Reviewerly/exHarmony huggingface dataset:
ESRNet: Efficient Search and Recognition Network for Image Manipulation Detection

With the widespread use of smartphones and the rise of intelligent software, we can manipulate captured photos anytime and anywhere, so the fake photos finally obtained look “Real.” If these intelligent operation methods are maliciously applied to our daily life, then fake news, fake photos, rumors, slander, fraud, threats, and other information security issues around us can happen all the time. Today’s intelligent retouching software can make various modifications to photos, some of which do not change the content that the photos themselves want to express, such as retouching, contrast improvement, and so on. In this article, we mainly study the three operation modes of changing the authenticity of photo contents, which are Copy-move, Splicing, and Removal. Few scholars have done relevant research due to the lack of a corresponding dataset. To address this issue, we elaborately collect a novel dataset, called the multi-realistic scene manipulation dataset ( MSM30K ), which consists of 30,000 images, including three types of tampering methods, and covering 32 different tampering scenes in life. In addition, we propose a unified detection network: the efficient search and recognition network ( ESRNet ) for three tampering methods. It mainly includes four main modules: Efficient feature pyramid network ( EFPN ), Residual receptive field block with attention ( RFBA ), Hierarchical decoding identification ( HDI ), and Cascaded group-reversal attention ( GRA ) blocks. On these three datasets, ESRNet can reach 0.81 on the S-measure, 0.72 on the F-measure, and 0.85 on the E-measure. The inference speed is ~53 fps on a single GPU without I/O time. ESRNet outperforms various state-of-the-art manipulation detection baselines on three image manipulation datasets.
Research on Underwater Object Detection Based on Improved YOLOv4

The complex underwater environment and lighting conditions make underwater images suffer from texture distortion and color variations. In this paper, we propose an improved YOLOv4 detection method to detect four underwater organisms: holothurian, echinus, scallop, starfish and waterweeds. Firstly, we modified the network structure, added a deep separable convolution to the backbone network, and added a 152&#x00D7;152 feature map, which is conducive to the detection of small targets. Secondly, k-means clustering algorithm is used to cluster the bounding box in the data set, and the size of the bounding box is improved according to the clustering results. Thirdly, we propose a new module (EASPP, Spatial Pyramid Pooling), which increases slightly the model complexity, but the improvement effect is significant. Finally, when training the model, we use multi-scale training to better train targets with different scales. The experimental results show that on our test set, the improved method in the underwater object detection method is 4.8% higher than the original YOLOv4 model in accuracy (AP), the F1-score is 5.1% higher than that of the original method, and for mAP@0.5 it reaches 81.5%, which is 5.6% higher than that of the original method, which can be concluded that our method is effective.
Single image dehazing using generative adversarial networks based on an attention mechanism

Most existing image dehazing methods rely on the solution of the atmospheric scattering model or supervised learning based on paired images. However, owing to incomplete prior knowledge and the lack of paired hazy and haze-free images of the same scenes as training samples, their performances for single image dehazing are unsatisfactory. Here, the authors present an unpaired image learning method based on the attention mechanism for single image dehazing problems. The method uses the constraint transfer learning ability and circulatory structure of CycleGAN to carry out an unsupervised image dehazing task for unpaired data. Considering the complexity of the haze distribution in actual imaging and human visual characteristics, the improved channel attention and domain attention mechanisms are integrated into the network to process different features and different regions non-uniformly. The experimental results show that the proposed method achieves good results on both synthetic datasets and real hazy images.
Factors Influencing Teachers’ Use of ICT in Class: Evidence from a Multilevel Logistic Model

Information and Communication Technologies (ICTs) have become a key factor in the educational context, especially in the aftermath of the COVID-19 pandemic, and, correctly implemented, can help to improve academic performance. The aim of this research was to analyse the factors that influence teachers’ decisions to use ICT more- or less frequently to carry out tasks and exercises in their classes. To this end, we estimated a multilevel logistic model with census data from the individualized evaluation of students of the Community of Madrid (Spain) carried out at the end of the 2018–2019 academic year in primary and secondary education. Additionally, we applied multiple imputation techniques to deal with missing values. Based on our results, we found that motivated teachers who have received ICT training, teach calm and respectful classes, and work at schools where students have access to digital devices and frequently use ICT at home, have a high predisposition to use ICT in their classes. Considering our results, our recommendations are aimed at improving teacher training in ICT, encouraging a frequent but responsible use of ICT at home, and increasing the provision of technological resources in schools.
Organizational commitment: an ever-shifting concept forever changed by COVID-19

