Reviewerly/exHarmony huggingface dataset:
Could the Quantum Internet Be Comprised of Molecular Spins with Tunable Optical Interfaces?

One goal of the second quantum revolution is developing an approach to transmit coherent quantum information across the world, forming the basis of the quantum Internet. Achieving this lofty goal will require scalable and tunable materials that allow for cross-platform interconnections. Solution-processable molecular systems designed from the ground up are poised to introduce the necessary tunability to meet these needs. In this Perspective, we describe the components of a quantum network, outline the criteria to create molecular systems suitable for networking applications, and discuss the pathway to generate spin-photon entanglement, a first step toward remote entanglement between molecular spins in a network. We also describe some initial steps to optimize molecular spins for potential integration into quantum networks as readily deployable qubits in network nodes. Throughout this Perspective, we highlight the tremendous potential of molecular systems for the quantum Internet and illustrate openings for chemists in this nascent field.
Serval Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems

This article introduces a new metaheuristic algorithm called the Serval Optimization Algorithm (SOA), which imitates the natural behavior of serval in nature. The fundamental inspiration of SOA is the serval's hunting strategy, which attacks the selected prey and then hunts the prey in a chasing process. The steps of SOA implementation in two phases of exploration and exploitation are mathematically modeled. The capability of SOA in solving optimization problems is challenged in the optimization of thirty-nine standard benchmark functions from the CEC 2017 test suite and CEC 2019 test suite. The proposed SOA approach is compared with the performance of twelve well-known metaheuristic algorithms to evaluate further. The optimization results show that the proposed SOA approach, due to the appropriate balancing exploration and exploitation, is provided better solutions for most of the mentioned benchmark functions and has superior performance compared to competing algorithms. SOA implementation on the CEC 2011 test suite and four engineering design challenges shows the high efficiency of the proposed approach in handling real-world optimization applications.
Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual dataset and benchmark experiments

The spread of Hate Speech on online platforms is a severe issue for societies and requires the identification of offensive content by platforms. Research has modeled Hate Speech recognition as a text classification problem that predicts the class of a message based on the text of the message only. However, context plays a huge role in communication. In particular, for short messages, the text of the preceding tweets can completely change the interpretation of a message within a discourse. This work extends previous efforts to classify Hate Speech by considering the current and previous tweets jointly. In particular, we introduce a clearly defined way of extracting context. We present the development of the first dataset for conversational-based Hate Speech classification with an approach for collecting context from long conversations for code-mixed Hindi (ICHCL dataset). Overall, our benchmark experiments show that the inclusion of context can improve classification performance over a baseline. Furthermore, we develop a novel processing pipeline for processing the context. The best-performing pipeline uses a fine-tuned SentBERT paired with an LSTM as a classifier. This pipeline achieves a macro F1 score of 0.892 on the ICHCL test dataset. Another KNN, SentBERT, and ABC weighting-based pipeline yields an F1 Macro of 0.807, which gives the best results among traditional classifiers. So even a KNN model gives better results with an optimized BERT than a vanilla BERT model.
FP-DARTS: Fast parallel differentiable neural architecture search for image classification

Neural Architecture Search (NAS) has made remarkable progress in automatic machine learning. However, it still suffers massive computing overheads limiting its wide applications. In this paper, we present an efficient search method, Fast Parallel Differential Neural Architecture Search (FP-DARTS). The proposed method is carefully designed from three levels to construct and train the super-network. Firstly, at the operation-level, to reduce the computational burden, different from the standard DARTS search space (8 operations), we decompose the operation set into two non-overlapping operator sub-sets (4 operations for each). Adopting these two reduced search spaces, two over-parameterized sub-networks are constructed. Secondly, at the channel-level, the partially-connected strategy is adopted, where each sub-network only adopts partial channels. Then these two sub-networks construct a two-parallel-path super-network by addition. Thirdly, at the training-level, the binary gate is introduced to control whether a path participates in the super-network training. It may suffer an unfair issue when using softmax to select the best input for intermediate nodes across two operator sub-sets. To tackle this problem, the sigmoid function is introduced, which measures the performance of operations without compression. Extensive experiments demonstrate the effectiveness of the proposed algorithm. Specifically, FP-DARTS achieves 2.50% test error with only 0.08 GPU-days on CIFAR10, and a state-of-the-art top-1 error rate of 23.7% on ImageNet using only 2.44 GPU-days for search.
Central bank digital currencies and the international payment system: The demise of the US dollar?

