Reviewerly/exHarmony huggingface dataset:
A Compendium of Practices for Central Bank Digital Currencies for Multinational Financial Infrastructures

Over five thousand digital currencies have been issued by private sector actors since the release of the Bitcoin digital currency in 2009.Private sector issuance of distributed ledger technology (DLT)based digital currencies such as Bitcoin, Ethereum and other altcoins threaten the stability of financial market infrastructures and preservation of monetary policy.Consequently, many central banks and monetary authorities have begun research and experimentation on central bank-issued digital currencies (CBDCs) to mitigate this threat.In this paper, we present a comprehensive survey of publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes from across the world to enable an understanding of the motivations and best practice approaches for undertaking CBDC experiments.We provide a classification and generic framework for CBDCs and highlight existing DLT platform limitations and use cases in the financial services industry.Overall, our paper organizes in one place, all the relevant, publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes to serve as a reference point for central banks, monetary authorities and researchers desiring to undertake research on DLT-based CBDCs.Ultimately, we present a survey on the technical feasibility and challenges of leveraging DLT to issue the selected CBDC experiments surveyed in this paper.
Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions

In problems such as sports video analytics, it is difficult to obtain accurate frame level annotations and exact event duration because of the lengthy videos and sheer volume of video data. This issue is even more pronounced in fast-paced sports such as ice hockey. Obtaining annotations on a coarse scale can be much more practical and time efficient. We propose the task of event detection in coarsely annotated videos. We introduce a multi-tower temporal convolutional network architecture for the proposed task. The network, with the help of multiple receptive fields, processes information at various temporal scales to account for the uncertainty with regard to the exact event location and duration. We demonstrate the effectiveness of the multi-receptive field architecture through appropriate ablation studies. The method is evaluated on two tasks - event detection in coarsely annotated hockey videos in the NHL dataset and event spotting in soccer on the SoccerNet dataset. The two datasets lack frame-level annotations and have very distinct event frequencies. Experimental results demonstrate the effectiveness of the network by obtaining a 55% average F1 score on the NHL dataset and by achieving competitive performance compared to the state of the art on the SoccerNet dataset. We believe our approach will help develop more practical pipelines for event detection in sports video.
Unsupervised Domain Adaptation Through Synthesis For Person Re-Identification

Person re-identification is a hot topic because of its widespread applications in video surveillance and public security. However, it remains a challenging task because of drastic variations in illumination or background across surveillance cameras, which causes the current methods can not work well in real-world scenarios. In addition, due to the scarce dataset, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic random scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose a novel unsupervised Re-ID method via domain adaptation, which can exploit the synthetic data to boost the performance of re-identification in a completely unsupervised way, and free humans from heavy data annotations. Extensive experiments show that our proposed method achieves the state-of-the-art performance on two benchmark datasets, and is very competitive with current cross-domain Re-ID method.
Geochemical exploration for lithium in NE Iran using the geochemical mapping prospectivity index, staged factor analysis, and a fractal model

Geochemical exploration for rare metals, specifically lithium, is essential on a regional scale based on their demand and consumption in recent years. The main objective of this study was to delineate lithium anomalies in regional exploration utilizing the geochemical mapping prospectivity index (GMPI), staged factor analysis (SFA), and a concentration-number (C-N) fractal model based on stream sediments. The case study area is 26000 km 2 and is located in the Khorasan Razavi province (NE Iran). In addition, rock samples were used to validate the Li anomalies identified. Results derived via the SFA show that Li was located on a factor denoted as F1-3 with Be, Cs, F, Nb, Sn, Th, U and W, which was used for calculation of the GMPI values. The GMPI data were classified by the C-N fractal method for determination of the Li anomalies. The main anomalies with GMPI ≥ 0.7 and Li ≥ 48 ppm were situated in the SE, SW, north and south parts of the study region. Li grades of rock samples were categorized by the C-N fractal technique for validation of F2-2 anomalies using a log-ratio matrix. The main anomalies were correlated with related lithological units of Li mineralization types. This correlation indicates that the main GMPI–Li anomalies are associated with granitic–pegmatitic units in the central and SE parts, and overlap with clay minerals in the northern and southern sectors of this region. There is good potential for Li mineralization as demonstrated by this hybrid method.
Learning a Unified Sample Weighting Network for Object Detection

