Reviewerly/exHarmony huggingface dataset:
Simultaneous Classification and Regression for Zakat Under-Reporting Detection

Tax revenue represents an essential budget source for most countries around the world. Accordingly, the modernization of relevant technological infrastructure has become a key factor of tax administration strategy for improving tax collection efficiency. In particular, the fiscal consolidation of the Kingdom of Saudi Arabia has been supported by considerable development in tax policy and administration, aimed at raising more taxes from non-oil activities. In fact, non-Saudi investors are liable for income tax in Saudi Arabia. On the other hand, Saudi citizen investors (and citizens of the GCC countries) are liable for Zakat, an Islamic assessment. Typically, taxpayers are in charge of preparing and accurately reporting their Zakat declaration. This allows tax authorities to overview and audit their business activities. However, despite administration efforts to increase taxpayer compliance, considerable revenue remains at under-reporting risk. In this paper, we introduce a novel intelligent approach to support tax authority efforts in detecting under-reporting among Zakat payer declarations. In particular, the proposed solution aims at improving detection accuracy and determining the fraud cases that correspond to a higher revenue at risk. Specifically, we formulate Zakat under-reporting detection as a supervised machine learning task through the design of a deep neural network that performs simultaneous classification and regression tasks. In particular, the proposed network contains an input layer, five hidden layers, and two output layers for classification and regression. Zakat declarations are mapped into the predefined “under-reporting” or “actual declaration” classes. Moreover, the revenue at risk caused by the predicted fraud cases is learned by the designed model. This allows the proposed approach to prioritize the auditing of specific Zakat payers based on the corresponding predicted revenue at risk. A real dataset including 51,919 Zakat declarations was used to validate and assess the designed model. Further, the Synthetic Minority Oversampling Technique (SMOTE) boosted the proposed model performance in terms of classification and prioritization.
Hybrid domain digital watermarking scheme based on improved differential evolution algorithm and singular value block embedding

Abstract The internet and related technologies have promoted the development of medical and health fields, especially remote diagnosis. As medical images may be stolen in the transmission process, the patient's personal information could be leaked. Aiming at the problem of privacy disclosure of colour medical images, a hybrid domain watermarking scheme based on improved differential evolution (DE) and the singular value block embedding (SVBE) is proposed in this study. Through this scheme, the image containing the patient's information is hidden in the patient's medical carrier image as a watermark. First, redistributed invariant lifting wavelet transform (RILWT), discrete wavelet transform (DWT), and singular value decomposition (SVD) are used to process the colour medical carrier image, and the high‐frequency information in the integer wavelet domain of the watermark is embedded into the processed carrier image using the proposed singular value block embedding method based on the improved differential evolution algorithm. At the same time, to increase the security of the watermark scheme, a digital signature based on the SHA‐384 hash function is designed for verification before watermark extraction. Compared with recent related research, the scheme has strong invisibility and robustness.
Analysis of 2D and 3D Convolution Models for Volumetric Segmentation of the Human Hippocampus

Extensive medical research has revealed evidence of a strong association between hippocampus atrophy and age-related diseases such as Alzheimer’s disease (AD). Therefore; segmentation of the hippocampus is an important task that can help clinicians and researchers in diagnosing cognitive impairment and uncovering the mechanisms behind hippocampal changes and diseases of the brain. The main aim of this paper was to provide a fair comparison of 2D and 3D convolution-based architectures for the specific task of hippocampus segmentation from brain MRI volumes to determine whether 3D convolution models truly perform better in hippocampus segmentation and also to assess any additional costs in terms of time and computational resources. Our optimized model, which used 50 epochs and a mini-batch size of 2, achieved the best validation loss and Dice Similarity Score (DSC) of 0.0129 and 0.8541, respectively, across all experiment runs. Based on the model comparisons, we concluded that 2D convolution models can surpass their 3D counterparts in terms of both hippocampus segmentation performance and training efficiency. Our automatic hippocampus segmentation demonstrated potential savings of thousands of clinician person-hours spent on manually analyzing and segmenting brain MRI scans
Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures

Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic features of both constituency parsing (CP) and dependency parsing (DP). The extraction results may also serve as distant supervision for zero-shot model training. Our approach outperforms existing methods from two perspectives: (1) the rules are simple yet effective, and (2) the results are more self-contained. We further propose a numerical information retrieval approach based on QuantityIE to answer analytical queries. Experimental results on information extraction and retrieval demonstrate the effectiveness of QuantityIE in extracting numerical information with high fidelity.
Retrospective content analysis of consumer product reviews related to chronic pain

