Reviewerly/exHarmony huggingface dataset:
Quantum Private Information Retrieval for Quantum Messages

Quantum private information retrieval (QPIR) for quantum messages is the protocol in which a user retrieves one of the multiple quantum states from one or multiple servers without revealing which state is retrieved. We consider QPIR in two different settings: the blind setting, in which the servers contain one copy of the message states, and the visible setting, in which the servers contain the description of the message states. One trivial solution in both settings is downloading all states from the servers and the main goal of this paper is to find more efficient QPIR protocols. First, we prove that the trivial solution is optimal for one-server QPIR in the blind setting. In one-round protocols, the same optimality holds even in the visible setting. On the other hand, when the user and the server share entanglement, we prove that there exists an efficient one-server QPIR protocol in the blind setting. Furthermore, in the visible setting, we prove that it is possible to construct symmetric QPIR protocols in which the user obtains no information of the non-targeted messages. We construct two-server symmetric QPIR protocols for pure states. Note that symmetric classical PIR is impossible without shared randomness unknown to the user.
Object Detection Using Convolution Neural Networks

Accurate and efficient object detection has been an important topic in the advancement of computer vision systems. With the sophistication of deep learning techniques, the precision of object detection has increased drastically. A huge number of visually impaired people in the world have inspired many smart solutions which use sophisticated technologies to aid them in their day to day life. This paper describes a system that aims to perform object detection with the goal of achieving high precision with acceptable real-time performance. The system is designed to help the visually impaired locate day today objects through object detection. The system is trained on CIFAR-100 [1] dataset which consists of 100 classes where each class has 500 training samples and 100 testing samples. The total training size is 50000 samples and the testing size is 10000 samples. The experimental results are determined by using convolution neural network combined with dropout, batch normalization and data augmentation to determine which technique or combination of techniques provides the best performance for object detection. The system is then implemented with the combinations of different technique.
Writer Characterization from Handwriting on Papyri Using Multi-step Feature Learning

Identification of scribes from historical manuscripts has remained an equally interesting problem for paleographers as well as the pattern classification researchers. Though significant research endeavors have been made to address the writer identification problem in contemporary handwriting, the problem remains challenging when it comes to historical manuscripts primarily due to the degradation of documents over time. This study targets scribe identification from ancient documents using Greek handwriting on the papyri as a case study. The technique relies on segmenting the handwriting from background and extracting keypoints which are likely to carry writer-specific information. Using the handwriting keypoints as centers, small fragments (patches) are extracted from the image and are employed as units of feature extraction and subsequent classification. Decisions from fragments of an image are then combined to produce image-level decisions using a majority vote. Features are learned using a two-step fine-tuning of convolutional neural networks where the models are first tuned on contemporary handwriting images (relatively larger dataset) and later tuned to the small set of writing samples under study. The preliminary findings of the experimental study are promising and establish the potential of the proposed ideas in characterizing writer from a challenging set of writing samples.
Blockchain Applications in the Energy Industry

The current energy transition from a fossil-fuel-based economy to a zero-carbon has significantly accelerated in recent years, as the largest emitters have committed to achieving carbon-neutral goals in the next 20-30 years. The energy industry transition is characterized by modernization through digital technologies, increased renewable energy generation, and environmental sustainability. Blockchain technology can play a significant role in providing secure digital distributed platforms facilitating digitization, decarbonization, and decentralization of the energy systems. Several promising blockchain applications in the energy sector are under research and development, including peer-to-peer energy trading; carbon monitoring, management, and trading; and IoT-enabled electric grid management. However, several challenges are slowing down the commercialization of these applications, including outdated legislation and regulations, slow pace of adaptation from the traditional energy industry, and risks associated with the new, untested technology.
Attention Based Multiple Siamese Network for Offline Signature Verification

Offline handwritten signatures play an important role in biometrics and document forensics, and it has been widely used in the fields of finance, judiciary and commerce. However, the skilled signature forgeries bring challenges and difficulties to personal privacy protection. Thus it is vital to discover micro but critical details between genuine signatures and corresponding skilled forgeries in signature verification tasks. In this paper, we propose an attention based Multiple Siamese Network (MSN) to extract discriminative information from offline handwritten signatures. MSN receives the reference and query signature images and their corresponding inverse images. The received images are fed to four parallel branches. We develop an effective attention module to transfer the information from original branches to inverse branches, which attempts to explore prominent features of handwriting. The weight-shared branches are concatenated in a particular way and formed into four contrastive pairs, which contribute to learn useful representations by comparisons of these branches. The preliminary decisions are generated from each contrastive pair independently. Then, the final verification result is voted from these preliminary decisions. In order to evaluate the effectiveness of proposed method, we conduct experiments on three publicly available signature datasets: CEDAR, BHSig-B and BHSig-H. The experimental results demonstrate the proposed method outperforms that of other previous approaches.
An automatic trough line identification method based on improved UNet

