Reviewerly/exHarmony huggingface dataset:
Acute Effects of Virtual Reality Exercise on Young Adults’ Blood Pressure and Feelings

Virtual reality (VR) seems to have the potential to provide opportunities to promote physical activity (PA) in a fun way. This paper aimed to examine the acute effects of three different virtual reality-based exercise bikes on young adults’ blood pressure (BP) and feelings compared to a traditional exercise cycling session. Four exercise sessions (immersive VR cycling, two non-immersive VR cycling, and traditional cycling) were completed by 36 young adults (22 females; Mage = 23.6 years). BP was measured immediately before and after each session using a BP cuff and exercise-induced feelings were assessed via an established survey immediately after each session. Participants’ previous experience with VR was used as the covariate in the ANCOVA with repeated measures. Significant main effects were observed across cycling sessions for systolic blood pressure [F (2, 29) = 3.04, p = 0.02, η2= 0.38)] and feelings [F (3, 32) = 7.74, p < 0.01, η2 = .42]. In particular, immersive VR and traditional cycling significantly increased systolic blood pressure compared to the two non-immersive VR sessions. Moreover, immersive VR significantly increased feelings compared to the two non-immersive VR sessions, whereas these two nonimmersive VR exercises had significantly greater increased feelings compared to traditional cycling, respectively. Findings suggest immersive VR-based exercise cycling may lead to higher exercise intensities compared to nonimmersive VR cycling. Further, immersive VR cycling yielded higher feelings compared to non-immersive VR and traditional cycling. Thus, immersive VR-based exercise can be a fun and physically active health promotion tool among young adults.
Healthy Ageing: A Decision-Support Algorithm for the Patient-Specific Assignment of ICT Devices and Services

In response to rapid population ageing, digital technology represents the greatest resource in supporting the implementation of active and healthy ageing principles at clinical and service levels. However, digital information platforms that deliver coordinated health and social care services for older people to cover their needs comprehensively and adequately are still not widespread. The present work is part of a project that focuses on creating a new personalised healthcare and social assistance model to enhance older people's quality of life. This model aims to prevent acute events to favour the elderly staying healthy in their own home while reducing hospitalisations. In this context, the prompt identification of criticalities and vulnerabilities through ICT devices and services is crucial. According to the human-centred care vision, this paper proposes a decision-support algorithm for the automatic and patient-specific assignment of tailored sets of devices and local services based on adults' health and social needs. This decision-support tool, which uses a tree-like model, contains conditional control statements. Using sequences of binary divisions drives the assignation of products and services to each user. Based on many predictive factors of frailty, the algorithm aims to be efficient and time-effective. This goal is achieved by adequately combining specific features, thresholds, and constraints related to the ICT devices and patients' characteristics. The validation was carried out on 50 participants. To test the algorithm, its output was compared to clinicians' decisions during the multidimensional evaluation. The algorithm reported a high sensitivity (96% for fall monitoring and 93% for cardiac tracking) and a lower specificity (60% for fall monitoring and 27% for cardiac monitoring). Results highlight the preventive and protective behaviour of the algorithm.
AEDS-IoT: Adaptive clustering-based Event Detection Scheme for IoT data streams

The emergence of the Internet of Things is expected to produce a huge amount of data streams for sensing the phenomenon from the real world. These data patterns i.e. data distributions keep on evolving and are subject to change based on the changes in the external environment. Valuable and useful information should be extracted from this data stream in real-time- to find out the events of interest. Pattern extraction from stored data and its analytics is convenient, though stream analytics has major challenges associated with it like identifying the event patterns from limited data in the stream, concept drift detection, outlier detection, and handling, limited storage, and processing capacity etc. Traditional methods, in general, work well with historical data yet have limitations while dealing with real-time data streams. Hence, the present work proposes adaptive clustering-based Event Detection Scheme for IoT (AEDS-IoT) stream data, which can infer events of interest from data distribution patterns present in streaming data. Initially, the scheme uses an adaptive window to collect the streaming data and processes it to detect the events. Further, the event patterns are detected by adaptive clustering. After identifying event patterns, the user assigns labels based on spatial–temporal conditions. The proposed AEDS methodology updates the patterns generated from upcoming stream data received. The performance of the proposed scheme is compared against the state of the scheme using the Arhus dataset. To corroborate the scheme with experimental evidence, the scheme was applied to the data collected from the Ganga river(the life line of North India) using smart water sensors. The performance and efficacy of AEDS-IoT were also tested on these data to detect upcoming event patterns from the data stream.
Force-torque (F-T) based multi-drone cooperative transport using fuzzy logic and low-cost and imprecise F-T sensor

