Reviewerly/exHarmony huggingface dataset:
Who watches the new watchmen? The challenges for drone digital forensics investigations

The technological advance of drone technology has augmented the existing capabilities of flying vehicles rendering them a valuable asset of the modern society. As more drones are expected to occupy the airspace in the near future, security-related incidents, either malicious acts or accidents, will increase as well. The forensics analysis of a security incident is essential, as drones are flying above populated areas and have also been weaponised from radical forces and perpetrators. Thus, it is an imperative need to establish a Drone Digital Forensics Investigation Framework and standardise the processes of collecting and processing such evidence. Although there are numerous drone platforms in the market, the same principles apply to all of them; just like mobile phones. Nevertheless, due to the nature of drones, standardised forensics procedures to date do not manage to address the required processes and challenges that such investigations pose. Acknowledging this need, we detail the unique characteristics of drones and the gaps in existing methodologies and standards, showcasing that there are fundamental issues in terms of their forensics analysis from various perspectives, ranging from operational and procedural ones, and escalate to manufacturers, as well as legal restrictions. The above creates a very complex environment where coordinated actions must be made among the key stakeholders. Therefore, this work paves the way to address these challenges by identifying the main issues, their origins, and the needs in the field by performing a thorough review of the literature and a gap analysis.
Man-Made Threat Event Recognition Based on Distributed Optical Fiber Vibration Sensing and SE-WaveNet

Real-time identification of threats has been the focus of attention in the security field in recent years. Researchers have aimed to build a fast, efficient, and accurate model to address risks in threat event identification. This study proposes a fast, effective deep learning model-the squeeze and excitation WaveNet (SE-WaveNet). WaveNet is a 1-D convolutional neural network (CNN) model with a large receptive field. It is small, fast, and highly accurate. Thus, it has a great advantage in processing time series data. On this basis, the squeeze and excitation (SE) structure of the attention mechanism is introduced to adapt to matrix data with complete information. To ensure the integrity of information in the experimental verification, the distributed optical fiber vibration sensing (DVS) system is used to collect the data near the vibration point and form the time-space matrix as the input of the model. SE-WaveNet is compared with WaveNet and related models. Results show that the accuracy of SE-WaveNet based on WaveNet is increased by approximately 3.5%, the model size is reduced by 39%, and the speed is increased by 32%. Compared with the five other related models, the average performance is 3%-9% higher, and the final test accuracy rate can reach approximately 97.73%.
SolAR: Energy Positive Human Activity Recognition using Solar Cells

The high power consumption of inertial activity sensors limits the battery lifetime of today's wearable devices.Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements into electrical energy while using the harvesting signal to replace conventional activity sensors.However, in human-centric applications, the amount of harvested kinetic energy is not enough to power a realtime activity recognition algorithm and run the wearable device perpetually.In this paper, we propose Solar based human Activity Recognition (SolAR), which uses solar cells simultaneously as an activity sensor as well as an energy source.Our key observation is that the power available from a wrist-worn solar cell changes dynamically while a person moves, encoding information about the underlying activity.We collect empirical solar energy data to explore its activity sensing potential and implement the activity recognition pipeline on an ultra low-power micro-controller unit to evaluate the end-to-end power consumption of the system.Our analysis reveals that SolAR improves activity recognition accuracy by up to 8.3% and harvests more than one order of magnitude higher power compared to its kinetic counterpart.This enables SolAR to generate more energy than required for the entire activity recognition pipeline, which we term as energy positive activity recognition, achieving uninterrupted, autonomous, self-powered and real-time operation.
Area and energy efficient shift and accumulator unit for object detection in IoT applications

Convolutional Neural Networks (CNNs) exhibit significant performance enhancements in several machine learning tasks such as surveillance, intelligent transportation, smart grids and healthcare systems. With the proliferation of physical things being connected to internet and enabled with sensory capabilities to form an Internet of Thing (IoT) network, it is increasingly important to run CNN inference, a computationally intensive application, on the resource constrained IoT devices. Object detection is a fundamental computer vision problem that provides information for image understanding in several artificial intelligence (AI) applications in smart cities. Among various object detection algorithms, CNN has emerged as a new paradigm to improve the overall performance. The Multiply-accumulate (MAC) operations, which are used repeatedly in the convolution layers of CNN, hold extreme computational complexity. Hence, the overall computational workloads and their respective energy consumption of any CNN applications are on the rise. To overcome these escalating challenges, approximate computing mechanism has played a vital role in reducing power and area of computation intensive CNN applications. In this paper, we have designed an approximate MAC architecture, termed Shift and Accumulator Unit (SAC), for the error-resilient CNN based object detection algorithm targeting embedded platforms. The proposed computing unit deliberately trades accuracy to reduce design complexity and power consumption, thus suiting the resource constrained IoT devices. The pipeline architecture of the SAC unit saves approximately 1.8× clock cycles than the non-pipeline SAC architecture. The performance evaluation shows that the proposed computing unit has better energy efficiency and resource utilization than the accurate multiplier and state-of-the-art approximate multipliers without noticeable deterioration in overall performance.
HNS: Hierarchical negative sampling for network representation learning

