Reviewerly/exHarmony huggingface dataset:
Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices

The readout error on near-term quantum devices is one of the dominant noise factors, which can be mitigated by classical postprocessing called quantum readout error mitigation (QREM). The standard QREM applies the inverse of noise calibration matrix to the outcome probability distribution using exponential computational resources to the number of measured qubits. This becomes infeasible for the current quantum devices with tens of qubits or more. Here we propose two efficient QREM methods finishing in $O(n{s}^{2})$ time for probability distributions of $n$ qubits and $s$ shots, which mainly aim at mitigating sparse probability distributions such that only a few states are dominant. We compare the proposed methods with several recent QREM methods in the following three cases: expectation values of the GHZ state, its fidelities, and the estimation error of maximum likelihood amplitude estimation (MLAE) algorithm with a modified Grover iterator. The two cases of the GHZ state are on real IBM quantum devices, while the third is with numerical simulation. Using the proposed method, the mitigation of the 65-qubit GHZ state takes only a few seconds, and we witness the fidelity of the 29-qubit GHZ state exceeding 0.5. The proposed methods also succeed in reducing the estimation error in the MLAE algorithm, outperforming the results by other QREM methods in general.
Implementation of Digital Chaotic Encryption in THz Wireless Communication

We implement a digital chaos-based encryption scheme in a photonics-aided terahertz radio-over-fiber (ROF) system operating at 340 GHz. The encrypted PS-64QAM-OFDM signal is successfully transmitted over 20 km SSMF and 54 m wireless link.
Global Instance Tracking: Locating Target More Like Humans

Target tracking, the essential ability of the human visual system, has been simulated by computer vision tasks. However, existing trackers perform well in austere experimental environments but fail in challenges like occlusion and fast motion. The massive gap indicates that researches only measure tracking performance rather than intelligence. How to scientifically judge the intelligence level of trackers? Distinct from decision-making problems, lacking three requirements (a challenging task, a fair environment, and a scientific evaluation procedure) makes it strenuous to answer the question. In this article, we first propose the global instance tracking (GIT) task, which is supposed to search an arbitrary user-specified instance in a video without any assumptions about camera or motion consistency, to model the human visual tracking ability. Whereafter, we construct a high-quality and large-scale benchmark VideoCube to create a challenging environment. Finally, we design a scientific evaluation procedure using human capabilities as the baseline to judge tracking intelligence. Additionally, we provide an online platform with toolkit and an updated leaderboard. Although the experimental results indicate a definite gap between trackers and humans, we expect to take a step forward to generate authentic human-like trackers. The database, toolkit, evaluation server, and baseline results are available at http://videocube.aitestunion.com.
Co-VQA : Answering by Interactive Sub Question Sequence

Most existing approaches to Visual Question Answering (VQA) answer questions directly, however, people usually decompose a complex question into a sequence of simple sub questions and finally obtain the answer to the original question after answering the sub question sequence(SQS).By simulating the process, this paper proposes a conversation-based VQA (Co-VQA) framework, which consists of three components: Questioner, Oracle, and Answerer.Questioner raises the sub questions using an extending HRED model, and Oracle answers them one-by-one.An Adaptive Chain Visual Reasoning Model (ACVRM) for Answerer is also proposed, where the question-answer pair is used to update the visual representation sequentially.To perform supervised learning for each model, we introduce a well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2 datasets.Experimental results show that our method achieves state-of-the-art on VQA-CP v2.Further analyses show that SQSs help build direct semantic connections between questions and images, provide question-adaptive variable-length reasoning chains, and with explicit interpretability as well as error traceability.
Self-Paced Co-Training of Graph Neural Networks for Semi-Supervised Node Classification

