Reviewerly/exHarmony huggingface dataset:
Long-lived and multiplexed atom-photon entanglement interface with feed-forward-controlled readouts

Abstract Quantum interfaces (QIs) that generate entanglement between photonic and spin-wave (atomic memory) qubits are basic building block for quantum repeaters. Realizing ensemble-based repeaters in practice requires quantum memory providing long lifetimes and multimode capacity. Significant progress has been achieved on these separate goals. The remaining challenge is to combine the two attributes into a single QI. Here, by establishing spatial multimode, magnetic-field-insensitive and long-wavelength spin-wave storage in laser-cooled atoms inside a phase-passively-stabilized polarization interferometer, we constructed a multiplexed QI that stores up to three long-lived spin-wave qubits. Using a feed-forward-controlled system, we demonstrated that a multiplexed QI gives rise to a 3-fold increase in the atom–photon (photon–photon) entanglement-generation probability compared with single-mode QIs. For our multiplexed QI, the measured Bell parameter is 2.51±0.01 combined with a memory lifetime of up to 1 ms. This work represents a key step forward in realizing fiber-based long-distance quantum communications.
Human Factors in Cybersecurity: A Scoping Review

Humans are often considered to be the weakest link in the cybersecurity chain. However, traditionally the Computer Science (CS) researchers have investigated the technical aspects of cybersecurity, focusing on the encryption and network security mechanisms. The human aspect although very important is often neglected. In this work we carry out a scoping review to investigate the take of the CS community on the human-centric cybersecurity paradigm by considering the top conferences on network and computer security for the past six years. Results show that broadly two types of users are considered: expert and non-expert users. Qualitative techniques dominate the research methodology employed, however, there is a lack of focus on the theoretical aspects. Moreover, the samples have a heavy bias towards the Western community, due to which the results cannot be generalized, and the effect of culture on cybersecurity is a lesser known aspect. Another issue is with respect to the unavailability of standardized security-specific scales that can measure the cybersecurity perception of the users. New insights are obtained and avenues for future research are presented.
Correlation filters with adaptive convolution response fusion for object tracking

Recently, the rich features extracted by deep learning models have been widely used under the correlation filter tracking framework and achieved great success. However, the features in different layers are often combined with fixed weights, which do not consider the different importance of different layers. In this paper, we propose a novel tracking method which can adaptively tune the weights of the convolutional responses obtained by features in different layers. We propose two adaptive weighting strategies, i.e. the cosine weighting and quadratic optimization weighting, adaptively assigning weights to each submodel, and combining multiple view submodels. Moreover, Normalized Peak Value is used to estimate the tracking reliability. Experimental results demonstrate that the proposed adaptive fusion based method can achieve comparable performance to several state-of-the-art approaches on public dataset.
Text Mining Life Cycle for a Spatial Reading of Viet Thanh Nguyen's The Refugees (2017)

Textual analysis is traditionally used by literary critics as a central methodology to interpret creative writings, however this method is significantly affected by human-error, which cause the failing to offer one correct interpretation of the text. As an example, the Vietnamese-American writer Viet Thanh Nguyen’s short story collection The Refugees (2017) has received opposing critical receptions: Whereas some critics applaud the stories for their truthful representations of the two countries, others criticize them for their biased depictions. This study aims to demonstrate how text mining can offer a more objective analysis of the representation of the two main countries in the selected stories. We propose a Big Data Analytics lifecycle that consider two empirical methods. The first method used N-grams, while the second method propose sentiment analysis using lexicon dictionary. The study revealed that text mining is useful in discovering the hidden pattern of textual data and resolving the problem of human error that occurs in performing the analytics manually.
Unconditional Communication-Efficient MPC via Hall’s Marriage Theorem

The best known n party unconditional multiparty computation protocols with an optimal corruption threshold communicates O(n) field elements per gate. This has been the case even in the semi-honest setting despite over a decade of research on communication complexity in this setting. Going to the slightly sub-optimal corruption setting, the work of Damgård, Ishai, and Krøigaard (EUROCRYPT 2010) provided the first protocol for a single circuit achieving communication complexity of $$O(\log |C|)$$ elements per gate. While a number of works have improved upon this result, obtaining a protocol with O(1) field elements per gate has been an open problem. In this work, we construct the first unconditional multi-party computation protocol evaluating a single arithmetic circuit with amortized communication complexity of O(1) elements per gate.
Text classification using improved bidirectional transformer