Evolving from the concept of organizational loyalty, organizational commitment (OC) is a construct that we propose is both dated and lacking in the literature. Our paper aims to address critical gaps in OC literature as it pertains to human resource development for the modern work setting by highlighting three salient elements that have a significant impact on OC on an international scale. We posit that technology, dynamic markets, and globalization are crucial aspects of OC that are largely omitted by the literature in this domain and should be further examined to enhance our understanding of OC as it pertains to human resources development. We also highlight the profound influence that the COVID-19 pandemic has had on OC from a human resources development perspective across the globe. Implications for practitioners and researchers are discussed.
A Light Stage on Every Desk

Every time you sit in front of a TV or monitor, your face is actively illuminated by time-varying patterns of light. This paper proposes to use this time-varying illumination for synthetic relighting of your face with any new illumination condition. In doing so, we take inspiration from the light stage work of Debevec et al. [4], who first demonstrated the ability to relight people captured in a controlled lighting environment. Whereas existing light stages require expensive, room-scale spherical capture gantries and exist in only a few labs in the world, we demonstrate how to acquire useful data from a normal TV or desktop monitor. Instead of subjecting the user to uncomfortable rapidly flashing light patterns, we operate on images of the user watching a YouTube video or other standard content. We train a deep network on images plus monitor patterns of a given user and learn to predict images of that user under any target illumination (monitor pattern). Experimental evaluation shows that our method produces realistic relighting results. Video results are available at grail.cs.washington.edu/projects/Light_Stage_on_Every_Desk/.
Ambiguousness-Aware State Evolution for Action Prediction

In this paper, we propose an ambiguousness-aware state evolution (AASE) method which represents the uncertainty of the input sequence and evolves the subsequent skeletons to generate a reasonable full-length sequence for action prediction. Unlike most existing methods that enforce partial sequences with the labels of full-length videos and ignore the semantic information of the subsequent action, we develop an evolution method by predicting the instructional actions and generating the reasonable candidate subsequent actions, so that the ambiguity of the full sequence’s label supervising for the partial actions can be effectively alleviated. Our method generates the rational subsequent actions under the instructional action class to complement the partially observed action sequence. We design two criteria for a rational generation: 1) the instruction of subsequent action keeps the semantic consistency with the observed sequence; 2) the generation sequence is satisfied with the distribution of the sequence of real data. Moreover, we design an uncertainty module to decide the instructional action class for the generation network. AASE predicts instructional actions with uncertainty learning and evolves different instructional actions by generating the subsequent skeletons, which find the most probable action to represent the partially observed action by learning the way of perceiving the tendency of the ongoing action. We conduct experiments on seven widely used action datasets: NTU-60, NTU-120, UCF101, UT-Interaction, BIT, PKU-MMD and HMDB51, and our experimental results clearly demonstrate that our method achieves very competitive performance with state-of-the-art.
Secure Trust Model Based on Blockchain for Internet of Things Enable Smart Agriculture

Blockchain technology has a lot of potentials to solve non-financial applications though it is derived from peer-to-peer financial transactions. In the last decade, both from industry and academician much research is already done on Blockchain technology its usability to the different non-financial applications. The Blockchain is the secure decentralized network and integrated with other emerging technology like fog computing, cloud computing, and Machine learning to address many challenges existing in different domains. The Internet of Things (IoT) has been used to automate the system which provides efficient and reliable services with minimum human interaction. IoT is integrated with smart agriculture for different purposes like sensing the moisture content, monitoring water level, growth of the plant, etc. However, the authors have identified various challenges including security and privacy issues in IoT enabled smart agriculture systems. In this work, Blockchain assisted trust model for smart agriculture is proposed. The proposed Blockchain based trust model uses Ethereum network and smart contract prototype to addressed some of the security issues in IoT enabled smart agriculture.
Loopy sum-product algorithm based joint detection, tracking and classification of extended objects with analytic implementations

This paper addresses the problem of joint detection, tracking and classification (JDTC) of multiple extended objects (EOs), where each EO is restricted to be an ellipse modeled as a random matrix, and the classification of an EO is obtained on the basis of its size information. In order to reduce the computational complexity of data association and calculating the marginal posterior probability density function (PDF), the loopy sum-product algorithm (LSPA) is carried out based on a suitably devised factor graph. The goal of LSPA-based JDTC is achieved by iteratively computing the belief of each EO which is used to approximate the marginal posterior. Furthermore, in order to get a closed-form implementation, the belief of each EO is modeled via a sum of belief of each class, which is further modeled as the class probability multiplied by a mixture Gamma-Gaussian-inverse-Wishart (GGIW) distribution conditional on the class. The details of implementation are discussed in this paper and, finally, the effectiveness of the proposed approach is assessed via simulation experiments.
Online Graph-Guided Inference Using Ensemble Gaussian Processes of Egonet Features

