Reviewerly/exHarmony huggingface dataset:
Hacking Quantum Key Distribution via Injection Locking

Unconditionally secure communication, which has been pursued for thousands of years hasn't, however, yet been reached due to a continuous competition between encryption and hacking. Quantum key distribution (QKD), harnessing the quantum mechanical nature of superposition and noncloning, may promise unconditional security by incorporating the one-time pad algorithm rigorously proved by Claude Shannon. Massive efforts have been made in building practical and commercial QKD systems; in particular, decoy states have been employed to detect photon-number splitting attacks against a single-photon source loophole, and measurement-device-independent (MDI) QKD has further closed all loopholes on the detection side, which leads to a seemingly real-life application. Here, we propose and experimentally demonstrate a MDI-QKD hacking strategy on the trusted-source assumption by using an injection-locking technique. An eaves-dropper (Eve) injects near off-resonance photons with randomly chosen polarization into a sender's laser, where injection locking in a shifted frequency can happen only when Eve's choice matches the sender's state. By setting a shifted window and switching the frequency of photons back afterwards, Eve in principle can obtain all the keys without terminating the real-time QKD. We observe the dynamics of a semiconductor laser with injected photons, and obtain a hacking success rate reaching $60.0\mathrm{%}$ of raw keys. Our results suggest that the spear-and-shield competition of unconditional security may continue until all potential loopholes are discovered and closed ultimately.
Speech feature selection and emotion recognition based on weighted binary cuckoo search

In this paper, a hybrid system is proposed for speech emotion recognition (SER). The algorithm in this paper adopts a two-stage design concept. In the first stage, we use the ensemble learning model random forest algorithm to obtain the importance of each feature. We use Emo-DB for experimental comparison and find that the combination of the logistic regression algorithm and the WBCS algorithm achieves best results. Cross-training method is used to ensure the features adapt to various situations. Under 100 training sets, the sentiment classification results are satisfactory. The proposed method is more accurate than state-of-the-art intelligent optimization dimensionality reduction algorithms.
DAAC: Digital Asset Access Control in a Unified Blockchain Based E-Health System

The use of the Internet of Things and modern technologies has boosted the expansion of e-health solutions significantly and allowed access to better health services and remote monitoring of patients. Every service provider usually implements its information system to manage and access patient data for its unique purpose. Hence, the interoperability among independent e-health service providers is still a major challenge. From the structure of stored data to its large volume, the design of each such big data system varies, hence the cooperation among different e-health systems is almost impossible. In addition to this, the security and privacy of patient information is a challenging task. Building a unified solution for all creates significant business and economic issues. In this article, we present a solution to migrate existing e-health systems to a unified Blockchain-based model, where access to large scale medical data of patients can be achieved seamlessly by any service provider. A core blockchain network connects individual & independent e-health systems without requiring them to modify their internal processes. Access to patient data in the form of digital assets stored in off-chain storage is controlled through patient-centric channels and policy transactions. Through emulation, we show that the proposed solution can interconnect different e-health systems efficiently.
Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records

Abstract Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption. In this study, we employed a deep learning model for the natural language process to extract keywords from pathology reports and presented the supervised keyword extraction algorithm. We considered three types of pathological keywords, namely specimen, procedure, and pathology types. We compared the performance of the present algorithm with the conventional keyword extraction methods on the 3115 pathology reports that were manually labeled by professional pathologists. Additionally, we applied the present algorithm to 36,014 unlabeled pathology reports and analysed the extracted keywords with biomedical vocabulary sets. The results demonstrated the suitability of our model for practical application in extracting important data from pathology reports.
Adaptive multi-view graph convolutional networks for skeleton-based action recognition