Abstract Text data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.
Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.
A comprehensive review on resolving ambiguities in natural language processing

Natural language processing is a known technology behind the development of some widely known AI assistants such as: SIRI, Natasha, and Watson. However, NLP is a diverse technology used for numerous purposes. NLP based tools are widely used for disambiguation in requirement engineering which will be the primary focus of this paper. A requirement document is a medium for the user to deliver one's expectations from the software. Hence, an ambiguous requirement document may eventually lead to misconceptions in a software. Various tools are available for disambiguation in RE based on different techniques. In this paper, we analyzed different disambiguation tools in order to compare and evaluate them. In our survey, we noticed that even though some disambiguation tools reflect promising results and can supposedly be relied upon, they fail to completely eliminate the ambiguities. In order to avoid ambiguities, the requirement document has to be written using formal language, which is not preferred by users due to its lack of lucidity and readability. Nevertheless, some of the tools we mentioned in this paper are still under development and in future might become capable of eliminating ambiguities. In this paper, we attempt to analyze some existing research work and present an elaborative review of various disambiguation tools.
A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.
Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction

We introduce a new open-source software library Jet, which uses task-based parallelism to obtain speed-ups in classical tensor-network simulations of quantum circuits. These speed-ups result from i) the increased parallelism introduced by mapping the tensor-network simulation to a task-based framework, ii) a novel method of reusing shared work between tensor-network contraction tasks, and iii) the concurrent contraction of tensor networks on all available hardware. We demonstrate the advantages of our method by benchmarking our code on several Sycamore-53 and Gaussian boson sampling (GBS) supremacy circuits against other simulators. We also provide and compare theoretical performance estimates for tensor-network simulations of Sycamore-53 and GBS supremacy circuits for the first time.
SAKG-BERT: Enabling Language Representation With Knowledge Graphs for Chinese Sentiment Analysis

Sentiment analysis of online reviews is an important task in natural language processing. It has received much attention not only in academia but also in industry. Data have become an important source of competitive intelligence. Various pretraining models such as BERT and ERNIE have made great achievements in the task of natural language processing, but lack domain-specific knowledge. Knowledge graphs can enhance language representation. Furthermore, knowledge graphs have high entity / concept coverage and strong semantic expression ability. We propose a sentiment analysis knowledge graph (SAKG)-BERT model that combines sentiment analysis knowledge and the language representation model BERT. To improve the interpretability of the deep learning algorithm, we construct an SAKG in which triples are injected into sentences as domain knowledge. Our investigation reveals promising results in sentence completion and sentiment analysis tasks.
Fine-Grained Elastic Partitioning for Distributed DNN Towards Mobile Web AR Services in the 5G Era

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach. The common theme is, under the premise that Quality of Service (QoS) is satisfied, to balance multiple interests by orchestrating computations across heterogeneous computing platforms.
Multiorgan segmentation from partially labeled datasets with conditional nnU-Net

Accurate and robust multiorgan abdominal CT segmentation plays a significant role in numerous clinical applications, such as therapy treatment planning and treatment delivery. Almost all existing segmentation networks rely on fully annotated data with strong supervision. However, annotating fully annotated multiorgan data in CT images is both laborious and time-consuming. In comparison, massive partially labeled datasets are usually easily accessible. In this paper, we propose conditional nnU-Net trained on the union of partially labeled datasets for multiorgan segmentation. The deep model employs the state-of-the-art nnU-Net as the backbone and introduces a conditioning strategy by feeding auxiliary information into the decoder architecture as an additional input layer. This model leverages the prior conditional information to identify the organ class at the pixel-wise level and encourages organs’ spatial information recovery. Furthermore, we adopt a deep supervision mechanism to refine the outputs at different scales and apply the combination of Dice loss and Focal loss to optimize the training model. Our proposed method is evaluated on seven publicly available datasets of the liver, pancreas, spleen and kidney, in which promising segmentation performance has been achieved. The proposed conditional nnU-Net breaks down the barriers between nonoverlapping labeled datasets and further alleviates the problem of data hunger in multiorgan segmentation. • We propose a 3D conditional nnU-Net for multiorgan segmentation. • Model trained on the union of partially labeled datasets by a conditioning strategy. • Apply the combination of dice loss and focal loss to optimize the training model. • Proposed method alleviates the problem of data hunger in multiorgan segmentation.
Post-OCR Paragraph Recognition by Graph Convolutional Networks

