Reviewerly/exHarmony huggingface dataset:
Distributed Timestamp Mechanism Based on Verifiable Delay Functions

In the data communication system, the real-time information interaction of communication device increases the risk of privacy sensitive data being tampered with.Therefore, maintaining data security is one of the most important issues in network data communication.Because the timestamp is the most important way to authenticate data in information interaction, it is very necessary to provide timestamp service in the data communication system.However, the existing centralized timestamp mechanism is difficult to provide credible timestamp service, and users can conspire with timestamping servers to forge timestamps.Therefore, this paper designs a distributed timestamp mechanism based on continuous verifiable delay functions.It utilizes multiple independent timestamp servers to provide timestamp services in a distributed model and appends the timestamp to the data once the data is generated.Thus, it can prove that the data already exists at a certain time and ensure the accuracy of the timestamp.Moreover, a digital blind signature based on elliptic curve cryptography is utilized to solve the problem of timestamp forgery in timestamp service.Finally, the security analysis of the scheme ensures the data security of data communication system and the concurrency rate of timestamp.The experimental results also show that the scheme greatly improves the efficiency of digital signatures.
Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells

White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98.97%, 99.0%, 96.05%, and 98.79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96.35% and 98.09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6.5 million trainable parameters.
Exploring structural relations among computer self-efficacy, perceived immersion, and intention to use virtual reality training systems

The use of virtual reality (VR) training systems for education has grown in popularity in recent years. Scholars have reported that self-efficacy and interactivity are important predictors of learning outcomes in virtual learning environments, but little empirical research has been conducted to explain how computer self-efficacy (as a subcategory of self-efficacy) and perceived immersion (as a correlate of interactivity) are connected to the intention to use VR training systems. The present study aims to determine which factors significantly influence behavioral intention when students are exposed to VR training systems via an updated technology acceptance frame by incorporating the constructs of computer self-efficacy and perceived immersion simultaneously. We developed a VR training system regarding circuit connection and a reliable and validated instrument including 9 subscales. The sample data were collected from 124 junior middle school students and 210 senior high school students in two schools located in western China. The samples were further processed into a structural equation model with path analysis and cohort analysis. The results showed that the intention to use VR training systems was indirectly influenced by computer self-efficacy but directly influenced by perceived immersion (β = 0.451). However, perceived immersion seemed to be influenced mostly by learner interaction (β = 0.332). Among external variables, learner interaction (β = 0.149) had the largest total effect on use intention, followed by facilitating conditions (β = 0.138), computer self-efficacy (β = 0.104), experimental fidelity (β = 0.083), and subjective norms (β = 0.077). The moderating roles of gender differences, grade level, and previous experience in structural relations were also identified. The findings of the present study highlight the ways in which factors and associations are considered in the practical development of VR training systems.
Contextualizing the impact of blockchain technology on the performance of new firms: The role of corporate governance as an intermediate outcome

The modern digitalization trends have totally transformed business practices and business models. Blockchain as a distributed technology makes possible the basic structure of a database with a combination of data blocks and hash chains. Using distributed software architecture and advanced computation, blockchain might change the method of data exchange among chain actors. Blockchain causes administration of a network in peer-to-peer and decentralized form; and influences the way of data exchange and interactions in organizations; hence, this study looks for evaluation of the impact of blockchain technology on corporate performance and governance of new firms. In the first section of the study, the research conceptual model was extracted by means of the previous studies and the hypotheses were formulated. In the second section, the relationships between the variables of the model among newly-established active business were assessed. The statistical population of the study was managers of SMEs in Iran and 397 completed questionnaires were received. Structural equation model and Smart PLS software have been used for data analysis. The research findings showed that the more the blockchain adoption in a new firm; the more direct effect it would have on the firm's performance. Similarly, corporate governance has an effect on the relationship of blockchain technology on corporate performance. Thus, managers of new firms by means of blockchain, can prepare the grounds for good corporate governance as well as the achievement of better performance.
Traffic sign detection algorithm based on improved YOLOv4-Tiny

