Reviewerly/exHarmony huggingface dataset:
Pseudo magic cubes: A multidimensional data hiding scheme exploiting modification directions for large payloads

In this paper, a multidimensional data hiding scheme exploiting modification directions (EMD) that can embed high payload information without causing much distortion, is proposed. Traditional, EMD embeds a secret digit represented in the (2n+1)−ary system has a maximum 1.161 bpp payload. In the proposed work, a secret-digit represented in the n3−ary notational system is hidden, on a group of 3 pixels that are chosen based on a random sequence yields a payload of log2(n). Hence, an increase in the dimension, n− of the neighborhood set results in the increased payload. We have conducted extensive experiments on the standard image set to verify the embedding efficiency and visual quality of the stego image. The PSNR of the proposed algorithm for embedding 2.3219 bpp in a cover image of 512 × 512 is 45.13 dB, which implies that the visual quality of the stego image has not degraded even for large payloads. We have also verified that the proposed technique is undetectable during steganalysis.
Data Augmentation and Text Recognition on Khmer Historical Manuscripts

Analysis and recognition of historical documents faces many challenges, one of which is the scarcity of the ground truth data needed for most machine learning techniques, deep learning in particular. In this paper, we present a novel approach which significantly augments the word image samples generated from an existing dataset of Khmer ancient palm leaf manuscripts. Instead of segmenting real Khmer words, we combine the annotated glyphs into groups called sub-syllables. A new text recognition method is also proposed to take into account the spatially complex structure of Khmer writing. The proposed method is composed of two main modules: a feature generator and a decoder. The generator utilizes convolutional blocks, inception blocks, and also a bi-directional LSTM to encode information extracted from the input image so that it can be decoded by the attention-based decoder to predict the final text transcription. Experiments are conducted on a new dataset of groups of sub-syllables constructed from annotated glyphs of the SleukRith Set.
Enrichment of Arabic TimeML Corpus

Automatic temporal information extraction is an important task for many natural language processing systems. This task requires thorough knowledge of the ontological and grammatical characteristics of temporal information in the text as well as annotated linguistic resources of the temporal entities. Before creating the resources or developing the system, it is first necessary to define a structured schema which describes how to annotate temporal entities. In this paper, we present a revised version of Arabic TimeML, and we propose an enriched Arabic corpus, called "ARA-TimeBank", for events, temporal expressions and temporal relations based on the new Arabic TimeML. We describe our methodology which combines a pre-annotation phase with manuel validation and verification. ARA-TimeBank is the first corpus constructed for Arabic, which meets the needs of TimeML and addresses the limitations of existing Arabic TimeBank.
Driving-behavior-oriented trajectory planning for autonomous vehicle driving on urban structural road

A novel driving-behavior-oriented method is proposed in this paper for improving trajectory planning performance of autonomous vehicle driving on urban structural road. Differ from the irregularity and unpredictability of escaping a maze or travelling on off-road, the driving on road emphasizes more on the compliance of road traffic rules and the satisfaction of passenger comfort rather than purely pursuing the shortest route or the shortest time. Therefore, the driving-behavior-oriented framework is employed in trajectory planning, which divides trajectory into lane change, turn and U-turn, according to the basic traffic rules and the daily behaviors of drivers. The presented approach mainly includes basic path planning, fast-bias RRT path planning and velocity planning. The basic path planning consists of lane change, turn and U-turn behaviors, which generates smooth path with continuous curvature. In order to ensure the completeness of the programming algorithm, a fast-bias RRT (FB-RRT) algorithm is embedded. As guiding by the driving behavior, normal random, goal-bias and Gaussian sampling strategies are fused to form FB-RRT, which could make the best use of the basic path planning and reduce the randomness of node’s extension to save the computation time. After collision-free path generating, cubic polynomial curve is employed to schedule velocity profile for coping with vehicle stability requirements, actuator constraints and comfort conditions. The planner has been tested in simulation and a real vehicle in various typical scenarios. Test results illustrate that the presented method could generate a trajectory with controllable extrema of curvature as well as with continuous and smooth enough curvature. Besides, generated trajectory has short length, high success rate (no less than 80% average success rate in complex environment) and real time (the average period is less than 100 ms). Moreover, the velocity profile meets the vehicle stability requirements, actuator constraints, and comfort conditions.
Virtual Reality (VR) in Engineering Education and Training