A trough is an elongated region of relatively low atmospheric pressure. Automatic analysis and recognition of trough lines in upper weather charts are challenging and of great significance to weather analysis. The existing automatic identification methods mainly depend on manually setting rules which cannot cover all trough line types and have low generalization ability. This paper proposes an automatic trough line identification method based on an improved model which can extract the trough line from meteorological element data at 500 hPa. The model adopts the UNet of a U-shaped encoder and decoder as basic structure, which is designed to enable precise localization by continuously combining low-level and high-level features. To extract abstract semantic features of the trough, the Xception, which takes depthwise separable convolution as basic unit, is adopted to replace the encoder of the original UNet. In addition, the Squeeze and Excitation (SE) module with an attention mechanism is added after each ordinary convolution in the decoder part to improve the recognition accuracy by increasing the weighting of the trough area. The experiments are conducted on a meteorological dataset and the results show that the recognition accuracy with our proposed method on the testing dataset can reach over 80%. We also compare our results to several other types of networks and traditional automatic identification methods, which demonstrates that the performance of the proposed network is superior to other methods.
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning

Meta-learning aims at optimizing the hyperparameters of a model class or training algorithm from the observation of data from a number of related tasks. Following the setting of Baxter [1], the tasks are assumed to belong to the same task environment, which is defined by a distribution over the space of tasks and by per-task data distributions. The statistical properties of the task environment thus dictate the similarity of the tasks. The goal of the meta-learner is to ensure that the hyperparameters obtain a small loss when applied for training of a new task sampled from the task environment. The difference between the resulting average loss, known as meta-population loss, and the corresponding empirical loss measured on the available data from related tasks, known as meta-generalization gap, is a measure of the generalization capability of the meta-learner. In this paper, we present novel information-theoretic bounds on the average absolute value of the meta-generalization gap. Unlike prior work [2], our bounds explicitly capture the impact of task relatedness, the number of tasks, and the number of data samples per task on the meta-generalization gap. Task similarity is gauged via the Kullback-Leibler (KL) and Jensen-Shannon (JS) divergences. We illustrate the proposed bounds on the example of ridge regression with meta-learned bias.
IoT and Blockchain combined: for decentralized security

Blockchain technology, a version of distributed ledger technology, has been grabbing a huge amount of attention in fields beyond its roots in crypto-currencies: blockchain and finance, blockchain and logistics, blockchain and the Internet of Things (IoT), blockchain and cybersecurity, and the list goes on. Blockchain (there are actually many blockchains, and it’s not limited to the technology that powers bitcoin), has been mentioned by virtually every research study firm as a rapidly accelerating evolution, and it’s not just about financial services companies, the domain in which we first discussed blockchain outside of its cryptographic roots (i.e., FinTech or financial technologies). The converging of blockchain and the Internet of Things is actually on the table for many companies and there are existing deployments, solutions and initiatives in several areas outside of IoT and financial services as well. Combine blockchain and IoT, and you have two bigger-than-Internet businesses that need each other for many reasons that we explain in this IoT blockchain overview.
ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain

Abstract Background Biomedical language translation requires multi-lingual fluency as well as relevant domain knowledge. Such requirements make it challenging to train qualified translators and costly to generate high-quality translations. Machine translation represents an effective alternative, but accurate machine translation requires large amounts of in-domain data. While such datasets are abundant in general domains, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge, a parallel corpus does not exist for this language pair in the biomedical domain. Description We developed an effective pipeline to acquire and process an English-Chinese parallel corpus from the New England Journal of Medicine (NEJM). This corpus consists of about 100,000 sentence pairs and 3,000,000 tokens on each side. We showed that training on out-of-domain data and fine-tuning with as few as 4000 NEJM sentence pairs improve translation quality by 25.3 (13.4) BLEU for en $$\rightarrow$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> zh (zh $$\rightarrow$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> en) directions. Translation quality continues to improve at a slower pace on larger in-domain data subsets, with a total increase of 33.0 (24.3) BLEU for en $$\rightarrow$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> zh (zh $$\rightarrow$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> en) directions on the full dataset. Conclusions The code and data are available at https://github.com/boxiangliu/ParaMed .
Spatio-Temporal Activity Detection and Recognition in Untrimmed Surveillance Videos

This work presents a spatio-temporal activity detection and recognition framework for untrimmed surveillance videos consisting of a three-step pipeline: object detection, tracking, and activity recognition. The framework relies on the YOLO v4 architecture for object detection, Euclidean distance for tracking, while the activity recognizer uses a 3D Convolutional Deep learning architecture employing spatio-temporal boundaries and addressing it as multi-label classification. The evaluation experiments on the VIRAT dataset achieve accurate detections of the temporal boundaries and recognitions of activities in untrimmed videos, with better performance for the multi-label compared to the multi-class activity recognition.
The Larger Picture: A Designerly Approach to Making the Invisible Domestic Workloads of Working Women Visible