The international monetary system is marked by a hierarchical relationship between currencies, where the US dollar is widely used. Recently, central banks have started to launch Central Bank Digital Currencies (CBDCs), which, in contrast to cryptocurrencies, are issued by monetary authorities. The purpose of this paper is (i) to analyse and explain domestic retail CBDCs in detail, and (ii) to assess whether the creation of CBDCs poses a threat to the US dollar as the key currency of the international monetary and financial system. It will be argued that, despite the innovations a CBDC may bring, the role of the US dollar will not be affected by the introduction of multiple CBDCs (mCBDCs) alone. Although mCBDC arrangements might decentralise the international payment system, the underlying structures supporting today's unipolar system would not automatically change. It is crucial that central banks work together to establish an alternative international monetary system.
Jointly modeling intra- and inter-session dependencies with graph neural networks for session-based recommendations

Recently, graph neural networks (GNNs) have achieved promising results in session-based recommendation. Existing methods typically construct a local session graph and a global session graph to explore complex item transition patterns. However, studies have seldom investigated the repeat consumption phenomenon in a local graph. In addition, it is challenging to retrieve relevant adjacent nodes from the whole training set owing to computational complexity and space constraints. In this study, we use a GNN to jointly model intra- and inter-session item dependencies for session-based recommendations. We construct a repeat-aware local session graph to encode the intra-item dependencies and generate the session representation with positional awareness. Then, we use sessions from the current mini-batch instead of the whole training set to construct a global graph, which we refer to as the session-level global graph. Next, we aggregate the K-nearest neighbors to generate the final session representation, which enables easy and efficient neighbor searching. Extensive experiments on three real-world recommendation datasets demonstrate that RN-GNN outperforms state-of-the-art methods.
AMS-PAN: Breast ultrasound image segmentation model combining attention mechanism and multi-scale features

Breast ultrasound medical images are characterized by poor imaging quality and irregular target edges. During the diagnosis process, it is difficult for physicians to segment tumors manually, and the segmentation accuracy required for diagnosis is high, so there is an urgent need for an automated method to improve the segmentation accuracy as a technical tool to assist diagnosis. This study designed an improved Pyramid Attention Network combining Attention mechanism and Multi-Scale features (AMS-PAN) for breast ultrasound image segmentation. On the encoding side, the model adopts the depthwise separable convolution strategy to achieve a multi-scale receptive field with cumulative small-size convolution, which performs multi-dimensional feature extraction and forms a feature pyramid. The model uses Global Attention Upsample (GAU) feature fusion on the decoding side. In order to further process the fused feature information, the proposed method uses a Spatial and Channel Attention (SCA) module to shift the model’s segmentation focus to the edge texture information. The good segmentation performance of our method is verified through experiments on BUSI and OASBUD. All the designed parts have contributed to the segmentation performance in practical applications. Compared with the traditional non-deep learning methods and the current mainstream deep learning methods, the improvement of the model in Dice and IoU metrics is pronounced. AMS-PAN has high computational efficiency, and its good performance has been proven to play a role in ultrasound detection tasks of breast tumors for physicians to specific auxiliary diagnostic roles to guide the subsequent diagnosis and treatment services for patients.
Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm

Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.
Label-attention transformer with geometrically coherent objects for image captioning

Encoder-decoder-based image captioning techniques are generally utilized to describe meaningful information present in an image. In this work, we investigate two unexplored ideas for image captioning using the transformer: 1) an object-focused label attention module (LAM), and 2) a geometrically coherent proposal (GCP) module that focuses on the scale and position of objects to benefit the transformer model by attaining better image perception. These modules demonstrate the enforcement of objects’ relevance in the surrounding environment. Furthermore, they explore the effectiveness of learning an explicit association between vision and language constructs. LAM and GCP tolerate the variation in objects’ class and its association with labels in multi-label classification. The proposed framework, label-attention transformer with geometrically coherent objects (LATGeO), acquires proposals of geometrically coherent objects using a deep neural network (DNN) and generates captions by investigating their relationships using LAM. The module LAM associates the extracted objects classes to the available dictionary using self-attention layers. Object coherence is acquired in the GCP module using the localized ratio of the proposals’ geometrical features. In this study, experimentation results are performed on MSCOCO dataset. The evaluation of LATGeO on MSCOCO advocates that objects’ relevance in surroundings and their visual features binding with geometrically localized ratios and associated labels generate improved and meaningful captions.
AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT

Impairment and a substantial decline in the mobility, independence, and quality of life of an elderly person. In this regard, the current work suggests a novel IoT-based system that makes the use of low-power wireless sensing the networks, big data, cloud computing and smart devices to detect falls of older persons in interior situations. A Three-dimensional axis accelerometer integrated into a wearable sixLowPAN device is utilised for this purpose and is in charge of gathering data collected from older people's movements in real-time. The Signals of the sensor are processed and analysed using a machine learning model on a sophisticated IoT gateway to give high efficiency in fall detection. We make use of low-cost wearable sensing devices from Apache Flink and MbientLab an open source broadcast engine, a short-term with a long memory network architecture, and categorization of fall. We examine the ideal Nyquist rate, sensor positioning, and multiple channeling information change using the training set, which was developed using the published dataset “MobiAct.” With a 95.87% accuracy rate, our edge computing system can detect falls using real-time data stream analytics.
An analysis of the current status of metaverse research based on bibliometrics