Although Virtual Reality (VR) was first mentioned in the 60s, the research interest into the technology and its application are still gaining much attention globally. VR technology had evolved and had found niche application in many fields including entertainment, tourism, healthcare, manufacturing, education and more. A notable characteristic of the VR technology is that it seeks to immerse the user into the intended environment and narrative, allowing high user interaction and involvement. These qualities are very beneficial for engineering education. In the past, cost, hardware capabilities and availability, connectivity and other issues had hindered VR wider adoption and application. However, it is changing with the advent of more devices, the involvement of more consumer electronic players and the growth of digitization. This paper seeks to report on the research on virtual reality in engineering education over the past 26 years. The study conducts a bibliometric analysis to reflect the trend of VR in engineering education settings, thus identifying the possible emerging trends. An extensive literature search was conducted using the Scopus database and was analyzed using Vosviewer and Excel. There is an increasing trend of VR research related to the engineering education settings with an evident increase in the scope, the coverage and the citations figures. There is tremendous growth in the number of publications and citations in the recent past four years of the study. The most active author in the field is Sampaio, and the top universities where the publications are affiliated with are Purdue University and Clemson University. The majority of the publications were also found to be affiliated with the "ASEE Annual Conference and Exposition Conference Proceedings". The United States had the honor of being the country with the majority of publications from this study. There is a continuous shift of research interest observed through the publications keywords, and this provides an indication of the dynamic progression of research direction among researcher in the field of study. The findings of this study provide a good overview of the trend in research related to VR in engineering education and can serve as a guide to academics seeking to research or adapt the usage of VR in engineering settings.
Deterministic hierarchical remote state preparation of a two‐qubit entangled state using Brown <i>et al.</i> state in a noisy environment

Quantum communication is one of the cutting-edge research areas today, where the scheme of remote state preparation (RSP) has drawn significant attention of researchers. The authors propose here a hierarchical RSP protocol for sending a two-qubit entangled state using a seven-qubit highly entangled state derived from Brown et al. state. They have also studied here the effects of two well-known noise models namely amplitude damping (AD) and phase damping (PD). An investigation on the variation of the fidelity of the state with respect to the noise operator and receiver is made. PD noise is found to affect the fidelity more than the AD noise. Furthermore, the higher power receiver obtains the state with higher fidelity than the lower power receiver under the effect of noise. To the best of their knowledge, they believe that they have achieved the highest fidelity for the higher power receiver, 0.89 in the presence of maximum AD noise and 0.72 in the presence of maximum PD noise, compared to all the previously proposed RSP protocols in noisy environments. The study of noise is described in a very pedagogical manner for a better understanding of the application of noise models to a communication protocol.
Identification of Radar Emitter Type with Recurrent Neural Networks

In this paper, we present a method for the identification of different multifunction radar emitter types. It is based on Long Short-Term Memory recurrent neural networks and a previously published hierarchical modelling approach. This approach maps radar pulses to different levels of symbols which can be regarded as parts of a radar language. We evaluate our method with an example emitter that can make use of three different resource management techniques. The results show that it is possible to distinguish between radar types that mainly use the same emission parameters but differ in the resource management method.
Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments

In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments. And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM). The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates. Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers. It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features. Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning

We propose a novel adaptive transfer learning framework, learning to transfer learn (L2TL), to improve performance on a target dataset by careful extraction of the related information from a source dataset. Our framework considers cooperative optimization of shared weights between models for source and target tasks, and adjusts the constituent loss weights adaptively. The adaptation of the weights is based on a reinforcement learning (RL) selection policy, guided with a performance metric on the target validation set. We demonstrate that L2TL outperforms fine-tuning baselines and other adaptive transfer learning methods on eight datasets. In the regimes of small-scale target datasets and significant label mismatch between source and target datasets, L2TL shows particularly large benefits.
Secure IoT Analytics for Fast Deterioration Detection in Emergency Rooms

