Reviewerly/exHarmony huggingface dataset:
Blockchain-enabled digital asset tokenization for crowdsensing in environmental, social, and governance disclosure

Mobile crowdsensing has the potential to facilitate Environmental, Social, and Governance (ESG) disclosure in supply chain networks. Traditional crowdsensing involves smartphone users sharing information to fulfill specific tasks while maximizing their rewards. But, implementing crowdsensing for ESG demands (1) cost-sharing to support technology adoption by information providers, (2) tracking the ownership and use of information, and (3) tackling the drawback of monetary incentives. This study investigates a novel digital asset tokenization mechanism for crowdsensing in ESG disclosure. Its blockchain tokens can keep track of each user's identity and consumption behavior to deliver necessary symbolic incentives to the information or digital asset provider. We model the interaction between providers and users as a two-stage Stackelberg game to analyze the participants' usage levels and optimal investment in technology. The incentive mechanism considers various factors such as technology adoption costs, level of cost sharing, and symbolic incentives to reveal their impact on participant benefits. Moreover, we endogenize the cost-sharing to evaluate/study the factors influencing the ideal/optimal cost-sharing level from digital asset consumers. We find that an increase in reputation incentive per token stimulates token consumption, which leads to more significant payoffs for the participants. Conversely, increasing technology adoption costs decreases token consumption, leading to lesser profits for the participants. However, in the case of cost-sharing, there exists a unique level of cost-sharing before (after) which utility of DA consumer increase (decreases), i.e., an incentive mechanism built on endogenous cost-sharing exhibits a competitive advantage over exogenous cost-sharing in terms of the profit it delivers to the DA consumers.
A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms

To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes.
Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions.
Efficient deep learning models based on tension techniques for sign language recognition

Communication by speaking prevails among the various ways of self-expression and communication between people. Speech presents a significant challenge for some disabled people, such as deaf people, deaf and hard of hearing, dumb and wordless persons. Therefore, these people rely on sign language to interact with others. Sign language is a system of movements and visual messages that ensure the integration of these individuals into groups that communicate vocally. On the other side, it is necessary to understand these individuals' gestures and linguistic semantics. The main objective of this work is to establish a new model that enhances the performance of the existing paradigms used for sign language recognition. This study developed three improved deep-learning models based on YOLOv5x and attention methods for recognizing the alphabetic and numeric information hand gestures convey. These models were evaluated using the MU HandImages ASL and OkkhorNama: BdSL datasets. The proposed models exceed those found in the literature, where the accuracy reached 98.9% and 97.6% with the MU HandImages ASL dataset and the OkkhorNama: BdSL dataset, respectively. The proposed models are light and fast enough to be used in real-time ASL recognition and to be deployed on any edge-based platform.
Learning after progress? Isabelle Stengers, artificial learning, and the future as problem

Abstract The aim of this article is to rethink the relation between education and progress, claiming that discourses of progress tend to project specific visions of the future and thereby instrumentalize education to achieve these visions while foreclosing other possible futures. The first part of the paper argues that the historical pact between education and progress has been recently recast in terms of learning. Learning receives at the same time an economic and a political interpretation in this context, turning issues such as unemployment or social justice into learning problems. What both interpretations seem to share, however, is that learning is conceived of as a pathway to futures already known. Drawing on the philosophy of Isabelle Stengers, in whose work the concept of learning acquires a vital position, the article reframes learning in terms of a situated encounter made possible within an artificial environment, whereby the future is not being projected, but becomes thoroughly problematic—a matter of collective concern. Recent calls to ‘learn to live with’ Covid-19, but also the effects of climate change, form the point of departure for reworking the concept of learning from the inside out.
The EnTPACK rubric: development, validation, and reliability of an instrument for measuring pre-service science teachers’ enacted TPACK

Science instruction can benefit from the use of digital technologies if pre-service teachers are given opportunities to acquire Technological Pedagogical And Content Knowledge (TPACK) as part of their studies. However, the prevailing self-report approach to TPACK measurement does not allow conclusions to be drawn about enacted TPACK, which is rarely assessed in real classroom situations. In addition, instruments designed to measure TPACK enactment lack descriptive clarity and no single instrument is used to assess the three relevant phases of teacher competencies (lesson planning, implementation, and reflection). The present paper addresses this gap by presenting the development and validation of a comprehensive rubric for assessing the enacted TPACK of pre-service science teachers. To operationalize the “fuzzy” aspects of the framework, the rubric targets a specific use of digital media and instructional approach in science teaching: student-generated explainer videos and animations. At the core of the development process is a theory- and literature-based systematic review of (1) existing instruments for assessing pre-service science teachers’ enacted TPACK and (2) instructional criteria for student-generated explainer videos in science classes. The resulting rubric allows valid conclusions given the appropriate conditions, has demonstrated reliability, and excels due to its specific focus, high degree of differentiation, systematic grounding in theory and literature, objective grading criteria, and comprehensive applicability to all three phases of teacher competencies.
INK: Knowledge graph representation for efficient and performant rule mining