This paper presents a novel approach to perform the task of cooperative transportation (CT) by using multiple quadcopter drones. A leader-follower approach is utilized. Considering that the leader drone can be controlled using different means, such as Proportional, Integral, and Derivative (PID) control or remote control by an operator, this paper focuses on designing the control scheme for the follower drone. This paper specifically considers outdoor application of such systems that requires usage of Global Positioning System (GPS) to receive the positional information of the drones. However, GPS has inherent errors of order of magnitude that can destabilize the system. In order to address this major limitation, a Force-Torque Feedback Controller (FT-FC) is proposed to control the follower drone. The FT-FC provides control based on the interaction forces and torques acting at the point of contact between the follower drone and the payload. Using such passive control schemes, drones are thus not required to communicate with each other. Fuzzy Logic (FL) is used to implement the FT-FC. Fuzzy Logic provides effective force-torque coordination between drones, emulates human behavior during CT, and allows use of noisy inexpensive force-torque sensors. This paper presents results from numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking. Additionally, the effectiveness of the fuzzy-based FT-FC in terms of handling disturbances is also demonstrated. The proposed FL FT-FC is also experimentally validated for two applications viz. three-dimensional physical Human Drone Interaction (HDI) and CT.
Blockchain-based decentralized management of IoT devices for preserving data integrity

Nowadays, internet of things (IoT) and blockchain technologies are being leveraged to help in multiple domains. IoT devices are capable of providing sensory data in real-time that is collected in order for it to be analyzed and processed. The collected data is then computed, processed, and stored in a centralized manner. This can sometimes be an issue due to a single point of failure, tampering, breaching of privacy, or manipulation of data. Employing blockchain technology can help in overcoming these issues as it provides a decentralized way to compute and store data. The primary aim of this chapter is to provide an overview of decentralized management of IoT devices for the integrity of data storage and transmission using blockchain. Additionally, the chapter also gives a detailed description of the strategies and techniques used in decentralization, and enumerates the applications of blockchain in the IoT decentralization concept.
Applications of blockchain technology for improving security in the internet of things (IoT)

The blockchain protocol is a digitally distributed system that records and verifies Bitcoin transactions, preserved by numerous distributed autonomous computers. The blockchain system is composed of multiple decentralized connected blocks (chains of blocks) that are managed using distributed ledger technology (DLT). Every block in the chain holds several transactions, and a new block is attached to the chain when a new transaction occurs. Their decentralized nature and architecture are what make blockchain systems robust, secure, and challenging to attack or cheat. Therefore, the blockchain system has gained significant attention from cybersecurity researchers/scholars who have applied this technology to address security concerns in various applications such as money laundering monitoring. In this chapter, we will first revisit and investigate the robust decentralized architecture of blockchain technology and the different features that correlate the blockchain system with various security applications. Then, we will consider the review for the current trends of blockchain security. Thereafter, we will consider the investigating of the security breaches paradigms of blockchain. Subsequently, we will navigate through several e-security services and blockchain technologies for improved IoT security applications and services.
An optimal and efficient data security technique through crypto-stegano for E-commerce

E-Commerce or Electronic commerce is the buying and selling of goods and services in which any commercial transactions through wireless electronic devices such as hand-held computers (tablets), mobile phones or laptops is conducted anytime & anywhere via Internet technology. But, E-Commerce transactions or services are suffered by many attacks such as Man in the Middle attack, eavesdropping attacks, and etc. due to the lack of secured security infrastructure. Here, data security is one of the ways to keep the confidential information secure through E-Commerce transactions. In this connection, we have proposed an optimal and efficient data security with the combination of Elgamal cryptosystem and LSB image steganography technique for E-Commerce. In our proposed work, at the merchant side, Elgamal encryption technique is used to protect sensitive information during E-Commerce transactions from intruders and LSB image steganography process is also applied to hide generated Elgamal encrypted data and produce a stego-image (steganography image). Then, DCT (Discrete Cosine Transform) technique through autoencoder is imposed on stego-image to make an optimal image to increase the throughput of the work. After that, the produced optimal image with cipher text is sent to the customer end. At the customer end, first, stego-image is extracted from the optimal image using LSB retrieval process. Then, Elgamal decryption process is used to retrieve the original data and secure the E-Commerce transactions in efficiently. Based on the experiment, we have plotted the performance metrics such as MSE, PSNR and SSIM on the work and entropy of the optimal image is also calculated with respect to the sample image. Thereby, a high level performance metrics is obtained in our proposed work.
A Selective Expression Manipulation With Parametric 3D Facial Model

