Reviewerly/exHarmony huggingface dataset:
Efficient Fall Detection Method Using Time-of-Flight Sensors and Decision Tree Model

This work presents a shoe-mounted Time-of-Flight (ToF) sensors system for fall detection, utilizing only two ToF sensors. These ToF sensors measure the distance between the sensors and the ground, which can be used for fall detection. The range data captured by the sensors are discretized by the quantile-based method and classified by the decision tree model. The proposed system employs a fall detection algorithm, resulting in low computing complexity, which makes the system suitable for implementation in low-power wearable devices. Experiment result shows that the decision tree model with proposed quantile-based discretization method, achieves an accuracy of 99.08% in distinguishing activities of daily living (ADLs) and falls among different person.
Why Is the Current XAI Not Meeting the Expectations?

Seeking better explanations for how algorithmic decisions are made.
A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient’s histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.
R/B-SecArch: A strong isolated SoC architecture based on red/black concept for secure and efficient cryptographic services

Cryptographic chips usually act as a secure base for information security systems and provide corresponding cryptographic services. Trusted execution environment (TEE) techniques for on-chip security have been widely proposed and effectively validated in academia and industry. However, existing mainstream TEEs still have certain security risks, especially in the face of emerging microarchitectural attacks. To address this issue, this paper proposes a strong isolation SoC architecture for high-security application requirements based on the red/black concept. Meanwhile, to provide efficient cryptographic services, we design an isolation interface that separates data and cryptographic tasks, and a cryptographic service descriptor that matches the isolation interface is defined. In addition, we designed a secure boot mechanism with OTP as the root of trust to ensure the system's power on security. Finally, we conduct performance tests and security analysis; the results show that the architecture can still provide efficient cryptographic services while ensuring security.
Non-Fungible Tokens (NFTs) as a Means for Blockchain Networks Integration in Healthcare

This paper addresses the challenge of interoperability between blockchain networks in healthcare. It presents a new conceptual model that uses non-fungible tokens (NFTs) to promote seamless communication between different blockchain systems. These tokens play a key role in securing data exchange and protecting medical information by serving as unique identifiers. Expected outcomes of this approach include improved interoperability, streamlined workflows, and improved patient outcomes.
The Impact of Social Media on the Development of Women Especially in Transition States

The main goal of this paper was to highlight the importance of social media in the development of women entrepreneurs in the state of Kosovo, where the number of women entrepreneurs is increasing every day. In this research, two objectives are presented: to analyze the influence of social media on women’s entrepreneurship in the case of Kosovo and to measure the impact of social media usage on sales of women’s entrepreneurship in the case of Kosovo. To realize this empirical research, a questionnaire containing 25 questions was used, and 750 women entrepreneurs answered this questionnaire over a period of 5 months. The results of the analysis are presented through descriptive analysis, Pearson correlation, and the OLS model. The results of this research show that social media has a positive effect on increasing sales in women-led businesses; they have easier access to communication. Also, the results indicate that these media have a positive impact on increasing the audience as well as reducing expenses during the marketing campaign. Based on the presented results, it is stated that social media is the primary influencer in the development of women’s entrepreneurship, and these findings are nearly similar to the results of research conducted by authors from various countries. Doi: 10.28991/HIJ-2023-04-03-07 Full Text: PDF
Assessing Knowledge, Acceptance, and Anticipated Impact of Telepathology in Saudi Arabia: Insights From Healthcare Workers and Patients

