Reviewerly/exHarmony huggingface dataset:
Mobile Application for Dementia and Alzheimer Patients

Dementia is an incessant neurodegenerative disease that to a degree influences millions of people around the world. Alzheimer’s disease is a major reason for dementia, a term for the decline of memory and cognition, severe enough to impact everyday human performance. According to various studies, Alzheimer’s disease has been found to account for 60 to 80% of dementia cases. Alzheimer’s disease consists of 7 stages with different indicators. As the stage progresses, the patient’s condition worsens over time and the dependency on the caregivers also increase. In this work, an Android application to provide a virtual guide for people with dementia and make tasks easier for caregivers is developed. This application will provide several features such as Family and Relationship information, Location Tracking using the Android location provider API, medication and exercise reminders with the alarm manager class, memory quizzes, and music therapy using Convolutional Neural Networks (CNN). The novelty of this application is the connection with volunteers in the vicinity using a spatial index for emergency assistance in situations where the nurse cannot physically attend to the patients. The aim of the application is to provide an integrated solution for counseling in all aspects of life in every stage of dementia.
Explainable Driver Activity Recognition Using Video Transformer in Highly Automated Vehicle

Distracted driving is one of the leading causes of road accidents. With the recent introduction of advanced driver assistance systems and L2 vehicles, the role of driver attention has gained renewed interest. It is imperative for vehicle manufacturers to develop robust systems that can identify distractions and aid in preventing such accidents in highly automated vehicles. This paper mainly focuses on studying secondary behaviors, and their relative complexity to develop a guide for auto manufacturers. In recent years, a few driver secondary action datasets and deep learning algorithms have been created to address this problem. Despite their success in many domains, Convolutional Neural Network based deep learning methods struggle to fully consider the overall context of an image, and focus on specific image features. We present the use of Video Transformers on two challenging datasets, one of them being a grayscale low-quality dataset. We also demonstrate how the novel concept of a Visual Dictionary can be used to understand the structural components of any secondary behavior. Finally, we validate different components of the visual dictionary by studying the attention modules of the transformer-based model and incorporating explainability in the computer vision model. An activity is decomposed into multiple small actions and attributes and the corresponding attention patches are highlighted in the input frame. Our code is available at github.com/VTTI/driver-secondary-action-recognition
DL-CMFD: Deep Learning-Based Copy-Move Forgery Detection Using Parallel Feature-Extractor

Due to readily available software, anyone can easily manipulate the image and create a piece of false information. Among different types of image forgery, copy-move image forgery is widely practiced due to its sophistication and difficulty distinguishing it from the original image. We proposed a new deep learning-based copy-move forgery detection (DL-CMFD) technique to identify the forged image by successfully localizing the forged area to address this challenge. To attain this, the scale-invariant features are extracted from the forged image using the parallel feature extraction technique utilizing a batch normalization-based convolution neural network with the aid of the ReLU activation function. In the next step, to identify the identical features, self-correlation has been calculated, and further, percentile pooling is used to find the top k-percent features. Lastly, a masked image is produced as the output. The proposed scheme has been deployed on the different available datasets like CoMoFD and COCO to testify to the efficiency and accuracy. The proposed scheme is found to be robust because it can detect the forged area accurately when deployed on post-processed forged images. We made various statistical parametric evaluations to evaluate the efficacy and conformity of the proposed copy-move forgery detection scheme, and a comparison analysis is also made.
A novel forgery classification method based on multi‐scale feature capsule network in mobile edge computing

Abstract Face recognition is one of the most important applications of MEC. However, there have been many fake face data that can deceive MEC devices, causing serious problems such as information leakage. Face forgery detection can effectively solve this problem. Current face forgery detection methods have achieved high accuracy. However, most of the methods are researched on the classification of face authenticity. We find that studying multi‐classification of forgery methods can not only improve the accuracy of the model to identify fake faces, but also help improve the generalization ability of fake face classification. We argue that multi‐scale features and high‐frequency features can expose more detailed forgery artifacts. So, we design four modules, which take advantage of the complementarity of RGB features and frequency features, global features and local features. The first module is a residual‐guided multi‐scale spatial attention module, which uses residuals to guide the RGB feature extractor to extract fake features from a multi‐scale perspective. The second module is a multi‐scale retinal feature extraction module. The third module is a multi‐scale channel attention‐guided local frequency statistics module, which extracts local frequency responses using sliding‐window DCT. The last module is a capsule network classification module with overall correlation to classify the fused features. The information transfer between the subject capsule and the classification capsule can increase the integrity of the model, making the model converge faster. We conduct experiments on the databases FaceForensics++, DeepfakeDetection, and FakeAVCeleb. Experimental result shows that our method performs well on forgery classification.
Effect of Convulsion Layers and Hyper-parameters on the Behavior of Adversarial Neural Networks