This paper proposes a novel method to represent expressive 3D facial shapes called the Selective Expression Manipulation (SEM) by fitting the expression coefficients of delta-blendshapes, which is the standard parametric facial model widely used in industries. SEM focuses on preserving blendshape semantics to characterize the facial shapes since the facial shape obtained by minimizing the distance to sparse facial landmarks might fail to signify a facial expression from a human being’s perspective. Assuming each delta-blendshape corresponds to a facial movement with semantic meaning, SEM finds a series of facial motions required to compose the target facial expression. In addition, SEM sequentially determines a sufficient number of expressions and coefficients closely resembling the target facial movements by introducing similarities to quantify the directional correlation of facial motions between a target and a blendshape, excluding redundant expressions in terms of motions from the neutral shape. As a result, far fewer inter-correlated expressions that significantly increase the target correlation can be obtained. Furthermore, SEM exhibits substantial improvement in accuracy, correlation, semantics, and stability in experiments over previous facial fitting schemes and state-of-the-art methods. It is demonstrated that SEM enables accurate and realistic 3D facial shape generation by semantically manipulating expression delta-blendshapes.
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Named Entity Recognition is an information extraction task that serves as a pre-processing step for other natural language processing tasks, such as machine translation, information retrieval, and question answering.Named entity recognition enables the identification of proper names as well as temporal and numeric expressions in an open domain text.For Semitic languages such as Arabic, Amharic, and Hebrew, the named entity recognition task is more challenging due to the heavily inflected structure of these languages.In this study, we annotate a new comparatively large Amharic named entity recognition dataset and make it publicly available.Using this new dataset, we build multiple Amharic named entity recognition systems based on recent deep learning approaches including transfer learning (RoBERTa), and bidirectional long short-term memory coupled with a conditional random fields layer.By applying the Synthetic Minority Over-sampling Technique to mitigate the imbalanced classification problem, our best performing RoBERTa based named entity recognition system achieves an f1-score of 93%, which is the new state-of-the-art result for Amharic named entity recognition.
SAR Image Compression Based on Multi-Resblock and Global Context

The synthetic aperture radar (SAR) image is widely used in many remote sensing applications. In order to store and transmit the increasing SAR image data, more efficient compression algorithms are needed. The purpose of this letter is to introduce a new framework for compressing SAR images. First, we propose a novel analysis and synthesis transform based on multi-Resblocks for transforming the original SAR image into a compact latent representation. Then, a Gaussian mixture model (GMM) is used to estimate the latent representation’s distribution. In order to explore the redundancy within the latent representation, the entropy model parameter is estimated by combining the local context, global context, and hyperprior information. In order to evaluate the performance of the proposed algorithm, we conduct experiments on a dataset of SAR images. The results show that the proposed algorithm outperforms JPEG2000 and some state-of-the-art learned image compression schemes in terms of compression performance.
Correlation-and-Correction Fusion Attention Network for Occluded Pedestrian Detection

As a significant issue in computer vision, pedestrian detection has achieved certain achievements with the support of deep learning. However, pedestrian detection in congested scenes still encounters the challenging problem of feature loss and obfuscation. To address the issue, we propose a pedestrian detection network based on a correlation-and-correction fusion attention mechanism. First, a multimask correction attention module is proposed, which generates visible part masks of pedestrians, enhancing the visible region’s features and correcting the false one. Besides, the module preserves the features of multiclass pedestrians by generating multiple masks. Then, we fuse a correlation channel attention module to enhance the correlation of various pedestrians’ body features. Next, we studied three fusion methods of correlation and correction attention mechanisms and found that the serial connection of “correlation first and correction behind” works best. Finally, we extend our method to multiclass pedestrian detection in congested scenes. Experimental results on the CityPersons, Caltech, and CrowdHuman datasets demonstrate the effectiveness of our method. On the CityPersons dataset where more than 70% of pedestrians are occluded, our method outperforms the baseline method by 1.12% on the heavy occlusion subset and surpasses many outstanding methods.
Low-Resource Speech Synthesis with Speaker-Aware Embedding

Speech synthesis has been successfully exploited for mapping from text sequence to speech waveform where high-resource languages have been well studied and learned from a large amount of text-speech paired data in public-domain corpora. However, developing speech synthesis under low-resource languages is challenging for speech communication in local regions since the collection of training data is expensive. In particular, the speaker-aware speech generation under low-resource settings is crucial in real world. Such a problem is increasingly difficult in case of very limited speaker-specific data. This paper presents a speaker-aware speech synthesis under low-resource settings based on an encoder-decoder framework by using transformer. Knowledge transfer is performed by incorporating a speaker-aware embedding through first learning a pretrained transformer from multi-speaker data of a low-populated spoken language and then fine-tuning the transformer to a target speaker with very limited speaker-specific embeddings. Experiments on low-resource Taiwanese speech synthesis are evaluated to show the merit of speaker-aware transformer in terms of Mel cepstral distortion and mean opinion score.
BERT for Natural Language Processing in Bahasa Indonesia