Semantic rule mining can be used for both deriving task-agnostic or task-specific information within a Knowledge Graph (KG). Underlying logical inferences to summarise the KG or fully interpretable binary classifiers predicting future events are common results of such a rule mining process. The current methods to perform task-agnostic or task-specific semantic rule mining operate, however, a completely different KG representation, making them less suitable to perform both tasks or incorporate each other’s optimizations. This also results in the need to master multiple techniques for both exploring and mining rules within KGs, as well losing time and resources when converting one KG format into another. In this paper, we use INK, a KG representation based on neighbourhood nodes of interest to mine rules for improved decision support. By selecting one or two sets of nodes of interest, the rule miner created on top of the INK representation will either mine task-agnostic or task-specific rules. In both subfields, the INK miner is competitive to the currently state-of-the-art semantic rule miners on 14 different benchmark datasets within multiple domains.
Haters in the spotlight

Abstract This study investigates the interplay between commenters’ gender and their language use in Slovene socially unacceptable Facebook comments by inspecting the frequency of opinion markers and impoliteness triggers, and the pragmatic functions of opinion markers which were investigated according to a newly developed typology. The analysis shows statistically significant differences between men and women in their formulation of opinions and use of impoliteness triggers. Comments written by men are characteristically formed as fact-like covert opinions and contain explicit impoliteness triggers (e.g., insults). Comments written by women are formed as overt opinions explicitly encoding subjectivity and include implicit impoliteness triggers (e.g., tropes). A common feature is the use of opinion markers as self-oriented face-saving devices. But while men use opinion markers to explicitly communicate their desire to save face, women use them to perform face-saving implicitly.
An Effective Obstacle Avoidance and Motion Planning Design for Underwater Telescopic Arm Robots Based on a Tent Chaotic Dung Beetle Algorithm

As the underwater environment is complex, the existence of obstacles will produce a certain collision interference to underwater robot operations, which causes the overall path planning and time costs to increase. In this paper, we propose a Tent chaotic mapping and dung beetle hybrid algorithm (MDBO) application for trajectory optimal planning and effective obstacle avoidance for an underwater telescopic arm robot. The method invokes the unique obstacle avoidance habit and foraging optimization idea of the dung beetle algorithm. Introducing it into the chaotic Tent mapping idea prevents the dung beetle algorithm (DBO) from falling into local optimality and increases the coverage of a global search. Simulation results show that the MDBO algorithm exhibits strong optimization ability and stability when multiple algorithms are verified using eight test functions. The MATLAB test reflects the performance indexes of the six joints of the underwater telescopic arm, and compared with various algorithms, the MDBO algorithm has an obvious convergence trend and strong global search ability. The algorithm is applied to real underwater experiments to verify that the improved dung beetle algorithm has better obstacle avoidance ability and reduces trajectory planning time by 30%, which helps the underwater robot to complete motion planning.
Blockchain technology applications in retail branding: Insights from retailers in the developing world

Abstract The research on blockchain use cases is becoming critical across all disciplines as this technology is expected to transform business processes and their individual operations. However, there is a scarcity of empirical research that focuses on the implication of blockchain applications in retailing and retailers in developing countries. Thus, there are three main aims of our research; first, we explore both the drivers and the barriers for the retailers in developing countries in adopting blockchain technology. Second, we attempt to explore the impacts on the retailers of blockchain technology adoption on their retail brand communication and customer experience. Finally, we examine whether the application of blockchain technology by retailers in developing countries can help to advance the retail brand‐customer relationship. The data in this study are collected from 21 semistructured interviews with senior managers, and 3 focus groups with 21 customers from 4 case retailers. Our research is among the first attempts to explore the blockchain technology adoption empirically, and its implications by retailers in developing countries.
CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers

Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the citation in a given context. We present a tool Citeprompt which uses the hitherto unexplored approach of prompt learning for citation intent classification. We argue that with the proper choice of the pretrained language model, the prompt template, and the prompt verbalizer, we can not only get results that are better than or comparable to those obtained with the state-of-the-art methods but also do it with much less exterior information about the scientific document. We report state-of-the-art results on the ACL-ARC dataset, and also show significant improvement on the SciCite dataset over all baseline models except one. As suitably large labelled datasets for citation intent classification can be quite hard to find, in a first, we propose the conversion of this task to the few-shot and zero-shot settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and 10-shot settings respectively.
Deep Hybrid Model with Trained Weights for Multimodal Sarcasm Detection

