Reviewerly/exHarmony huggingface dataset:
A study of vocabulary learning using annotated 360° pictures

Second language (L2) learning research suggests that virtual reality (VR) has the potential to enhance the development of language skills due to its immersive nature and its situated learning opportunities. This quantitative, between-subjects study compared the effectiveness of three learning conditions. University students (N = 63) studied vocabulary annotated on: (1) 360° pictures viewed using a VR headset, (2) 360° pictures viewed on a desktop monitor, and (3) standard two-dimensional (2D) pictures viewed on a desktop monitor (control condition). After the experiment, the students completed productive and receptive posttests measuring vocabulary recall. Through multiple analyses of variance (MANOVA), the study revealed that learning new vocabulary with annotated 360° pictures viewed on a desktop monitor is associated with significantly higher posttests scores, compared with learning using a VR headset or standard 2D pictures. Kruskal-Wallis H tests showed vocabulary learning with 360° to be engaging, but effective only when studied on the 2D monitor. This study has practical implications for VR-assisted language learning and for the design of teaching materials to enhance L2 vocabulary learning.
From Easy to Hard: Learning Language-Guided Curriculum for Visual Question Answering on Remote Sensing Data

Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. Although VQA in computer vision has been widely researched, VQA for remote sensing data (RSVQA) is still in its infancy. There are two characteristics that need to be specially considered for the RSVQA task. 1) No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation; 2) There are questions with clearly different difficulty levels for each image in the RSVQA task. Directly training a model with questions in a random order may confuse the model and limit the performance. To address these two problems, in this paper, a multi-level visual feature learning method is proposed to jointly extract language-guided holistic and regional image features. Besides, a self-paced curriculum learning (SPCL)-based VQA model is developed to train networks with samples in an easy-to-hard way. To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work. The proposed model is evaluated on three public datasets, and extensive experimental results show that the proposed RSVQA framework can achieve promising performance.
Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing of multi-layer neural networks within the networking of local clients, whereas, without disclosing the original local training data. Notably, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey paper, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.
An Exploration of the Cybersecurity Workforce Shortage

There is a colossal cyber security workforce shortage. Global estimates state there are over 1 million unfilled cyber security jobs. As everyone becomes increasingly hyper-connected and cybercrime intensifies in both scale and complexity, the need for experts in the field is dire. Innovative organizations are realizing that they need to tap into new talent pools of minorities and woman that have historically been overlooked. This article explores nature of this this workforce shortage and how to find new sources of talent.
Definable Conditionals

Abstract The variably strict analysis of conditionals does not only largely dominate the philosophical literature, since its invention by Stalnaker and Lewis, it also found its way into linguistics and psychology. Yet, the shortcomings of Lewis–Stalnaker’s account initiated a plethora of modifications, such as non-vacuist conditionals, presuppositional indicatives, perfect conditionals, or other conditional constructions, for example: reason relations, difference-making conditionals, counterfactual dependency, or probabilistic relevance. Many of these new connectives can be treated as strengthened or weakened conditionals. They are definable conditionals . This article develops a technique to infer the logic for such definable conditionals from the known logic of the underlying defining conditional. The technique is applied to central examples. The results show that a large part of the zoo of conditionals arises from a basic conditional—a constant nucleus of the different contextual and conceptual variations of variably strict conditionals.
Augmented reality

Abstract In this series, we explore technology-related themes and topics. The series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers.
Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective.Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods.Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods.However, the their performance must be improved.This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence using video data.In the proposed framework, the keyframe extraction technique eliminates duplicate consecutive frames.This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames.For feature selection and classification tasks, the applied sequential CNN uses one kernel size, whereas the inception v4 CNN uses multiple kernels for different layers of the architecture.For empirical analysis, four widely used standard datasets are used with diverse activities.The results confirm that the proposed approach attains 98% accuracy, reduces the computational cost, and outperforms the existing techniques of violence detection and recognition.
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations

