Reviewerly/exHarmony huggingface dataset:
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model

Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community. One of the most popular research topics in this domain is Food Retrieval, due to its profound influence on health-oriented applications. In this paper, we focus on the task of cross-modal retrieval between food images and cooking recipes. We present Modality-Consistent Embedding Network (MCEN) that learns modality-invariant representations by projecting images and texts to the same embedding space. To capture the latent alignments between modalities, we incorporate stochastic latent variables to explicitly exploit the interactions between textual and visual features. Importantly, our method learns the cross-modal alignments during training but computes embeddings of different modalities independently at inference time for the sake of efficiency. Extensive experimental results clearly demonstrate that the proposed MCEN outperforms all existing approaches on the benchmark Recipe1M dataset and requires less computational cost.
Robust Decentralised Trust Management for the Internet of Things by Using Game Theory

Due to the large scale of the typical deployments and the involvement of moving objects to the Internet of Things, participating nodes opportunistically establish data exchanging connections, spanning across multiple organizations and security domains. This opportunistic behavior causes the impossibility of defining valid security policies to rule node authorization, and the ineffectiveness of traditional static access control models based on roles or attributes. Trust management is a promising solution to complement these conventional rules and models by realizing a more dynamic security approach and regulating connection request acceptance or rejection based on monitored behaviors. As a centralized authority cannot be established within multi-tenant and large scale infrastructures, decentralized approaches have recently emerged, supported by the blockchain technology, and applied to the case of useful Internet of Things implementations. However, they are vulnerable to possible attacks aiming at discrediting honest nodes (by lowering their trust degree) and/or redeem malicious nodes (by increasing their trust degree). The widely-accepted protection consists of securing the communications by using SSL/TLS, and restricting the nodes allowed to update the trust degree. However, they are known to be ineffective against compromised nodes that, despite holding legitimate security claims and cryptographic material, they deviate from the correct behavior by sending false and mendacious scores. This work proposes to exploit on game theory to realize robust decentralized trust management able to tolerate malicious nodes sending mendacious scores. Explicitly, a signaling node has been formalized to model the interactions between the IoT and the edge nodes by refusing potentially untrue scores. Moreover, the evolutionary Dempster-Shafer theory is used to combine the collected scores to update nodes’ trust degrees, by excluding diverging scores far from the majority. Such solutions have been implemented within the context of a blockchain-supported trust management solution for IoT, and an empirical assessment is provided to show the quality of the proposed approach.
Cross-Linguistic Syntactic Evaluation of Word Prediction Models

A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatical sentences with high accuracy.However, these studies are based primarily on monolingual evidence from English.To investigate how these models' ability to learn syntax varies by language, we introduce CLAMS (Cross-Linguistic Assessment of Models on Syntax), a syntactic evaluation suite for monolingual and multilingual models.CLAMS includes subject-verb agreement challenge sets for English, French, German, Hebrew and Russian, generated from grammars we develop.We use CLAMS to evaluate LSTM language models as well as monolingual and multilingual BERT.Across languages, monolingual LSTMs achieved high accuracy on dependencies without attractors, and generally poor accuracy on agreement across object relative clauses.On other constructions, agreement accuracy was generally higher in languages with richer morphology.Multilingual models generally underperformed monolingual models.Multilingual BERT showed high syntactic accuracy on English, but noticeable deficiencies in other languages.
Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction

3D Morphable Model (3DMM) is a statistical model of facial shape and texture using a set of linear basis functions. Most of the recent 3D face reconstruction methods aim to embed the 3D morphable basis functions into Deep Convolutional Neural Network (DCNN). However, balancing the requirements of strong regularization for global shape and weak regularization for high level details is still ill-posed. To address this problem, we properly control generality and specificity in terms of regularization by harnessing the power of uncertainty. Additionally, we focus on the concept of nonlinearity and find out that Graph Convolutional Neural Network (Graph CNN) and Generative Adversarial Network (GAN) are effective in reconstructing high quality 3D shapes and textures respectively. In this paper, we propose to employ (i) an uncertainty-aware encoder that presents face features as distributions and (ii) a fully nonlinear decoder model combining Graph CNN with GAN. We demonstrate how our method builds excellent high quality results and outperforms previous state-of-the-art methods on 3D face reconstruction tasks for both constrained and in-the-wild images.
Deep Facial Non-Rigid Multi-View Stereo

