Reviewerly/exHarmony huggingface dataset:
Designing with Genius Loci: An Approach to Polyvocality in Interactive Heritage Interpretation

Co-design with communities interested in heritage has oriented itself towards designing for polyvocality to diversify the accepted knowledges, values and stories associated with heritage places. However, engagement with heritage theory has only recently been addressed in HCI design, resulting in some previous work reinforcing the same realities that designers set out to challenge. There is need for an approach that supports designers in heritage settings in working critically with polyvocality to capture values, knowledges, and authorised narratives and reflect on how these are negotiated and presented in the designs created. We contribute “Designing with Genius Loci” (DwGL)—our proposed approach to co-design for polyvocality. We conceptualised DwGL through long-term engagement with volunteers and staff at a UK heritage site. First, we used ongoing recruitment to incentivise participation. We held a series of making workshops to explore participants’ attitudes towards authorised narratives. We built participants’ commitments to collaboration by introducing the common goal of creating an interactive digital design. Finally, as we designed, we enacted our own commitments to the heritage research and to participants’ experiences. These four steps form the backbone of our proposed approach and serve as points of reflexivity. We applied DwGL to co-creating three designs: Un/Authorised View, SDH Palimpsest and Loci Stories, which we present in an annotated portfolio. Grounded in research through design, we reflect on working with the proposed approach and provide three lessons learned, guiding further research efforts in this design space: (1) creating a conversation between authorised and personal heritage stories; (2) designing using polyvocality negotiates voices; and (3) designs engender existing qualities and values. The proposed approach places polyvocality foremost in interactive heritage interpretation and facilitates valuable discussions between the designers and communities involved.
A Hierarchical Approach toward Prediction of Human Biological Age from Masked Facial Image Leveraging Deep Learning Techniques

The lifestyle of humans has changed noticeably since the contagious COVID-19 disease struck globally. People should wear a face mask as a protective measure to curb the spread of the contagious disease. Consequently, real-world applications (i.e., electronic customer relationship management) dealing with human ages extracted from face images must migrate to a robust system proficient to estimate the age of a person wearing a face mask. In this paper, we proposed a hierarchical age estimation model from masked facial images in a group-to-specific manner rather than a single regression model because age progression across different age groups is quite dissimilar. Our intention was to squeeze the feature space among limited age classes so that the model could fairly discern age. We generated a synthetic masked face image dataset over the IMDB-WIKI face image dataset to train and validate our proposed model due to the absence of a benchmark masked face image dataset with real age annotations. We somewhat mitigated the data sparsity problem of the large public IMDB-WIKI dataset using off-the-shelf down-sampling and up-sampling techniques as required. The age estimation task was fully modeled like a deep classification problem, and expected ages were formulated from SoftMax probabilities. We performed a classification task by deploying multiple low-memory and higher-accuracy-based convolutional neural networks (CNNs). Our proposed hierarchical framework demonstrated marginal improvement in terms of mean absolute error (MAE) compared to the one-off model approach for masked face real age estimation. Moreover, this research is perhaps the maiden attempt to estimate the real age of a person from his/her masked face image.
Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms.
Low-Rate Denial-of-Service Attack Detection: Defense Strategy Based on Spectral Estimation for CV-QKD

Although continuous-variable quantum key distribution (CVQKD) systems have unconditional security in theory, there are still many cyber attacking strategies proposed that exploit the loopholes of hardware devices and algorithms. At present, few studies have focused on attacks using algorithm vulnerabilities. The low-rate denial-of-service (LDoS) attack is precisely an algorithm-loophole based hacking strategy, which attacks by manipulating a channel’s transmittance T. In this paper, we take advantage of the feature that the power spectral density (PSD) of LDoS attacks in low frequency band is higher than normal traffic’s to detect whether there are LDoS attacks. We put forward a detection method based on the Bartlett spectral estimation approach and discuss its feasibility from two aspects, the estimation consistency and the detection accuracy. Our experiment results demonstrate that the method can effectively detect LDoS attacks and maintain the consistency of estimation. In addition, compared with the traditional method based on the wavelet transform and Hurst index estimations, our method has higher detection accuracy and stronger pertinence. We anticipate our method may provide an insight into how to detect an LDoS attack in a CVQKD system.
A machine learning approach to detecting fraudulent job types