Purpose The rise of the metaverse has brought profound changes to the economic and social operation models and injected new vitality into academic research. Although a large number of studies have emerged, there are few quantitative analyses of development frontiers and trends. Design/methodology/approach From a bibliometric perspective, this paper selects 183 pieces of metaverse-related literature in the WoS core database since 2000 as the object of analysis. This paper sums up the characteristics of the literature using the methods of descriptive statistical analysis, keywords analysis, thematic evolution analysis and summarizes the core themes and the laws of metaverse development in each stage. Findings The digital economy vision brought by the metaverse has led to an increasing number of researchers and achievements in this field. But the depth and breadth of research are still insufficient and unevenly distributed in the region, and the cross-fertilization fields need to be expanded. From the industry's point of view, VR games represented by Second Life and My World have contributed to the popularity of the metaverse. As technology progresses, the research hotspots in the field of metaverse gradually develop from conceptual research to artificial intelligence, blockchain, NFT and other technical applications. However, academic research has not yet caught up with the industry's pace and stays more in the concept discussion and preliminary application stage. Originality/value A systematic overview of the current status, knowledge structure and hot issues of metaverse research is shown, which provides a thematic axis for this field, enriches and improves the quantitative analysis of its literature and provides a clear picture for researchers to continuously promote the development of this field. At the same time, it is necessary to warn that technological development is a double-edged sword. The process of metaverse development should return to rationality, respect the laws of its development and guarantee the healthy development of the metaverse by strengthening legal regulation and the ethical review of science and technology.
Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human-computer interaction more natural and flexible, bringing richer interactive experience to teaching, on-board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi-level feature fusion of two-stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB-D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi-level feature fusion of two-stream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single-channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1-miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.
Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.
Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review

Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer.Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach.A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy.US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.
Does ICTs decrease the spread of informal economy in Africa?

This paper assesses the effect of information and communication technologies (ICTs) on the informal economy. We applied the Generalized Method of Moments (GMM) on a sample of 45 African countries from 2000 to 2017. According to the findings, the use of ICTs (mobile phone and internet) decreases the spread of the informal economy in Africa. These results are robust to a battery of robustness checks. Furthermore, the results of the mediation analysis show that the effect of ICTs on the informal economy is mediated by financial development, human capital and control of corruption. From a policy perspective, we suggest a quantitative and qualitative consolidation of technological infrastructures, for a sustainable mitigation of the rise of informal sector in Africa.
A bibliometric study on blockchain-based supply chain: a theme analysis, adopted methodologies, and future research agenda

The emergence of the underlying blockchain technology of bitcoin has gained extensive attention from researchers and practitioners. As distributed ledger technology, blockchain widely finds its applications in the supply chain to mitigate issues related to transparency, information sharing, process efficiency, and traceability. This study employed a knowledge-based visualization technique to create a vision beyond other review studies on the blockchain-based supply chain. We used bibliometric and network analysis to synthesize the previous literature. In total, 431 articles in the timespan of 2017 to April 2022 from Scopus and Web of Science (WOS) databases were analyzed after applying search string, inclusion, and exclusion criteria. Basic information was extracted from initial data screening; then, data was analyzed on the grounds of co-occurrence, bibliographic coupling, citation, co-authorship, and co-citation analysis. In addition, thematic analysis was performed to analyze the content of the previous studies, adopted research methods, and dynamic industries in the literature. Besides all these, we identified various research gaps and proposed research directions for future study. We believe that this study provides adequate knowledge to academic scholars and supply chain practitioners to fast-track the current research in the supply chain domain using blockchain technology.
Fine-Grained Object Classification via Self-Supervised Pose Alignment

Semantic patterns offine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, distinctive de-tails carried by local regions can be spatially distributed or even self-occluded, leading to a large variation on ob-ject representation. For discounting pose variations, this paper proposes to learn a novel graph based object rep-resentation to reveal a global configuration of local parts for self-supervised pose alignment across classes, which is employed as an auxiliary feature regularization on a deep representation learning network. Moreover, a coarse-to-fine supervision together with the proposed pose-insensitive constraint on shallow-to-deep sub-networks encourages discriminative features in a curriculum learning manner. We evaluate our method on three popular fine-grained ob-ject classification benchmarks, consistently achieving the state-of-the-art performance. Source codes are available at https://github.com/yangxhll/P2P-Net.