Sarcasm detection is one the most challenging task in natural language processing. Though sentiment semantics are necessary to improve sarcasm detection performance, existing DL-based sarcasm detection models do not fully incorporate them. This research suggested the Hybrid RNN and Optimized LSTM for Multimodal Sarcasm Detection (HROMSD) model. The model is processed under the four stages: preprocessing, feature extraction, feature level fusion, and classification. The initial stage of this proposed technique is preprocessing, here input of the multimodal data, which comprises of text, video, and audio are preprocessed. Here, the text will be preprocessed under tokenization and stemming, the video will be preprocessed under face detection and the audio will be preprocessed under filtering technique. Then, the stage of feature extraction takes place, where the features from preprocessed text, video, and audio are extracted, here, n-grams, TF-IDF, improved Bag of Visual Words, and emojis are extracted as the text features; then CLM and improved SLBT based video features are extracted from the video features, and chroma, MFCC, jitter and special features are extracted from the audio features. The resultant extracted features set are subjected for feature level fusion stage, which makes use of an improved multilevel CCA fusion technique. The classification is carried out using Hybrid RNN and Optimized LSTM for detection purpose, where Improved BES (IBES) method utilized to increase the detection system’s performance. When compared to earlier research, the proposed work is more accurate.
Prehistoric Mining Data: How to create Open Data from archaeological research for the ARIADNE community and beyond

Making archaeological data available for the scientific community received a major boost through the ARIADNE Infrastructure projects that began in 2013. The purpose of this article is to present a methodology for converting excavation and field survey documentation from its original source into a format that corresponds to the FAIR principles and commits to the ARIADNE guidelines, with the ultimate goal to make the data publicly available and incorporating them into the ARIADNE portal. The methodology is illustrated by excavation and field survey reports that have been created according to the requirements of the Austrian Federal Monuments Office and document the archaeological investigations undertaken in the project 'Prehistoric copper production in the eastern and central Alps'. These were further processed in an Open Research Data pilot project, which initiated the development of the methodology. This consists of a pipeline to convert excavation and field survey reports to lasting file formats and extract information on sites, archaeological structures, stratigraphic units and finds from the reports to create CIDOC CRM encoded RDF data. The goal of the pipeline is to lower the entry threshold for creating such data by making data entry easy, using spreadsheets and applying easy to install and open-source (at least freely available) tools. All components of the pipeline are freely available, and detailed documentation with installation instructions and sample data can be downloaded for those who wish to test the methodology and try it out using their own data.
OfpCNN: On-Demand Fine-Grained Partitioning for CNN Inference Acceleration in Heterogeneous Devices

Collaborative inference is a promising method for balancing the limited computational power of Internet of Things (IoT) devices with the huge computational demands of convolutional neural networks (CNNs). In this approach, a CNN is divided into multiple partitions and placed on multiple devices to run simultaneously. However, two major challenges are raised. (1) Computational latencies vary when the central processing unit (CPU) loads of devices are different. However, no suitable methods are available for accurately determining computation latencies on the basis of CPU utilization. (2) Existing methods partition a CNN model either vertically or horizontally. The granularity of these methods is extremely coarse and their accuracy is low. To address the aforementioned issues, this study proposes a distributed collaborative inference framework that supports a fine-grained partitioning scheme for CNN in heterogeneous devices (hereafter referred to as OfpCNN). First, the framework uses the layer latency prediction model based on floating-point operations and CPU load (FCPM) to accurately predict the computation latency of each layer of CNN in different devices. Subsequently, OfpCNN uses horizontal and vertical partitioning methods (HVPM) to partition the input feature maps and the structure of CNN respectively in accordance with network conditions and computing capacity, then assigns them to multiple devices for execution. The HVPM solution overall considers the execution position of the layer, parallelism, and location of devices responsible for data aggregation and distribution, which can consequently obtain more fine-grained partition schemes. Experimental results show that FCPM can achieve a minimum accuracy of 88% and HVPM can improve the inference speed by 1-2.54 times compared with other state-of-the-art methods.
XFaaS: Hyperscale and Low Cost Serverless Functions at Meta

Function-as-a-Service (FaaS) has become a popular programming paradigm in Serverless Computing. As the responsibility of resource provisioning shifts from users to cloud providers, the ease of use of FaaS for users may come at the expense of extra hardware costs for cloud providers. Currently, there is no report on how FaaS platforms address this challenge and the level of hardware utilization they achieve.
Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co‐existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model‐based (including frequentist and Bayesian) approaches and algorithm‐based approaches. To evaluate their performance, we compare these approaches using real‐life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.