Chronic pain (CP) lasts for more than 3 months, causing prolonged physical and mental burdens to patients. According to the US Centers for Disease Control and Prevention, CP contributes to more than 500 billion US dollars yearly in direct medical cost plus the associated productivity loss. CP is complex in etiology and can occur anywhere in the body, making it difficult to treat and manage. There is a pressing need for research to better summarize the common health issues faced by consumers living with CP and their experience in accessing over-the-counter analgesics or therapeutic devices. Modern online shopping platforms offer a broad array of opportunities for the secondary use of consumer-generated data in CP research. In this study, we performed an exploratory data mining study that analyzed CP-related Amazon product reviews. Our descriptive analyses characterized the review language, the reviewed products, the representative topics, and the network of comorbidities mentioned in the reviews. The results indicated that most of the reviews were concise yet rich in terms of representing the various health issues faced by people with CP. Despite the noise in the online reviews, we see potential in leveraging the data to capture certain consumer-reported outcomes or to identify shortcomings of the available products.
Special Issue “Data Science in Insurance”

Within the insurance field, the digital revolution has enabled the collection and storage of large quantities of information [...]
Service Selection Using an Ensemble Meta-Learning Classifier for Students with Disabilities

Students with special needs should be empowered to use assistive technologies and services that suit their individual circumstances and environments to maximize their learning attainment. Fortunately, modern distributed computing paradigms, such as the Internet of Things (IoT), cloud computing, and mobile computing, provide ample opportunities to create and offer a multitude of digital assistive services and devices for people with disabilities. However, choosing the appropriate services from a pool of competing services while satisfying the unique requirements of disabled learners remains a challenging research endeavor. In this article, we propose an ensemble meta-learning model that ranks and selects the best IoT services while considering the diverse needs of disabled students within the educational context. We train and test our deep ensemble meta-learning model using two synthetically generated assistive services datasets. The first dataset incorporates 50,000 records representing the possible use of 12 learning activities, fulfilled by 60 distinct assistive services. The second dataset includes a range of 120,000 service ratings of seven quality features, including response, availability, successibility, latency, cost, quality of service, and accessibility. Our deep learning model uses an ensemble of multiple input learners fused using a meta-classification network shared by all the outputs representing individual assistive services. The model achieves significantly better results than traditional machine learning models (i.e., support vector machine and random forest) and a simple feed-forward neural network model without the ensemble technique. Furthermore, we extended our model to utilize the accessibility rating of services to suggest appropriate educational services for disabled learners. The empirical results show the acceptability of our assistive service recommender for learners with disabilities.
Unsupervised deep learning of bright-field images for apoptotic cell classification

The classification of apoptotic and living cells is significant in drug screening and treating various diseases. Conventional supervised methods require a large amount of prelabelled data, which are often costly and consume immense human resources in the biological field. In this study, unsupervised deep learning algorithms were used to extract cell characteristics and classify cells. A model integrating a convolutional neural network and an autoencoder network was utilised to extract cell characteristics, and a hybrid clustering approach was employed to obtain cell feature clustering results. Experiments on both public and private datasets revealed that the proposed unsupervised strategy performs well in cell categorisation. For instance, in the public dataset, our method obtained a precision of 96.72% on only 1000 unlabelled cells. To the best of our knowledge, this is the first time unsupervised deep learning has been applied to distinguish apoptosis and live cells with high accuracy.
Velocity Filtering Using Quantum 3D FFT

In this work, the quantum version of 3D FFT is proposed for constructing velocity filters. Velocity filters are desirable when we need to separate moving objects with a specific velocity range in amplitude and direction in a rapidly changing background. These filters are useful in many application fields, such as for monitoring regions for security reasons or inspecting processes in experimental physics. A faster and more attractive way to implement this filtering procedure is through 3D FFT instead of using 3D FIR filters. Additionally, 3D FFT provides the capability to create banks of ready-made filters with various characteristics. Thus, 3D filtering is carried out in the frequency domain by rejecting appropriate frequency bands according to the spectral content of the trajectory of the object to be isolated. The 3D FFT procedure and the corresponding inverse one are required in the beginning and end of the filtering process. Although 3D FFT is computationally effective, it becomes time-consuming when we need to process large data cubes. The implementation of velocity filters by means of the quantum version of 3D FFT is investigated in this work. All necessary quantum circuits and quantum procedures needed are presented in detail. This proposed quantum structure results in velocity filtering with a short execution time. For this purpose, a review of the necessary quantum computational units is presented for the implementation of quantum 3D FFT and representative examples of applications of velocity filtering are provided.
Chaotic Optical Communication with Wavelength-Hopping Technology Based on Tunable Lasers

