Reviewerly/exHarmony huggingface dataset:
Sustaining Academic Staff’s Engagement Through Leader’s Motivating Language In Universiti Selangor

Lack of job satisfaction and low motivation level lead to an increasing rate of turnover intention among academic staff in higher learning institutions.With the growing number of private institutions, it increases the choices for voluntary turnover among academicians who seek for better institutions.The best approach in retaining quality workforce is to maintain staff's engagement to the institution, which can be achieved with the establishment of job satisfaction.The Leader's Motivating Language (LML) specifically focuses on the three main aspects of leader's communication which can elevate employees' job satisfaction.This study investigated the use of LML by faculty's academic administrators and examined the relationship between the use of LML and academic staff's job satisfaction level.A quantitative approach was employed, with 185 academic staff from Universiti Selangor (UNISEL) recruited through purposive sampling to provide the required data for this study through a questionnaire.The findings revealed that the LML is practiced at a moderately high level within the faculties of UNISEL.It was also found that there is a strong, significant and positive relationship between the use of LML by the academic administrators and academic staff's job satisfaction.This study has theoretical implications for understanding how the use of LML strongly relates to the level of job satisfaction which can help sustain UNISEL academic staff's engagement in the institution hence reducing staff turnover intention and increasing talent retention.
Chaotic Mapping-Based Anti-Sorting Radio Frequency Stealth Signals and Compressed Sensing-Based Echo Signal Processing Technology

Radio frequency (RF) stealth anti-sorting technology can improve the battlefield survival rate of radar and is one of the research hotspots in the radar field. In this study, the signal design principle of anti-sequential difference histogram (SDIF) sorting was explored for the main sorting algorithm of the SDIF. Furthermore, we designed a piecewise linear chaotic system with interval number parameterization based on random disturbance and proposed a method to modulate the repetition period of widely spaced signal pulses using a chaotic system. Then, considering the difficulty of the traditional signal processing method to measure the velocity of the highly random anti-sorting signals designed in this paper, we used compressed sensing (CS) technology to process the echoes of the signals to solve the velocity and distance of the detection targets. Finally, simulation verification was performed from the correctness of the signal design principle, the performance of the chaotic system, the anti-sorting performance of the designed signals and the recovery and reconstruction performance of the signals by CS. The results show that: (a) the signal design principle presented in this paper can guide the signal design correctly; (b) the performance of the piecewise linear chaotic system with interval number parameterization is better than that of the classical one-dimensional chaotic system; (c) the anti-sorting signal modulated by the chaotic system can achieve anti-SDIF sorting, and the anti-sorting signals designed in this paper can be processed to obtain the velocity and distance of the targets.
Twitter-based classification for integrated source data of weather observations

&lt;span lang="EN-US"&gt;Meteorology and weather forecasting are crucial for predicting future climate conditions. Forecasts can be helpful when they provide information that can assist people in making better decisions. People today use big data to analyze social media information accurately, including those who rely on the weather forecast. Recent years have seen the widespread use of machine learning and deep learning for managing messages on social media sites like Twitter. In this study, authors analyzed weather-related text in Indonesia based on the searches made on Twitter. A total of three machine learning algorithms were examined: support vector machine (SVM), multinomial logistic regression (MLR), and multinomial Naive Bayes (MNB), as well as the pretrained bidirectional encoder representations of transformers (BERT), which was fine-tuned over multiple layers to ensure effective classification. The accuracy of the BERT model, calculated using the F1-score of 99%, was higher than that of any other machine learning method. Those results have been incorporated into a web-based weather information system. The classification result was mapped using Esri Maps application programming interface (API) based on the geolocation of the data.&lt;/span&gt;
CORRELATION BETWEEN FINE NEEDLE ASPIRATION CYTOLOGY AND CORRESPONDING ULTRASOUND BIRADS SCORE IN MAMMARY MALIGNANCY

The aim of this study is to evaluate accuracy of ultrasound in diagnosing breast cancer and assessing correlation of radiological findings with findings of fine needle aspiration cytology (FNAC). Retrospective study was conducted at IbneSina Hospital&amp; Multan Medical &amp; Dental College Multan from July 2021 to December 2021. Study was conducted on 40 patients aged between 30 to 90 years having breast limps. Subjects underwent clinical and ultrasonic examination and BIRADS score was correlated with cytology data. Major ultrasonic finding was that irregular lesion was found in 21 (52.5%) subjects. In 10 (25%) patientsill defined lesion was found. FNAC revealed severe atypia in 7.5% and mammary carcinoma in 92.5% cases.BIRADS score V, IV and III were assigned to 75%, 20% and 5% cases respectively. Correlation of FNAC results with ultrasonic findings showed 90.6% accuracy of ultrasound in diagnosis of breast cancer. Ultrasonography provides accurate diagnosis of breast lesions and is recommended radiographic modality for assessing underlying cause of palpable breast mass.
Context-Aware Querying, Geolocalization, and Rephotography of Historical Newspaper Images