Indonesian is the national language of Indonesia. Besides Indonesian, there are 700 foreign and local languages used to communicate in Indonesia. Even though it is used by more than 275.7 million people, Indonesian and local Indonesian are still not getting more attention in the Natural Language Processing (NLP) community. Currently the Bidirectional Encoder Representations from Transformers or BERT model is a state of the art performance in NLP. This article aims to conduct a review of the BERT model in Indonesian and local Indonesian language. Some of the findings in this article can be used as ideas for developing NLP using the BERT model in Indonesian. From the search, found 7 pretrained BERT models in Indonesian and local Indonesian language. 5 of them are monolingual BERT Model in Bahasa Indonesia and 1 monolingual in Local Indonesian Language (Sundanese). Meanwhile, only 1 multilingual BERT model in Indonesian-Javanese-Sundanese. The majority of monolingual BERT models are in Indonesian, while only 1 monolingual BERT model is in the local Indonesian language, Sundanese. The downstream task of the Indonesian BERT model and the Indonesian local BERT model are sentiment analysis, classification, and text summarization. There are 3 extrinsic evaluation benchmarks for Indonesian BERT, namely IndoNLU, IndoNLG, and IndoLEM.
Deep Unfolding of the Half-Quadratic Splitting Algorithm for ISAR Image Super-resolution

This paper targets on an effective and interpretable model-based deep learning method for Inverse Synthetic Aperture Radar (ISAR) image super-resolution. Due to the intrinsic hardware limitations of the radar system, ISAR images are typically blurred and of low-resolution which lead to difficulties in down-streaming tasks including target detection, recognition and classification. The traditional model-based methods would be less effective when competing with the data-driven meth-ods. However, these deep learning model seldom has a good interpretative formulation to be studied mathematically. In this paper, we propose an unfolded half-quadratic splitting network (UHQSNet) that establishes a hierarchical network by unfolding the iterative optimization process into the fine-designed network architecture. The experimental results on a three-target ISAR datasets show that the proposed UHQSNet gains significantly better performance than both of the model-based method and deep learning method.
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition

The choice of modeling units is crucial for automatic speech recognition (ASR) tasks. In mandarin scenarios, the Chinese characters represent meaning but are not directly related to the pronunciation. Thus only considering the writing of Chinese characters as modeling units is insufficient to capture speech features. In this paper, we present a novel method involves with multi-level modeling units, which integrates multi-level information for mandarin speech recognition. Specifically, the encoder block considers syllables as modeling units and the decoder block deals with character-level modeling units. To facilitate the incremental conversion from syllable features to character features, we design an auxiliary task that applies cross-entropy (CE) loss to intermediate decoder layers. During inference, the input feature sequences are converted into syllable sequences by the encoder block and then converted into Chinese characters by the decoder block. Experiments on the widely used AISHELL-I [1] corpus demonstrate that our method achieves promising results with CER of 4.1%/4.6% and 4.6%/5.2%, using the Conformer and the Transformer backbones respectively.
Design of Intelligent Positioning Shoes for Elderly Fall Monitoring Based on GPS and MPU-6000 Acceleration Sensor

To monitor the occurrence of the fall of the elderly and provide help for the later search for the elderly, this paper proposes an intelligent positioning shoe based on GPS and MPU-6000 accelerometer and supplemented with security fence, ultra-long standby, track playback, and other functions. The acceleration sensor MPU-6000 and the pressure sensor FSR402 jointly detect whether the elderly fall and send the positioning information and alarm information obtained by the GPS module to the guardian mobile phone client through the GPRS module. Through both the function of the safety fence to prevent the fall in the old man out, and in the fall after the incident can search to the old man's position in a timely and effective manner, to a large extent, has realized the old man safe travel, played a good monitoring effect, solve the problem of the old man travel safety, for the social development has brought the positive social value.
Feature Refinement Deformable Network for Aircraft Detection in SAR images

Aircraft with advantages of mobility and carrying ability plays an indispensable role in modern transportation areas. However, detecting aircraft in SAR images still meets serious challenges due to discrete spatial distributions of back-scattering points, diversified gestures of aircraft as well as complex surroundings. In this paper, a Feature Refinement Deformable Network (FRDN) is proposed by carefully designing a Feature Fusion Attention Module (FFAM) and a Deformable Latent Convolutional Module (DLCM).Moreover, FFAM is designed to fuse structural and abstract features and highlight significant information of aircraft effectively. DLCM focus on capturing significant features of discrete-distributed back-scattering points of aircraft. The contributions of FFAM and DLCM are carefully exploited and the detection performance of our method is compared with other specific and general CNN-based methods on a self-built SAR aircraft detection data-set.Experimental results illustrates the effectiveness of our method for aircraft detection in SAR images.