Abstract Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants’ decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this paper explores to what extent different categorizations of fraudulent jobs can be classified. In addition, this paper seeks to find what type of features are most relevant in classifying the type of fraudulent job. In this paper, we develop and validate a machine learning system for identifying identity theft, corporate identity theft and multi-level marketing amongst fraudulent job advertisements. We utilized four classes of features: empirical rule set-based features, bag-of-word models, most recent state-of-the-art word embeddings and transformer models for various machine learning classifiers. The machine learning models were validated by evaluating them on a publicly available job description dataset. Our results indicate that the word embeddings and transformer-based features consistently outperformed the handcrafted rule-set based features class. Ultimately, a Gradient Boosting classifier with a combination of empirical rule-set based features, parts-of-speech tags and bag-of-words vectors achieved the best performance with an F1-score of 0.88.
Facial Expression Recognition with Squeeze-and-Excitation Network

In this paper, we aim to propose a facial expression recognition algorithm by utilizing the spatial attention mechanism. We adopt 3 basic CNN models, including AlexNet, VGGNet and ResNet for comparison. Then we add the attention module, i.e., SENet to VGGNet and ResNet for futher feature enhancement. Our results on FER2013 show the effectiveness of our method. Our VGG+SENet achieves 65.0% accuracy and our ResNet+SENet achieves 66.8% accuracy. Both method with attention can get an obvious performance promotion, which validates the effectiveness of attention mechanism.
From Simple to Complex: The Relationship Between Educational Leaders’ Cognitive Complexity and Leadership-Style Profiles

This innovative study examines the relations between principals’ cognitive complexity (CC) levels and their leadership-style profiles. Participants were 341 principals and 3,209 teachers in Israel. Results indicated four leadership-style profiles: transformational; mixed – including transformational and transactional; transactional; and passive-avoidant. School principals were further found to juggle their perspectives from simple to complex, resulting in passive-avoidant to transactional, to transformational and to mixed leadership profiles. The mixed leadership-style profile (transactional and transformational) was characterized by high CC, and construed social behavior in a multidimensional way; passive-avoidant leadership was characterized by simple thinking (low CC).
Optimal Aggressive Constrained Trajectory Synthesis and Control for Multi-Copters

In this paper, we propose a novel time and control effort optimal aggressive trajectory synthesis and control design methodology. The trajectory synthesis is a modified minimum snap design but with specific position and orientation constraints on a multi-copter, such as flying through tight spaces (windows) at specific orientations. The paper also introduces a means to stitch together multiple flight segments, enforce smoothness, and minimize segment times as well as the overall time, thereby resulting in very aggressive and feasible trajectories. A novel analysis for a specific scenario when no yaw angle specifications are provided is conducted, wherein a trade-off results in additional aggressiveness. The control algorithms to follow these trajectories are based on an inverse dynamics approach. Several candidate high-fidelity simulations are performed to verify the effectiveness of the proposed approach.
Enhancing Distributed Data Integrity Verification Scheme in Cloud Environment Using Machine Learning Approach

DML (Distributed machine learning) is considered one of the most important AI technologies (AI). Nonetheless, data integrity isn't taken into account in the present distributed machine learning configuration. If network intruders falsify the data, however, change or destroy the data. As a result, ensuring data integrity in the DML is critical. To verify that the training data is accurate, it presents a distributed machine-learned ness initiates data integrity checking method. To begin, it advocates for the implementation of a Provable Data Possession (PDP) selection checking method to obtain data integrity confirmation, as a result of DML-DIV technique will be able to resist manipulation as well as fraud assaults. Second, in the TPA verification process, researchers produce a unique integer, called the blinded variable, and use the discrete logarithm problem (DLP) to establish evidence and assure data protection. To invoke the data possessor's public/private set of keys, it uses two-step authentication and identity-based cryptogra phic pivotal generating technology. As a result of which our DML-DIV scheme may be able to tackle the critical escrow issue while also lowering document management expenses. Lastly, a rigorous theoretical analysis is performed, formal theoretical analysis and empirical data confirm the safety and effectiveness of the DML-DN system.
OutCast: Outdoor Single‐image Relighting with Cast Shadows