IoT data analytics can potentially bring benefits to several critical application domains, especially in healthcare.In fact, especially in emergency rooms the detection of critical patients can be a critical task when the number of patients to be monitored is high with respect to the available medical personnel.However, it is also necessary to pay attention to ethics, privacy, and security issues, aiming to prevent attacks and unauthorized access to sensitive data of patients, guaranteeing the correct functioning of the system in a secure environment.To this end, this paper presents a knowledge representation framework enabling the intelligent video surveillance of patients, which can be used in combination with IoT-based systems to enhance the detection of critical patients in emergency rooms, while dealing with ethics, privacy, and security issues.These are guaranteed by means of an event-based visual access control specification method, constraining the access to both devices and users.We also describe a clinical scenario related to the early treatment of sepsis in an emergency room, showing how the proposed framework can enhance the detection of such critical disease while guaranteeing ethics, privacy, and security.
SyrAgri: A Recommender System for Agriculture in Mali

This paper focuses on recommender system for agriculture in Mali called SyrAgri. The goal is to guide and improve the quality-of-experience of farmers by offering them good farming practices according to their needs. Two types of recommendations are essentially taken into account: the recommendation of crops and the recommendation of farming practices based on some predefined criteria which are: yield, life cycle of the crop, type of soil, growing season, etc. SyrAgri also informs farmers about crop rotation and the similarity between different types of crops based on the following parameters: crop families, growing seasons and appropriate soil types. For the development of this system a hybrid recommendation approach was used: demographic, semantic and collaborative methods. Each method is adapted to a specific stage of a user’s visit to the system. The demographic approach is first activated in order to offer recommendations to new users of the system, which resolves the concept of cold start (immediate inclusion of a new item or a new user in the system). The semantic approach is then activated to recommend to the user items (crops, agricultural practices) semantically close to those (s)he has appreciated. Finally, the collaborative approach is used to recommend items that similar users have liked.
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize the target object using only the image-level supervision. Recent methods encourage the model to activate feature maps over the entire object by dropping the most discriminative parts. However, they are likely to induce excessive extension to the backgrounds which leads to over-estimated localization. In this paper, we consider the background as an important cue that guides the feature activation to cover the sophisticated object region and propose contrastive attention loss. The loss promotes similarity between foreground and its dropped version, and, dissimilarity between the dropped version and background. Furthermore, we propose foreground consistency loss that penalizes earlier layers producing noisy attention regarding the later layer as a reference to provide them with a sense of backgroundness. It guides the early layers to activate on objects rather than locally distinctive backgrounds so that their attentions to be similar to the later layer. For better optimizing the above losses, we use the non-local attention blocks to replace channel-pooled attention leading to enhanced attention maps considering the spatial similarity. Last but not least, we propose to drop background regions in addition to the most discriminative region. Our method achieves state-of-the-art performance on CUB-200-2011 and ImageNet benchmark datasets regarding top-1 localization accuracy and MaxBoxAccV2, and we provide detailed analysis on our individual components. The code will be publicly available online for reproducibility.
VenueTrace: a privacy-by-design COVID-19 digital contact tracing solution

Rapid spread of the COVID-19 pandemic is making traditional manual contact tracing challenging; in response, digital contact tracing mobile apps have been developed by the software industry and promoted by governments and health authorities worldwide. However, deploying contact tracing apps across a population at scale have raised many privacy concerns. In this paper, we propose a venue-access-based contact tracing solution, VenueTrace, which preserves user privacy by designs by: (i) enabling the contact tracing of venue-to-user, instead of user-to-user; (ii) avoiding information exchanges between users; and (iii) ensuring no private data is exposed to back-end servers, while enabling proximity contact tracing.
Bidirectional Attention for Text-Dependent Speaker Verification

Automatic speaker verification provides a flexible and effective way for biometric authentication. Previous deep learning-based methods have demonstrated promising results, whereas a few problems still require better solutions. In prior works examining speaker discriminative neural networks, the speaker representation of the target speaker is regarded as a fixed one when comparing with utterances from different speakers, and the joint information between enrollment and evaluation utterances is ignored. In this paper, we propose to combine CNN-based feature learning with a bidirectional attention mechanism to achieve better performance with only one enrollment utterance. The evaluation-enrollment joint information is exploited to provide interactive features through bidirectional attention. In addition, we introduce one individual cost function to identify the phonetic contents, which contributes to calculating the attention score more specifically. These interactive features are complementary to the constant ones, which are extracted from individual speakers separately and do not vary with the evaluation utterances. The proposed method archived a competitive equal error rate of 6.26% on the internal “DAN DAN NI HAO” benchmark dataset with 1250 utterances and outperformed various baseline methods, including the traditional i-vector/PLDA, d-vector, self-attention, and sequence-to-sequence attention models.
Exploiting Temporal Coherence for Self-Supervised One-Shot Video Re-identification

