Reviewerly/exHarmony huggingface dataset:
Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP

Natural language processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence, which is concerned with developing methods to process and generate language at scale. Modern NLP tools have the potential to support humanitarian action at multiple stages of the humanitarian response cycle. Both internal reports, secondary text data (e.g., social media data, news media articles, or interviews with affected individuals), and external-facing documents like Humanitarian Needs Overviews (HNOs) encode information relevant to monitoring, anticipating, or responding to humanitarian crises. Yet, lack of awareness of the concrete opportunities offered by state-of-the-art techniques, as well as constraints posed by resource scarcity, limit adoption of NLP tools in the humanitarian sector. This paper provides a pragmatically-minded primer to the emerging field of humanitarian NLP, reviewing existing initiatives in the space of humanitarian NLP, highlighting potentially impactful applications of NLP in the humanitarian sector, and describing criteria, challenges, and potential solutions for large-scale adoption. In addition, as one of the main bottlenecks is the lack of data and standards for this domain, we present recent initiatives (the DEEP and HumSet ) which are directly aimed at addressing these gaps. With this work, we hope to motivate humanitarians and NLP experts to create long-term impact-driven synergies and to co-develop an ambitious roadmap for the field.
Uniform consistency in nonparametric mixture models

We study uniform consistency in nonparametric mixture models as well as closely related mixture of regression (also known as mixed regression) models, where the regression functions are allowed to be nonparametric and the error distributions are assumed to be convolutions of a Gaussian density. We construct uniformly consistent estimators under general conditions while simultaneously highlighting several pain points in extending existing pointwise consistency results to uniform results. The resulting analysis turns out to be nontrivial, and several novel technical tools are developed along the way. In the case of mixed regression, we prove L1 convergence of the regression functions while allowing for the component regression functions to intersect arbitrarily often, which presents additional technical challenges. We also consider generalizations to general (i.e., nonconvolutional) nonparametric mixtures.
Clustering ensemble-based novelty score for outlier detection

Recently, One-class classification algorithms have been successfully used for outlier detection problems in several industrial fields. However, in case of that the target class has complex structures, single outlier detection model with one-class classifier often poorly performs because it cannot appropriately reflect intrinsic data structures. To address this limitation, we propose a clustering ensemble-based novelty score algorithm. The proposed algorithm calculates novelty score from the mixture of multiple clustering solutions generated by both random subspace and random-K ensemble approaches. Then, final ensemble novelty score is defined by summarizing multiple novelty scores obtained from individual clustering results. Because these multiple novelty scores are computed from many possible characteristics of target class information, the proposed ensemble novelty score can appropriately reflect the inherent structures of target class. Experiments were conducted on various benchmark datasets to compared with existing methods and investigate the properties of the proposed algorithm. The experimental results confirm that the proposed algorithm outperforms existing one-class classification methods in various cases.
Slimmable Multi-Task Image Compression for Human and Machine Vision

In the Internet of Things (IoT) communications, visual data is frequently processed among intelligent devices using artificial intelligence algorithms, replacing humans for analyzing and decision-making while only occasionally requiring human’s scrutiny. However, due to high redundancy of compressive encoders, existing image coding solutions for machine vision are not efficient at runtime. To balance the rate-accuracy performance and efficiency of image compression for machine vision while attaining high-quality reconstructed images for human vision, this paper introduces a novel slimmable multi-task compression framework for human and machine vision in visual IoT applications. Firstly, the image compression for human and machine vision under the constraint of bandwidth, latency, computational resources are modelled as a multi-task optimization problem. Secondly, slimmable encoders are employed to multiple human and machine vision tasks in which the parameters of the sub-encoder for machine vision tasks are shared among all tasks and jointly learned. Thirdly, to solve the feature match between latent representation and intermediate features of deep vision networks, feature transformation networks are introduced as decoders of machine vision feature compression. Finally, the proposed framework is successfully applied to human and machine vision tasks’ scenarios, e.g., object detection and image reconstruction. Experimental results show that the proposed method outperforms baselines and other image compression approaches on machine vision tasks with higher efficiency (shorter latency) in two vision tasks’ scenarios while retaining comparable quality on image reconstruction.
Slender Flexible Object Segmentation Based on Object Correlation Module and Loss Function Optimization