Pretrained language models (PLMs) learn stereotypes held by humans and reflected in text from their training corpora, including gender bias.When PLMs are used for downstream tasks such as picking candidates for a job, people's lives can be negatively affected by these learned stereotypes.Prior work usually identifies a linear gender subspace and removes gender information by eliminating the subspace.Following this line of work, we propose to use DensRay, an analytical method for obtaining interpretable dense subspaces.We show that DensRay performs on-par with prior approaches, but provide arguments that it is more robust and provide indications that it preserves language model performance better.By applying DensRay to attention heads and layers of BERT we show that gender information is spread across all attention heads and most of the layers.Also we show that DensRay can obtain gender bias scores on both token and sentence levels.Finally, we demonstrate that we can remove bias multilingually, e.g., from Chinese, using only English training data.
Citizen Humanities

Abstract Citizen humanities is the term for citizen ‘science’ in the humanities. It has a long tradition and, since the object of investigation is human culture, raises questions about values, cultural significance, and deeper meaning of phenomena related to human culture. The development of digital technologies not only led to the emergence of digital humanities but also to new ways of involving citizens in the activities of cultural heritage institutions and academic research. Participants’ contributions to academic research and to the preservation of cultural heritage range from uncovering treasures hidden in archives and digital environments to tapping local knowledge. Their tasks have included tagging, transcribing, or cataloguing artefacts, through which they acquire specialist knowledge and competences, while assisting scholars and researchers to gain new insights. Challenges in the citizen humanities include biases, participant training and retention, as well as the advancement of digital technologies, such as artificial intelligence. Citizen humanities can combine topical issues in society with academic knowledge, demonstrate the relevance of the humanities for society, and establish a direct link to its members. In addition to the advancement of knowledge, the citizen humanities can unlock the potential of embedded, diverse, and culturally sensitive knowledge and play a crucial role in preserving and enriching cultural heritage.
Reno-Inst: An ontology to support renovation projects planning and renovation products installation

While the use of semantic models has been explored in different areas of the Architecture, Engineering and Construction (AEC) industry to map and formalize knowledge and support different tasks, building renovation seems to be a neglected area in this field. Therefore, this paper presents the Renovation-Installation (Reno-Inst) ontology, which maps knowledge from the renovation domain considering different requirements, constraints, and other elements related to the installation of common renovation products such as windows, thermal insulation panels, and heat radiators. The development of the Reno-Inst ontology relies on an approach combining input from experts and knowledge from related engineering documents. The verification and validation process includes a content evaluation workshop with experts, a verification design stage, and the analysis and implementation of a real case study. The paper provides another example of the power of ontologies as a method for mapping and representing knowledge, gathering, and retrieving relevant information. Particularly, renovation projects encounter diverse challenges that lead to cost and schedule overruns, making their performance typically low. Therefore, the proposed Reno-Inst ontology provides a basis from which new applications can be developed, tested, and deployed to support improvements in the building renovation field, especially in the planning and execution of renovation activities.
Mining Compact High Utility Itemsets Without Candidate Generation

Though the research topic of high utility itemset (HUI) mining has received extensive attention in recent years, current algorithms suffer from the crucial problem that too many HUIs tend to be produced. This seriously degrades the performance of HUI mining in terms of execution and memory efficiency. Moreover, it is very hard for users to discover meaningful information in a huge number of HUIs. In this paper, we address this issue by proposing a promising framework with a novel algorithm named CHUI (Compact High Utility Itemset)-Mine to discover closed $$^{+}$$ HUIs and maximal HUIs, which are compact representations of HUIs. The main merits of CHUI-Mine lie in two aspects: First, in terms of efficiency, unlike existing algorithms that tend to produce a large amount of candidates during the mining process, CHUI-Mine computes the utility of itemsets directly without generating candidates. Second, in terms of losslessness, unlike current algorithms that provide incomplete results, CHUI-Mine can discover the complete closed $$^{+}$$ or maximal HUIs with no miss. A comprehensive investigation is also presented to compare the relative advantages of different compact representations in terms of computational cost and compactness. To our best knowledge, this is the first work addressing the issue of mining compact high utility itemsets in terms of closed $$^{+}$$ and maximal HUIs without candidate generation. Experimental results show that CHUI-Mine achieves a massive reduction in the number of HUIs and is several orders of magnitude faster than benchmark algorithms.
Machine Learning for Internet of Things

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.
Optimal privacy control for transport network data sharing