Introduction: Telepathology, a rapidly evolving field in modern healthcare, has the potential to significantly impact the diagnosis and management of diseases. This study aimed to assess the prevalence of acceptance and knowledge, the likelihood of future use, and the perceived advantages and disadvantages of telepathology among healthcare workers (HCWs) and patients in Saudi Arabia. Methods: A cross-sectional study was conducted with 388 participants, including 179 HCWs and 209 adult patients across Saudi Arabia. Data were collected using a bilingual, self-administered, and anonymous computer-based questionnaire. The Statistical Package for Social Sciences (SPSS, IBM Corp., Armonk, NY) was employed for data analysis. Results: The participants had an average knowledge rate of 80.3% (n=312) concerning telepathology. A substantial 88.16% (n=342) were inclined towards its acceptance, and an optimistic 89.97% (n=349) foresaw its potential utility. Among the respondents, 70% (n=272) pinpointed "expedited results" as the principal merit of telepathology. However, 60% (n=233) flagged "the necessity for costly infrastructure" as its chief limitation. Conclusions: The insights derived underscore a prominent knowledge and endorsement of telepathology among the Saudi population and HCWs. Despite certain drawbacks, participants believe that telepathology is feasible and offers numerous benefits that could greatly enhance the healthcare system in Saudi Arabia. Future research should focus on evaluating its practical implementation and efficacy within healthcare facilities.
The Adoption of Blockchain Technology in Higher Education: The Impact of Leadership Readiness

The emergence of blockchain technology has captured considerable interest across various industries due to its ability to revolutionize traditional systems by ensuring enhanced security, transparency, traceability and efficiency. More recently, the higher education sector has begun exploring the adoption of blockchain technology, acknowledging its potential to transform multiple facets of the educational landscape, including academic administration, the learning and teaching process, and academic research. The primary goal of this study is to ascertain the factors that influence individuals' behavioral intention to adopt and use blockchain-based educational platforms. Moreover, the research aims to underscore the pivotal role played by educational leadership in advancing the adoption of blockchain technology within higher education institutions. Blockchain technology is recognized as a cutting-edge solution that has the potential to streamline processes, reduce costs, and foster trust within the educational ecosystem. A novel conceptual model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was developed and tested for the purpose of this study. Data was gathered from 175 respondents across eight European countries through a structural questionnaire. Partial least squares structural equation modeling (PLS-SEM) was employed to examine the hypotheses of the model. The study unveils perceived trust as the primary determinant influencing the behavioral intention to use blockchain-based educational solutions, underscoring its pivotal role in embracing innovative technologies by instilling confidence and reducing uncertainty among users. Furthermore, educational partner readiness, performance expectancy, and effort expectancy also have a positive impact on the usage intention. Additionally, the results also highlight the significance of leadership blockchain readiness in shaping the actual usage behavior. The findings offer valuable insights into the adoption of blockchain technology in higher education. Also, the study extends previous research by utilizing an extended UTAUT framework, and it contributes to the emerging literature on blockchain implementation and technology acceptance models.
Deep Joint Source-Channel Coding for Semantic Communications

Semantic communications is considered a promising technology that will increase the efficiency of next-generation communication systems, particularly human-machine and machine-type communications. In contrast to the source-agnostic approach of conventional wireless communication systems, semantic communications seek to ensure that only relevant information for the underlying task is communicated to the receiver. Considering most semantic communication applications have strict latency, bandwidth, and power constraints, a prominent approach is to model them as a joint source-channel coding (JSCC) problem. Although JSCC has been a long-standing open problem in communication and coding theory, remarkable performance gains have been made recently over existing separate source and channel coding systems, particularly in low-la-tency and low-power scenarios. Recent progress has been made thanks to the adoption of deep learning techniques for joint source-channel code design that outperform the concatenation of state-of-the-art compression and channel coding schemes, which are the result of decades-long research efforts. In this article, we present an adaptive deep learning based JSCC (DeepJSCC) architecture for semantic communications, introduce its design principles, highlight its benefits, and outline future research challenges that lie ahead.
A Novel Approach for Detecting and Preventing Security attacks using Machine Learning in IoT