Region sampling or weighting is significantly important to the success of modern region-based object detectors. Unlike some previous works, which only focus on "hard'' samples when optimizing the objective function, we argue that sample weighting should be data-dependent and task-dependent. The importance of a sample for the objective function optimization is determined by its uncertainties to both object classification and bounding box regression tasks. To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights. Our framework is simple yet effective. It leverages the samples' uncertainty distributions on classification loss, regression loss, IoU, and probability score, to predict sample weights. Our approach has several advantages: (i). It jointly learns sample weights for both classification and regression tasks, which differentiates it from most previous work. (ii). It is a data-driven process, so it avoids some manual parameter tuning. (iii). It can be effortlessly plugged into most object detectors and achieves noticeable performance improvements without affecting their inference time. Our approach has been thoroughly evaluated with recent object detection frameworks and it can consistently boost the detection accuracy. Code has been made available at https://github.com/caiqi/sample-weighting-network.
Hardware Architecture of a Number Theoretic Transform for a Bootstrappable RNS-based Homomorphic Encryption Scheme

Homomorphic encryption (HE) is one of the most promising solutions to secure cloud computing. The number theoretic transform (NTT) that is widely used for convolution operations in HE requires a large amount of computation and has high parallelism, and therefore it has been a good candidate for hardware acceleration. Nevertheless, prior NTT hardware solutions for HE-based applications are impractical in most applications because they do not seriously consider the critical bootstrapping procedure that allows unlimited homomorphic operations on encrypted data. In this paper, we suggest practical bootstrappable parameters, specifically for an established residue number system (RNS)based HE scheme, and apply them to our NTT hardware design. In addition, to limit the size of internal memory for roots of unity increased by the bootstrappable parameters, only a few roots of unity are stored and others are generated on the fly. In our NTT hardware architecture, multiple NTT butterfly units (BUs) are efficiently deployed for high throughput and high resource utilization. In particular, several groups of BUs for respective moduli work in a parallel and pipelined manner, which is effective in an RNS-based HE scheme with a number of moduli. Our implementation on a Xilinx UltraScale FPGA with the bootstrappable parameters achieves a $118 \times$ faster processing speed than a software implementation, and it further provides various trade-off choices such as the number of DSP slices against BRAMs based on available FPGA resources.
Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

With much research has been conducted into trajectory planning for quadrotors, planning with spatial and temporal optimal trajectories in real-time is still challenging. In this letter, we propose a framework for large-scale waypoint-based polynomial trajectory generation, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. Algebraic convenience of both sub-problems is leveraged to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our method is provided. Moreover, based on polynomial theory, an extremely fast feasibility checker is designed for various kinds of constraints. By incorporating it into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art waypoint-based methods regarding efficiency, optimality, and scalability. The algorithm can be incorporated in a high-level waypoint planner, which can rapidly search over a three-dimensional space for aggressive autonomous flights. The capability of our algorithm is experimentally demonstrated by quadrotor fast flights in a limited space with dense obstacles. We release our implementation as an open-source ros-package. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Multi-point shortest path planning based on an Improved Discrete Bat Algorithm

Multi-point shortest path planning problem is a typical problems in discrete optimization. The bat algorithm is a nature-inspired metaheuristic optimization algorithm that is used in a wide range of fields. However, there is one problem with the BA, which is easy to premature. To solve multi-point shortest path planning problem, an improved discrete bat algorithm (IDBA) is proposed in this paper. In this algorithm, the Floyd–Warshall algorithm is first used to transform an incomplete connected graph into a complete graph whose vertex set consists of a start point and necessary points. Then the algorithm simulates the bats' foraging and obstacle avoidance process to find the shortest path in the complete graph to satisfy the constraints. Finally, the path is transferred to the original incomplete graph to get the solution. In order to overcome the premature phenomenon of a discrete bat algorithm, the modified neighborhood operator is proposed. To prove the effectiveness of our method, we compared its performance in 26 instances with the results obtained by three different algorithms: DBA, IBA and GSA-ACS-PSOT. We also performed a sensitivity analysis on the parameters. The results indicate that the improved bat algorithm outperforms all the other alternatives in most cases.
Adversarial View Confusion Feature Learning for Person Re-Identification

The performances of person re-identification tasks can be seriously degraded because of variations caused by view changes. In recent years, there are many methods focusing on how to solve cross view challenges which can be roughly divided into two categories: 1) learning view-invariant features without the help of view information. 2) combining view-wise features with the guide of view information. However, these methods are neither perfect enough. Methods of the first category are not roust enough for different kinds of view-invariants while methods of the other category can not generalize well in real-world applications. In this paper, we aim to learn view-invariant features with the help of view information. We proposed an end-to-end trainable framework, called View Confusion Feature Learning (VCFL), to learn view-invariant features by getting rid of view specific information. To the best of our knowledge, VCFL is originally proposed to learn view-invariant identity-wise features, and it is a kind of combination of view-generic and view-specific methods. The whole view confusion learning mechanism consists of three parts: 1) adversarial learning between feature extractor and the view classifier; 2) drawing the features with the same ID close to centers; 3) the guidance of SIFT, for seamlessly integration of hand-crafted features and deep features. In order to make the whole confusion mechanism work better, we further propose a VCFL+ model, which improves the fusion process in the feature map level through the thoughts of attention mechanism. Experiments on three benchmark datasets including Market1501, CUHK03, and DukeMTMC prove the superiority of our method over state-of-the-art approaches.
Mudra: Convolutional Neural Network based Indian Sign Language Translator for Banks