Slender flexible objects are ubiquitous in real-world circumstances. The existing object detection and segmentation algorithms have achieved high accuracy and speed in rigid objects, but the detection effect of slender flexible objects is not ideal. Extreme aspect ratios and dynamically changeable geometric appearance characterize slender flexible objects, to the extent that it is difficult to locate them in instance segmentation. In this paper, a new instance segmentation method based on the object correlation module and loss function optimization is proposed for the detection of slender flexible objects. In order to achieve more accurate anchor box positioning, a GIoU bounding-box regression loss function is selected to overcome the problem of inconsistency between training objectives and assessment indicators. Furthermore, due to the Mask Scoring R-CNN detection network ignoring the relationships between objects, the object correlation module is proposed to achieve end-to-end learning and modeling of the correlation features between all objects in the image to improve slender flexible objects detection accuracy. The results of the experiments on the self-built flexible object dataset for the power grid operation sites demonstrate that the method presented in this research can efficiently recognize and segment slender flexible objects, with a detection accuracy of 44.8%. The ablation experiment also shows that the addition object correlation module and the revised bounding-box regression loss function are both effective and can enhance slender flexible object detection accuracy by 1.2% and 0.5%, respectively. The proposed instance segmentation method considers the correlation characteristics between objects and improves the bounding-box regression loss function to increase the segmentation accuracy of slender flexible objects.
Redesigning the Serpent Algorithm by PA-Loop and Its Image Encryption Application

This article presents a cryptographic encryption standard whose model is based on Serpent presented by Eli Biham, Ross Anderson, and Lars Knudsen. The modification lies in the design of the Cipher, we have used power associative (PA) loop and group of permutations. The proposed mathematical structure is superior to Galois Field (GF) in terms of complexity and has the ability to create arbitrary randomness due to a larger key space. The proposed method is simple and speedy in terms of computations, meanwhile it affirms higher security and sensitivity. In contrast to GF, PA-loop are non-isomorphic and have several Cayley table representations. This validates the resistance to cryptanalytic attacks, particularly those targeting mathematical structures. This cryptographic scheme’s full description of encryption and decryption is measured and rigorously assessed to support its multimedia applications. The observed speed of this technique, which uses a key of 256 bits and a block size of 128 bits, is comparable to three-key triple-DES.
Higher education teachers' digital competencies for a blended future

Welcome to this special issue, focussing on teachers' digital competencies in Higher Education. The articles in this special issue explore the question of how digital competencies in higher education are conceptualized after the Great Online Transition, during and after the pandemic. A few of the topics addressed in the special issue are: new competency frameworks, the issue of disciplines and artificial intelligence and looks into the future of higher education learning and teaching. In this preface to the special issue, we first present a brief introduction to the context and the problem statement. We then provide a summary of each of the ten papers included in this special issue, we present how they are related and how each article makes a unique contribution to the main goal of the special issue. Finally, the implications are discussed together with suggestions for future research.
Entrepreneurship innovation using social robots in tourism: a social listening study

Abstract The tourism sector has been one of the most impacted by the COVID-19 pandemic, due to restrictions on mobility and fear of social contact. In this context, business innovation through digital transformation is presented as a great opportunity for the tourism industry and the inclusion of social robots in service tasks is an example. This transformation requires new methodologies, skills and talent that must be promoted to improve the innovative tourism ecosystem. With this research, we try to determine how the inclusion of social or service robots in hotels can improve the image and perception held by clients or guests. For that, we first analyse the degree of knowledge and sentiment generated by social robots through a social listening study in social networks. In addition, we determine whether these perceptions on the subject are in tune with other more formal fields, such as scientific research, or with the strategies followed at a national or international level by companies, agencies and organisations related to the technology and innovation of social robotics. For both objectives, we use the Simbiu social listening tool, a software-based program on Talkwalker, and we obtain interesting results. Basically, people on Twitter have a neutral or positive feeling about the use of social robots, and people who write in English have a more positive attitude towards social robots than Spanish speakers. After COVID-19, are necessary changes in strategic decisions of the hospitality and it is essential to continue investigating the role of social robots in this new context.
Multi-level learning counting via pyramid vision transformer and CNN