Network representation learning (NRL) aims at modeling network graph by encoding vertices and edges into a low-dimensional space. These learned representations can be used for subsequent applications, such as vertex classification and link prediction. Negative Sampling (NS) is the most widely used method for boosting the performance of NRL. However, most of the existing work only randomly draws negative samples based on vertex frequencies, i.e., the vertices with higher frequency are more likely to be drawn, which ignores the situation that the sampled one may not be a true negative sample, thus, lead to undesirable embeddings. In this paper, we propose a new negative sampling method, called Hierarchical Negative Sampling (HNS), which is able to model the latent structures of vertices and learn the relations among them. During sampling, HNS can draw more appropriate negative samples and thereby obtain better performance on network embeddings. Firstly, we theoretically demonstrate the superiority of HNS over NS. And then we use experimental results to show that our proposed method outperforms the state-of-the-art models on vertex classification tasks at different training scales in real-world networks.
Correlation-based structural dropout for convolutional neural networks

Convolutional neural networks (CNNs) easily suffer from the over-fitting problem since they are often over-parameterized in the case of small training datasets. The conventional dropout that drops feature units randomly works well for fully connected networks, but fails to regularize CNNs well due to high spatial correlation of the intermediate features, which allows the dropped information to flow through the network, thus leading to the problem of under-dropping. To better regularize CNNs, some structural dropout methods such as SpatialDropout and DropBlock have been proposed by dropping feature units in continuous regions randomly. However, these methods may suffer from the over-dropping problem by discarding the critical discriminative features, thus limiting the performance of CNNs. To address these issues, we propose a novel structural dropout method, Correlation based Dropout (CorrDrop), to regularize CNNs by dropping feature units based on feature correlation. Unlike the previous dropout methods, our CorrDrop can focus on the discriminative information and drops features in a spatial-wise or channel-wise manner. Extensive experiments on different datasets, network architectures, and various tasks (e.g., image classification and object localization) demonstrate the superiority of our method over other methods.
Speculative Blackness: Considering Afrofuturism in the Creation of Inclusive Speculative Design Probes

Participatory design is positioned as an approach to address community challenges and reimagine potential futures by decentralizing systems of power and centering the marginalized, moving away from designers as experts and framing design as being community-driven. Methods of speculative participatory design are useful in imagining futures among marginalized groups while negotiating existing societal constructs. While research probes have been developed to support the practice of speculation in design, we have yet to see research probes in HCI that consider the lived experiences of Black and brown communities. In this pictorial, we illustrate the process of developing methods cards as design probes to encourage speculative and critical design thinking from an Afrofuturism lens. We highlight how Afrofuturism centers Black communities in design speculation lending to both equity-driven design outcomes, and an accessible resource that can be readily used by designers and non- designers alike in addressing community concerns.
Consensus Graph Representation Learning for Better Grounded Image Captioning

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the captioning model to dynamically link generated object words or phrases to appropriate regions of the image, i.e., the grounded image captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects) that has not solved the key issue of object hallucination, i.e., the semantic inconsistency. In this paper, we take a novel perspective on the issue above: exploiting the semantic coherency between the visual and language modalities. Specifically, we propose the Consensus Rraph Representation Learning framework (CGRL) for GIC that incorporates a consensus representation into the grounded captioning pipeline. The consensus is learned by aligning the visual graph (e.g., scene graph) to the language graph that consider both the nodes and edges in a graph. With the aligned consensus, the captioning model can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further. We validate the effectiveness of our model, with a significant decline in object hallucination (-9% CHAIRi) on the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several automatic metrics and human evaluation, the results indicate that the proposed approach can simultaneously improve the performance of image captioning (+2.9 Cider) and grounding (+2.3 F1LOC}).
A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.
Quantum Darwinism in a Composite System: Objectivity versus Classicality

We investigate the implications of quantum Darwinism in a composite quantum system with interacting constituents exhibiting a decoherence-free subspace. We consider a two-qubit system coupled to an N-qubit environment via a dephasing interaction. For excitation preserving interactions between the system qubits, an analytical expression for the dynamics is obtained. It demonstrates that part of the system Hilbert space redundantly proliferates its information to the environment, while the remaining subspace is decoupled and preserves clear non-classical signatures. For measurements performed on the system, we establish that a non-zero quantum discord is shared between the composite system and the environment, thus violating the conditions of strong Darwinism. However, due to the asymmetry of quantum discord, the information shared with the environment is completely classical for measurements performed on the environment. Our results imply a dichotomy between objectivity and classicality that emerges when considering composite systems.
Towards Optimal Low-Latency Live Video Streaming

Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live streaming by developing dynamic models and optimal adaptation strategies to establish QoE upper bounds as a function of the allowable end-to-end latency. We further develop practical live streaming algorithms within the iterative Linear Quadratic Regulator (iLQR) based Model Predictive Control and Deep Reinforcement Learning frameworks, namely MPC-Live and DRL-Live, to maximize user live streaming QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live streaming algorithms can achieve close-to-optimal performance within the latency range of two to five seconds.
Research and application of 3D visualization and Internet of Things technology in urban land use efficiency management

Land is the basis for human beings to engage in economic and social activities, but the continuous acceleration of the process of economic development and urban expansion has brought about a series of ecological and urban problems. Therefore, the simulation of urban spatial expansion is very important for the rational planning of urban land and the promotion of sustainable urban development. The three-dimensional visualization of urban land allows users to have a perceptual understanding of urban construction, and enables decision makers, designers and users to have a more vivid and rational understanding of the current state of urban construction and planning and design blueprints. Based on this, this article combines three-dimensional imaging and Internet of Things technology to carry out the research on urban land utilization. First of all, this article discusses image modeling techniques such as texture mapping and panoramas, and applies these techniques to the model reconstruction of complex urban land. The model effectively combines graphics-based and image-based model reconstruction technology to construct a highly realistic three-dimensional urban landscape. Secondly, this article combines the Internet of Things and wavelet analysis to design an Internet of Things architecture for urban land use efficiency management. Finally, on the premise of taking into account the important results and the status quo of urban development, the countermeasures and suggestions for increasing the urban land use rate are put forward.
Self-Training for Domain Adaptive Scene Text Detection

Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask R-CNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.
Secondary School Teachers Self-Perception of Digital Teaching Competence in Spain Following COVID-19 Confinement

The education system has become even more complex following the global pandemic, which saw face-to-face teaching transition to virtual teaching. To cope with this abrupt transition, it is essential that teachers have a sufficient level of digital teaching competence. This article aims to increase awareness of teachers’ self-perception of their digital teaching competences in the educational field. Specifically, this study explores Spanish secondary school teachers’ knowledge and use of different ICT tools by evaluating their competence based on different areas proposed by The National Institute of Technology and Professional Development (Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado, INTEF), Madrid, Spain. From the results of the questionnaires, we have determined that teachers consider themselves to have an upper intermediate level of digital teaching competence, although there are still shortcomings that need to be addressed in order to improve this level of competence, and its true integration in the teaching–learning process.
Uncertainty-Aware Few-Shot Image Classification

Few-shot image classification learns to recognize new categories from limited labelled data. Metric learning based approaches have been widely investigated, where a query sample is classified by finding the nearest prototype from the support set based on their feature similarities. A neural network has different uncertainties on its calculated similarities of different pairs. Understanding and modeling the uncertainty on the similarity could promote the exploitation of limited samples in few-shot optimization. In this work, we propose Uncertainty-Aware Few-Shot framework for image classification by modeling uncertainty of the similarities of query-support pairs and performing uncertainty-aware optimization. Particularly, we exploit such uncertainty by converting observed similarities to probabilistic representations and incorporate them to the loss for more effective optimization. In order to jointly consider the similarities between a query and the prototypes in a support set, a graph-based model is utilized to estimate the uncertainty of the pairs. Extensive experiments show our proposed method brings significant improvements on top of a strong baseline and achieves the state-of-the-art performance.
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for visual commonsense reasoning

Reasoning is a critical ability towards complete visual understanding. To develop machine with cognition-level visual understanding and reasoning abilities, the visual commonsense reasoning (VCR) task has been introduced. In VCR, given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer. The methods adopting the powerful BERT model as the backbone for learning joint representation of image content and natural language have shown promising improvements on VCR. However, none of the existing methods have utilized commonsense knowledge in visual commonsense reasoning, which we believe will be greatly helpful in this task. Therefore, we incorporate commonsense knowledge into the cross-modal BERT, and propose a novel Knowledge Enhanced Visual-and-Linguistic BERT (KVL-BERT for short) model. Besides taking visual and linguistic contents as input, external commonsense knowledge is integrated into the multi-layer Transformer. In order to preserve the structural information and semantic representation of the original sentence, we propose an algorithm called RMGSR (Relative-position-embedding and Mask-self-attention Guided Semantic Representations). Compared to other task-specific models and general task-agnostic pre-training models, our KVL-BERT outperforms them.
A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

Purpose Previous research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics. Design/methodology/approach First, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity. Findings The new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f -measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results. Originality/value This study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.