Abstract We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and clouds. Previous solutions for this problem rely on reconstructing occluder geometry, e. g., using multi‐view stereo, which requires many images of the scene. Instead, in this work we make use of a noisy off‐the‐shelf single‐image depth map estimation as a source of geometry. Whilst this can be a good guide for some lighting effects, the resulting depth map quality is insufficient for directly ray‐tracing the shadows. Addressing this, we propose a learned image space ray‐marching layer that converts the approximate depth map into a deep 3D representation that is fused into occlusion queries using a learned traversal. Our proposed method achieves, for the first time, state‐of‐the‐art relighting results, with only a single image as input. For supplementary material visit our project page at: dgriffiths.uk/outcast .
Neural network programming: Integrating first principles into machine learning models

This work introduces Neural Network Programming (NNP) as an integrated hybrid modelling approach. NNP consists in formulating a set of first principles equations that is later decomposed and transcribed into an Algorithmically Structured artificial Neural Network (ASNN). NNP leverages the advantages of the universal approximation theorem and neural network optimization algorithms in order to generate physically coherent machine learning models. Since ASNNs are not mere approximations of physics equations, it is not necessary to modify either the gradient or performance function in order to account for errors with respect to the first principles equations. ASNNs are trained faster and more accurately than typical hybrid models because the gradient is computed through automatic differentiation instead of numeric differentiation. It is shown that the same ASNN architecture is transferable between processes with similar characteristics. In particular, a flash separator, distillation column, and a biogas upgrading process, were modelled using an identical architecture.
AI-based Home Security System with Face Recognition

Facial identification is biometric technology; it is about recognizing human facia features and the equipment and software will do processing and analysis. With the quick advancement of video observing, the huge data of the observing pictures have taken over a broad range of human capabilities. Smart video retrieval technology is getting an undeniably irreplaceable piece of smart surveillance systems. The results of the simulation can demonstrate that the algorithm has great recognition execution either for a single face or multiple-face pictures and can recognize the face in different poses. Simultaneously, that algorithm is quick and can essentially match the essentials of real-time face recognition.
Automated Dataset Generation with Blender for Deep Learning-based Object Segmentation

"Give a man a synthetic dataset and you help him for a project; teach a man to generate his own dataset and you help him for a lifetime." Developments in digital technology led to an unprecedented increase in the amount of data in industry and in science as well, including the field of computer vision. This enabled the evolution of convolutional neural networks (CNNs) and other deep learning (DL) architectures that require a large amount of data for their training. Even though the large-scale collection of visual data is relatively cheap and easy to implement, the annotation of the collected data is a time-consuming process. Although, with the use of pre-trained models only a small-scale fine-tuning dataset is required, the labeling of only a few hundred or few thousand images for object segmentation or instance segmentation is still a laborious task, if done manually. Recent trends suggest that synthetic data will play a significant role in the future of DL and there are numerous research papers promoting various synthetic datasets and/or models trained on them. Unfortunately, there is much less information on how to create such a dataset and what are the important points one has to keep in mind when creating their own. In this paper, we summarize some of the available tools for creating a synthetic dataset for object segmentation and provide a detailed example using the Blender 3D creation suite. During our example, we attract attention to the vital points and considerations for automatic dataset generation for object segmentation.
Research on Remote Sensing Image Classification Algorithm Based on EfficientNet

Accurate classification of remote sensing images is important in remote sensing applications. In order to verify the efficiency and accuracy of efficientnet algorithm in remote sensing image classification, this paper classifies the UCMerced LandUse dataset based on EfficientNet. The experimental results show that compared with VGGNet, ResNet and MobileNet, the EfficientNet network introduces composite parameters and scales depth, width and resolution at the same time. The accuracy of EfficientV2-s in the verification set is 16.5%, 5.2%, 1.8% and 1.7% higher than that of VGG, MobileNetV2, ResNet34 and EfficientNet-b0, which shows the efficiency and accuracy of EfficientNet network in remote sensing image classification data set.
One Improved Model of Named Entity Recognition by Combining BERT and BiLSTM-CNN for Domain of Chinese Railway Construction