We present a method for 3D face reconstruction from multi-view images with different expressions. We formulate this problem from the perspective of non-rigid multi-view stereo (NRMVS). Unlike previous learning-based methods, which often regress the face shape directly, our method optimizes the 3D face shape by explicitly enforcing multi-view appearance consistency, which is known to be effective in recovering shape details according to conventional multi-view stereo methods. Furthermore, by estimating face shape through optimization based on multi-view consistency, our method can potentially have better generalization to unseen data. However, this optimization is challenging since each input image has a different expression. We facilitate it with a CNN network that learns to regularize the non-rigid 3D face according to the input image and preliminary optimization results. Extensive experiments show that our method achieves the state-of-the-art performance on various datasets and generalizes well to in-the-wild data.
Detecting A Twitter Cyberbullying Using Machine Learning

Social media is a platform where many young people are getting bullied. As social networking sites are increasing, cyberbullying is increasing day by day. To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model automatically detect social media bullying actions. However, many social media bullying detection techniques have been implemented, but many of them were textual based. The goal of this paper is to show the implementation of software that will detect bullied tweets, posts, etc. A machine learning model is proposed to detect and prevent bullying on Twitter. Two classifiers i.e. SVM and Naïve Bayes are used for training and testing the social media bullying content. Both Naive Bayes and SVM (Support Vector Machine) were able to detect the true positives with 71.25% and 52.70% accuracy respectively. But SVM outperforms Naive Bayes of similar work on the same dataset. Also, Twitter API is used to fetch tweets and tweets are passed to the model to detect whether the tweets are bullying or not.
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI). First, the problem is cast as a stochastic optimization problem over a predefined training duration and solved using the Lyapunov optimization framework. In order to learn and track the wireless channel, a Gaussian process regression (GPR)-based channel prediction method is leveraged and incorporated into the scheduling decision. The proposed scheduling policies are evaluated via numerical simulations, under both perfect and imperfect CSI. Results show that the proposed method reduces the loss of accuracy up to 25.8% compared to state-of-the-art client scheduling and RB allocation methods.
Meta-Learning for Few-Shot Land Cover Classification

The representations of the Earth's surface vary from one geographic region to another. For instance, the appearance of urban areas differs between continents, and seasonality influences the appearance of vegetation. To capture the diversity within a single category, such as urban or vegetation, requires a large model capacity and, consequently, large datasets. In this work, we propose a different perspective and view this diversity as an inductive transfer learning problem where few data samples from one region allow a model to adapt to an unseen region. We evaluate the modelagnostic meta-learning (MAML) algorithm on classification and segmentation tasks using globally and regionally distributed datasets. We find that few-shot model adaptation outperforms pre-training with regular gradient descent and fine-tuning on the (1) Sen12MS dataset and (2) DeepGlobe dataset when the source domain and target domain differ. This indicates that model optimization with meta-learning may benefit tasks in the Earth sciences whose data show a high degree of diversity from region to region, while traditional gradient-based supervised learning remains suitable in the absence of a feature or label shift.
What should teacher educators know about technology? Perspectives and self-assessments

This article offers a first look at teacher educators' (N = 336) perceptions of their technology competencies based on the Teacher Educator Technology Competencies (TETCs; Foulger, Graziano, Schmidt-Crawford, & Slykhuis, 2017). The participants generally rated their competence levels highly in relation to the TETCs. Although many participants reported that the TETCs adequately reflected the competencies required of them, they suggested various additions and changes to the TETCs. This mixed-method study advances understanding of teacher educators' perceptions of the importance of various competences to their work and offers feedback from the field regarding which competencies might be missing from the TETCs.
Person Reidentification by Multiscale Feature Representation Learning With Random Batch Feature Mask