Newspapers contain a wealth of historical information in the form of articles and illustrations. Libraries and cultural heritage institutions have been digitizing their collections for decades to enable web-based access to and retrieval of information. A number of challenges arise when dealing with digitized collections, such as those of KBR, the Royal Library of Brussels (used in this study), which contain only page-level metadata, making it difficult to extract information from specific contexts. A context-aware search relies heavily on metadata enhancement. Therefore, when using metadata at the page level, it is even more challenging to geolocalize less-known landmarks. To overcome this challenge, we have developed a pipeline for geolocalization and visualization of historical photographs. The first step of this pipeline consists of converting page-level metadata to article-level metadata. In the next step, all articles with building images were classified based on image classification algorithms. Moreover, to correctly geolocalize historical photographs, we propose a hybrid approach that uses both textual metadata and image features. We conclude this research paper by addressing the challenge of visualizing historical content in a way that adds value to humanities research. It is noteworthy that a number of historical urban scenes are visualized using rephotography, which is notoriously challenging to get right. This study serves as an important step towards enriching historical metadata and facilitating cross-collection linkages, geolocalization, and the visualization of historical newspaper images. Furthermore, the proposed methodology is generic and can be used to process untagged photographs from social media, including Flickr and Instagram.
The Role of Metadata and Vocabulary Standards in Enabling Scientific Data Interoperability: A Study of Earth System Science Data Facilities

Objective: Journal publishers within many sciences are increasingly expecting data to be deposited into repositories that support the FAIR principles. Data repositories are thus needing to determine what implications the FAIR principles have on their existing services and systems. Metadata standards and controlled vocabularies are specifically called out as core components of the FAIR principles related to interoperability.Methods: This paper looks specifically at the ways that metadata standards and controlled vocabularies are used by Earth system science data repositories. Data sets from 55 data facilities were examined to determine which metadata standards and controlled subject / keyword vocabularies were used.&amp;nbsp;Results: The findings indicate that only the ISO 19115:2003 and DataCite metadata standards are used by more than 40% of the data facilities, and the NASA Global Change Master Directory (GCMD) keywords are the only keyword vocabulary of broad use within this community.&amp;nbsp;Conclusions: These findings raise questions about the extent to which metadata standards and keyword vocabularies can facilitate interoperability beyond narrow sub-sections of the data facility communities. This study also points to systematic challenges related to migration to new standards.
Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access

&#x0D; Representing a sparse histogram, or more generally a sparse vector, is a fundamental task in differential privacy.&#x0D; An ideal solution would use space close to information-theoretical lower bounds, have an error distribution that depends optimally on the desired privacy level, and allow fast random access to entries in the vector.&#x0D; However, existing approaches have only achieved two of these three goals.&#x0D; &#x0D; In this paper we introduce the Approximate Laplace Projection (ALP) mechanism for approximating k-sparse vectors. This mechanism is shown to simultaneously have information-theoretically optimal space (up to constant factors), fast access to vector entries, and error of the same magnitude as the Laplace-mechanism applied to dense vectors.&#x0D; A key new technique is a unary representation of small integers, which we show to be robust against ''randomized response'' noise. This representation is combined with hashing, in the spirit of Bloom filters, to obtain a space-efficient, differentially private representation.&#x0D; &#x0D; Our theoretical performance bounds are complemented by simulations which show that the constant factors on the main performance parameters are quite small, suggesting practicality of the technique.&#x0D;
Motion Capture 3D Sign Language Resources

Abstract The new 3D motion capture data corpus expands the portfolio of existing language resources by a corpus of 18 hours of Czech sign language. This helps alleviate the current problem, which is a critical lack of quality data necessary for research and subsequent deployment of machine learning techniques in this area. We currently provide the largest collection of annotated sign language recordings acquired by state-of-the-art 3D human body recording technology for the successful future deployment of communication technologies, especially machine translation and sign language synthesis.
Comparative Analysis of Machine Learning Classifiers for Phishing Detection