Graph neural networks (GNNs) have demonstrated great success in many graph data-based applications. The impressive behavior of GNNs typically relies on the availability of a sufficient amount of labeled data for model training. However, in practice, obtaining a large number of annotations is prohibitively labor-intensive and even impossible. Co-training is a popular semi-supervised learning (SSL) paradigm, which trains multiple models based on a common training set while augmenting the limited amount of labeled data used for training each model via the pseudolabeled data generated from the prediction results of other models. Most of the existing co-training works do not control the quality of pseudolabeled data when using them. Therefore, the inaccurate pseudolabels generated by immature models in the early stage of the training process are likely to cause noticeable errors when they are used for augmenting the training data for other models. To address this issue, we propose a self-paced co-training for the GNN (SPC-GNN) framework for semi-supervised node classification. This framework trains multiple GNNs with the same or different structures on different representations of the same training data. Each GNN carries out SSL by using both the originally available labeled data and the augmented pseudolabeled data generated from other GNNs. To control the quality of pseudolabels, a self-paced label augmentation strategy is designed to make the pseudolabels generated at a higher confidence level to be utilized earlier during training such that the negative impact of inaccurate pseudolabels on training data augmentation, and accordingly, the subsequent training process can be mitigated. Finally, each of the trained GNN is evaluated on a validation set, and the best-performing one is chosen as the output. To improve the training effectiveness of the framework, we devise a pretraining followed by a two-step optimization scheme to train GNNs. Experimental results on the node classification task demonstrate that the proposed framework achieves significant improvement over the state-of-the-art SSL methods.
From Human Pose Similarity Metric to 3D Human Pose Estimator: Temporal Propagating LSTM Networks

Predicting a 3D pose directly from a monocular image is a challenging problem. Most pose estimation methods proposed in recent years have shown ‘quantitatively’ good results (below <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 50 <i>mm</i> ). However, these methods remain ‘perceptually’ flawed because their performance is only measured via a simple distance metric. Although this fact is well understood, the reliance on ‘quantitative’ information implies that the development of 3D pose estimation methods has been slowed down. To address this issue, we first propose a perceptual <i>Pose SIMilarity</i> (PSIM) metric, by assuming that human perception (HP) is highly adapted to extracting structural information from a given signal. Second, we present a perceptually robust 3D pose estimation framework: <i>Temporal Propagating Long Short-Term Memory networks</i> (TP-LSTMs). Toward this, we analyze the information-theory-based spatio-temporal posture correlations, including joint interdependency, temporal consistency, and HP. The experimental results clearly show that the proposed PSIM metric achieves a superior correlation with users’ subjective opinions than conventional pose metrics. Furthermore, we demonstrate the significant quantitative and perceptual performance improvements of TP-LSTMs compared to existing state-of-the-art methods.
Device fingerprinting using deep convolutional neural networks

Device fingerprinting is a problem of identifying a network device using network traffic data to secure against cyber-attacks. Automated device classification from a large set of network traffic features space is challenging for the devices connected in the cyberspace. In this work, the idea is to define a device-specific unique fingerprint by analysing solely inter-arrival time of packets as a feature to identify a device. Neural networks are the universal function approximation which learn abstract, highlevel, nonlinear representation of training data. Deep convolution neural network is used on images of inter-arrival time signature for device fingerprinting of 58 non-IoT devices of 5-11 types. To evaluate the performance, we compared ResNet-50 layer and basic CNN-5 layer architectures. We observed that device type identification models perform better than device identification. We also found that when deep learning models are attacked over device signature, the models identify the change in signature, and classify the device in the wrong class thereby the classification performance of the models degrades. The performance of the models to detect the attacks are significantly different from each other though both models indicate the system under attack.
A Tensor-Based Catheter and Wire Detection and Tracking Framework and Its Clinical Applications

Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task.In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed.The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved.A novel and fully automatic detection framework and its clinical applications are developed.The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.
An Extended DBSCAN Clustering Algorithm

Finding clusters of different densities is a challenging task.DBSCAN "Density-Based Spatial Clustering of Applications with Noise" method has trouble discovering clusters of various densities since it uses a fixed radius.This article proposes an extended DBSCAN for finding clusters of different densities.The proposed method uses a dynamic radius and assigns a regional density value for each object, then counts the objects of similar density within the radius.If the neighborhood size ≥ MinPts, then the object is a core, and a cluster can grow from it, otherwise, the object is assigned noise temporarily.Two objects are similar in local density if their similarity ≥ threshold.The proposed method can discover clusters of any density from the data effectively.The method requires three parameters; MinPts, Eps (distance to the k th neighbor), and similarity threshold.The practical results show the superior ability of the suggested method to detect clusters of different densities even with no discernible separations between them.
Post-Quantum Era in V2X Security: Convergence of Orchestration and Parallel Computation

