Reviewerly/exHarmony huggingface dataset:
Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach

We have entered a new technological paradigm with the emergence of Internet-embedded software and hardware, so-called the Internet of Things (IoT). Although IoT offers pan-industry business opportunities, most industries are only just beginning to employ it. We thus determine and prioritize the most important factors that influence IoT adoption, and reveal how IoT adoption affects the performance of manufacturing companies. We use a hybrid method that integrates the adaptive neuro-fuzzy inference system with the decision-making trial and evaluation laboratory, a novelty of the study. The literature on this subject informs our selection of the critical adoption factors, namely, technological, environmental, and organizational. The data are acquired from industrial managers involved in the decision-making process of information technology procurement in manufacturing companies in Malaysia. Our results can support IoT adoption guidelines geared to yield maximum efficiency in manufacturing industries, service providers, and governments.
Self-supervised Visual Attribute Learning for Fashion Compatibility

Many self-supervised learning (SSL) methods have been successful in learning semantically meaningful visual representations by solving pretext tasks. However, prior work in SSL focuses on tasks like object recognition or detection, which aim to learn object shapes and assume that the features should be invariant to concepts like colors and textures. Thus, these SSL methods perform poorly on downstream tasks where these concepts provide critical information. In this paper, we present an SSL framework that enables us to learn color and texture-aware features without requiring any labels during training. Our approach consists of three self-supervised tasks designed to capture different concepts that are neglected in prior work that we can select from depending on the needs of our downstream tasks. Our tasks include learning to predict color histograms and discriminate shapeless local patches and textures from each instance. We evaluate our approach on fashion compatibility using Polyvore Outfits and In-Shop Clothing Retrieval using Deep-fashion, improving upon prior SSL methods by 9.5-16%, and even outperforming some supervised approaches on Polyvore Outfits despite using no labels. We also show that our approach can be used for transfer learning, demonstrating that we can train on one dataset while achieving high performance on a different dataset.
Multiple access control scheme for EHRs combining edge computing with smart contracts

To make up the insufficient computing power of remote medical devices and solve the transmission delay problem, edge computing has been widely studied in academic community. The application of edge computing in E-health makes it more convenient for medical providers to view the patient’ electronic health records (EHRs), which not only improves the work efficiency of medical providers, but also provides patients with more personalized treatments. However,there are some security problems in the E-health system, such as EHRs leakage or tampering. Therefore, attribute-based encryption (CP-ABE) is widely used in the access control of EHRS. In order to reduce the computing cost of resource-constrained devices and achieve more flexible fine-grained access control, this paper proposes an effective ABE scheme that outsources part of the computing load to the fog nodes, which safely shares data with lower overheads. It also supports users revocation and attribute revocation. In addition, we combine smart contract and blockchain to share more fine-grained EHRs. The security proof and experimental results show that our scheme has higher efficiency to protect the patients’ EHRs privacy.
Teegraph: A Blockchain consensus algorithm based on TEE and DAG for data sharing in IoT

Blockchain offers new ways to the data sharing-based collaboration among IoT devices when a centralized IT infrastructure is unavailable. As one of the critical elements in a Blockchain system, the existing consensus algorithms still have some weaknesses, such as energy-wasting, low throughput, high latency, and increased network communication requirements. In this paper, we focus on designing a highly efficient Blockchain consensus algorithm for data sharing among IoT devices. We present the detailed design of Teegraph, which is a Trusted Execution Environment (TEE) and Directed Acyclic Graph (DAG)-based consensus algorithm. A proof-of-concept implementation of Teegraph is presented. The simulation results demonstrate that TEE usage in Teegraph is more efficient than that of the existing state of the art TEE-based consensus algorithms such as MinBFT and MinZyzzyva. Moreover, Teegraph outperforms Hashgraph, one of the most popular DAG-based consensus algorithms in throughput and latency.
Modern Development Trends of Chatbots Using Artificial Intelligence (AI)