We propose a new approach for paragraph recognition in document images by spatial graph convolutional networks (GCN) applied on OCR text boxes. Two steps, namely line splitting and line clustering, are performed to extract paragraphs from the lines in OCR results. Each step uses a β-skeleton graph constructed from bounding boxes, where the graph edges provide efficient support for graph convolution operations. With pure layout input features, the GCN model size is 3~4 orders of magnitude smaller compared to RCNN based models, while achieving comparable or better accuracies on PubLayNet and other datasets. Furthermore, the GCN models show good generalization from synthetic training data to real-world images, and good adaptivity for variable document styles.
Recognition of Doctors’ Cursive Handwritten Medical Words by using Bidirectional LSTM and SRP Data Augmentation

Inability to read doctors’ handwritten prescriptions causes 7,000 deaths a year in a developed country like the US. The situation should be worse in developing countries where more doctors use handwriting prescriptions. In Bangladesh, the writings become more indecipherable as they contain both English and Bangla words with Latin abbreviations of medical terms. As a result, patients and pharmacists find them difficult to read and the pharmacists provide wrong medicines. In order to ease the difficulty of reading doctors’ prescriptions, this paper proposes an online handwritten recognition system to predict the doctors’ handwriting and develop a digital prescription. To build this system, the “Handwritten Medical Term Corpus” dataset is introduced which contains 17,431 data samples of 480 words (360 English and 120 Bangla) from 39 Bangladeshi doctors and medical professionals. A bigger sample size can improve the recognition efficiency. A new data augmentation technique SRP (Stroke Rotation and Parallel shift) method is proposed to widen the variety of handwriting styles and increase the sample size. A sequence of line data is extracted from the augmented image dataset of 1,591,100 samples which is fed to a Bidirectional LSTM model. The proposed method has achieved 89.5% accuracy which is 16.1% higher than the recognition accuracy with no data expansion. This technology can reduce medical errors and save medical cost and ensure healthy living.
Exploring AMD GPU Scheduling Details by Experimenting With “Worst Practices”

Graphics processing units (GPUs) have been the target of a significant body of recent real-time research, but research is often hampered by the “black box” nature of GPU hardware and software. Now that one GPU manufacturer, AMD, has embraced an open-source software stack, one may expect an increased amount of real-time research to use AMD GPUs. Reality, however, is more complicated. Without understanding where internal details may differ, researchers have no basis for assuming that observations made using NVIDIA GPUs will continue to hold for AMD GPUs. Additionally, the openness of AMD’s software does not mean that their scheduling behavior is obvious, especially due to sparse, scattered documentation. In this paper, we gather the disparate pieces of documentation into a single coherent source that provides an end-to-end description of how compute work is scheduled on AMD GPUs. In doing so, we start with a concrete demonstration of how incorrect management triggers extreme worst-case behavior in shared AMD GPUs. Subsequently, we explain the internal scheduling rules for AMD GPUs, how they led to the “worst practices,” and how to correctly manage some of the most performance-critical factors in AMD GPU sharing.
Extreme Rotation Estimation using Dense Correlation Volumes

We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap. We observe that, even when images do not overlap, there may be rich hidden cues as to their geometric relationship, such as light source directions, vanishing points, and symmetries present in the scene. We propose a network design that can automatically learn such implicit cues by comparing all pairs of points between the two input images. Our method therefore constructs dense feature correlation volumes and processes these to predict relative 3D rotations. Our predictions are formed over a fine-grained discretization of rotations, bypassing difficulties associated with regressing 3D rotations. We demonstrate our approach on a large variety of extreme RGB image pairs, including indoor and outdoor images captured under different lighting conditions and geographic locations. Our evaluation shows that our model can successfully estimate relative rotations among non-overlapping images without compromising performance over overlapping image pairs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Generation of annotated multimodal ground truth datasets for abdominal medical image registration

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom, therefore the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging (MRI), computed tomography (CT), and cone beam CT (CBCT) images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.