In recent years, communication over the Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Nevertheless, the rapid increase use of digital platforms has led to a significant growth of Phishing Attacks. Phishing attacks are one of the most common security issues in digital worlds that can affects both individual and organization in keeping their confidential information secure. Various modern approaches can be used to target an individual and trick them into leaking their sensitive information, which can later, purposely be used to harm the targeted victim or entire organization depending on the cybercriminal's intent and type of data leaked. This paper evaluates phishing detection by using Naïve Bayes, Simple Logistic, Random Forest, Ada Boost and MLP classifications. This study discusses the comparative analysis on the effectiveness of classification for detecting phishing attacks. The results indicated that the detection system trained with the Random Forest produce higher accuracy of 97.98% than another classifier method.
Latency Evaluation in Ad-hoc IoT-Blockchain Network

Many researchers are studying the applications that ensemble blockchain and IoT. In such applications, a blockchain is normally deployed on top of the infrastructure-based underlying IoT network (e.g., Wi-Fi), which incurs the single point of failure issue. This work considers using infrastructure-less IoT for blockchain (i.e., ad-hoc IoT-blockchain network) to avoid the issue. Like in other networks, latency is one of the essential parameters to assess the network quality in the ad-hoc IoT -blockchain system. The latency is inherently related to the synchronization of transactions and blocks messages in the blockchain. Therefore, latency evaluation in an ad-hoc IoT-blockchain network is necessary. This research evaluates two latency metrics named Transaction-Oriented Latency (TOL) and Block-Oriented Latency (BOL) in an ad-hoc IoT-Blockchain system with Ethereum and two well-known consensus mechanisms: Proof of Work (PoW) and Proof of Authority (PoA). We create a smart contract that the different number of transactions can trigger; we then adopt the measurement method of the latency values. The evaluation results reveal the TOL and BOL following the transaction number in both the ad-hoc IoT-blockchain with PoA and PoW.
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation

Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results. However, deploying CNNs on resource-constrained edge devices is constrained by limited memory bandwidth for transmitting large intermediated data during inference, i.e., activation. Existing research utilizes mixed-precision and dimension reduction to reduce computational complexity but pays less attention to its application for activation compression. To further exploit the redundancy in activation, we propose a learnable mixed-precision and dimension reduction co-design system, which separates channels into groups and allocates specific compression policies according to their importance. In addition, the proposed dynamic searching technique enlarges search space and finds out the optimal bit-width allocation automatically. Our experimental results show that the proposed methods improve 3.54%/1.27% in accuracy and save 0.18/2.02 bits per value over existing mixed-precision methods on ResNet18 and MobileNetv2, respectively.
An Interactive Chatbot for University Open Day

Macau Polytechnic University (MPU) welcomes many visitors on its open day, and it is therefore necessary to provide them with excellent service and guidance. However, there are many difficulties in providing information to visitors, such as the occupation of human resources, language barriers and inaccurate answers, etc. By developing a chatbot to answer the questions from visitors instantly and consistently, helps to save human resources and increase the efficiency of work. An intelligent chatbot can not only provide accurate and appropriate responses but also bring a great communication experience to the visitors. This research uses Natural Language Processing (NLP) and Deep Learning (DL) techniques to develop a chatbot that can engage in interactive conversations with visitors for the MPU open day. A high-performance neural network is built to learn from the given dataset and can accurately answer questions about the MPU open day. Besides, STT (Speech-to-Text) and TTS (Text-to-Speech) APIs are used in this work to assist the communication between computers and humans. This work can be used as a reference framework of fast implementation of a chatbot for any knowledge area with an easy-to-adapt web interface.
SST-GAN: Single Sample-based Realistic Traffic Image Generation for Parallel Vision

To improve their adaptability to various kinds of driving situations, deep learning-based vision algorithms need images from rare scenes, such as extreme weather conditions and traffic congestions. However, most datasets collected from physical driving environments are lack of such images, making vision models trained on these datasets do not work well in scarce scenes. Thus, we design an SST-GAN method for controllably generating realistic images of scarce driving scenes based on the framework of parallel vision. Trained on only a single sample, SST-GAN can produce hundreds of rare scene images from two directions: style transfer and content generation. Specifically, a transition retraining method is designed to transfer the weather and lighting styles from common scenes to scarce scenes, and a structural similarity index loss is used as reconstruction loss to guarantee the trained network can obtain more realistic content modification and generation during the image reconstruction. Experimental results show that SST-GAN outperforms the state-of-the-art method on expanding the amount of scarce scene images from both style and content. The method is highly adaptable and works flexibly on handling image generation problems for various types of rare scenes.
Reliable Video Forensics Evidence Cataloguing using Video Source device Identification on the Blockchain