It is said 80% of business owners will use chatbots in the future. Chatbots become more and more popular in terms of business, that is why it is necessary for businesses implement innovative approaches to provide customer service twenty-four hours. Such business especially is needed in terms of the challenging Covid-19 times. Artificial Intelligence-powered chatbots can work as intelligent teaching systems, for providing a personalized way of learning for students. Chatbot reviews student’s responses and his learning progress. One of the most convenient features of chatbots is the opportunity to send lecture materials in the form of messages to students as if it is just a chat with a friend. Apart from personalized chatbot usage in the studying process, it can be used to streamline business processes, e.g., such as sales. Nowadays chatbots are able to help customers to search products they need, place orders to the cart and pay for it, and track delivery processes of orders. Chatbot intelligence is being developed every day and every year now, so very soon chatbots will be able to perform even more difficult tasks to make the life of the user easier. In this paper, authors will deliver theoretical materials and historical background of chatbots, describe classification and techniques of chatbots, then describe the modern development trends of chatbots using artificial intelligence (AI), and finally discuss the role of chatbots in education and e-commerce.
B<i>l</i>-IEA: A Bit-Level Image Encryption Algorithm for Cognitive Services in Intelligent Transportation Systems

In Intelligent Transportation Systems, images are the main data sources to be analyzed for providing intelligent and precision cognitive services. Therefore, how to protect the privacy of sensitive images in the process of information transmission has become an important research issue, especially in future no non-private data era. In this article, we design the Rearrangement-Arnold Cat Map (R-ACM) to disturb the relationship between adjacent pixels and further propose an efficient Bit-level Image Encryption Algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{B}{l}$ </tex-math></inline-formula> -IEA) based on R-ACM. Experiments show that the correlation coefficients of two adjacent pixels are 0.0022 in the horizontal direction, -0.0105 in the vertical direction, and -0.0035 in the diagonal direction respectively, which are obviously weaker than that of the original image with high correlations of adjacent pixels. What’s more, the NPCR is 0.996120172, and the UACI is 0.334613406, which indicate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{B}{l}$ </tex-math></inline-formula> -IEA has stronger ability to resist different attacks compared with other solutions. Especially, the lower time complexity and only one round permutation make it particularly suitable to be used in the time-limited intelligent transportation field.
Distributed cognition based localization for AR-aided collaborative assembly in industrial environments

The existing (augmented reality) AR-aided assembly is highly associated with AR devices, which mainly provides guidance for one operator, and it is hard to share augmented assembly instructions for large-scale products which require multiple operators working together. To address this problem, the paper proposes a distributed cognition based localization method for AR-aided collaborative assembly. Firstly, a scene cognition using multi-view acquisition about industrial environments is performed with incremental modeling in advance, providing the foundation for the subsequent pose estimate of multi-AR clients. Then, based on feature extracting and matching against the pre-built shop floor model, a pose recovery of AR-aided system is derived from different views of AR operators in a global coordinate system, followed by a distributed motion tracking with the complementary features of visual and inertial data, resulting in a co-located collaborative AR instruction for assembly. Finally, experiments are carried out to validate the proposed method, and experimental results illustrate that the proposed method can achieve distributed cognition-based localization accurately and robustly. Therefore, shared visual communications among multiple operators are synchronized, and assembly status is aware by all the operators.
Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation

Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain. In this paper, we propose a pair-based SSDA method that adapts a model to the target domain using self-distillation with sample pairs. Each sample pair is composed of a teacher sample from a labeled dataset (i.e., source or labeled target) and its student sample from an unlabeled dataset (i.e., unlabeled target). Our method generates an assistant feature by transferring an intermediate style between the teacher and the student, and then train the model by minimizing the output discrepancy between the student and the assistant. During training, the assistants gradually bridge the discrepancy between the two domains, thus allowing the student to easily learn from the teacher. Experimental evaluation on standard benchmarks shows that our method effectively minimizes both the inter-domain and intra-domain discrepancies, thus achieving significant improvements over recent methods.
Authorized Shared Electronic Medical Record System with Proxy Re-Encryption and Blockchain Technology

With the popularity of the internet 5G network, the network constructions of hospitals have also rapidly developed. Operations management in the healthcare system is becoming paperless, for example, via a shared electronic medical record (EMR) system. A shared electronic medical record system plays an important role in reducing diagnosis costs and improving diagnostic accuracy. In the traditional electronic medical record system, centralized database storage is typically used. Once there is a problem with the data storage, it could cause data privacy disclosure and security risks. Blockchain is tamper-proof and data traceable. It can ensure the security and correctness of data. Proxy re-encryption technology can ensure the safe sharing and transmission of relatively sensitive data. Based on the above situation, we propose an electronic medical record system based on consortium blockchain and proxy re-encryption to solve the problem of EMR security sharing. Electronic equipment in this process is connected to the blockchain network, and the security of data access is ensured through the automatic execution of blockchain chaincodes; the attribute-based access control method ensures fine-grained access to the data and improves the system security. Compared with the existing electronic medical records based on cloud storage, the system not only realizes the sharing of electronic medical records, but it also has advantages in privacy protection, access control, data security, etc.
Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image