Along with the potential emergence of quantum computing, safety and security of new and complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous, and secure operation of these scenarios, quantum-resistant security algorithms (QRSAs) that enable secure connectivity must be integrated into the network management and orchestration systems of mobile networks. This article explores a road-map study of post-quantum era convergence with cellular connectivity using the Service &#x0026; Computation Orchestrator (SCO) framework for enhanced data security in radio access and backhaul transmission with a particular focus on vehicle-to-everything services. Using NTRU as a QSRA, we show that the parallelization performance of the Toom-Cook and Karatsuba computation methods can vary based on different CPU load conditions through extensive simulations, and that the SCO framework can facilitate the selection of the most efficient computation for a given QRSA. Finally, we discuss the evaluation results, identify the current standardization efforts, and present possible directions for the coexistence of post-quantum and mobile network connectivity through an SCO framework that leverages parallel computing.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has received increasing attention in recent years. In contrast to conventional ranking models that score each item individually, re-ranking aims to explicitly model the mutual influences among items to further refine the ordering of items given an initial ranking list. In this paper, we present a personalized re-ranking model (dubbed PEAR) based on contextualized transformer. PEAR makes several major improvements over the existing methods. Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list. In addition to item-level ranking score prediction, we also augment the training of PEAR with a list-level classification task to assess users’ satisfaction on the whole ranking list. Experimental results on both public and production datasets have shown the superior effectiveness of PEAR compared to the previous re-ranking models.
Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation

Knowledge graph embedding (KGE) is effectively exploited in providing precise and accurate recommendations from many perspectives in different application scenarios. However, such methods that utilize entire embedded Knowledge Graph (KG) without applying information-relevance regulatory constraints fail to stop the noise penetration into the underlying information. Moreover, higher computational time complexity is a CPU overhead in KG-enhanced systems and applications. The occurrence of these limitations significantly degrade the recommendation performance. Therefore, to cope with these challenges we proposed novel KGEE (Knowledge Graph Embedding Enhancement) approach of Hashing-based Semantic-relevance Attributed Graph-embedding Enhancement (H-SAGE) to model semantically-relevant higher-order entities and relations into the unique Meta-paths. For this purpose, we introduced Node Relevance-based Guided-walk (NRG) modeling technique. Further, to deal with the computational time-complexity, we converted the relevant information to the Hash-codes and proposed Deep-Probabilistic (dProb) technique to place hash-codes in the relevant hash-buckets. Again, we used dProb to generate guided function-calls to maximize the possibility of Hash-Hits in the hash-buckets. In case of Hash-Miss, we applied Locality Sensitive (LS) hashing to retrieve the required information. We performed experiments on three benchmark datasets and compared the empirical as well as the computational performance of H-SAGE with the baseline approaches. The achieved results and comparisons demonstrate that the proposed approach has outperformed the-state-of-the-art methods in the mentioned facets of evaluation.
Chinese Evolution in Recent 150 Years: A Diachronic Study of Word Frequency in The Gospel of Mark

This study quantitatively investigated word frequencies in parallel Chinese texts at three different periods, namely, Classical Chinese (1855), Beijing Mandarin (1908) and Modern Mandarin (2010), attempting to shed some light on the evolution of the Chinese language over the last 150 years. With the evolution of Chinese: both the types and tokens increased diachronically, and sharp changes can be found in the high-frequency function words, vocabulary richness, and the distribution of word classes. The syntactic structure has also become increasingly complex. In addition, although the length of words (measured in character) has increased diachronically, disyllabic words have not always been preponderant in Chinese after the opium war of 1840–1842. The results show that the complication is a crucial trend in the evolution process of Chinese.
Artificial intelligence and machine learning in armed conflict: A human-centred approach

Note: This is an edited version of a paper published by the ICRC in June 2019.
Conclusion stability for natural language based mining of design discussions