Videos and images are plentiful, shared, modified, and retrieved through different sources. They are used for the purposes of data collection and evidence gathering, as well as entertainment. The widespread use and misuse of video and/or image capturing devices has caused the need for authenticity to be preserved and the source of information to be identified for trust and security. In today’s world, these devices are used to commit crimes and are sometimes used to catch them. The videos and/or images are collected as key evidence and are part of the forensics tasks. Crime investigators require access to the origin of the data, and it is usually stored in a centralized storage that produces vulnerabilities like that of a single point of failure as well as is an easy gateway to attacks. Proposed here is a decentralized evidence storage architecture where video is stored in a decentralized storage with its source identification automated and metadata stored on the chain. This provides confidentiality, integrity, and security of the video data stored. In addition, privacy is preserved through off-chain storage systems and private permissioned blockchains.
Implementation of parallel AES Processing with CAM-based Massive-parallel SIMD Matrix Core

Recently, several multimedia applications, such as digital image compression, digital video compression, and digital audio processing, are executed on the mobile devices. The processing core in the mobile device requires high performance, versatility, and programmability. Generally, multimedia applications consist of repeated arithmetic operation and table-lookup coding operation. To have high performance, versatility, and programmability by improving the processing speed of the both operations on a processing core, Content Addressable Memory-based massive-parallel SIMD matrix core (CAMX) is proposed. The role of CAMX is an accelerator for mobile CPU core. CAMX has highly parallel processing capability and is configured by two CAM modules which are used in fast table-lookup coding operation. This paper has shown the result of AES encryption. The number of AES encryption total clock cycles are 1,362,699, and also the detail number of clock cycles is confirmed: SubBytes is 1,312,160, ShiftRows and MixColumns is 17,161 in total, AddRoundKey is 2,519. Furthermore, CAMX can process AES encryption in 83.17 clock cycles / bytes, and it is confirmed to be about 50 % performance improvement than existing mobile processor.
Special Issue on Security and Privacy in Blockchains and the IoT

The increasing digitalization in all areas of life is leading step-by-step to a data-driven society [...]
FHIR Based ContSys Ontology to Enable Continuity of Care Data Interoperability

In the midst of a global pandemic, perspectives on how digital can enhance healthcare service delivery and workflow to address the global crisis is underway. Action plans collating existing digital transformation programs are being scrutinized to set in place core infrastructure and foundations for sustainable healthcare solutions. Reforming health and social care to personalize the home care setting can for example assist in avoiding treatment in a crowed acute hospital setting and improve the experience and impact on both health care professionals and service users alike. In this information intensive domain addressing the interoperability challenge through standards based roadmaps is the lynchpin to enable health and social care services to connect effectively. Thus facilitating safe and trustworthy data workflow from one healthcare systems provider to another. In this paper we showcase a methodology on how we can extract, transform and load data in a semi-automated process using a Common Semantic Standardized Data Model (CSSDM) to generate personalized healthcare knowledge graph (KG). CSSDM is based on formal ontology of ISO 13940:2015 ContSys for conceptual grounding and FHIR based specification to accommodate structural attributes to generate KG. CSSDM we suggest enables data harmonization and data linking. The goal of CSSDM is to offer an alternative pathway to speak about interoperability by supporting a different kind of collaboration between a company creating a health information system and a cloud enabled health service. This pathway of communication provides access to multiple stakeholders for sharing high quality data and information.
Automatic Recognition of Teachers' Nonverbal Behavior Based on Dilated Convolution

Recognition of teachers' nonverbal behavior is an important tool for assessing teachers' teaching behavior. Within the continuous progress of artificial intelligence technology, automatic recognition of nonverbal behavior has been more and more popular. Therefore, we take the teaching videos in the actual classroom as the data source, acquires and labels the teachers' nonverbal behavior dataset, extracts the teacher skeleton points information in the images using the target detection algorithm and the human pose estimation algorithm, and adopts a residual block model with dilated convolution for automatic recognition of teachers' nonverbal behavior. It is proved that the proposed method can effectively improve the accuracy of teachers' nonverbal behavior recognition. The research in this paper is to help solve the problem of automatic recognition of teachers' nonverbal behavior and assist teachers to optimize teaching strategies and improve teaching efficiency.
Unsupervised Domain-Adaptive Person Re-Identification with Multi-Camera Constraints

Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines pseudo-labels estimated via a self-training scheme by imposing multi-camera constraints. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. In addition, we develop a method that appropriately selects a person from the pair that contributes to the performance improvement. We evaluate the performance of the network using public and private datasets and confirm the performance surpasses state-of-the-art methods in domains with overlapping camera views. To the best of our knowledge, this is the first study on domain-adaptive learning with multi-camera constraints that can be obtained in real environments.