Graph-guided semi-supervised learning (SSL) and inference has emerged as an attractive research field thanks to its documented impact in a gamut of application domains, including transportation and power networks, biological, social, environmental, and financial ones. Distinct from SSL approaches that yield point estimates of the variables to be inferred, the present work puts forth a Bayesian interval learning framework that utilizes Gaussian processes (GPs) to allow for uncertainty quantification – a key component in safety-critical applications. An ensemble (E) of GPs is employed to offer an expressive model of the learning function that is updated incrementally as nodal observations become available – what caters also for delay-sensitive settings. For the first time in graph-guided SSL and inference, egonet features per node are utilized as input to the EGP learning function to account for higher order interactions than the one-hop connectivity of each node. Further enhancing these attributes through random features that encrypt sensitive information per node offers scalability and privacy for the EGP-based learning approach. Numerical tests on real and synthetic datasets corroborate the effectiveness of the novel method.
Tracking Individuals in Classroom Videos via Post-processing OpenPose Data

Analyzing classroom video data provides valuable insights about the interactions between students and teachers, albeit often through time-consuming qualitative coding or the use of bespoke sensors to record individual movement information. We explore measuring classroom posture and movement in secondary classroom video data through computer vision methods (especially OpenPose), and introduce a simple but effective approach to automatically track movement via post-processing of OpenPose output data. Analysis of 67 videos of mathematics classes from middle school and high school levels highlighted the challenges associated with analyzing movement in typical classroom videos: occlusion from low camera angles, difficulty detecting lower body movement due to sitting, and the close proximity of students to one another and their teachers. Despite these challenges, our approach tracked person IDs across classroom videos for 93.0% of detected individuals. The tracking results were manually verified through randomly sampling 240 instances, which revealed notable OpenPose tracking inconsistencies. Finally, we discuss the implications for supporting more scalability of video data classroom movement analysis, and future potential explorations.
Refugee Food Insecurity &amp; Technology: Surfacing Experiences of Adaptation, Navigation, Negotiation and Sharing

Abstract With the Syrian crisis still ongoing, 91% of Syrian refugee families in Lebanon do not have adequate access to safe and sufficient food. There has been a drive for technological innovation in humanitarian food assistance. To further inform such innovation, we used an Experience-Centred Design approach to gain a holistic understanding of the experiences of refugee food insecurity and how refugees envision technologies can support them. Design engagements with 13 women in an informal refugee settlement in Lebanon were conducted. The findings presented in this paper highlight that coping with food insecurity consists of experiences of adaptation, navigation, negotiation and sharing. We found that a multitude of technologies may be designed to enhance the aforementioned experiences and enable refugees to better cope with food insecurity. Through the lens of our findings, we discuss how technologies may support information sharing, the furthering of new practices and sharing of resources to support refugee food security.
NR-CNN: Nested-Residual Guided CNN In-loop Filtering for Video Coding

Recently, deep learning for video coding, such as deep predictive coding, deep transform coding, and deep in-loop filtering, has been an emerging research area. The coding gain of hybrid coding framework could be extensively promoted by the data-driven models. However, previous deep coding tools especially deep in-loop filtering mainly consider the performance improvement while pay less attention to the reliability, usability, and adaptivity of the networks. In this article, a nested-residual guided convolutional neural network (NR-CNN) structure with cascaded global shortcut and configurable residual blocks is proposed for in-loop filtering. By taking advantage of the correlation between different color components, we further extend the NR-CNN by utilizing luminance as textural and structural guidance for chrominance filtering, which significantly improves the filtering performance. To fully exploit the proposed network into codec integration, we subsequently introduce an efficient and adaptive framework consisting of an adaptive granularity optimization and a parallel inference pipeline for deep learning based filtering. The former contributes to the coding performance improvement through an adaptive decision-making based on rate-distortion analysis at various granularities. The latter reduces the running time of network inference. The extensive experimental results show the superiority of the proposed method, achieving 8.2%, 14.9%, and 13.2% BD-rate savings on average under random access (RA) configuration. Meanwhile, the proposed method also obtains better subjective quality.
Downsampling and Transparent Coding for Blockchain

