Reviewerly/exHarmony huggingface dataset:
Can Blockchain Technology Facilitate International Trade?

This study considers the potential role for blockchain technologies in international trade, specifically how such technologies might affect trade finance, customs procedures, and provenance (origin) of goods. For trade finance, blockchain could reduce the expense and time required to facilitate trade that depends on third-party lending or insurance. For customs procedures, blockchain could reduce costs, expedite customs procedures, and boost both global trade volumes and economic output more than the worldwide elimination of tariffs. For provenance, blockchain could improve management of supply chains by providing real-time information on the origin and movement of goods. Blockchain may also be used to improve detection of illicit trade flows and deter illegitimate efforts to circumvent trade rules. Such applications could aid customs and law enforcement in facilitating the flow of legitimate trade. Numerous efforts are underway to explore the benefits of blockchain as well as how to mitigate its risks for international commerce.
Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference

Color mapping is a foundational technique for visualizing scalar data. Prior literature offers guidelines for effective colormap design, such as emphasizing luminance variation while limiting changes in hue. However, empirical studies of color are largely focused on perceptual tasks. This narrow focus inhibits our understanding of how generalizable these guidelines are, particularly to tasks like visual inference that require synthesis and judgement across multiple percepts. Furthermore, the emphasis on traditional ramp designs (e.g., sequential or diverging) may sideline other key metrics or design strategies. We study how a cognitive metric-color name variation-impacts people's ability to make model-based judgments. In two graphical inference experiments, participants saw a series of color-coded scalar fields sampled from different models and assessed the relationships between these models. Contrary to conventional guidelines, participants were more accurate when viewing colormaps that cross a variety of uniquely nameable colors. We modeled participants' performance using this metric and found that it provides a better fit to the experimental data than do existing design principles. Our findings indicate cognitive advantages for colorful maps like rainbow, which exhibit high color categorization, despite their traditionally undesirable perceptual properties. We also found no evidence that color categorization would lead observers to infer false data features. Our results provide empirically grounded metrics for predicting a colormap's performance and suggest alternative guidelines for designing new quantitative colormaps to support inference. The data and materials for this paper are available at: https://osf.io/tck2r/.
Access to finance among small and medium-sized enterprises and job creation in Africa

In the past decade inclusive growth, that is job-rich growth, has topped the policy agenda in developing countries. This paper investigates how the access to finance affects employment in small and medium-sized enterprises (SMEs) in Sub-Saharan Africa. It first presents a model where firm creation requires entrepreneurial search and paying the start-up costs, while the firm's size in terms of employment depends on the access to credit. Under the financial market imperfections, access to credit can be a binding constraint on firm entry and employment even when the banks have sufficient liquidity. Using an impact evaluation-based approach on firm-level data from 42 African countries, we show that SMEs with access to formal financing create more jobs than firms without access, with employment in firms having access to more affordable and larger loans growing the fastest. The impact of access to finance is stronger for firms in manufacturing than in services, pointing to sectoral targeting of finance as a possible policy supporting industrialization.
A Reliable Data-Transmission Mechanism Using Blockchain in Edge Computing Scenarios

With the advent of the Internet-of-Things (IoT) era, more and more devices are connected to the IoT. Under the traditional cloud-thing centralized management mode, the transmission of massive data is facing many difficulties, and the reliability of data is difficult to be guaranteed. As emerging technologies, blockchain technology and edge computing (EC) technology have attracted the attention of academia in improving the reliability, privacy, and invariability of IoT technology. In this article, we combine the characteristics of the EC and blockchain to ensure the reliability of data transmission in the IoT. First, we propose a data transmission mechanism based on blockchain, which uses the distributed architecture of blockchain to ensure that the data is not tampered with; second, we introduce the three-tier structure in the architecture in turn; and finally, we introduce the four working steps of the mechanism, which are similar to the working mechanism of blockchain. In the end, the simulation results show that the proposed scheme can ensure the reliability of data transmission in the IoT to a great extent.
Data-driven ICU management: Using Big Data and algorithms to improve outcomes

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" can be used to refer to the analysis of these datasets that collect enormous amount of data of different origin and format. Complexity and variety define the value of Big Data. In fact, the retrospective analysis of these datasets allows to generate new knowledge, with consequent potential improvements in the clinical practice. Despite the promising start of Big Data analysis in medical research, which has seen a rising number of peer-reviewed articles, very limited applications have been used in ICU clinical practice. A close future effort should be done to validate the knowledge extracted from clinical Big Data and implement it in the clinic. In this article, we provide an introduction to Big Data in the ICU, from data collection and data analysis, to the main successful examples of prognostic, predictive and classification models based on ICU data. In addition, we focus on the main challenges that these models face to reach the bedside and effectively improve ICU care.
International Comparative Study on PISA Mathematics Achievement Test Based on Cognitive Diagnostic Models