With the development of communication technology, there is a more urgent demand for enhancing the security of data transmission. In order to improve the security of the traditional chaotic laser communication system, we propose a new scheme by the introduction of wavelength-hopping technology based on tunable lasers. In our new scheme, due to the hopping of wavelengths and the pseudo-randomness of the wavelength-hopping sequence, it is difficult for the eavesdropper to intercept the information and predict to which wavelength the hopping station will hop. The numerical simulation results show that the average bit error rate of eavesdropping is about five orders of magnitude higher than that of working normally. This indicates that the introduction of wavelength-hopping technology can improve the difficulty of message decoding and improve the confidentiality of the system. This scheme can be used to realize a high level of privacy in the design of chaotic laser communication systems in the future.
Wagging Tails – An Android App for Dog

The increasing popularity of pets, especially dogs, has led to a surge in demand for pet-related services and products. Pet owners face numerous challenges when it comes to taking care of their furry friends, including finding reliable information about pet care, scheduling veterinarian appointments, and finding high-quality pet products. To address these challenges, we are developing a mobile application called Wagging Tails using the technologies like flutter and dart. The primary problem that Wagging Tails aims to solve is to provide pet owners with a comprehensive platform for managing their pet's needs. The application includes features like adoption of a dog, veterinarian appointment scheduling, and access to pet products and essential information. The purpose of this project is to develop a user-friendly and feature-rich mobile application for pet owners. The main objective is to provide pet owners with a one-stop solution for managing their pet's needs, including adoption, healthcare, and access to high-quality pet products. The application aims to be accessible to users of all technical backgrounds and to provide a seamless user experience. We used the Flutter and Dart technologies to develop the mobile application, which includes a user-friendly interface and seamless functionality. The application includes features like adoption of a dog, veterinarian appointment scheduling, access to pet products, and essential information. In conclusion, the Wagging Tails project is a comprehensive mobile application that provides pet owners with a one-stop solution for managing their pet's needs. The application includes features like adoption of a dog, veterinarian appointment scheduling, access to pet products, and essential information. The application aims to be accessible to users of all technical backgrounds and to provide a seamless user experience.
On Two Intuitionistic Fuzzy Modal Topological Structures

The concept of an Intuitionistic Fuzzy Modal Topological Structure (IFMTS) was introduced previously, and some of its properties were studied. So far, there are two different IFMTSs based on the classical intuitionistic fuzzy operations: “union” (∪) and “intersection” (∩). In the present paper, two new IFMTSs are developed. They are based on new intuitionistic fuzzy topological operators from closure and interior types, introduced here for the first time, and on the two standard intuitionistic fuzzy modal operators □ and ◊. Some basic properties of the new IFMTSs are discussed. The newly presented IFMTSs could be considered as a basis for the next research on the IFMTSs. Some ideas for the future development of the IFMTS theory and open problems are formulated, related to the existence of other intuitionistic fuzzy operations that can generate new intuitionistic fuzzy topological operators and, respectively, new IFMTSs.
Widening Blockchain Technology toward Access Control for Service Provisioning in Cellular Networks

The attention on blockchain technology (BCT) to create new forms of relational reliance has seen an explosion of new applications and initiatives, to assure decentralized security and trust. Its potential as a game-changing technology relates to how data gets distributed and replicated over several organizations and countries. This paper provides an introduction to BCT, as well as a review of its technological aspects. A concrete application of outsource access control and pricing procedures in cellular networks, based on a decentralized access control-as-a-service solution for private cellular networks, is also presented. The application can be used by service and content providers, to provide new business models. The proposed method removes the single point of failure from conventional centralized access control systems, increasing scalability while decreasing operational complexity, regarding access control and pricing procedures. Design and implementation details of the new method in a real-world scenario using a private cellular network and a BCT system that enables smart contracts are also provided.
Smart Contracts-Based Demand Response Bidding Mechanism to Enhance the Load Aggregator Model in Thailand