The most significant models of neuronal networks for unsupervised machine learning now include the generative adversarial networks (GAN) as a separate branch. They have recently shown some degree of success. However, there is still a major obstacle. Many different loss functions have been created to train GAN discriminators, and they all share a similar structure: a total amount of real and fake losses that are solely dependent on the real losses and the generated data respectively. A challenge with a sum equal to two losses is that the training may benefit from one loss while harming the other, as we shall demonstrate causes mode collapse and instability. First, we suggest that the current loss function may prevent learning fine details in the data while trying to contract the discriminator. In this paper, we provide a new class of loss discriminant functions that promotes the training of the discriminator with a higher frequency than that of the generator for make the discriminator more demanding compared to the generator and vice versa. We may choose appropriate weights to train the discriminator in a way that takes advantage of the GAN’s stability by using gradients of the real and fake loss parties. Our methodology may be used with any discriminator model where the loss is the total of true and fake pieces. Our approach entails appropriately adjusting the hyper-parameters in order to enhance the training of the two opposing models. The effectiveness of our loss functions on tasks involving the creation of curves and images has been demonstrated through experiments, improving the base results by a statistically significant margin on several datasets.
Meta-optic accelerators for object classifier

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accuracy in classifying handwritten digits.
Secure Sharing of Student Credentials Using Blockchain

Sharing students’ credentials is an essential process for any educational ecosystem that includes diverse stakeholders such as students, teachers, various committees of institutes, administrations, government agencies, and many more. All of these stakeholders take extensive measures to ensure the authenticity and confidentiality of students’ testimonials when those are being shared. In spite of these concerns, the distribution of students’ credentials is a vulnerable, potentially complex, and sometimes insecure process. The goal is to reduce the existing security-related issues when sharing the students’ credentials with the help of blockchain technology. The technology of blockchain can be used effectively to solve the issue of securely exchanging student credentials. We proposed a secure, immutable, authentic, tamper-proof, privacy-preserving, and easily shareable blockchain-based mechanism for securely exchanging students’ credentials. In order to make the process of sharing credentials scalable, we proposed a secure off-chain store mechanism. A decentralized application based on Ethereum technology is used for conducting an analysis of the architecture with regard to its performance as well as its potential feasibility.
WDM Photonic Synaptic Interconnects for Sparse Photonic Neural Networks

We propose a new wavelength-division-multiplexed silicon photonic synaptic interconnect based on silicon microring resonators in between two silicon nitride star couplers. Further, we experimentally observe the reconfigurability of the proposed scheme on foundry-enabled chip.
LPR: learning point-level temporal action localization through re-training

Abstract Point-level temporal action localization (PTAL) aims to locate action instances in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the localization-by-classification paradigm to locate action boundaries in the temporal class activation map (TCAM) by thresholding, also known as TCAM-based method. However, TCAM-based methods are limited by the gap between classification and localization tasks, since TCAM is generated by a classification network. To address this issue, we propose a re-training framework for the PTAL task, also known as LPR. This framework consists of two stages: pseudo-label generation and re-training. In the pseudo-label generation stage, we propose a feature embedding module based on a transformer encoder to capture global context features and optimize pseudo-labels’ quality by leveraging point-level annotations. In the re-training stage, LPR uses the above pseudo-labels as supervision to locate action instances with a temporal action localization network rather than generating TCAMs. Furthermore, to alleviate the effects of label noise in the pseudo-labels, we propose a joint learning classification module (JLCM) in the re-training stage. This module contains two classification sub-modules that simultaneously predict action categories and are guided by a jointly determined clean set for network training. The proposed framework achieves state-of-the-art localization performance on both the THUMOS’14 and BEOID datasets.
Inpainting with Separable Mask Update Convolution Network

Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are many existing methods that can produce high-quality restoration results, they often struggle when dealing with images that have large missing areas, resulting in blurry and artifact-filled outcomes. This is primarily because of the presence of invalid information in the inpainting region, which interferes with the inpainting process. To tackle this challenge, the paper proposes a novel approach called separable mask update convolution. This technique automatically learns and updates the mask, which represents the missing area, to better control the influence of invalid information within the mask area on the restoration results. Furthermore, this convolution method reduces the number of network parameters and the size of the model. The paper also introduces a regional normalization technique that collaborates with separable mask update convolution layers for improved feature extraction, thereby enhancing the quality of the restored image. Experimental results demonstrate that the proposed method performs well in restoring images with large missing areas and outperforms state-of-the-art image inpainting methods significantly in terms of image quality.
Object tracking based on siamese network with 3D attention and multiple graph attention

Currently, the object tracking algorithm based on the siamese network is the most popular research direction of object tracking. However, most siamese network trackers are unable to update the template, resulting in hardly dealing with the fuzzy target well, which requires the trackers to extract the target features in the template more accurately. Moreover, most of the siamese network trackers compute the similarity between the target template and the search region in a global matching way. Thus they cannot well handle the rapid change of the target. To address the above limitations, we propose an object tracking algorithm based on 3D attention SimAM and multiple graph attention. We combine siamese networks with 3D attention to improve the feature extraction capability of the network. We frame the target template and search region to improve feature-matching accuracy by introducing multiple graph attention mechanisms. We test our model against three authoritative benchmarks, GOT10K, UAV123 and OTB100, and compare it with advanced trackers, and the results show that our trackers achieve better results.
Two-photon Interference at a Single Input Port of a Beam-Splitter

We present experimental evidence for two-photon interference (TPI) in a variation of the Hong-Ou-Mandel experiment in which photons with different polarizations enter only one of the beam-splitter input ports.
Promuovere l’inclusione nella classe di lingua: dalle procedure didattiche tradizionali al Ciclo Glottodidattico

Within the long-standing educational linguistic research tradition on the so-called ‘operational models’, little attention has been devoted to inclusive practices, particularly when learners with specific language needs are present in mainstream classes. This paper aims to fill this gap by presenting a new teaching procedure (Language Teaching Cycle) which brings together some key-principles derived from traditional models with new insights into the psycholinguistic processes that characterize language learning and should be supported in order to promote inclusion. The first part of the paper proposes a critical literature review of two major teaching procedures (the PPP sequence and the Teaching/Learning Unit), while the second part presents the theoretical bases of the Language Teaching Cycle, as well as its potential future developments and applications.
Blockchain model based on human DNA to modify blocks in P2P networks

The problems are persistent in the general model of a chain of blocks, highlighting two fundamental characteristics: the impossibility of removing blocks of data once they are added to the chain and the limitation of storing only a small amount of data in each block. Although these characteristics, in addition to the place where this information is stored and the P2P network where it works, make it unique and have generated innovative paradigms in ICT, with the development of cryptocurrencies and NFTs, they have also raised drawbacks, such as the impossibility to modify transactions or blocks once they have been validated and stored in the blockchain. Turned out, a proposal for a model based on human DNA and its way of storing information, a procedure manual that describes in detail how to implement a chain of blocks based on human DNA, which was presented as an alternative to the identified problems, which allow modifying misinformation to expand data storage on the blockchain. It was concluded that a new blockchain model is presented as an adequate solution to face the limitations of the traditional blockchain. By incorporating the ability to modify misinformation to improve data storage, the functionality and versatility of the blockchain is significantly improved. This study opens new perspectives in the ICT field, providing a promising alternative for the implementation of more flexible and adaptable.
Building Student Loyalty Through Study Program Commitment

&#x0D; &#x0D; &#x0D; &#x0D; This study aims to determine the effect of study program commitment on student loyalty. This is conducted through a survey. Data are taken from students of the Economics Education Study Program, Teacher Training and Education Faculty of State Islamic University of Sultan Syarif Kasim, Riau by using a questionnaire in the form of google form link and analyzed with SPSS program. The results show that study program commitment has a significant effect on student loyalty. The finding also indicates that the study program commitment and student loyalty can be developed based on social exchange theory.&#x0D; &#x0D; &#x0D; &#x0D;
Chapter 4. An eye-tracking study of productivity and effort in Chinese-to-English translation and post-editing

For several language pairs, an emerging consensus finds that post-editing of machine translations is faster and less cognitively effortful than from-scratch human translation, resulting in increased translator productivity and decreased translator fatigue. These benefits have yet to be robustly established in some language pairs that are linguistically and culturally remote with very different writing systems. We carry out a systematic Chinese-to-English study using keystroke logger timing measures and eye-tracking measures of cognitive effort, taking into account translator education levels, different source text domains, and quality of the translation product. We observe significant post-editing productivity gains for more highly educated participants and for more straightforward and less technical texts. Measures of cognitive effort show significantly reduced cognitive effort in post-editing.
Dynamic Self-Attention with Guided-Attention Network for Visual Question Answering using Capsules Dynamic Routing