As one of the most influential international large-scale educational assessments, the Program for International Student Assessment (PISA) provides a valuable platform for the horizontal comparisons and references of international education. The cognitive diagnostic model, a newly generated evaluation theory, can integrate measurement goals into the cognitive process model through cognitive analysis, which provides a better understanding of the mastery of students of fine-grained knowledge points. On the basis of the mathematical measurement framework of PISA 2012, 11 attributes have been formed from three dimensions in this study. Twelve test items with item responses from 24,512 students from 10 countries participated in answering were selected, and the analyses were divided into several steps. First, the relationships between the 11 attributes and the 12 test items were classified to form a Q matrix. Second, the cognitive model of the PISA mathematics test was established. The liner logistic model (LLM) with better model fit was selected as the parameter evaluation model through model comparisons. By analyzing the knowledge states of these countries and the prerequisite relations among the attributes, this study explored the different learning trajectories of students in the content field. The result showed that students from Australia, Canada, the United Kingdom, and Russia shared similar main learning trajectories, while Finland and Japan were consistent with their main learning trajectories. The primary learning trajectories of the United States and China were the same. Furthermore, the learning trajectory for Singapore was the most complicated, as it showed a diverse learning process, whereas the trajectory in the United States and Saudi Arabia was relatively simple. This study concluded the differences of the mastery of students of the 11 cognitive attributes from the three dimensions of content, process, and context across the 10 countries, which provided a reference for further understanding of the PISA test results in other countries and shed some evidence for a deeper understanding of the strengths and weaknesses of mathematics education in various countries.
Introduction to the Tezos Blockchain

Tezos is an innovative blockchain that improves on several aspects compared to more established blockchains. It offers an original proof-of-stake consensus algorithm and can be used as a decentralized smart contract platform. It has the capacity to amend its own economic protocol through a voting mechanism and focuses on formal methods to improve safety.
Argentina and the COVID-19: Lessons learned from education and technical colleges in Buenos Aires Province

This study looks at the impact of the novel coronavirus (COVID-19) pandemic on four education and technical colleges from the San Nicolas District in Buenos Aires Province, Argentina. We analyse information collected from directors, a regional teacher trainer, and an education specialist on how colleges react, adapt, and respond to student-teacher needs during the tine of the pandemic. We also examine the changes to the practicum for 21 teachers. The reality of the pandemic has inspired management and teachers from training and technical institutions to propose an innovative educational response for the student-teachers and to contemplate how teacher training can reshape practice and learning moving from the old to the new normal.
DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems

In Internet-of-Vehicles (IoV) ecosystems, intelligent toll gates (ITGs) connect nearby metropolitan cities through smart highways. At ITGs, existing solutions integrate blockchain (BC) and deep-learning schemes to leverage trusted and responsive analytics support for connected smart vehicles (CSVs) at ITGs. BC eliminates third-party intermediaries, and secures payments between vehicle owners (VO) and governing authorities (GA). Deep-Learning, on the other hand, facilitates accurate predictions for diverse and complex urban traffic conditions. However, due to fixed toll pricing schemes based on connected smart vehicles (CSV) type, VOs suffer from variable delays at different lanes due to dynamic congestion scenarios. To address the research gaps of such a fixed pricing schemes, we propose a BC-envisioned scheme DwaRa, that operates in three phases. In the first phase, future traffic is predicted based on Markov queues to balance the congestion at different lanes at ITGs efficiently. Then, we propose a novel spatially induced-long-short term memory (SI-LSTM) model to predict current traffic and weather based on historical repositories. Second, based on inputs by the Markov model, SI-LSTM, lane type, and vehicle type, a dynamic pricing algorithm is presented to improve the quality of experience (QoE) of the VO. Finally, based on dynamic price fixation between the VO and the GA, smart contracts (SCs) are executed and transactional data is secured through BC. The proposed scheme is compared against parameters like average mean-squared error (MSE), predicted traffic, scalability, interplanetary file system (IPFS) storage, computation (CC), and communication cost (CCM). At n = 100 test samples, and arrival rate β = 80, the obtained MSE is 0.0012, with a peak average value of 0.00526. The overall CC is 45.88 milliseconds (ms) and CCM is 53 bytes that indicate the proposed scheme efficacy against conventional approaches.
Adversarial co-distillation learning for image recognition

Knowledge distillation is an effective way to transfer the knowledge from a pre-trained teacher model to a student model. Co-distillation, as an online variant of distillation, further accelerates the training process and paves a new way to explore the “dark knowledge” by training n models in parallel. In this paper, we explore the “divergent examples”, which can make the classifiers have different predictions and thus induce the “dark knowledge”, and we propose a novel approach named Adversarial Co-distillation Networks (ACNs) to enhance the “dark knowledge” by generating extra divergent examples. Note that we do not involve any extra dataset, and we only utilize the standard training set to train the entire framework. ACNs are end-to-end frameworks composed of two parts: an adversarial phase consisting of Generative Adversarial Networks (GANs) to generate the divergent examples and a co-distillation phase consisting of multiple classifiers to learn the divergent examples. These two phases are learned in an iterative and adversarial way. To guarantee the quality of the divergent examples and the stability of ACNs, we further design “Weakly Residual Connection” module and “Restricted Adversarial Search” module to assist in the training process. Extensive experiments with various deep architectures on different datasets well demonstrate the effectiveness of our approach.
Blockchain-Based Secure Computation Offloading in Vehicular Networks

Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.
GPU-NEST: Characterizing Energy Efficiency of Multi-GPU Inference Servers

Cloud inference systems have recently emerged as a solution to the ever-increasing integration of AI-powered applications into the smart devices around us. The wide adoption of GPUs in cloud inference systems has made power consumption a first-order constraint in multi-GPU systems. Thus, to achieve this goal, it is critical to have better insight into the power and performance behaviors of multi-GPU inference system. To this end, we propose GPU-NEST, an energy efficiency characterization methodology for multi-GPU inference systems. As case studies, we examined the challenges presented by, and implications of, multi-GPU scaling, inference scheduling, and non-GPU bottleneck on multi-GPU inference systems' energy efficiency. We found that inference scheduling in particular has great benefits in improving the energy efficiency of multi-GPU scheduling, by as much as 40 percent.
Convolutional Neural Network Pruning: A Survey

Deep convolutional neural networks have enabled remarkable progress over the last years on a variety of visual tasks, such as image recognition, speech recognition, and machine translation. These tasks contribute many to machine intelligence. However, developments of deep convolutional neural networks to a machine terminal remains challenging due to massive number of parameters and float operations that a typical model contains. Therefore, there is growing interest in convolutional neural network pruning. Existing work in this field of research can be categorized according to three dimensions: pruning method, training strategy, estimation criterion.
360-Degree Video Streaming: A Survey of the State of the Art

360-degree video streaming is expected to grow as the next disruptive innovation due to the ultra-high network bandwidth (60–100 Mbps for 6k streaming), ultra-high storage capacity, and ultra-high computation requirements. Video consumers are more interested in the immersive experience instead of conventional broadband televisions. The visible area (known as user’s viewport) of the video is displayed through Head-Mounted Display (HMD) with a very high frame rate and high resolution. Delivering the whole 360-degree frames in ultra-high-resolution to the end-user significantly adds pressure to the service providers’ overall intention. This paper surveys 360-degree video streaming by focusing on different paradigms from capturing to display. It overviews different projections, compression, and streaming techniques that either incorporate the visual features or spherical characteristics of 360-degree video. Next, the latest ongoing standardization efforts for enhanced degree-of-freedom immersive experience are presented. Furthermore, several 360-degree audio technologies and a wide range of immersive applications are consequently deliberated. Finally, some significant research challenges and implications in the immersive multimedia environment are presented and explained in detail.
A Plug-and-Play Scheme to Adapt Image Saliency Deep Model for Video Data

With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information. However, due to the lesser consideration of temporal information, the image saliency deep models may become fragile in the video sequences dominated by temporal information. Thus, the most recent video saliency detection approaches have adopted the network architecture starting with a spatial deep model that is followed by an elaborately designed temporal deep model. However, such methods easily encounter the performance bottleneck arising from the single stream learning methodology, so the overall detection performance is largely determined by the spatial deep model. In sharp contrast to the current mainstream methods, this paper proposes a novel plug-and-play scheme to weakly retrain a pretrained image saliency deep model for video data by using the newly sensed and coded temporal information. Thus, the retrained image saliency deep model will be able to maintain temporal saliency awareness, achieving much improved detection performance. Moreover, our method is simple yet effective for adapting any off-the-shelf pre-trained image saliency deep model to obtain high-quality video saliency detection. Additionally, both the data and source code of our method are publicly available.
A black-box adversarial attack for poisoning clustering

Clustering algorithms play a fundamental role as tools in decision-making and sensible automation processes. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. To the best of our knowledge, however, only a few works have currently addressed this problem. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
Assessment of Mixed-Reality Technology Use in Remote Online Anatomy Education

This survey study uses survey responses from first-year medical students to evaluate the initial experience of an anatomy lesson delivered remotely using teleconferencing and mixed-reality technology.
Usability of mental illness simulation involving scenarios with patients with schizophrenia via immersive virtual reality: A mixed methods study

Schizophrenia is one of the most prevalent mental illnesses contributing to national burden worldwide. It is well known that mental health nursing education, including clinical placement, is still insufficient to reach the optimal level of competency in nursing students. This study suggests a new form of mental health virtual reality (VR) simulation that is user-friendly and engaging to improve education about schizophrenia, thereby improving its treatment.A mixed-methods study was conducted with a total of 60 nursing students, using 360-degree videos of five different scenarios reflecting clinical symptoms of schizophrenia patients and related treatment tasks delivered via head-mounted displays (HMDs). We used a 17-item quantitative questionnaire and a 7-item open-ended qualitative questionnaire to evaluate the ease of use and usefulness of the VR simulation program and to identify areas where further improvement is required.The VR simulation program was perceived as useful and exciting. Participants stressed that the high realism of the simulation increased their engagement in and motivation to learn about mental health nursing. Some participants made suggestions, such as further refining the picture and sound quality in order to achieve satisfactory educational outcomes.VR simulation using 360-degree videos and HMDs could serve as an effective alternative form of clinical training in mental health nursing. Education could be enhanced by its benefits of being engaging and exciting, as reported by this study's participants.