Person reidentification (PReID) has received increasing attention due to its significant importance in intelligent video surveillance. However, most existing multiscale feature learning methods embed the multiscale feature extraction modules for PReID, which increases the complexity of the inference network and reduces the timeliness. Moreover, jointly using the small-scale and large-scale features to learn feature representations may weaken the local detailed features extraction and spatial information learning. Besides, some attentive local features are often suppressed when introducing the attention mechanisms for deep PReID models. To address these issues, a deep model with multiscale feature representation learning (MFRL) and random batch feature mask (RBFM) is proposed for PReID in this study. To ensure the feature representations discriminability and spatial information learning, two identity losses are adopted to supervise the small-scale and large-scale features learning in the MFRL module, respectively. To alleviate the situation of local attentive features being suppressed by using attention mechanisms, RBFM branch with random feature block dropping strategy which can learn the attentive local feature representations. The proposed methods are only performed in the training phase and discarded in the testing phase, thus, enhancing the effectiveness of the model. Our model achieves the state-of-the-art on the popular benchmark data sets, including Market-1501, DukeMTMC-reID, and CUHK03. Besides, we conduct a set of ablation experiments to verify the effectiveness of the proposed methods.
Systematic Review on Fully Homomorphic Encryption Scheme and Its Application

Security and efficiency of transferred data is the main concern in big data and cloud computing service. Fully Homomorphic Encryption is one of the most recent solutions that ensure the security and confidently. This paper expected to lead precise writing on the subject of Fully Homomorphic Encryption and its Application. The primary point of the audit is to pick up knowledge of the Fully Homomorphic Encryption and to locate the best way to deal with executing it. First, Fully Homomorphic Encryption Algorithms were discovered since 2009. After this year, different approaches were followed in order to enhance the scheme such as Fully Homomorphic Encryption based on Integer (DGHV) Scheme, Fully Homomorphic Encryption based on BGV schemes, Multi-key Fully Homomorphic Encryption Scheme and Fully Homomorphic Encryption based on GSW. This paper uses a systematic review of Fully Homomorphic Encryption technologies. The technique is performed to lead this methodical writing included utilizing the examination inquiries or research questions to determine keywords which were then used to search for bits of peer-reviewed papers, articles or book at scholastic directories. Through introductory hunts, 250 papers and scholarly works were found and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 24. Jadad scale used to measure the quality of selected publications. Most of the papers developed new schemes on Fully Homomorphic Encryption to enhance efficiency and reduce noise. Other papers developed a new algorithm to improve the security of the transferred data. Last Thematic modeling analysis presented in this paper to ensure that all the articles were related to Fully Homomorphic Encryption and cloud computing.
Consumer resistance to innovation: smart clothing

Abstract Smart clothing is believed to have an enormous growth potential, but at present, it is not so attractive in terms of sales. This study identified various obstacles affecting consumers’ interest in smart clothing. Interviews were conducted with consumers who are resistant to innovation, those who would continue to use their phones and wearable devices of which safety and functionality have already been proven but reject smart clothing. Availability, which refers to the extent to which consumers can use a product or service, is also a significant factor influencing the innovation resistance of smart clothing. It was also observed that consumers reject smart clothing as an act of resisting innovation; many stated that they would not buy smart clothing unless a trustable brand produces it with appropriate functions and lower prices. Some people said that they refuse to purchase smart clothing due to aesthetic dissatisfaction despite the improved quality and performance. These results indicate that manufacturers should consider what functionality or technology would be appropriate to incorporate in clothing while ensuring fashionable styles and availability in many sizes.
Adversarial Attacks on Graph Neural Networks

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g., the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we present a study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node’s features and the graph structure , thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain, we propose an efficient algorithm N ettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given. For the first time, we successfully identify important patterns of adversarial attacks on graph neural networks (GNNs) — a first step towards being able to detect adversarial attacks on GNNs.
Manifesting construction activity scenes via image captioning

This study proposed an automated method for manifesting construction activity scenes by image captioning – an approach rooted in computer vision and natural language generation. A linguistic description schema for manifesting the scenes is developed initially and two unique dedicated image captioning datasets are created for method validation. A general model architecture of image captioning is then instituted by combining an encoder-decoder framework with deep neural networks, followed by three experimental tests involving the selection of model learning strategies and performance evaluation metrics. It is demonstrated the method's performance is comparable with that of state-of-the-art computer vision methods in general. The paper concludes with a discussion of the feasibility of the practical application of the proposed approach at the current technical level.
A Survey on Interactive Reinforcement Learning