There are three problems in YOLOv4-Tiny when it is used for traffic sign detection: the feature pyramid network fails to fuse high-level and low-level features sufficiently, the importance of low-level features for small object detection is not considered, and the ability to extract the features of small objects in the backbone network is not strong. Focusing on these problems, this paper proposes an improved YOLOv4 -Tiny for real-time traffic sign detection. Firstly, this paper improves YOLOv4-Tiny’s feature fusion method and proposes an adaptive feature pyramid network (AFPN), which aims to adaptively fuse the two feature layers with different scales. Secondly, two receptive field blocks (RFB) are added after the two feature layers of the backbone network. These two RFBs are composed of multi-branch structures and dilated convolution with different dilation rates, which can enhance the feature extraction ability of the backbone network. The CCTSDB and GTSDB datasets are used to evaluate the effectiveness of the improved method. The experimental results show that our proposed network is superior to the original network in the precision, recall rate, and mAP. In addition, compared with other state-of-the-art approaches on traffic sign detection, our proposed network has good comprehensive performance in accuracy and speed. The above results show that our improved method is effective in improving the performance of traffic sign detection. • A novel feature fusion method is proposed based on an Adaptive Feature Pyramid Network (AFPN). AFPN can better fuse the output results of the two scale feature layers of the backbone network, so the fused features have more semantic information and location information. • We proposed to add a receptive field block [16] (RFB) after the output layer of the backbone network. RFBs use a multi-branch structure and a dilated convolution layer to superimpose different scale receptive fields and enhance the feature extraction ability of the convolutional neural network, thereby improving the detection accuracy of the network. • The experimental results show that compared with the original network (i.e., YOLOv4-tiny), the proposed network improves the precision, recall rate, and mAP by 2.62%, 2.17%, and 1.34%, respectively, on the CCTSDB dataset, and works better on the CCTSDB_s dataset with smaller traffic signs. At the same time, the frame processing rate of the proposed network is still up to 145.7 FPS.
Augmented Reality Marketing: A Systematic Literature Review and an Agenda for Future Inquiry

Augmented reality (AR) is a potentially disruptive technology that enriches the consumer experience and transforms marketing. With the surging popularity of AR in marketing practice, academic efforts to investigate its effects on consumer experience, response, and behavior have increased significantly. To obtain an integrated and comprehensive view of the front-line in AR marketing research and identify the gaps for future research, we analyze the existing AR marketing literature through a systematic literature review. Using 99 journal articles selected from the Web of Science core collections, this research sheds light on the general characteristics such as publication year, publication outlet, research design, and research method. Moreover, this research also gains insight into the AR marketing relevant factors such as application area, application context, AR type, and theoretical lenses. The findings of the analyses reveal the state-of-the-art of scholarly publications on AR marketing research. First, the number of journal articles on AR marketing increased rapidly in the past few years, and the journals that published articles on AR marketing cover a wide range of disciplines. Second, the empirical studies in most literature adopted the quantitative research design and used survey or experiment methods. Third, the studies in more than half of the journal articles used mobile AR applications in various online contexts. Fourth, the Technology Acceptance Model (TAM) and the Stimulus-Organism-Response (S-O-R) framework are the two most widely used theoretical lenses used in the literature. After that, the major application areas of AR in marketing are retail, tourism, and advertising. To identify the focal themes discussed in the three application areas, this research summarizes the studies by the outcome variables. Specifically, the outcome variables have five categories: technology-related, product-related, brand-related, tourist destination-related, and advertisement-related. Finally, this research proposes the agenda for future academic efforts in AR marketing.
Progressive Language-Customized Visual Feature Learning for One-Stage Visual Grounding

Visual grounding is a task to localize an object described by a sentence in an image. Conventional visual grounding methods extract visual and linguistic features isolatedly and then perform cross-modal interaction in a post-fusion manner. We argue that this post-fusion mechanism does not fully utilize the information in two modalities. Instead, it is more desired to perform cross-modal interaction during the extraction process of the visual and linguistic feature. In this paper, we propose a language-customized visual feature learning mechanism where linguistic information guides the extraction of visual feature from the very beginning. We instantiate the mechanism as a one-stage framework named Progressive Language-customized Visual feature learning (PLV). Our proposed PLV consists of a Progressive Language-customized Visual Encoder (PLVE) and a grounding module. We customize the visual feature with linguistic guidance at each stage of the PLVE by Channel-wise Language-guided Interaction Modules (CLIM). Our proposed PLV outperforms conventional state-of-the-art methods with large margins across five visual grounding datasets without pre-training on object detection datasets, while achieving real-time speed. The source code is available in the supplementary material.
ChaCha20–Poly1305 Authenticated Encryption with Additional Data for Transport Layer Security 1.3