The pandemic in 2020 and the resultant global lockdown led to new dynamics in the homestead. Families faced an accelerated process of digitization of conventional processes like education and white collar jobs. Working women with children of school or kindergarten age were forced to redraw the boundaries between the personal and the professional as the entire family essentially functioned from home, which increased prevalent gender inequalities. Tasks like 'planning', care-giving, emotional care etc. go unaccounted for and remain invisible to other members of the household. We present a feminist design research project that explores how design research can contribute to greater social justice and gender equality by a fairer distribution of domestic labour. We conducted semi-structured interviews in India during the pandemic lockdown, to elicit both explicit as well as tacit information on daily workloads of working women with families. We found disparity in domestic duties between genders, with specific regard to the circumstances created by the 2020 pandemic conditions. Based on our findings and analysis, we prototyped a visualization tool that makes invisible domestic work visible. Our research contribution is a design research methodology to generate insights to enable creation of design interventions for social justice. This was achieved through use of established design research methods, that are conventionally used in face-to-face settings, which were then adapted to exceptional pandemic conditions that required social distancing where communication was remote, and usually virtual.
Dynamic Particle Filter Framework for Robust Object Tracking

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization.
STRIDE-AI: An Approach to Identifying Vulnerabilities of Machine Learning Assets

We propose a security methodology for Machine Learning (ML) pipelines, supporting the definition of key security properties of ML assets, the identification of threats to them as well as the selection, test and verification of security controls.Our proposal is based on STRIDE, a widely used approach to threat modeling originally developed by Microsoft.We adapt STRIDE to the Artificial Intelligence domain by taking a security property-driven approach that also provides guidance in selecting the security controls needed to alleviate the identified threats.Our proposal is illustrated via an industrial case study.
A Hybrid Encryption and Decryption Algorithm using Caesar and Vigenere Cipher

Abstract This paper presents a hybrid encryption and decryption algorithm, which combines the concept of Caesar Cipher and Vigenere Cipher algorithms. This was done by comparing the proposed design with some of the well-known ciphers such as Hill Cipher, Caesar Cipher and Vigenere Cipher through MATLAB simulations. Parameters such as letter frequency and graph behavior were evaluated. Results show that the proposed algorithm performs excellently. It has the most evenly distributed graph among other ciphers being compared in terms of their frequency of the ciphertext. Also, the decryption algorithm was able to achieve 99.99% accuracy.
Reinforced Neighbour Feature Fusion Object Detection with Deep Learning

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, previous works have tried to improve the performance in various object detection necks but have failed to extract features efficiently. To solve the insufficient features of objects, this work introduces some of the most advanced and representative network models based on the Faster R-CNN architecture, such as Libra R-CNN, Grid R-CNN, guided anchoring, and GRoIE. We observed the performance of Neighbour Feature Pyramid Network (NFPN) fusion, ResNet Region of Interest Feature Extraction (ResRoIE) and the Recursive Feature Pyramid (RFP) architecture at different scales of precision when these components were used in place of the corresponding original members in various networks obtained on the MS COCO dataset. Compared to the experimental results after replacing the neck and RoIE parts of these models with our Reinforced Neighbour Feature Fusion (RNFF) model, the average precision (AP) is increased by 3.2 percentage points concerning the performance of the baseline network.
Deep Face Video Inpainting via UV Mapping

This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of any prior knowledge of the face to help retrieve correspondences for the corrupted face. They therefore only achieve sub-optimal results, particularly for faces under large pose and expression variations where face components appear very differently across frames. In this paper, we propose a two-stage deep learning method for face video inpainting. We employ 3DMM as our 3D face prior to transform a face between the image space and the UV (texture) space. In Stage I, we perform face inpainting in the UV space. This helps to largely remove the influence of face poses and expressions and makes the learning task much easier with well aligned face features. We introduce a frame-wise attention module to fully exploit correspondences in neighboring frames to assist the inpainting task. In Stage II, we transform the inpainted face regions back to the image space and perform face video refinement that inpaints any background regions not covered in Stage I and also refines the inpainted face regions. Extensive experiments have been carried out which show our method can significantly outperform methods based merely on 2D information, especially for faces under large pose and expression variations. Project page: https://ywq.github.io/FVIP.
Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, and that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm as base clusterings and then refine the summarized data using a link-based approach. Based on the evaluation metric of NMI (Normalized Mutual Information) that is averaged across different combinations of benchmark datasets and experimental settings, these new models reach the improved level of 0.34, while the best model found in the literature obtains only around the mark of 0.24. Besides, parameter analysis included herein helps to enhance their performance even further, given relations of clustering quality and algorithmic variables specific to the underlying link-based models. Moreover, another significant factor of ensemble size is examined in such a way to justify a tradeoff between complexity and accuracy.
JUWELS Booster – A Supercomputer for Large-Scale AI Research

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the Jülich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligence (AI) research and applications. We detail its system architecture, parallel, distributed model training, and benchmarks indicating its outstanding performance. We exemplify its potential for research application by presenting large-scale AI research highlights from various scientific fields that require such a facility.