Signlanguageisamediumofexpressing thoughts and feelings by the deaf-dumb community. It could be extremely challenging for deaf-mute people to communicate efficiently in banks, where they might have to explain their needs. There are very few people who can understand sign language. The main focus of our proposed method is to design an ISL (Indian Sign Language) hand gesture motion translation tool for banks for helping the deaf-mute community to convey their ideas by converting them to text format. In the fields of ASL (American Sign Language) and other languages, ample amounts of work have been done. Apart from other algorithms, our proposed method recognizes human actions considering isolated dynamic Indian signs related to the bank as a novel approach. There are very few research works carried out in this field of ISL recognition for banks. Over and above that, an insufficient amount of dataset along with dissimilarity in gestures length was a difficulty. We used a self-recorded ISL dataset for training the model for recognizing the gestures. Unlike image data, the video domain was a new challenge. Larger lengthened video gestures were taken and actions were recognized from a series of video frames. CNN (Convolutional Neural Network) named inception V3 was used to extract the image features. LSTM (Long Short Term Memory), an architecture of RNN (Recurrent neural network) classified these gestures and are translated into text. Experimental results display that this approach towards isolated word dynamic hand gesture recognition systems provides an accurate and effective method for the interaction between non-signer and signer.
Semi-Supervised Learning with Scarce Annotations

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.
Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming

Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.
TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.
Multi-Objective Data Placement for Workflow Management in Cloud Infrastructure Using NSGA-II

The cloud computing paradigm provides massive storage and rich computing resources for workflow deployment and implementation. Nevertheless, workflow applications (e.g., meteorological prediction and financial analysis) are usually data intensive, and substantial data resources with privacy information tend to be accessed during the workflow implementation. Therefore, it remains challenging to design a data placement method for seeking tradeoffs among multiple performance metrics, i.e., resource usage, data acquisition time, and energy cost, while avoiding privacy conflicts of information-overlapping datasets for workflow implementation of the cloud infrastructure. To address this challenge, a multi-objective data placement method for workflow management in the cloud infrastructure with privacy protection is proposed in this paper. Technically, the BCube topology is adopted to establish the resource model in the cloud infrastructure, and the potential privacy conflicts of datasets required for workflow implementation are analyzed. Then, a non-dominated sorting genetic algorithm II is leveraged to promote the resource usage, reduce the data acquisition time, and optimize the energy cost of the cloud infrastructure, while achieving the privacy protection for data placement. Finally, experimental evaluations demonstrate that the performance of the cloud infrastructure is optimized for workflow management.
A Key-Recovery Timing Attack on Post-quantum Primitives Using the Fujisaki-Okamoto Transformation and Its Application on FrodoKEM

In the implementation of post-quantum primitives, it is well known that all computations that handle secret information need to be implemented to run in constant time. Using the Fujisaki-Okamoto transformation or any of its different variants, a CPA-secure primitive can be converted into an IND-CCA secure KEM. In this paper we show that although the transformation does not handle secret information apart from calls to the CPA-secure primitive, it has to be implemented in constant time. Namely, if the ciphertext comparison step in the transformation is leaking side-channel information, we can launch a key-recovery attack. Several proposed schemes in round 2 of the NIST post-quantum standardization project are susceptible to the proposed attack and we develop and show the details of the attack on one of them, being FrodoKEM. It is implemented on the reference implementation of FrodoKEM, which is claimed to be secure against all timing attacks. Experiments show that the attack code is able to extract the secret key for all security levels using about $$2^{30}$$ decapsulation calls.
Multi-reservoir echo state computing for solar irradiance prediction: A fast yet efficient deep learning approach

Accurate solar irradiance prediction plays an important role in renewable energy systems. Based on time series analysis, a serially connected multi-reservoir echo state network (MR-ESN) is developed to predict solar irradiance. MR-ESN is a fast yet efficient approach, which makes use of the high efficiency of ESN and the advantages of deep learning. MR-ESN consists of multiple reservoirs in series, which are responsible for encoding the input signals into a richer state representation. The time series analysis is adopted to provide more appropriate input and output for MR-ESN. Various prediction horizons including one-hour-ahead and multi-hour-ahead prediction are conducted, respectively. The effect of reservoir layer number on the MR-ESN performance is explored in detail. Three internal qualitative indicators are adopted to investigate the performance differences of MR-ESN, i.e., probability distribution, correlation analysis, and principal component analysis (PCA) of network states. Simulation results demonstrate that MR-ESN outperforms than traditional ESN, backpropagation (BP) and Elman neural networks.