Reviewerly/exHarmony huggingface dataset:
Business on Chain: A Comparative Case Study of Five Blockchain-Inspired Business Models

Blockchain technology, despite its origins as the underlying infrastructure for value transfer in the era of cryptocurrency, has been touted as the main disruptive force in modern businesses.Blockchain has the capacity to chronologically capture and store transactional data in a standardized and tamperproof format that is transparent to all stakeholders involved in the transaction.This, in turn, has prompted companies to rethink preexisting business practices, thereby yielding a myriad of fascinating business models anchored in blockchain technology.In this study, we advance contemporary knowledge of business applications of blockchain by drawing on the theoretical lens of the digital business model and value configuration to decipher how pioneers in this space are leveraging blockchain to create and capture value.Through a comparative, multiple case study approach, we analyzed five companies in mainland China that have rolled out blockchain initiatives.From our case analyses, we derived a typology of five blockchain-inspired business models, each of which embodies a distinctive logic for market differentiation.For each business model, we offer insights into its value creation logic, its value capturing mechanism, and the challenges that could threaten its longer-term viability.Grounded in our findings, we discuss key implications for theory and practice.
Multi-scale fully convolutional network for gland segmentation using three-class classification

Automated precise segmentation of glands from the histological images plays an important role in glandular morphology analysis, which is a crucial criterion for cancer grading and planning of treatment. However, it is non-trivial due to the diverse shapes of the glands under different histological grades and the presence of tightly connected glands. In this paper, a novel multi-scale fully convolutional network with three class classification (TCC-MSFCN) is proposed to achieve gland segmentation. The multi-scale structure can extract different receptive field features corresponding to multi-size objects. However, the max-pooling in the convolution neural network will cause the loss of global information. To compensate for this loss, a special branch called high-resolution branch in our framework is designed. Besides, for effectively separating the close glands, a three-class classification with additional consideration of edge pixels is applied instead of the conventional binary classification. Finally, the proposed method is evaluated on Warwick-QU dataset and CRAG dataset with three reliable evaluation metrics, which are applied to our method and other popular methods. Experimental results show that the proposed method achieves the-state-of-the-art performance. Discussion and conclusion are presented afterwards.
Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy

Many real-world social networks are decentralized in nature, and the only way to analyze such a network is to collect local views of the social graph from individual participants. Since local views may contain sensitive information, it is often desirable to apply differential privacy in the data collection process, which provides strong and rigorous privacy guarantees. In many practical situations, the local view of a participant contains not only her own connections, but also those of her neighbors, which are private and sensitive for the neighbors, but not directly so for the participant herself. We call such information beyond direct connections an extended local view (ELV)</>, and study two fundamental problems related to ELVs: first, how do we correctly enforce differential privacy for all participants in the presence of ELVs? Second, how can the data collector utilize ELVs to obtain accurate estimates of global graph properties?
Diversity Transfer Network for Few-Shot Learning

Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in the lack of intra-class diversity within insufficient training samples. To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. The learning problem of the sample generation (i.e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works. Besides, an organized auxiliary task co-training over known categories is proposed to stabilize the meta-training process of DTN. We perform extensive experiments and ablation studies on three datasets, i.e., miniImageNet, CIFAR100 and CUB. The results show that DTN, with single-stage training and faster convergence speed, obtains the state-of-the-art results among the feature generation based few-shot learning methods. Code and supplementary material are available at: https://github.com/Yuxin-CV/DTN.
Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese

As the problem of an aging population becomes more and more serious, social robots have an increasingly significant influence on human life. By employing regular question-and-answer conversations or wearable devices, some social robotics products can establish personal health archives. But those robots are unable to collect diet information automatically through robot vision or audition. A healthy diet can reduce a person's risk of developing cancer, diabetes, heart disease, and other age-related diseases. In order to automatically perceive the dietary composition of the elderly by listening to people's chatting, this paper proposed a chat-based automatic dietary composition perception algorithm (DCPA). DCPA uses social robot audition to understand the semantic information and percept dietary composition for Mandarin Chinese. Firstly, based on the Mel-frequency cepstrum coefficient and convolutional neural network, a speaker recognition method is designed to identify speech data. Based on speech segmentation and speaker recognition algorithm, an audio segment classification method is proposed to distinguish different speakers, store their identity information and the sequence of expression in a speech conversation. Secondly, a dietetic lexicon is established, and two kinds of dietary composition semantic understanding algorithms are proposed to understand the eating semantics and sensor dietary composition information. To evaluate the performance of the proposed DCPA algorithm, we implemented the proposed DCPA in our social robot platform. Then we established two categories of test datasets relating to a one-person and a multi-person chat. The test results show that DCPA is capable of understanding users' dietary compositions, with an F1 score of 0.9505, 0.8940 and 0.8768 for one-person talking, a two-person chat and a three-person chat, respectively. DCPA has good robustness for obtaining dietary information.
Fast and Deep Graph Neural Networks

We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.
Improved Visual-Semantic Alignment for Zero-Shot Object Detection

Zero-shot object detection is an emerging research topic that aims to recognize and localize previously ‘unseen’ objects. This setting gives rise to several unique challenges, e.g., highly imbalanced positive vs. negative instance ratio, proper alignment between visual and semantic concepts and the ambiguity between background and unseen classes. Here, we propose an end-to-end deep learning framework underpinned by a novel loss function that handles class-imbalance and seeks to properly align the visual and semantic cues for improved zero-shot learning. We call our objective the ‘Polarity loss’ because it explicitly maximizes the gap between positive and negative predictions. Such a margin maximizing formulation is not only important for visual-semantic alignment but it also resolves the ambiguity between background and unseen objects. Further, the semantic representations of objects are noisy, thus complicating the alignment between visual and semantic domains. To this end, we perform metric learning using a ‘Semantic vocabulary’ of related concepts that refines the noisy semantic embeddings and establishes a better synergy between visual and semantic domains. Our approach is inspired by the embodiment theories in cognitive science, that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary) and the visual perception (seen/unseen object images). Our extensive results on MS-COCO and Pascal VOC datasets show significant improvements over state of the art.1
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification

Although great progress in supervised person re-identification (Re-ID) has been made recently, due to the viewpoint variation of a person, Re-ID remains a massive visual challenge. Most existing viewpoint-based person Re-ID methods project images from each viewpoint into separated and unrelated sub-feature spaces. They only model the identity-level distribution inside an individual viewpoint but ignore the underlying relationship between different viewpoints. To address this problem, we propose a novel approach, called Viewpoint-Aware Loss with Angular Regularization (VA-reID). Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level. In addition, rather than modeling different viewpoints as hard labels used for conventional viewpoint classification, we introduce viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation. VALSR can effectively solve the ambiguity of the viewpoint cluster label assignment. Extensive experiments on the Market1501 and DukeMTMC-reID datasets demonstrated that our method outperforms the state-of-the-art supervised Re-ID methods.
Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future.
Scene recognition: A comprehensive survey

With the success of deep learning in the field of computer vision, object recognition has made important breakthroughs, and its recognition accuracy has been drastically improved. However, the performance of scene recognition is still not sufficient to some extent because of complex configurations. Over the past several years, scene recognition algorithms have undergone important evolution as a result of the development of machine learning and Deep Convolutional Neural Networks (DCNN). This paper reviews many of the most popular and effective approaches to scene recognition, which is expected to create benefits for future research and practical applications. We seek to establish relationships among different algorithms and determine the critical components that lead to remarkable performance. Through the analysis of some representative schemes, motivation and insights are identified, which will help to facilitate the design of better recognition architectures. In addition, current available scene datasets and benchmarks are presented for evaluation and comparison. Finally, potential problems and promising directions are highlighted.
A probability distribution detection based hybrid ensemble QoS prediction approach