The accurate segmentation of cerebral vessels from time-of-flight magnetic resonance angiography (TOF-MRA) data is crucial for the diagnosis and treatment of cerebrovascular diseases. However, cerebrovascular segmentation remains challenging due to the tiny and complex structure of the vessels. This paper proposed a GAN-based deep-based method that maps the TOF-MRA data to 3D cerebral vessels. To improve the capability of feature expression, the proposed network integrates squeeze-and-excite blocks into a Vnet structure to extract and accumulate features at different scales. In addition, we introduce adversarial loss for network training, which can help the model to learn the distribution of the cerebral vessels. Besides, we introduce island number and minimum diameter to evaluate the spatial consistency and recognized minimal vessel of the cerebrovascular segmentation results. The proposed method is validated on 102 TOF-MRA cases with manually annotated labels. The experimental results indicate that our method can identify more tiny cerebral vessels meanwhile maintaining the spatial consistency of the cerebral vessel structure. Besides, our GAN-based cerebrovascular segmentation method reaches an average dice similar coefficient of 89.89%, outperforming other state-of-the-art segmentation methods.
FPGA-Based Convolutional Neural Network Accelerator with Resource-Optimized Approximate Multiply-Accumulate Unit

Convolutional neural networks (CNNs) are widely used in modern applications for their versatility and high classification accuracy. Field-programmable gate arrays (FPGAs) are considered to be suitable platforms for CNNs based on their high performance, rapid development, and reconfigurability. Although many studies have proposed methods for implementing high-performance CNN accelerators on FPGAs using optimized data types and algorithm transformations, accelerators can be optimized further by investigating more efficient uses of FPGA resources. In this paper, we propose an FPGA-based CNN accelerator using multiple approximate accumulation units based on a fixed-point data type. We implemented the LeNet-5 CNN architecture, which performs classification of handwritten digits using the MNIST handwritten digit dataset. The proposed accelerator was implemented, using a high-level synthesis tool on a Xilinx FPGA. The proposed accelerator applies an optimized fixed-point data type and loop parallelization to improve performance. Approximate operation units are implemented using FPGA logic resources instead of high-precision digital signal processing (DSP) blocks, which are inefficient for low-precision data. Our accelerator model achieves 66% less memory usage and approximately 50% reduced network latency, compared to a floating point design and its resource utilization is optimized to use 78% fewer DSP blocks, compared to general fixed-point designs.
Improving application performance with biased distributions of quantum states

We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity. In particular, we analyze mixtures of Haar-random pure states with Dirichlet-distributed coefficients. We analytically derive the concentration parameters required to match the mean purity of the Bures and Hilbert--Schmidt distributions in any dimension. Numerical simulations suggest that this value recovers the Hilbert--Schmidt distribution exactly, offering an alternative and intuitive physical interpretation for ensembles of Hilbert--Schmidt-distributed random quantum states. We then demonstrate how substituting these Dirichlet-weighted Haar mixtures in place of the Bures and Hilbert--Schmidt distributions results in measurable performance advantages in machine-learning-based quantum state tomography systems and Bayesian quantum state reconstruction. Finally, we experimentally characterize the distribution of quantum states generated by both a cloud-accessed IBM quantum computer and an in-house source of polarization-entangled photons. In each case, our method can more closely match the underlying distribution than either Bures or Hilbert--Schmidt distributed states for various experimental conditions.
Eyes-Free Tongue Gesture and Tongue Joystick Control of a Five DOF Upper-Limb Exoskeleton for Severely Disabled Individuals