Severe scale variation has become a challenging issue for hindering the improvement of accuracy in crowd counting task. To tackle the problem, we propose a Pyramid Transformer CNN Network (PTCNet), an effective combination of the transformer and the CNN, which possesses both the global receptive fields and the locality to deal with the severe scale variation problems and boost the prediction accuracy. Firstly, we utilize the pyramid vision transformer to extract multi-level global context information of the crowd, aiming at different head scales. And then, the multi-level information is fully fused in the multi-level feature aggregation module where detailed crowd characteristics from all feature spaces are preserved to be further processed. Finally, we design a multi-branch regression head to enrich the crowd features for strong representations and regress the density maps. Extensive experiments on challenging datasets with complex scenarios and multiple scales demonstrate the effectiveness of the our method. The proposed method achieves competitive results comparing with the state-of-the-art approaches and achieves state-of-the-art results(MAE:51.7, RMSE:79.6) on ShanghaiTech Part_A dataset.
Decolonial design practices: Creating safe spaces for plural voices on contested pasts, presents, and futures

This article introduces the concept of the ‘safe space’ in design interventions: a socially developed environment for thoughts, actions, and mutual learning in political and contested contexts. The safe space responds to current challenges within decolonial design practices and calls for a plurality of knowledge and voices. We present a study in which we developed safe spaces with young Namibians by combining core elements of design anthropology and community-based participatory design through interventions, dialogic engagements, and co-design. We demonstrate how the intentional design practice of a safe space enables new ways of working in a contested context. The article presents a framework for designing and developing safe spaces, focusing on working with multiple temporalities and the social production of spaces.
HeadPose-Softmax: Head pose adaptive curriculum learning loss for deep face recognition

Face recognition has been one of the most popular applications in the field of target detection. Currently, frontal faces can be easily detected, but multi-view face detection remains a difficult task because of various factors such as illumination, various poses, occlusions, and facial expressions. Margin-based loss functions are used to increase the feature margins between different classes, thus enhancing the discriminability of face recognition models, but the performance in face detection in complex scenes (e.g., high pitch angle face detection in surveillance environments) can be significantly degraded. Recently, the idea of a mining-based strategy to emphasize hard samples has been used to achieve good results in multi-view face detection. However, most of the existing methods do not explicitly emphasize samples based on their importance, resulting in the underutilization of hard samples. In this paper, we propose a curriculum learning loss function (HeadPose-Softmax) to classify the difficulty of a sample based on its facial pose, and embed the concept of curriculum learning into the loss function to implement a novel training strategy for deep face recognition. The loss function explicitly emphasizes the importance of the samples according to the different difficulty of each sample, which allows the model to make fuller use of hard samples, focus on learning pose invariant features, and improve the accuracy of the model in multi-view face detection tasks. Specifically, our HeadPose-Softmax dynamically adjusts the relative importance of the hard samples according to the pose angle of the face in the hard samples during the training phase. At each stage, different samples are assigned different importance according to their corresponding difficulty. Extensive experimental results under popular benchmarks show that our HeadPose-Softmax can enhance the accuracy of the model in multi-view face detection and outperform the state-of-the-art competitors.
Nowcasting bitcoin’s crash risk with order imbalance

The spectacular nature of bitcoin price crashes baffles market spectators and prompts routine warnings from regulators cautioning that cryptocurrencies behave in contra to the fundamental properties that traditionally define what constitutes money. Arguably most concerning to the public is, first, bitcoin’s unprecedented price volatility relative to other asset classes and, second, its seemingly detached price behavior relative to time-honored economic and market fundamentals. In an attempt to create an early warning system of bitcoin price crash risk using generalized extreme value (GEV) and logistic regression modeling, this study integrates order flow imbalance, along with several control factors which reflect blockchain activity and network value, in order to nowcast bitcoin’s price crashes. From a data analysis perspective, and despite their dissimilar distributional underpinnings, the GEV and logistic models perform comparably. When evaluating the type I and type II errors which these models yield, it is shown that their performance is comparable in terms of accuracy. In addition, it is also shown how the proportion of type I and type II errors can shift dramatically across probability cutoff tolerances. Towards the end of this study, time varying probabilities of a price crash are shown and evaluated. The sample range in this study encompasses the SARS-CoV-2 (Covid-19) time period as well as the recent scandal and collapse of the FTX cryptocurrency exchange.
Toward Keyword Generation through Large Language Models