Recently, with the rapid increase in the number of web services, how to discover services to meet personalized requirements of users in large-scale scenarios has become a popular topic in both industry and academia. The quality of service (QoS) prediction-based web service recommendation system has been widely studied as an effective method for solving this problem. However, due to the sparsity and imbalance of the observed QoS data, existing QoS prediction approaches suffer from limited prediction accuracy and poor scalability. Moreover, existing QoS prediction approaches do not consider the probability distribution of the observed QoS data, which have remarkable impacts on the prediction performance. In this paper, we propose a novel probability distribution detection-based hybrid ensemble QoS prediction model that can dynamically integrate a set of basic prediction models to improve the prediction accuracy instead of designing complex and time-consuming models. Specifically, we first propose an enhanced CF (collaborative filtering)-based approach as the basis of the prediction model. Second, given the results of a set of other basic prediction models, in addition, we propose a distribution detection algorithm to calculate the PCWs (probability confidence weights) of those results. Finally, we combine them dynamically to obtain final results based on PCWs. Experiments based on real datasets show that our approach has higher prediction accuracy and better scalability than existing mainstream QoS prediction approaches.
Unsupervised Anomaly Detection and Localization Based on Deep Spatiotemporal Translation Network

Anomaly detection is of great significance for intelligent surveillance videos.Current works typically struggle with object detection and localization problems due to crowded and complex scenes.Hence, we propose a Deep Spatiotemporal Translation Network (DSTN), novel unsupervised anomaly detection and localization method based on Generative Adversarial Network (GAN) and Edge Wrapping (EW ).In training, we use only the frames of normal events in order to generate their corresponding dense optical flow as temporal features.During testing, since all the video sequences are input into the system, unknown events are considered as anomalous events due to the fact that the model knows only the normal patterns.To benefit from the information provided by both appearance and motion features, we introduce (i) a novel fusion of background removal and real optical flow frames with (ii) a concatenation of the original and background removal frames.We improve the performance of anomaly localization in the pixellevel evaluation by proposing (iii) the Edge Wrapping to reduce the noise and suppress non-related edges of abnormal objects.Our DSTN has been tested on publicly available anomaly datasets, including UCSD pedestrian, UMN, and CUHK Avenue.The results show that it outperforms other state-of-the-art algorithms with respect to the frame-level evaluation, the pixel-level evaluation, and the time complexity for abnormal object detection and localization tasks.
Domain Knowledge-Based Deep-Broad Learning Framework for Fault Diagnosis

Intelligent fault diagnosis is a vital role in smart manufacturing. And deep-learning-based fault diagnosis has become a hot topic due to its strong feature extraction ability. However, traditional deep-learning-based methods show two limitations. One is that a large number of labeled samples are required to construct effective diagnosis models. Another is that these methods lack flexibility, especially for homologous multitasking problems. In this article, a novel domain-knowledge-based deep-broad learning framework (DK-DBLF) is proposed to overcome abovementioned limitations. A DK-DBLF consists of two parts: a task-specific feature extractor and a flexible fault recognizer. The first part is constructed by several convolutional neural networks to obtain abstract features automatically, and the second part employs a broad learning system to improve the flexibility of the proposed framework. To combine these two parts more effectively, bridge label-based strategy is designed, which is a key connection that can integrate domain knowledge into the learning process. The performance of a DK-DBLF is tested on motor-bearing and pipeline defect datasets, which are health condition classification and homologous multitask estimation problems, respectively. The results have demonstrated that our framework can significantly reduce the usage of labeled samples in the learning process, and architecture adjustment can be easily performed when compared with traditional deep methods.
VPQC: A Domain-Specific Vector Processor for Post-Quantum Cryptography Based on RISC-V Architecture