In the era of smart cities, Internet of Things, and Mobility-as-a-Service, private operators need to share data with public agencies to support data exchanges for “living lab” ecosystems more than ever before. However, it is still problematic for private operators to share data with the public due to risks to competitive advantages. A privacy control algorithm is proposed to overcome this key obstacle for private operators sharing complex network-oriented data objects. The algorithm is based on information-theoretic k-anonymity and, using tour data as an example, where an operator’s data is used in conjunction with performance measure accuracy controls to synthesize a set of alternative tours with diffused probabilities for sampling during a query. The algorithm is proven to converge sublinearly toward a constrained maximum entropy under certain asymptotic conditions with measurable gap. Computational experiments verify the applicability to multi-vehicle fleet tour data; they confirm that reverse engineered parameters from the diffused data result in controllable sampling error; and tests conducted on a set of realistic routing records from travel data in Long Island, NY, demonstrate the use of the methodology from both the adversary and user perspectives.
When and Why Metaheuristics Researchers can Ignore “No Free Lunch” Theorems

The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible objective functions on a fixed search space, all search algorithms perform equally well. Several refined versions of the theorem find a similar outcome when averaging across smaller sets of functions. This paper argues that NFL results continue to be misunderstood by many researchers, and addresses this issue in several ways. Existing arguments against real-world implications of NFL results are collected and re-stated for accessibility and new ones are added. Specific misunderstandings extant in the literature are identified, with speculation as to how they may have arisen. This paper presents an argument against a common paraphrase of NFL findings—that algorithms must be specialised to problem domains to do well—after problematising the usually undefined term “domain”. It provides novel concrete counter-examples illustrating cases where NFL theorems do not apply. In conclusion, it offers a novel view of the real meaning of NFL, incorporating the anthropic principle and justifying the position that in many common situations researchers can ignore NFL.
Voxento 2.0: A Prototype Voice-controlled Interactive Search Engine for Lifelogs

In this paper, we describe an extended version of Voxento which is an interactive voice-based retrieval system for lifelogs that has been developed to participate in the fourth Lifelog Search Challenge LSC'21, at ACM ICMR'21. Voxento provides a spoken interface to the lifelog dataset, which facilitates a novice user to interact with a personal lifelog using a range of vocal commands and interactions. For the version presented here, Voxento has been enhanced with new retrieval features and better user interaction support. In this paper, we introduce these new features, which include dynamic result filtering, predefined interactive responses and the development of a new retrieval API. Although Voxento was proposed for wearable technologies such as Google Glass or interactive devices like smart TVs, the version of Voxento presented here uses a desktop computer in order to participate in the LSC'21 competition. In the current Voxento iteration, the user has the option to enable voice interaction or use standard text-based retrieval.
Time and frequency as quantum continuous variables

We present a second quantization description of frequency-based continuous variables quantum computation in the subspace of single photons. For this, we define frequency and time operators using the free-field Hamiltonian and its Fourier transform, and we show that these observables, when restricted to the one photon per mode subspace, reproduce the canonical position-momentum commutation relations. As a consequence, frequency and time operators can be used to define a universal set of gates in this particular subspace. We discuss the physical implementation of these gates as well as their effect on single photon states, and show that frequency and time variables can also be used to implement continuous variables quantum information protocols, in the same way than polarization is currently used as a two-dimensional quantum variable.
How Deep the Theory of Quantum Communications Goes: Superadditivity, Superactivation and Causal Activation

In the theory of quantum communications, a deeper structure has been recently unveiled, showing that the capacity does not completely characterize the channel ability to transmit information due to phenomena -namely, superadditivity, superactivation and causal activation -with no counterpart in the classical world.Although how deep goes this structure is yet to be fully uncovered, it is crucial for the communication engineering community to own the implications of these phenomena for understanding and deriving the fundamental limits of communications.Hence, the aim of this treatise is to shed light on these phenomena by providing the reader with an easy access and guide towards the relevant literature and the prominent results from a communication engineering perspective.
A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale PV installations are deployed at an unprecedented pace, and their integration into the grid can be challenging since public authorities often lack quality data about them. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be easily transferred from one region or data source to another due to differences in image acquisition. To address this issue known as domain shift and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8,000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.