Interactive reinforcement learning (RL) has been successfully used in various applications in different fields, which has also motivated HCI researchers to contribute in this area. In this paper, we survey interactive RL to empower human-computer interaction (HCI) researchers with the technical background in RL needed to design new interaction techniques and propose new applications. We elucidate the roles played by HCI researchers in interactive RL, identifying ideas and promising research directions. Furthermore, we propose generic design principles that will provide researchers with a guide to effectively implement interactive RL applications.
An intrusion detection scheme based on the ensemble of discriminant classifiers

The cyber-physical system and modern technology are useful in various applications of the network. However, various types of bugs and vulnerabilities are also brought along with modern technologies. The attacks caused by vulnerabilities create huge losses, necessitating the need to detect these attacks. Although, considerable work has been done by the researchers so far, but novel attacks are yet to be detected. Existing schemes are suitable for denial of service (DOS) type of attack class. However, these schemes are not efficiently detecting other types of attack classes such as Probing, Remote to User (R2L) and User to Root (U2R). In view of this, a new intrusion detection scheme based on ensemble of discriminant classifiers is proposed in this paper. In ensemble of discriminant classifier method, weak learners are converted into strong learners. KDDcup99 dataset has been used in the proposed scheme for empirical evaluation. The results show that the proposed scheme is superior in detecting all types of attack classes by achieving 98.9% overall accuracy. Network security, Cyber-physical system, Intrusion detection, Ensemble methods, Discriminant classifier, Denial of service (DOS)
Traffic Signal Violation Detection using Artificial Intelligence and Deep Learning

The number of new vehicles on the road is increasing rapidly, which in turn causes highly congested roads and serving as a reason to break traffic rules by violating them. This leads to a high number of road accidents. Traffic violation detection systems using computer vision are a very efficient tool to reduce traffic violations by tracking and Penalizing. The proposed system was implemented using YOLOV3 object detection for traffic violation detections such as signal jump, vehicle speed, and the number of vehicles. Further, the system is optimized in terms of accuracy. Using the Region of interest and location of the vehicle in the duration of frames, determining signal jump. This implementation obtained an accuracy of 97.67% for vehicle count detection and an accuracy of 89.24% for speed violation detection.
Perceptive Model Predictive Control for Continuous Mobile Manipulation

A mobile robot needs to be aware of its environment to interact with it safely. We propose a receding horizon control scheme for mobile manipulators that tracks task space reference trajectories. It uses visual information to avoid obstacles and haptic sensing to control interaction forces. Additional constraints for mechanical stability and joint limits are met. The proposed method is faster than state of the art sampling based planners, available as opensource and can be implemented on a broad class of robots. We validate the method both in simulation and through extensive hardware experiments with a multitude of mobile manipulation platforms. The resulting software package is released with this letter.
The strategic influence of school principal leadership in the digital transformation of schools

This paper analyzes the key factors for the successful digital transformation of schools through the use of Educational Digital Resources (EDRs), paying special attention to school principals and their leadership from a strategic pedagogical perspective. A model is proposed to determine which aspects of principal leadership influence EDR use, and factor analysis is conducted with all the items included in the model. Partial Least Squares Structural Equation Modeling (PLS-SEM) variance analysis is then used to perform a factor analysis of the resulting dimensions. The model is tested with a sample of 142 Spanish school principals. The results show that how useful school principals perceive EDRs to be is the most influential variable in the digital transformation of schools. Other key factors include school contextual variables, the technical support and service related to the EDRs, and the principal's professional and personal profile. Educational policies for digital transformation should thus integrate principals into their core definition, considering factors such as their age or teaching and leadership experience. They should also consider contextual factors, such as school size and complexity and the school's digital culture. School principals' leadership can facilitate a digital culture transformation, not through their authority or bureaucratic influence, but rather their capacity to foster an open debate that allows educational communities to see the integration of EDRs into pedagogical models as an opportunity to improve outcomes.