It is essential to understand research trends for researchers, decision-makers, and investors. One way to analyze research trends is to collect and analyze author-defined keywords in scientific papers. Unfortunately, while author-defined keywords are beneficial to researchers aiming to figure out the trends of their research fields, 45% of scientific papers in Microsoft Academic Graph did not contain their author-defined keywords. Additionally, six of the top seven AI conferences neither collect nor disclose keywords. This paper proposes a method for generating the keywords using Galactica, a pre-trained large language model published by Meta. We evaluate this method’s performance by comparing the keywords provided by authors in the CoRL’22 and report characteristics of the generated keywords. Our study shows the F1 score of our proposed method was ten times better than that of previous studies, and 42.7% of the generated keywords are relevant to author-defined keywords.
Scaffolding CS1 Courses with a Large Language Model-Powered Intelligent Tutoring System

Programming skills are rapidly becoming essential for many educational paths and career opportunities. Yet, for many international students, the traditional approach to teaching introductory programming courses can be a significant challenge due to the complexities of the language, the lack of prior programming knowledge, and the language and cultural barriers. This study explores how large language models and gamification can scaffold coding learning and increase Chinese students' sense of belonging in introductory programming courses. In this project, a gamification intelligent tutoring system was developed to adapt to Chinese international students' learning needs and provides scaffolding to support their success in introductory computer programming courses.
Statistical local descriptors for face recognition: a comprehensive study

The use of local statistical descriptors for image representation has emerged and gained a reputation as a powerful approach in the last couple of decades. Many algorithms have been proposed and applied, since then, in various application areas employing different datasets, classifiers, and testing parameters. In this paper, we felt the need to make a comprehensive study of frequently-used statistical local descriptors. We investigate the effect of using different histogram-based local feature extraction algorithms on the performance of the face recognition problem. Comparisons are conducted among 18 different algorithms. These algorithms are used for the extraction of the local statistical feature descriptors of the face images. Moreover, feature fusion/concatenation of different combinations of generated feature descriptors is applied, and the relevant impact on the system performance is evaluated. Comprehensive experiments are carried out using two well-known face databases with identical experimental settings. The obtained results indicate that the fusion of the descriptors can significantly enhance the system's performance.
Successful Alert Issuance with Sufficient Lead Time by Uttarakhand State Earthquake Early Warning System: Case Study of Nepal Earthquakes

Three moderate earthquakes on November 9, 2022, November 12, 2022, and January 24, 2023 occurred in Western Nepal region of Magnitudes 5.8, 5.4 and 5.8 respectively. The epicenters were ∼100 km away from the Uttarakhand State Earthquake Early Warning System (UEEWS) network which consists of 169 seismic sensors in oval shape network with larger axis of about 280 km in East-West and 120 km in North-South in Uttarakhand-Dharchula seismic gap, a region of central seismic gap of Indian Himalaya. The strong motions recorded by UEEWS were fetched in real-time at the central server in Roorkee through dedicated private network. The central server issued notifications and warning alerts to the public of Uttarakhand on the mobile app, 'Uttarakhand Bhookamp Alert' installed by them. The success story of issuance of warning along with location of triggered sensors, epicenter of earthquakes and lead time people got in these events are elaborated in this article. Specially, these three moderate earthquakes albeit not triggered in the instrumented area, issuance of warning to the public by UEEWS network is noteworthy. Encouragingly, these events have been considered an example to rationalize establishment of earthquake early warning system in the other parts of the Himalayan regions.
Integration of Data Science and IoT with Blockchain for Industry 4.0

The field of the Industrial Internet of Things (IIoT) is developing rapidly and gaining abundant attention throughout our society. The vast volume of raw information that these IoT devices generate opens the door for an evolving field that draws useful insights from raw data. This area of study that deals with the extraction of information and draws insights from IoT data using various algorithmic and scientific methods is called data science. This field is in high demand among various industries and academic institutions. However, due to the lack of fundamental security technology, vulnerabilities and security risks are emerging in these IoT devices. Therefore, there is a need to secure and store such extracted data and insights, since this information can be sensitive and must be tamper-proof. Blockchain technology is being used as a decentralized and distributed approach to ensure the security requirements of IoT, enhance IoT and IIoT development, and as an immutable storage system. However, scalability and transaction fees are the major challenges that restrict the use of blockchain technology for IIoT. This chapter introduces a Directed Acyclic Graph (DAG)-based blockchain called IOTA, which overcomes these major challenges of the traditional blockchain to enable seamless integration with IoT devices. Furthermore, three IIoT applications are proposed and elaborated, consisting of (1) Device Identity Management, (2) Sensor data anomaly detection using Artificial Intelligence (AI), and (3) Security of IIoT data using IOTA Tangle.