In the 5G era, massive devices need to be securely connected to the edge of communication networks, while emerging quantum computers can easily crack the traditional public-key ciphers. Lattice-based cryptography (LBC) is one of the most promising types of schemes in all post-quantum cryptography (PQC) due to its security and efficiency. To meet the requirements of high-throughput and diverse application scenarios of 5G, we investigate the vectorization of kernel algorithms of several LBC candidates and thus present a domain-specific vector processor, VPQC, leveraging the extensible RISC-V architecture. To support the parallel computation of number theoretic transform (NTT) of different dimensions (from 64 to 2048), a vector NTT unit is implemented in VPQC. Besides, a vector sampler executing both uniform sampling and binomial sampling is also employed. Evaluated under TSMC 28nm technology, the vector coprocessor of VPQC consumes 942k equivalent logic gates and 12KB memories. Experimental results show that VPQC can speed up several typical key encapsulation mechanisms (NewHope, Kyber and LAC) by an order of magnitude compared with previous state-of-the-art hardware implementations.
Post-hoc explanation of black-box classifiers using confident itemsets

Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks, have been widely utilized to build predictive models that can extract complex relationships in a dataset and make predictions for new unseen data records. However, it is difficult to trust decisions made by such methods since their inner working and decision logic is hidden from the user. Explainable Artificial Intelligence (XAI) refers to systems that try to explain how a black-box AI model produces its outcomes. Post-hoc XAI methods approximate the behavior of a black-box by extracting relationships between feature values and the predictions. Perturbation-based and decision set methods are among commonly used post-hoc XAI systems. The former explanators rely on random perturbations of data records to build local or global linear models that explain individual predictions or the whole model. The latter explanators use those feature values that appear more frequently to construct a set of decision rules that produces the same outcomes as the target black-box. However, these two classes of XAI methods have some limitations. Random perturbations do not take into account the distribution of feature values in different subspaces, leading to misleading approximations. Decision sets only pay attention to frequent feature values and miss many important correlations between features and class labels that appear less frequently but accurately represent decision boundaries of the model. In this paper, we address the above challenges by proposing an explanation method named Confident Itemsets Explanation (CIE). We introduce confident itemsets, a set of feature values that are highly correlated to a specific class label. CIE utilizes confident itemsets to discretize the whole decision space of a model to smaller subspaces.
RISQ-V: Tightly Coupled RISC-V Accelerators for Post-Quantum Cryptography

Empowering electronic devices to support Post-Quantum Cryptography (PQC) is a challenging task. PQC introduces new mathematical elements and operations which are usually not easy to implement on standard processors. Especially for low cost and resource constraint devices, hardware acceleration is usually required. In addition, as the standardization process of PQC is still ongoing, a focus on maintaining flexibility is mandatory. To cope with such requirements, hardware/software co-design techniques have been recently used for developing complex and highly customized PQC solutions. However, while most of the previous works have developed loosely coupled PQC accelerators, the design of tightly coupled accelerators and Instruction Set Architecture (ISA) extensions for PQC have been barely explored. To this end, we present RISQ-V, an enhanced RISC-V architecture that integrates a set of powerful tightly coupled accelerators to speed up lattice-based PQC. RISQ-V efficiently reuses processor resources and reduces the amount of memory accesses. This significantly increases the performance while keeping the silicon area overhead low. We present three contributions. First, we propose a set of powerful hardware accelerators deeply integrated into the RISC-V pipeline. Second, we extended the RISC-V ISA with 29 new instructions to efficiently perform operations for lattice-based cryptography. Third, we implemented our RISQ-V in ASIC technology and on FPGA. We evaluated the performance of NewHope, Kyber, and Saber on RISQ-V. Compared to the pure software implementation on RISC-V, our co-design implementations show a speedup factor of up to 11.4 for NewHope, 9.6 for Kyber, and 2.7 for Saber. For the ASIC implementation, the energy consumption was reduced by factors of up to 9.5 for NewHope, 7.7 for Kyber, and 2.1 for Saber. The cell count of the CPU was increased by a factor of 1.6 compared to the original RISC-V design, which can be considered as a moderate increase for the achieved performance gain.