There are currently few named entity recognition (NER) models in domain of Chinese railway construction. To mitigate such awkward situation, this paper uses the neural network method to sort out the basic information from Chinese text about Chinese railway construction. Concretely, this paper proposes one improved model of NER by combining bidirectional encoder representation from transformers (BERT) and convolutional long short-term memory (LSTM) network model so as to promote the NER performance of Chinese text about Chinese railway construction. Based on deep understandings of domain knowledge about Chinese railway construction, the proposed model performs targeted processing on the input, and designs a novel masking algorithm based on Chinese placenames and numbers. The proposed model further uses bidirectional LSTM (BiLSTM) network as the encoding layer, which can leverage the feature extraction capability of the convolution neural network (CNN) to improve the NER performance. Experimental results show that the F1 value of the proposed model is 7.28% higher than the traditional conditional random field (CRF) model, and the F1 value of the BERT model with mask of Chinese placenames and numbers is 3.43% higher than the original BERT model.
Blockchain Architectural Concerns: A Systematic Mapping Study

Blockchain is an emerging technology whose main characteristic is decentralized and distributed architecture with-out depending on a central server. Blockchain broadens the spectrum by providing a shared infrastructure that securely stores information and executes programs, making it a very disruptive technology. Because blockchain is a relatively novel technology, there is insufficient knowledge about architectural concerns for designing and evaluating blockchain-based solutions. In this paper, we report a systematic mapping study, giving a broader landscape of the blockchain-based architectural concerns, such as quality attributes, tactics, and patterns related to the architectural design space with blockchain. From the current literacy, this study unveils these concerns and their relationships. Software architects can use these concerns for analyzing, designing, assessing, or refactoring their blockchain-based solutions.
Translational relation embeddings for multi-hop knowledge base question answering

Multi-hop Knowledge Base Question Answering (KBQA) aims to predict answers that require multi-hop reasoning from the topic entity in the question over the Knowledge Base (KB). Relation extraction is a core step in KBQA, which extracts the relation path from the topic entity to the answer entity. Compared with single-hop questions, multi-hop ones have more complex syntactic structures to understand, and multi-hop relation paths lead to a larger search space, which makes it much more challenging to extract the correct relation paths. To tackle the above challenges, most existing relation extraction approaches focus on the semantic similarity between questions and relation paths. However, those approaches only consider the word semantics of the relation names but ignore the graph semantics inside the knowledge base. As a result, their generalization ability relying on the naming rules of the relations, making it more difficult to generalize over large knowledge bases. To address the current limitations and take advantage of the graph semantics of relations, we propose a novel translational embedding-based relation extractor that utilizes pretrained embeddings from TransE. In particular, we treat the multi-hop relation path as a translation from the first relation to the last one in the semantic space of TransE. Then we map the question into this space under the supervision of the path embeddings. To take full advantage of the pretrained graph semantics in TransE, we propose a KBQA framework that leverages pretrained relation semantics in relation extraction and pretrained entity semantics in answer selection. Our approach achieves state-of-the-art performance on two benchmark datasets, WebQuestionSP and MetaQA, demonstrating its effectiveness on the multi-hop KBQA task.
An internet of secure and private things: A service-oriented architecture

Low-cost networked IoT devices are fast becoming commonplace. From implanted medical devices to motion-activated surveillance cameras and from driverless smart cars to voice-operated home management systems, IoT devices continue to permeate further and deeper into our lives. However, this widespread adoption of IoT devices has also given rise to a wide range of security, privacy and trust issues that are unique to the IoT ecosystem. Conventional solutions are ill-suited for the IoT domain due to limited resources, network dynamics, and evolving trust boundaries. Hence, novel mechanisms are needed to address the specific challenges of the IoT landscape. To this end, we propose a user-centric cloud-based service that allows device owners to have fine-grained control over what kind and how much data is shared through their IoT devices. Our scheme builds on top of Intel Software Guard Extensions (SGX) to instantiate secure virtual clones (shadows) of actual devices in the cloud, substantially reducing the attack surface for IoT networks. Furthermore, a scalable infrastructure in the cloud allows us to deploy sophisticated policy enforcement and data scrubbing mechanisms on a per application basis giving users explicit control over data sharing. The presented approach requires little effort on part of device vendors and users as the service provider handles the bulk of the work. We demonstrate the effectiveness of our approach empirically by implementing the service on SGX hardware and deploying advanced data cleansing policies on device-generated data.