, which usually takes more than 50% computation complexity in traditional H.264/265. We propose an edge-based Joint Video Coding (eJVC) scheme, which can save up to 84.04% encoding complexity of UAV video collector. Specifically, the attention network is used to distinguish foreground blocks and background blocks in the frame. LSTM neural network is used on the edge server to predict the auxiliary data needed by UAV video collector for video coding. In addition, our proposed solution can also accommodate the movement direction change when the control signal is notified in advance. Finally, the prototype system is implemented with the real world data set, and the experiment results show that our proposed solution can significantly save the computation time with little bitrate distortion performance loss.
Pretrained Speech Encoders and Efficient Fine-tuning Methods for Speech Translation: UPC at IWSLT 2022

This paper describes the submissions of the UPC Machine Translation group to the IWSLT 2022 Offline Speech Translation and Speechto-Speech Translation tracks.The offline task involves translating English speech to German, Japanese and Chinese text.Our Speech Translation systems are trained end-to-end and are based on large pretrained speech and text models.We use an efficient fine-tuning technique that trains only specific layers of our system, and explore the use of adapter modules for the non-trainable layers.We further investigate the suitability of different speech encoders (wav2vec 2.0, HuBERT) for our models and the impact of knowledge distillation from the Machine Translation model that we use for the decoder (mBART).For segmenting the IWSLT test sets we fine-tune a pretrained audio segmentation model and achieve improvements of 5 BLEU compared to the given segmentation.Our best single model uses HuBERT and parallel adapters and achieves 29.42 BLEU at English-German MuST-C tst-COMMON and 26.77 at IWSLT 2020 test.By ensembling many models, we further increase translation quality to 30.83 BLEU and 27.78 accordingly.Furthermore, our submission for English-Japanese achieves 15.85 and English-Chinese obtains 25.63 BLEU on the MuST-C tst-COMMON sets.Finally, we extend our system to perform English-German Speech-to-Speech Translation with a pretrained Text-to-Speech model.