Visual Question Answering (VQA) takes an active role in aiding people, especially with visual issues. Since, VQA answers questions about a specific image, it needs a deep understanding of the image and question content together. Recently, attention is commonly used to build VQA models. It gives weight to each feature that leads to better interpretation of the images and questions representations. However, VQA faces some issues, it requires multi-attention layers for reasoning. In addition to that, the current works focus on the visual features and ignore the effect of question features. Also, they used the stacked layer approach, which increases the models’ parameters. In this work, we propose a dynamic attention mechanism based on capsules concept. It can be generalized as self-attention and co-attention based on the inputs. Furthermore, we make the self-attention improves the co-attention results by feeding its output to the proposed co-attention instead of using the ordinary embedding matrices. Motivated by capsule networks, our model computes the attention scores through an iterative process. The proposed VQA is evaluated using evaluation matrices such as accuracy and WUPS on the benchmark DQAUAR and VQA2.0 datasets. The results show an improvement in the VQA compared to the baseline models.
Dataglove for Sign Language Recognition of People with Hearing and Speech Impairment via Wearable Inertial Sensors

Finding ways to enable seamless communication between deaf and able-bodied individuals has been a challenging and pressing issue. This paper proposes a solution to this problem by designing a low-cost data glove that utilizes multiple inertial sensors with the purpose of achieving efficient and accurate sign language recognition. In this study, four machine learning models-decision tree (DT), support vector machine (SVM), K-nearest neighbor method (KNN), and random forest (RF)-were employed to recognize 20 different types of dynamic sign language data used by deaf individuals. Additionally, a proposed attention-based mechanism of long and short-term memory neural networks (Attention-BiLSTM) was utilized in the process. Furthermore, this study verifies the impact of the number and position of data glove nodes on the accuracy of recognizing complex dynamic sign language. Finally, the proposed method is compared with existing state-of-the-art algorithms using nine public datasets. The results indicate that both the Attention-BiLSTM and RF algorithms have the highest performance in recognizing the twenty dynamic sign language gestures, with an accuracy of 98.85% and 97.58%, respectively. This provides evidence for the feasibility of our proposed data glove and recognition methods. This study may serve as a valuable reference for the development of wearable sign language recognition devices and promote easier communication between deaf and able-bodied individuals.
Chaos Gray Wolf global optimization algorithm based on Opposition-based Learning

Gray wolf optimizer (GWO) is a new heuristic algorithm.It has few parameters and strong optimization ability and is used in many fields.However, when solving complex and multimodal functions, it is also easy to trap into the local optimum and premature convergence.In order to boost the performance of GWO, a tent chaotic map and opposition-based learning Grey Wolf Optimizer (CO-GWO) is proposed.Firstly, some better values of the population in the current generation are retained to avoid deterioration in the next generation.Secondly, tent chaotic map and opposition-based (OBL) are introduced to generate values that can traverse the whole feasible region as much as possible, which is conducive to jumping out of local optimization and accelerating convergence.Then, the coefficient a is dynamically adjusted by the polynomial attenuation function of the 2-decay method.Finally, the proposed algorithm is tested on 23 benchmark functions.The results show that the proposed algorithm is superior to the conventional heuristic algorithms, GWO and its variants in search-efficiency, solution accuracy and convergence rate.
Joint Upload-Download Transmission Scheme for Low-Latency Mobile Live Video Streaming

Variations in wireless network bandwidth will have a significant impact on the performance of mobile live video streaming. When multiple users have different network latency, the way of uploading a higher bitrate version of previously uploaded video segments may improve the quality of experience (QoE) of users with high network latency. In this paper, we propose an upload-download collaborative transmission scheme for mobile live video streaming with the goal of improving the overall QoE of all users. Moreover, we designed a frame-based transmission and scheduling mechanism to reduce the delay experienced by users watching live videos. Then, we design a joint upload-download transmission algorithm based on deep reinforcement learning (DRL) that takes into account the states of both the video upload and download sides. Through extensive simulation in multi-client mobile live video streaming scenarios, the proposed scheme outperforms existing solutions in terms of overall QoE, smoothness, and live video delay.