Skeleton based human action recognition has attracted more and more attentions recently thanks to the accessibility of depth sensors and the development of pose estimation techniques. Conventional approaches such as convolutional neural networks usually model skeletons with grid-shaped representations, which cannot explicitly explore the dependency between two correlated joints. In this paper, we treat the skeleton as a single graph with joints as nodes and bones as edges. Based on the skeleton graph, the improved graph convolutional network called adaptive multi-view graph convolutional networks(AMV-GCNs) is proposed to deal with skeleton based action recognition. We firstly construct a novel skeleton graph and two kinds of graph nodes are defined to model the spatial configuration and temporal dynamics respectively. Then the generated graphs along with feature vectors on graph nodes are fed into AMV-GCNs. In AMV-GCNs, an adaptive view transformation module is designed to reduce the impact of view diversity. Proposed module can automatically determine suitable viewpoints and transform skeletons to new representations under those viewpoints for better recognition. Further, we employ multiple GCNs based streams to utilize and learn action information from different viewpoints. Finally, the classification scores from multiple streams are fused to provide the recognition result. Extensive experimental evaluations on four challenging datasets, NTU RGB+D 60, NTU RGB+D 120, Northwestern-UCLA and UTD-MHAD, demonstrate the superiority of our proposed network.
Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference

Multi-task networks are commonly utilized to alleviate the need for a large number of highly specialized single-task networks. However, two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning). Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference). In this paper, we show that both can be achieved simply by reparameterizing the convolutions of standard neural network architectures into a non-trainable shared part (filter bank) and task-specific parts (modulators), where each modulator has a fraction of the filter bank parameters. Thus, our reparameterization enables the model to learn new tasks without adversely affecting the performance of existing ones. The results of our ablation study attest the efficacy of the proposed reparameterization. Moreover, our method achieves state-of-the-art on two challenging multi-task learning benchmarks, PASCAL-Context and NYUD, and also demonstrates superior incremental learning capability as compared to its close competitors. The code and models are made publicly available ( https://github.com/menelaoskanakis/RCM ).
Significance and Possibility of VR Technology Embedded in the Teaching of Ideological and Political Theory Course in Colleges and Universities

The main function of Ideological and political theory course in Colleges and universities is to carry out ideological and political education and moral education to help students establish a good world outlook, outlook on life and values. The main purpose of this paper is to combine VR technology with ideological and political theory course in Colleges and universities. The data of this experiment is from our school to select economics class 1, economics class 2, financial management class 1, and financial management class 2. The average score of Ideological and political theory course in the final examination of last semester of the four classes is close to and close to each other, and 50 people are selected as samples. They were randomly divided into two groups: VR teaching group (economics class 1: A1, financial management class 1: B1) and general teaching group (economics class 2: A2, financial management class 2: B2). VR teaching group lasted for one month, VR ideological and political theory class classroom teaching, ordinary teaching group or traditional teaching. One month later, the four groups of students were tested for ideological and political theory. Before and after the experiment, group A1 and group B1 were investigated: the learning attitude and classroom state of Ideological and political theory course. The experimental results show that: after VR classroom learning, the proportion of A1 and B2 groups who like to think about political theory class increased by 8.2% and 8.14%. The proportion of A1 and B1 aversion to ideological and political theory courses decreased to 1.2% and 3.54%. After VR classroom teaching, the average score of Ideological and political theory course in A1 and B1 groups was 13.81 and 9.68 points higher than that of A2 and B2 groups, and the scores were increased by 19.7% and 13.5%. VR classroom teaching can stimulate students' interest in learning; promote the understanding of knowledge, as well as the establishment of emotional attitude and values.
DeepHandMesh: A Weakly-Supervised Deep Encoder-Decoder Framework for High-Fidelity Hand Mesh Modeling

Human hands play a central role in interacting with other people and objects. For realistic replication of such hand motions, high-fidelity hand meshes have to be reconstructed. In this study, we firstly propose DeepHandMesh, a weakly-supervised deep encoder-decoder framework for high-fidelity hand mesh modeling. We design our system to be trained in an end-to-end and weakly-supervised manner; therefore, it does not require groundtruth meshes. Instead, it relies on weaker supervisions such as 3D joint coordinates and multi-view depth maps, which are easier to get than groundtruth meshes and do not dependent on the mesh topology. Although the proposed DeepHandMesh is trained in a weakly-supervised way, it provides significantly more realistic hand mesh than previous fully-supervised hand models. Our newly introduced penetration avoidance loss further improves results by replicating physical interaction between hand parts. Finally, we demonstrate that our system can also be applied successfully to the 3D hand mesh estimation from general images. Our hand model, dataset, and codes are publicly available( https://mks0601.github.io/DeepHandMesh/ ).
Explaining Image Classifiers Using Statistical Fault Localization