Spinal cord injury can leave the affected individual severely disabled with a low level of independence and quality of life. Assistive upper-limb exoskeletons are one of the solutions that can enable an individual with tetraplegia (paralysis in both arms and legs) to perform simple activities of daily living by mobilizing the arm. Providing an efficient user interface that can provide full continuous control of such a device-safely and intuitively-with multiple degrees of freedom (DOFs) still remains a challenge. In this study, a control interface for an assistive upper-limb exoskeleton with five DOFs based on an intraoral tongue-computer interface (ITCI) for individuals with tetraplegia was proposed. Furthermore, we evaluated eyes-free use of the ITCI for the first time and compared two tongue-operated control methods, one based on tongue gestures and the other based on dynamic virtual buttons and a joystick-like control. Ten able-bodied participants tongue controlled the exoskeleton for a drinking task with and without visual feedback on a screen in three experimental sessions. As a baseline, the participants performed the drinking task with a standard gamepad. The results showed that it was possible to control the exoskeleton with the tongue even without visual feedback and to perform the drinking task at 65.1% of the speed of the gamepad. In a clinical case study, an individual with tetraplegia further succeeded to fully control the exoskeleton and perform the drinking task only 5.6% slower than the able-bodied group. This study demonstrated the first single-modal control interface that can enable individuals with complete tetraplegia to fully and continuously control a five-DOF upper limb exoskeleton and perform a drinking task after only 2 h of training. The interface was used both with and without visual feedback.
An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace

Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is expected to be carried out by drones. Therefore, it is of great significance to construct a reasonable air route network for logistics UAV to ensure the safety and efficiency of operations. In this paper, a single route planning model and an air route network planning model for UAV were constructed by fully considering the complex urban low altitude environment, the flight performance of UAV and the characteristics of logistics tasks to regulate the flights of drones. Then, taking Jiangjun Road Campus of Nanjing University of Aeronautics and Astronautics as an example, the improved cellular automata (CA) was adopted to search for the optimal route between different waypoints, and the optimal spanning tree algorithm was used to construct the route network. The experimental results demonstrated that the improved CA could greatly reduce search time and obtain the optimal route while enhancing safety. With the satisfaction of the voyage, the needs of logistics and distribution constraints, a network that had smaller intersection points and redundancy was generated. The models and core ideas proposed in this paper can not only regulate operation of drones but also provide a solid foundation for the distribution of logistics UAV in the future.
Polymedia in interaction

Abstract This Special Issue on “Polymedia in interaction” theorizes and empirically investigates practices and ideologies of digitally mediated interaction under conditions of polymedia. We argue that the proliferation of mobile interpersonal communication in the 2010s calls for, and is reflected in, conceptual and methodological shifts in empirical research on digital language and communication in pragmatics and sociocultural linguistics. In this introduction, these shifts are crystallized in five interrelated themes: (1) a turn from ‘computer-mediated communication’ to ‘digitally mediated interaction’ as a bracket category; (2) a move beyond the on/offline divide and focus on the integration of mediated interaction in everyday communication in micro-units of social structure (e.g. transnational families, business or academic communication); (3) an empirical downscaling towards private and small-scale public data; (4) a shift from the study of single modes of digital communication to polymedia; and (5) a focus on semiotic repertoires and registers of digital mediation. Research that orients to (some or all of) these focal points is compared with other trends in digital language research, including computational methods. The papers in this issue flesh out these five dimensions with findings from qualitative research, based on multi-sited linguistic and digital ethnographies in various sociolinguistic settings.
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition

We focus on unsupervised representation learning for skeleton based action recognition. Existing unsupervised approaches usually learn action representations by motion prediction but they lack the ability to fully learn inherent semantic similarity. In this paper, we propose a novel framework named Prototypical Contrast and Reverse Prediction (PCRP) to address this challenge. Different from plain motion prediction, PCRP performs reverse motion prediction based on encoder-decoder structure to extract more discriminative temporal pattern, and derives action prototypes by clustering to explore the inherent action similarity within the action encoding. Specifically, we regard action prototypes as latent variables and formulate PCRP as an expectation-maximization (EM) task. PCRP iteratively runs (1) E-step as to determine the distribution of action prototypes by clustering action encoding from the encoder while estimating concentration around prototypes, and (2) M-step as optimizing the model by minimizing the proposed ProtoMAE loss, which helps simultaneously pull the action encoding closer to its assigned prototype by contrastive learning and perform reverse motion prediction task. Besides, the sorting can also serve as a temporal task similar as reverse prediction in the proposed framework. Extensive experiments on N-UCLA, NTU 60, and NTU 120 dataset present that PCRP outperforms main stream unsupervised methods and even achieves superior performance over many supervised methods. The codes are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LZUSIAT/PCRP</uri> .