Transport Layer Security (TLS) provides a secure channel for end-to-end communications in computer networks. The ChaCha20–Poly1305 cipher suite is introduced in TLS 1.3, mitigating the sidechannel attacks in the cipher suites based on the Advanced Encryption Standard (AES). However, the few implementations cannot provide sufficient speed compared to other encryption standards with Authenticated Encryption with Associated Data (AEAD). This paper shows ChaCha20 and Poly1305 primitives. In addition, a compatible ChaCha20–Poly1305 AEAD with TLS 1.3 is implemented with a fault detector to reduce the problems in fragmented blocks. The AEAD implementation reaches 1.4-cycles-per-byte in a standalone core. Additionally, the system implementation presents 11.56-cycles-per-byte in an RISC-V environment using a TileLink bus. The implementation in Xilinx Virtex-7 XC7VX485T Field-Programmable Gate-Array (FPGA) denotes 10,808 Look-Up Tables (LUT) and 3731 Flip-Flops (FFs), represented in 23% and 48% of ChaCha20 and Poly1305, respectively. Finally, the hardware implementation of ChaCha20–Poly1305 AEAD demonstrates the viability of using a different option from the conventional cipher suite based on AES for TLS 1.3.
Understanding cybercrime in ‘real world’ policing and law enforcement

Cybercrime is a growing issue, still not fully understood by researchers or policing/law enforcement communities. UK Government reports assert that victims of cybercrime were unlikely to report crimes immediately due to the perception that police were ill-equipped to deal with these offences. Additionally, these reports identify policing issues including a lack of cybercrime knowledge. This paper reviews current research, providing a comprehensive account of cybercrime and addressing issues in policing such offences. We achieve this by describing the technological, individual, social and situational landscapes conducive to cybercrime, and how this knowledge may inform strategies to overcome current issues in investigations.
Evaluation of the trade-off between performance and communication costs in federated learning scenario

In traditional Machine Learning (ML) approaches, the data are collected and stored by a single node and subsequently used for training and testing. However, the acquisition and management of a large amount of data in some domains, as for example healthcare, can be problematic on account of the adoption of centralized architectures which entail certain security and privacy risks. Federated Learning (FL) has recently emerged as a technological solution to address such questions, even though communication efficiency may be a significant issue. This paper presents a novel learning strategy aimed at reducing the total number of parameters shared during the FL process and therefore, at evaluating a trade-off between the requirement to bring down communication costs and the need to guarantee the highest classification performance. The results demonstrate the goodness of the solution in comparison with the traditional FedAvg algorithm since the accuracy of the proposed approach shows values ranging from 89.25% to 96.6% and, and in addition, the reduction of the communication overheads shows improvements ranging from 95.64% to 6%. The analysis of the proposed approach shows promising results in terms of performance and communication costs, especially in relation to the total amount of moved data since the challenge addressed by the paper concerns communication efficiency during the training process.
Blockchain Technology: The Role of Integrated Reverse Supply Chain Networks in Sustainability

This research explores the role of Blockchain Technology (BCT) integrated with Reverse Supply Chain Networks (RSCN) and evaluates the relationship between BCT and sustainability performance. A qualitative research design was employed to develop a conceptual framework for BCT in RSCN. This research collected and analysed primary and secondary data from four cases as focal-actors. These focal-actors are from multi-industries in Jordan, namely food, pharmaceuticals, electronics, and toys. These actors are lead-firms and have experience working with RSCNs and technology applications such as BCT. Primary data were acquired from interviews with managers working in these industries, which were supported by the analysis of secondary data that identified two types of themes: internally focused and externally focused BCT-integrated drivers of RSCNs. The analysis also identified how they leverage sustainability performance improvements, including their use of RSCN approaches and features. This research is one of the few attempts to explore BCT integrated into RSCN for better sustainability performance that contributes to the theoretical and practical knowledge of supply chains within emerging economies. All types of actors-as-stakeholders involved with national programs and projects can adopt the new framework. The key findings contribute to the field of RSCN where the adoption of BCT as a broad-based strategy to attain sustainability goals and reverse chain activities along the supply chain is a goal.
Building restaurant customers’ technology readiness through robot-assisted experiences at multiple product levels