Since most of the applications rely on machine learning algorithms, the customers are affected by the machine-made decisions. This can be resolved by adding more complexity to ML models. Common attacks that are possible to break the reliability of ML techniques are replay attacks, zero-dynamics attacks, and adversarial attacks. These attacks leverage the vulnerabilities of ML and collapse the reliability of data. The development of trusted ML systems can be achieved with the help of technical tools such as probabilistic modeling, Markov decision process. temporal decision process. Then, to achieve a reliable machine-learning framework, a simple way could be to utilize an ensemble for the regular models. This can develop reliable predictions. Another emerging technique is transfer learning that helps to overcome the performance loss and increases the trust score. However, estimating reliability will define the accuracy of predictions, it can work along with transductive reasoning. In general, reliability calculation is defined as the probability of count of true classes from the example set.
A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid

In the realm of modern medicine, medical imaging stands as an irreplaceable pillar for accurate diagnostics. The significance of precise segmentation in medical images cannot be overstated, especially considering the variability introduced by different practitioners. With the escalating volume of medical imaging data, the demand for automated and efficient segmentation methods has become imperative. This study introduces an innovative approach to heart image segmentation, embedding a multi-scale feature and attention mechanism within an inverted pyramid framework. Recognizing the intricacies of extracting contextual information from low-resolution medical images, our method adopts an inverted pyramid architecture. Through training with multi-scale images and integrating prediction outcomes, we enhance the network’s contextual understanding. Acknowledging the consistent patterns in the relative positions of organs, we introduce an attention module enriched with positional encoding information. This module empowers the network to capture essential positional cues, thereby elevating segmentation accuracy. Our research resides at the intersection of medical imaging and sensor technology, emphasizing the foundational role of sensors in medical image analysis. The integration of sensor-generated data showcases the symbiotic relationship between sensor technology and advanced machine learning techniques. Evaluation on two heart datasets substantiates the superior performance of our approach. Metrics such as the Dice coefficient, Jaccard coefficient, recall, and F-measure demonstrate the method’s efficacy compared to state-of-the-art techniques. In conclusion, our proposed heart image segmentation method addresses the challenges posed by diverse medical images, offering a promising solution for efficiently processing 2D/3D sensor data in contemporary medical imaging.
Structured dataset of human-machine interactions enabling adaptive user interfaces

Abstract This article introduces a dataset of human-machine interactions collected in a controlled and structured manner. The aim of this dataset is to provide insights into user behavior and support the development of adaptive Human-Machine Interfaces (HMIs). The dataset was generated using a custom-built application that leverages formally defined User Interfaces (UIs). The resulting interactions underwent processing and analysis to create a suitable dataset for professionals and data analysts interested in user interface adaptations. The data processing stage involved cleaning the data, ensuring its consistency and completeness. A data profiling analysis was conducted for checking the consistency of elements in the interaction sequences. Furthermore, for the benefit of researchers, the code used for data collection, data profiling, and usage notes on creating adaptive user interfaces are made available. These resources offer valuable support to those interested in exploring and utilizing the dataset for their research and development efforts in the field of human-machine interfaces.
ChatGPT vs. Crowdsourcing vs. Experts: Annotating Open-Domain Conversations with Speech Functions

This paper deals with the task of annotating open-domain conversations with speech functions. We propose a semi-automated method for annotating dialogs following the topic-oriented, multi-layered taxonomy of speech functions with the use of hierarchical guidelines using Large Language Models. These guidelines comprise simple questions about the topic and speaker change, sentence types, pragmatic aspects of the utterance, and examples that aid untrained annotators in understanding the taxonomy. We compare the results of dialog annotation performed by experts, crowdsourcing workers, and ChatGPT. To improve the performance of ChatGPT, several experiments utilising different prompt engineering techniques were conducted. We demonstrate that in some cases large language models can achieve human-like performance following a multi-step tree-like annotation pipeline on complex discourse annotation, which is usually challenging and costly in terms of time and money when performed by humans.
On the Impossibility of Surviving (Iterated) Deletion of Weakly Dominated Strategies in Rational MPC