In 2022, Thailand’s Demand Response (DR) business model was shifting from the Traditional Utility (TU) model to the Load Aggregator (LA) model in accordance with Thailand’s smart grid master plan. This research studied the current demand response model and mechanism to draw possible gaps in operations. This research deals with the data system owned by the individual load aggregator. The load aggregators collect meter data and evaluate demand adaptations before sending the results to claim compensation on behalf of their customers. This approach lacks transparency and facilitates distortion of the facts. Hence, this research introduces the data execution by smart contracts and data records on the blockchain that enhance transparent data sharing among multiple parties and maintain data integrity. Moreover, the proposed bidding algorithm allows customers to offer an expected price under the maximum incentive payment determined by the avoided costs of running the peaking power plants. Hence, the bidding helps reflect the DR operation costs on the customer side and control the budget for incentive payments. This study emphasized the smart contracts and decentralized application layer, so the public blockchain is a reasonable network for the test. However, implementation in real cases using the public blockchain requires careful considerations, such as network fees, transaction speeds, and the security of smart contract codes.
Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models

The practical applications of automatic recognition and categorization technology for next-generation systems are desired in the clinical laboratory. We approached the identification of reactive lymphocytosis using artificial intelligence (AI) technology and studied its clinical usefulness for blood smear screening. This study created one- and two-step AI models for the identification of reactive lymphocytosis. The ResNet-101 model was applied for deep learning. The original image set for supervised AI training consisted of 5765 typical nucleated blood cell images. The subjects for clinical assessment were 25 healthy cases, 25 erythroblast cases, and 25 reactive lymphocytosis cases. The total accuracy (mean ± standard deviation) of the one- and two-step models were 0.971 ± 0.047 and 0.977 ± 0.024 in healthy, 0.938 ± 0.040 and 0.978 ± 0.018 in erythroblast, and 0.856 ± 0.056 and 0.863 ± 0.069 in reactive lymphocytosis cases, respectively. The two-step AI model showed a sensitivity of 0.960 and a specificity of 1.000 between healthy and reactive lymphocytosis cases. As our two-step tandem AI model showed high performance for identifying reactive lymphocytosis in blood smear screening, we plan to apply this method to the development of AI models to differentiate reactive and neoplastic lymphocytosis.
Remote operation system for novice tractor drivers for situations where automatic driving is difficult

Abstract The development of automated tractors is anticipated to address the problem of a declining and aging agricultural labor force. However, there are numerous situations in which automatic driving is difficult in the field. We developed a remote operation system allowing novice tractor drivers to control a tractor remotely in situations where automatic driving is difficult and evaluated its performance on a real tractor in a field. Our challenge was to design a user interface (UI) that allows novices to comprehend the tractor's status and return the tractor situation where automatic operation is possible. The system's UI is inspired by popular video games. In experiments, even novice tractor drivers with little or no experience driving automobiles or playing video games could remotely control the tractor and till according to the goal line. In addition, the participants dealt with an invisible obstacle by viewing illustrations of the tractor sensor data. The designed UI's operation methods and operator attributes were analyzed. Through the experiment, the following facts were revealed. A method of operation that allows the analog stick to adhere to the mechanical guide results in smooth operation. In addition, the participant's preferred control method depends on his/her gaming experience. Thus, this study clarifies some of the specifications and design guidelines necessary for a novice‐friendly remote UI for the remote operation of a tractor.
The Dialectical Principle of Charity: A Procedure for a Critical Discussion

Abstract This paper aims to discuss a well-known concept from argumentation theory, namely the principle of charity. It will show that this principle, especially in its contemporary version as formulated by Donald Davidson, meets with some serious problems. Since we need the principle of charity in any kind of critical discussion, we propose the way of modifying it according to the presupponendum —the rule written in the sixteenth century by Ignatius Loyola. While also corresponding with pragma-dialectical rules, it also provides additional content. This will be termed the dialectical principle of charity, and it offers a few steps to be performed during an argument in order to make sure that the participants understand each other well and are not deceived by any cognitive bias. The meaning of these results could be of great significance for argumentation theory, pragma-dialectics and the practice of public discourse as it enhances the principle of charity and makes it easier to apply in argumentation.
Sensitive enhancement of cat state quantum illumination

Quantum illumination is a binary hypothesis testing to detect a possible low-reflective object. Theoretically, both cat state illumination and Gaussian-state illumination possess the upper bound of 3dB sensitivity gain, over the usual coherent state illumination, for the significantly low illuminating intensity. Here, we investigate further how to enhance the quantum advantage of quantum illumination by optimizing the illuminating cat states for larger illuminating intensity. By comparing the quantum Fisher information or error exponent, we show that the sensitivity of the quantum illumination with generic cat states proposed here can be optimized further, and the 10.3% sensitive enhancement over the previous cat state illumination can be obtained.