Blockchain is considered to be able to solve the problem of trust between distributed nodes. However, it's a challenge to store the large amount of data produced by blockchain. Therefore, it is necessary to reduce node storage overhead while ensuring node independence and data recoverability. This paper proposes to downsample these data to reduce the storage overhead of nodes. These nodes keep good independence, if downsampling follows the entropy of blockchain and the block bodies are continuously stored. Moreover, it demonstrates that the entire blockchain history can be efficiently recovered through the cooperative decoding of a group of nodes like fountain codes, if reserved data over these nodes obey the soliton distribution. However, these data on nodes are transparent (stored data are uncoded and can be used directly by the node without decoding). Thus, the proposed algorithm not only keeps decentralization and security, but also has good scalability in independence and recoverability.
A Robust Network for Embedded Traffic Sign Recognition

Traffic sign recognition systems are vital in real-world applications such as auto-driving and safety and driver assistance. While deep neural networks have achieved high accuracy in classifying traffic signs in recent years, there is always the discussion of the high computations of these networks and their many teachable parameters. A significant challenge is to design a compact deep neural network for the application of traffic sign recognition. This paper proposes a network that uses residual blocks in the network to obtain a top-1 accuracy of 99.51 for the German traffic sign recognition benchmark. The number of parameters is ~430,000, which is ~32x fewer than the state-of-the-art. Experiments have been performed to show the network's resistance to destructive factors and its comprehensiveness in the application of traffic sign recognition. These tests show that it is a comprehensive and robust network for the recognition of traffic signs.
Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis

Synthesized speech is common today due to the prevalence of virtual assistants, easy-to-use tools for generating and modifying speech signals, and remote work practices. Synthesized speech can also be used for nefarious purposes, including creating a purported speech signal and attributing it to someone who did not speak the content of the signal. We need methods to detect if a speech signal is synthesized. In this paper, we analyze speech signals in the form of spectrograms with a Compact Convolutional Transformer (CCT) for synthesized speech detection. A CCT utilizes a convolutional layer that introduces inductive biases and shared weights into a network, allowing a transformer architecture to perform well with fewer data samples used for training. The CCT uses an attention mechanism to incorporate information from all parts of a signal under analysis. Trained on both genuine human voice signals and synthesized human voice signals, we demonstrate that our CCT approach successfully differentiates between genuine and synthesized speech signals.
VigilRx: A Scalable and Interoperable Prescription Management System Using Blockchain

Achieving interoperability between healthcare providers is a major challenge.Current systems for managing prescription records suffer from data siloing, unnecessary record duplication, and slow record transfers.In many systems, patients do not retain control over their prescription data.Instead, they must use an intermediary to access or transfer their records.Furthermore, record transfers suffer from differing standards between providers, outdated communication methods, and information blocking.Solving these problems necessitates the creation of an interoperable prescription management system.Realizing such a system requires considering security, efficiency, scalability, and other challenges.Recent regulatory actions attempt to address these challenges, but fundamental issues persist.This paper proposes a patient-centric and interoperable prescription system that ensures patient control, prevents information blocking, and improves transfer efficiency.We call our solution VigilRx-a system that uses blockchain and smart contracts to manage prescriptions.Stakeholders exist as one of three role-based smart contracts within the system: patient, prescriber, or pharmacy.These role contracts ensure the system is patient-centric by assigning ownership of prescription records directly to patients.Our smart contracts also ensure the system's interoperability, as we use a standardized prescription contract to ensure records can be easily managed.VigilRx's use of blockchain also promotes transparency by providing patients an explicit list of parties that hold permission to access their records.Use of existing software patterns allows the system to adapt as needed.We implement VigilRx and show that it is both scalable and efficient.
Data Clustering Using Chimp Optimization Algorithm

In recent decades, many successful meta-heuristic algorithms are widely applied to data clustering problems due to their powerful capabilities. The Chimp Optimization Algorithm (ChOA) is a recently proposed meta-heuristic search algorithm that is inspired by chimps' individual intelligence and sexual motivation in their group hunting. The good performance of ChOA in a variety of optimization problems, prove the superiority of this algorithm over other swarm-based intelligence. This study presents a novel approach according to ChOA for data clustering. ChOA, like other Swarm Intelligence-based Algorithms (SIAs), starts with a random population and then calculates an objective value for them. In data clustering problems the population of candidate solutions is the position vectors of the centroids, and the objective function is the sum of intra-cluster distances (SICDs) between each sample and its nearest centroid. Following that, ChOA attempts to optimize the population by finding the best position vectors of optimal centroids. There are four different kinds of chimps in this regard: driver, chaser, barrier, and attacker. Furthermore, four major hunting moves including driving, blocking, chasing, and attacking are considered to find the promising solution. In this research, four datasets of the UCI machine learning repository are used to evaluate the performance of the proposed new approach. Experimental results illustrate that the proposed work significantly outperforms other clustering algorithms regarding the objective value, Error Rate (ER), and some other statistical tests.