Rational multiparty computation (rational MPC) provides a framework for analyzing MPC protocols through the lens of game theory. One way to judge whether an MPC protocol is rational is through weak domination: Rational players would not adhere to an MPC protocol if deviating never decreases their utility, but sometimes increases it. Secret reconstruction protocols are of particular importance in this setting because they represent the last phase of most (rational) MPC protocols. We show that most secret reconstruction protocols from the literature are not, in fact, stable with respect to weak domination. Furthermore, we formally prove that (under certain assumptions) it is impossible to design a secret reconstruction protocol which is a Nash equilibrium but not weakly dominated if (1) shares are authenticated or (2) half of all players may form a coalition.
Josephson parametric amplifier with Chebyshev gain profile and high saturation

We demonstrate a Josephson parametric amplifier design with a band-pass impedance-matching network based on a third-order Chebyshev prototype. We measured eight amplifiers operating at 4.6 GHz that exhibit gains of 20 dB with less than 1-dB gain ripple and bandwidth up to 500 MHz. The amplifiers further achieve high-output-saturation powers around $\ensuremath{-}73\phantom{\rule{0.2em}{0ex}}\mathrm{dBm}$ based on the use of rf superconducting quantum interference device arrays as their nonlinear element. We characterize the system readout efficiency and its signal-to-noise ratio near saturation using a Sycamore processor, finding the data consistent with near quantum limited noise performance of the amplifiers. In addition, we measure the amplifiers' intermodulation distortion in two-tone experiments as a function of input power and intertone detuning, and observe excess distortion at small detuning with a pronounced dip as a function of signal power, which we interpret in terms of power-dependent dielectric losses.
Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

Abstract Background Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5‐year survival time of two patient groups: one with preoperative radiotherapy and one without. Methods The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet‐scattering network. Our study combines the strengths of these two convolution‐based approaches to robustly extract image features related to protein expression. Results The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve. Conclusion These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.
A Radio Frequency Fingerprinting Scheme Using Learnable Signal Representation

Radio frequency (RF) fingerprinting is a promising technique for device authentication due to its advantages of difficult-to-tamper and inimitableness. Rather than employing hand-crafted features, deep learning-based methods that learn low-level signal representation from raw signals have been explored recently. This letter proposes a novel RF fingerprinting scheme using learnable short-time Fourier transform (STFT) and convolutional neural network (CNN). Instead of representing radio signals by spectrograms from a fixed STFT and processing signal representation and classification separately, this approach integrates a parameterized STFT-based signal representation module and a CNN classifier into a single framework. The signal representation module is jointly trained with the CNN classifier using a single identification loss, transforming the input radio signals into spectrograms desired by the CNN classifier. Experimental results show that the RF fingerprint identification accuracy of the proposed scheme with learnable signal representation is significantly improved compared to baseline schemes with traditional hand-crafted signal representations.
THE ROLE OF ARTIFICIAL INTELLIGENCE (AI) IN SHAPING SUSTAINABLE AND RESILIENT DIGITAL SUPPLY CHAINS

Purpose This research investigates the importance of advanced digital policy which have been use in the supply chain management globally. It investigates Capability of data analytics internally, External data analytics, Capability of procurement digitally, Automation, End to End Visibility their role in digital supply and by this how it contributes to business performance and its flexibility. Design/Methodology/Approach The method of collecting data by using Questionnaires and collecting their responses to identify their thoughts about the digital supply chain through digital platforms.&#x0D; Findings We have 12 tested hypotheses, which showed the results we found that our results do not support 6 which includes 3 direct 2 mediating, and 1 moderating. It includes the capability of procurement digitally and Supply chain digital, internal data analytics and Supply chain digital, End-to-end visibility and Supply chain digital. 2 mediating which includes Automation, Supply chain digitally and Business performance, End to End Visibility, Supply chain digitally and business performance. 1 moderating which includes prior to digital platforms that moderates with Supply chain digitally and business performance.&#x0D; Research Limitations / ImplicationsThe current research gives helpful details to those working in logistics firm. It can help some small Logistics firms to switch towards digital supply chain. By using modernized techniques it can help them in improving organizational performance. This can lead to smart supply chain and can go to next level.&#x0D; Originality/Value We have analysed the moderated effects of prior to digital platforms between the digital supply chain.