Developer discussions range from in-person hallway chats to comment chains on bug reports. Being able to identify discussions that touch on software design would be helpful in documentation and refactoring software. Design mining is the application of machine learning techniques to correctly label a given discussion artifact, such as a pull request, as pertaining (or not) to design. In this paper we demonstrate a simple example of how design mining works. We then show how conclusion stability is poor on different artifact types and different projects. We show two techniques—augmentation and context specificity—that greatly improve the conclusion stability and cross-project relevance of design mining. Our new approach achieves AUC of 0.88 on within dataset classification and 0.80 on the cross-dataset classification task.
Decision Making on Teachers’ adaptation to Cybergogy in Saturated Interval- valued Refined Neutrosophic overset /underset /offset Environment

Neutrosophic overset, neutrosophic underset and neutrosophic offset introduced by Smarandache are the special kinds of neutrosophic sets with values beyond the range [0,1] and these sets are pragmatic in nature as it represents the real life situations. This paper introduces the concept of saturated refined neutrosophic sets and extends the same to the special kinds of neutrosophic sets. The proposed concept is applied in decision making on Teacher’s adaptation to cybergogy. The decision making environment is characterized by different types of teachers, online teaching skills and various training methods. Fuzzy relation is used to match the most suitable method to the different kinds of teachers with the intervention of saturated interval valued neutrosophic refined oversets, offsets and undersets. The results obtained by applying the notion of saturated refined sets using various distance measures represent the effect of training methods on teacher’s adaptation to learner-centred teaching methods, which certainly give space to gain many insights on the relationship between quality of training and teacher’s adaptation rate. The proposed concept has wide scope and few limitations.
Predicting new composition relations between web services via link analysis

With the wide application of service-oriented architecture (SOA) and service-oriented computing (SOC), the past decade has witnessed a rapid growth of the number of web services on the internet. Against this background, combining different web services to create new applications has attracted great interest from developers. However, according to latest statistics, only a small number of popular services are frequently used by developers and the use rates of most web services are rather low. To help service users discover appropriate web services and promote service compositions has thus become a significant need. In the following paper, we propose a link-based approach to predict new composition relations between web services. The approach is based on exploration of known composition relations and similarity relations among web services. To measure the composition or similarity degrees, several link-based methods are exploited, and two reasonable heuristic rules for integrating the existing composition and similarity relations for service composition prediction are developed. Case studies and experiments based on real web service datasets validated the proposed approach.
Microcalcifications segmentation from mammograms for breast cancer detection

The presence of microcalcifications (MCs) in X-ray mammograms provides an important early sign of women breast cancer. However, their detection still remains very complex due to the diversity in shape, size, their distributions and to the low contrast between the cancerous areas and surrounding bright structures in mammograms. This paper presents an effective approach based on mathematical morphology for detection of MCs in digitised mammograms. The developed approach performs an initial step in order to extract the breast area and removing unwanted artefacts out of the mammogram. Subsequently, an enhancement process is applied to improve appearance and increase the contrast of images and to eliminate noise. Once the breast region has been found, a segmentation phase through morphological watershed is performed in order to detect MCs. The performance of our approach is evaluated using a total of 22 mammograms extracted from the MIAS mammographic database, showing the presence of MCs. The obtained results were compared with manual detection, marked by an expert mammographic radiologist. These results show that the system is very effective, especially in terms of sensitivity.
Improving Availability of Vertical Federated Learning: Relaxing Inference on Non-overlapping Data

Vertical Federated Learning (VFL) enables multiple parties to collaboratively train a machine learning model over vertically distributed datasets without data privacy leakage. However, there is a limitation of the current VFL solutions: current VFL models fail to conduct inference on non-overlapping samples during inference. This limitation seriously damages the VFL model’s availability because, in practice, overlapping samples may only take up a small portion of the whole data at each party which means a large part of inference tasks will fail. In this article, we propose a novel VFL framework which enables federated inference on non-overlapping data. Our framework regards the distributed features as privileged information which is available in the training period but disappears during inference. We distill the knowledge of such privileged features and transfer them to the parties’ local model which only processes local features. Furthermore, we adopt Oblivious Transfer (OT) to preserve data ID privacy during training and inference. Empirically, we evaluate the model on the real-world dataset collected from Criteo and Taobao. Besides, we also provide a security analysis of the proposed framework.