While supervised techniques in re-identification are extremely effective, the need for large amounts of annotations makes them impractical for large camera networks. One-shot re-identification, which uses a singular labeled tracklet for each identity along with a pool of unlabeled tracklets, is a potential candidate towards reducing this labeling effort. Current one-shot re-identification methods function by modeling the inter-relationships amongst the labeled and the unlabeled data, but fail to fully exploit such relationships that exist within the pool of unlabeled data itself. In this paper, we propose a new framework named Temporal Consistency Progressive Learning, which uses temporal coherence as a novel self-supervised auxiliary task in the one-shot learning paradigm to capture such relationships amongst the unlabeled tracklets. Optimizing two new losses, which enforce consistency on a local and global scale, our framework can learn richer and more discriminative representations. Extensive experiments on two challenging video re-identification datasets - MARS and DukeMTMC-VideoReID - demonstrate that our proposed method is able to estimate the true labels of the unlabeled data more accurately by up to $$8\%$$ , and obtain significantly better re-identification performance compared to the existing state-of-the-art techniques .
A Decoupled Learning Scheme for Real-World Burst Denoising from Raw Images

The recently developed burst denoising approach, which reduces noise by using multiple frames captured in a short time, has demonstrated much better denoising performance than its single-frame counterparts. However, existing learning based burst denoising methods are limited by two factors. On one hand, most of the models are trained on video sequences with synthetic noise. When applied to real-world raw image sequences, visual artifacts often appear due to the different noise statistics. On the other hand, there lacks a real-world burst denoising benchmark of dynamic scenes because the generation of clean ground-truth is very difficult due to the presence of object motions. In this paper, a novel multi-frame CNN model is carefully designed, which decouples the learning of motion from the learning of noise statistics. Consequently, an alternating learning algorithm is developed to learn how to align adjacent frames from a synthetic noisy video dataset, and learn to adapt to the raw noise statistics from real-world noisy datasets of static scenes. Finally, the trained model can be applied to real-world dynamic sequences for burst denoising. Extensive experiments on both synthetic video datasets and real-world dynamic sequences demonstrate the leading burst denoising performance of our proposed method.
3D telepresence for remote collaboration in extended reality (xR) application

Abstract Extended Reality (xR) encapsulates various computer-altered reality technologies that cover virtual reality (VR), augmented reality (AR), and mixed reality (MR). xR is a technology that merges a virtual element into the real-world with the aims to enhance reality on the virtual world immerse onto real-world space. xR has been improved from time to time as the advanced immersive technologies to extend the reality we experience by either combining the virtual and real worlds or by generating a fully immersive experience. Remote collaboration in xR is a challenge since both parties need to have the same system and to set to parallel to xR environments. In a collaborative interface context, the user can be in a remote collaboration or face-to-face to sense the immersive environment. Human teleportation is transferring a human from a local location to a remote location, where the reconstruction of a human appears in a realistic visual representation. However, creating a fully realistic representation of the human figure need a complex 3D reconstruction method. Therefore, the paper describes the human teleportation in the xR environment using the advanced RGB-D sensor devices. It explains the phases to develop the real human teleportation in xR. The paper also discusses the proposed collaborative xR system that has successfully actualized the user teleportation.
Sensing finger input using an RFID transmission line

We introduce a passive Radio Frequency IDentification (RFID) based system to detect finger gesture input for Human-Computer Interaction applications. The device is simple, inexpensive and does not require calibration to accommodate changes in the device location or the Radio Frequency (RF) environment. This is achieved by connecting the chips of two RFID tags together using a strip transmission line. The key observation is that touching different positions along the transmission line changes the impedance matching between each chip and its antenna, changing Received Signal Strength (RSS) values for each tag. When a finger slides in different directions between key positions along the transmission line, there are relative RSS patterns and trends that are robust to changes in the device location and the RF environment. We implemented and evaluated an detection algorithm and system using a commercial RFID reader and two commercial RFID chips. Results show that precision and recall are greater than 95% and 94% when detecting 10 finger gesture inputs across 48 different device locations.