The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for “Explainable AI”. In this paper, we show that statistical fault localization (SFL) techniques from software engineering deliver high quality explanations of the outputs of DNNs, where we define an explanation as a minimal subset of features sufficient for making the same decision as for the original input. We present an algorithm and a tool called DeepCover, which synthesizes a ranking of the features of the inputs using SFL and constructs explanations for the decisions of the DNN based on this ranking. We compare explanations produced by DeepCover with those of the state-of-the-art tools gradcam, lime, shap, rise and extremal and show that explanations generated by DeepCover are consistently better across a broad set of experiments. On a benchmark set with known ground truth, DeepCover achieves $$76.7\%$$ accuracy, which is $$6\%$$ better than the second best extremal.
Accuracy assessment of fringe projection profilometry and digital image correlation techniques for three-dimensional shape measurements

Abstract With ever-increasing demand for three-dimensional (3D) imaging and shape measurements in a variety of fields, measurement accuracy has become of vital importance to numerous scientific and engineering applications. This paper presents an experimental investigation into the accuracy comparison of two prevalent 3D imaging and shape measurement methods: fringe projection profilometry (FPP) and 3D digital image correlation (3D-DIC) techniques. A detailed description of their principles reveals their inherent similarities and fundamental differences. A measurement system composed of both techniques is employed in the study, and a test target with speckle checkerboard patterns on its surface is adopted to allow simultaneous FPP and 3D-DIC measurements. The evaluation puts emphasis on how the geometric angles between key hardware components affect the 3D measurement accuracy. Experiments show that the depth and height measurements of both techniques can reach sub-micron accuracy, and the relative accuracy of the 3D shape or position measurements can reach 1/600 000.
DELTAS: Depth Estimation by Learning Triangulation and Densification of Sparse Points

Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems. However, this accuracy comes at a high computational cost which impedes practical adoption. Distinct from cost volume approaches, we propose an efficient depth estimation approach by first (a) detecting and evaluating descriptors for interest points, then (b) learning to match and triangulate a small set of interest points, and finally (c) densifying this sparse set of 3D points using CNNs. An end-to-end network efficiently performs all three steps within a deep learning framework and trained with intermediate 2D image and 3D geometric supervision, along with depth supervision. Crucially, our first step complements pose estimation using interest point detection and descriptor learning. We demonstrate state-of-the-art results on depth estimation with lower compute for different scene lengths. Furthermore, our method generalizes to newer environments and the descriptors output by our network compare favorably to strong baselines.
Real-time distance-based outlier detection in data streams

Real-time outlier detection in data streams has drawn much attention recently as many applications need to be able to detect abnormal behaviors as soon as they occur. The arrival and departure of streaming data on edge devices impose new challenges to process the data quickly in real-time due to memory and CPU limitations of these devices. Existing methods are slow and not memory efficient as they mostly focus on quick detection of inliers and pay less attention to expediting neighbor searches for outlier candidates. In this study, we propose a new algorithm, CPOD, to improve the efficiency of outlier detections while reducing its memory requirements. CPOD uses a unique data structure called "core point" with multi-distance indexing to both quickly identify inliers and reduce neighbor search spaces for outlier candidates. We show that with six real-world and one synthetic dataset, CPOD is, on average, 10, 19, and 73 times faster than M_MCOD, NETS, and MCOD, respectively, while consuming low memory.
Multi-level similarity learning for image-text retrieval

Image-text retrieval task has been a popular research topic and attracts a growing interest due to it bridges computer vision and natural language processing communities and involves two different modalities. Although a lot of methods have made a great progress in image-text task, it remains challenging because of the difficulty to learn the correspondence between two heterogeneous modalities. In this paper, we propose a multi-level representation learning for image-text retrieval task, which utilizes semantic-level, structural-level and contextual information to improve the quality of visual and textual representation. To utilize semantic-level information, we firstly extract the nouns, adjectives and number with high frequency as the semantic labels and adopt multi-label convolutional neural network framework to encode the semantic-level information. To explore the structure-level information of image-text pair, we firstly construct two graphs to encode the visual and textual information with respect to the corresponding modality and then, we apply graph matching with triplet loss to reduce the cross-modality discrepancy. To further improve the retrieval results, we utilize the contextual-level information from two modalities to refine the rank list and enhance the retrieval quality. Extensive experiments on Flickr30k and MSCOCO, which are two commonly datasets for image-text retrieval, have demonstrated the superiority of our proposed method.
Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine

Extreme learning machine (ELM) is an effective classification and prediction learning algorithm based on feedforward neural network (FNN). This paper presents the Phasmatodea (stick insect) population evolution algorithm (PPE), which is different from other algorithms, in which each solution represents a population and has two attributes: quantity and growth rate. Combining the concept of similar evolution and the model of population competition, it is a new local search method. The algorithm is compared with the other algorithms on benchmark functions and engineering problems. Then use it to enhance a variant of the ELM model. The results show that the proposed algorithm has a certain competitiveness.
Investigating the causality between financial inclusion, financial development and sustainable development in <scp>Sub‐Saharan</scp> Africa economies: The mediating role of foreign direct investment

In developing economies, the role of the financial sector and foreign capital in the stimulation of sustainable production practices has not been very clear cut. In a bid to obtain a much clearer empirical perspective, the present study investigates the causal relationship between financial development, financial inclusion, foreign direct investment (FDI) and sustainable development in a panel of 33 Sub‐Saharan African (SSA) economies within the 2004–2018 study periods. Panel cointegration tests uncover the presence of a long‐run relationship among the variables in the model. Prior to determining the direction of causality, panel estimation procedures show the magnitude and signs of the long run coefficients. Panel Granger causality tests uncover bidirectional causality between financial inclusion and FDI as well as between financial development and FDI. Also uncovered is unidirectional causality from FDI towards sustainable development and resource rents. This study suggests that the policymakers in SSA should optimize the level of financial development which requires a vigorous improvement so as to ensure higher potential benefits for the sustainability of SSA region through financial sector.
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search

With the flourish of differentiable neural architecture search (NAS), automatically searching latency-constrained architectures gives a new perspective to reduce human labor and expertise. However, the searched architectures are usually suboptimal in accuracy and may have large jitters around the target latency. In this paper, we rethink three freedoms of differentiable NAS, i.e. operation-level, depth-level and width-level, and propose a novel method, named Three-Freedom NAS (TF-NAS), to achieve both good classification accuracy and precise latency constraint. For the operation-level, we present a bi-sampling search algorithm to moderate the operation collapse. For the depth-level, we introduce a sink-connecting search space to ensure the mutual exclusion between skip and other candidate operations, as well as eliminate the architecture redundancy. For the width-level, we propose an elasticity-scaling strategy that achieves precise latency constraint in a progressively fine-grained manner. Experiments on ImageNet demonstrate the effectiveness of TF-NAS. Particularly, our searched TF-NAS-A obtains 76.9% top-1 accuracy, achieving state-of-the-art results with less latency. Code is available at https://github.com/AberHu/TF-NAS .
Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm

With the unprecedented development in the internet technology, the information overload issues have become more and more complex, resulting in users being unable to obtain the target information accurately and effectively in selecting the required information from a large pool of surfed data. In view of this a recommendation system can be used to predict the user's selection probability for different potential objects as an important tool, which can help to solve the information overload issues. So far, many personalized recommendation algorithms based on bipartite graphS have been proposed, most of which are based on the similarity degree among users or items, such as collaborative filtering (CF), mass diffusion (MD) and heat conduction (HC). Among many recommendation algorithms, the performances of algorithms are varied. MD algorithm has high recommendation accuracy but poor diversity, while HC algorithm has good diversity but low accuracy. In order to solve the dilemma in accuracy and diversity, some hybrid recommendation algorithm have been proposed. This paper has mainly focused on the hybrid recommendation algorithm HHM, and pointed out its shortcomings. Based on the reconsideration of the effect of item popularity in the recommendation process, an improved hybrid recommendation algorithm using dual parameter called IHM was proposed. The particle swarm optimization (PSO) algorithm was applied to the parameter optimal of the hybrid recommendation algorithm to obtain the parameters of the algorithm. Experiments on 3 real datasets indicated that the IHM algorithm is better than HHM algorithms in terms of the recommendation accuracy, diversity and novelty. Meanwhile, the IHM algorithm can also improve the recommendation for items with lower popularity and solve the cold start problem.