The growing popularity of robot-related research contexts in hospitality and tourism calls for in-depth analysis of how different product/service designs strategies integrating robots may influence customers' experiences. Employing a scenario-based 2 × 2 × 2 experimental research design, this study assesses service robots applied at three different product/service levels (i.e., core, facilitating, and augmented). From surveying 378 customers of mid-priced casual restaurants and 312 tourists of a mid-priced theme park restaurant, findings of the study suggest that using robots at all three product/service levels lead to a more positive educational experience but not entertainment experience. The study further extends the literature by positioning dining at a robotic restaurant as an important occasion to showcase the latest technologies to customers. By providing memorable entertainment and educational experiences, customers’ technology readiness could be enhanced, making them more willing to try new technologies. Such a focus brings in unique contributions both in literature and practice.
The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design

There have been increasing calls for centering impacted communities – both online and offline – in the design of the AI systems that will be deployed in their communities. However, the complicated nature of a community’s goals and needs, as well as the complexity of AI’s development procedures, outputs, and potential impacts, often prevents effective participation. In this paper, we present the Model Card Authoring Toolkit, a toolkit that supports community members to understand, navigate and negotiate a spectrum of machine learning models via deliberation and pick the ones that best align with their collective values. Through a series of workshops, we conduct an empirical investigation of the initial effectiveness of our approach in two online communities – English and Dutch Wikipedia, and document how our participants collectively set the threshold for a machine learning based quality prediction system used in their communities’ content moderation applications. Our results suggest that the use of the Model Card Authoring Toolkit helps improve the understanding of the trade-offs across multiple community goals on AI design, engage community members to discuss and negotiate the trade-offs, and facilitate collective and informed decision-making in their own community contexts. Finally, we discuss the challenges for a community-centered, deliberation-driven approach for AI design as well as potential design implications.
TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring

Medical document classification is one of the active research problems and the most challenging within the text classification domain. Medical datasets often contain massive feature sets where many features are considered irrelevant, redundant, and add noise, thus, reducing the classification performance. Therefore, to obtain a better accuracy of a classification model, it is crucial to choose a set of features (terms) that best discriminate between the classes of medical documents. This study proposes TextNetTopics, a novel approach that applies feature selection by considering Bag-of-topics (BOT) rather than the traditional approach, Bag-of-words (BOW). Thus our approach performs topic selections rather than words selection. TextNetTopics is based on the generic approach entitled G-S-M (Grouping, Scoring, and Modeling), developed by Yousef and his colleagues and used mainly in biological data. The proposed approach suggests scoring topics to select the top topics for training the classifier. This study applied TextNetTopics to textual data to respond to the CAMDA challenge. TextNetTopics outperforms various feature selection approaches while highly performing when applying the model to the validation data provided by the CAMDA. Additionally, we have applied our algorithm to different textual datasets.
CASVA: Configuration-Adaptive Streaming for Live Video Analytics

The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has fulled the development of live video analytics, where camera videos need to be streamed over the network to edge or cloud servers with sufficient computational resources. Although it is promising to strike a balance between available bandwidth and server-side DNN inference accuracy by adjusting video encoding configurations, the influences of fine-grained network and video content dynamics on configuration performance should be addressed. In this paper, we propose CASVA, a Configuration-Adaptive Streaming framework designed for live Video Analytics. The design of CASVA is motivated by our extensive measurements on how video configuration affects its bandwidth requirement and inference accuracy. To handle the complicated dynamics in live video analytics streaming, CASVA trains a deep reinforcement learning model which does not make any assumptions about the environment but learns to make configuration choices through its experiences. A variety of real-world network traces are used to drive the evaluation of CASVA. The results on a multitude of video types and video analytics tasks show the advantages of CASVA over state-of-the-art solutions.