Reviewerly/exHarmony huggingface dataset:
Blockchain technology in pharmaceutical supply chains: a transaction cost perspective

Purpose Adopting new technologies to improve supply chain activities and processes is essential due to increasingly complex and dynamic business environments. Particularly in the pharmaceutical industry, high-quality standards must be met, requiring transparency and visibility in the supply chain. This research aims at investigating the implementation of blockchain technology in the supply chain of an Egyptian pharmaceutical company. Design/methodology/approach The research applies a single case-study approach building on the theoretical underpinnings of transaction cost economics. Twenty-five semistructured interviews were conducted with pharmacies and employees of the case company to identify the blockchain technologies' potential for pharmaceutical supply in Egypt. Further analyzing the frequencies of the codes, the authors elaborate on specific relationships between the observed practices. Findings The research revealed the potential benefits of adopting blockchain technology. Transaction costs are indeed positively impacted by reduced contracting costs, processing costs and lead times, also ensuring the safe delivery of medications. However, the findings also highlight obstacles related to running costs, awareness and company culture. Regarding supply chain governance, blockchain technology can enhance collaboration within the supply chain as well as with important stakeholders. Practical implications Insufficient management of pharmaceutical supply chains (PSC) may affect a company's reputation but also disrupt the patient's healing process due to temperature damage and counterfeit medicines. Blockchain governance, in this vein, can ensure a safer and more reliable supply of pharmaceutical products. For intraorganizational purposes, however, cloud solutions, barcoding and generally digital platforms are rated more frequently than blockchain solutions. Originality/value The present study contributes to an advanced understanding how blockchain technology supports PSC, particularly in an emerging country context like Egypt. It thereby confirms and extends previous research as well as adds to the theoretical underpinnings of digitalized supply chains.
Virtual and Augmented Reality for Environmental Sustainability: A Systematic Review

In recent years, extended reality (XR) technology has seen a rise in use in environmental subjects, i.e., climate change or biodiversity loss, as a potential tool to inform and engage the public with current and future environmental issues. However, research on the potential of XR technology for environmental sustainability is still in the early stages, and there is no clear synthesis of the methods studied in this field. To provide a clearer view of existing approaches and research objectives, we systematically reviewed current literature dealing with XR use in environmental topics. Although the results indicate that the volume of literature exploring XR in environmental applications is increasing, empirical evidence of its impact is limited, hindering the possibility of presently drawing significant conclusions on its potential benefits. Based on our analyses, we identified thematic, theoretical, and methodological knowledge gaps and provide a guideline to aid future research in the field.
Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation

Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge and users’ historical behaviors for the next-item prediction. In this paper, we focus on the cross-domain sequential recommendation problem. This commonly exist problem is rather challenging from two perspectives, i.e., the implicit user historical rating sequences are difficult in modeling and the users/items on different domains are mostly non-overlapped. Most previous sequential CDR approaches cannot solve the cross-domain sequential recommendation problem well, since (1) they cannot sufficiently depict the users’ actual preferences, (2) they cannot leverage and transfer useful knowledge across domains. To tackle the above issues, we propose joint Internal multi-interest exploration and External domain alignment for cross domain Sequential Recommendation model (IESRec). IESRec includes two main modules, i.e., internal multi-interest exploration module and external domain alignment module. To reflect the users’ diverse characteristics with multi-interests evolution, we first propose internal temporal optimal transport method in the internal multi-interest exploration module. We further propose external alignment optimal transport method in the external domain alignment module to reduce domain discrepancy for the item embeddings. Our empirical studies on Amazon datasets demonstrate that IESRec significantly outperforms the state-of-the-art models.
Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond

This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS ) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate feedback from the users. One of the main reasons for that is the lack of system-user conversational interaction data. To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems. Specifically, we develop a user simulator, dubbed ConvSim, that, once initialized with an information need description, is capable of providing feedback to system's responses, as well as answering potential clarifying questions. Our experiments on a wide variety of state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to 16% retrieval performance increase in terms of nDCG@3. Moreover, we observe consistent improvements as the number of feedback rounds increases (35% relative improvement in terms of nDCG@3 after three rounds). This points to a research gap in the development of specific feedback processing modules and opens a potential for significant advancements in CS. To support further research in the topic, we release over 30 000 transcripts of system-simulator interactions based on well-established CS datasets.
Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales

Emotion artificial intelligence, the algorithm that recognizes and interprets various human emotions beyond valence (positive and negative polarity), is still in its infancy but has attracted attention from industry and academia. Based on discrete emotion theory and statistical language modeling, this work proposes an algorithm to enable automatic domain-adaptive emotion lexicon construction and multidimensional emotion detection in texts. Using a large-scale dataset of China’s movie market from 2012 to 2018, we constructed and validated a domain-specific emotion lexicon and demonstrated the predictive power of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) in online reviews on box office sales. We found that representing overall emotions through discrete emotions yields higher prediction accuracy than valence or latent emotion variables generated by topic modeling. To understand the source of the predictive power from a theoretical perspective and to test the cross-culture generalizability of our prediction study, we further conducted an experiment in the U.S. movie market based on theories on emotion, judgment, and decision-making. We found that discrete emotions, mediated by perceived processing fluency, significantly affect the perceived review helpfulness, which further influences purchase intention. Our work shows the economic value of emotions in online reviews, generates insight into the mechanism of their effects, and has managerial implications for online review platform design, movie marketing, and cinema operations.
Exploring gold mineralization in altered ultramafic rocks in south Abu Marawat, Eastern Desert, Egypt

Gold mining is an important strategic sector. The search for mineral reserves is moving deeper as more accessible shallow resources are discovered. Geophysical techniques are now being employed more frequently in mineral exploration because they are quick and can provide crucial subsurface information for discovering potential metal deposits, particularly in high-relief and inaccessible places. The potential for gold in a large-scale gold mining (LSGM) locality in the South Abu Marawat area is investigated using a geological field investigation that includes rock sampling, structural measurements, detailed petrography, reconnaissance geochemistry, and thin section analysis, integrated with various transformation filters of surface magnetic data (analytic signal, normalized source strength, tilt angle), contact occurrence density maps, and tomographic modelling for the subsurface magnetic susceptibilities. The benefits of remote sensing (RS) and its technology in mapping detailed rock differentiation, and characterizing physical objects on the land surface using various spatial, and spectral resolution datasets are integrated. Both aeromagnetic and measured land magnetic profiles are used to investigate the area's present geological conditions and possible future mining localities. Results indicate that gold mineralization in the study area is linked to the altered ultramafic zones that are associated with faulting and shearing and characterized by a low magnetic susceptibility anomaly.
Synthetic data for face recognition: Current state and future prospects

Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition.
Agile Methodology for the Standardization of Engineering Requirements using Large Language Models

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and in particular, a shift towards Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies that hinder their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP) in general and Large Language Models (LLMs) in particular can serve as an enabler for the conversion of NL requirements into semi-machine-readable requirements. This is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into semi-machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach is an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs were utilized. Two of these models were fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERT), specifically aeroBERT-NER and aeroBERT-Classifier, which were trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, was utilized to identify sentence chunks present in NL requirements. All three language models were utilized together to achieve the standardization of requirements. To demonstrate the effectiveness of the methodologies, requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs) were employed, and a total of two, five, and three boilerplate templates were identified for design, functional, and performance requirements, respectively.
Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.
Even Shorter Quantum Circuit for Phase Estimation on Early Fault-Tolerant Quantum Computers with Applications to Ground-State Energy Estimation

We develop a phase-estimation method with a distinct feature: its maximal run time (which determines the circuit depth) is δ/ϵ, where ϵ is the target precision, and the preconstant δ can be arbitrarily close to 0 as the initial state approaches the target eigenstate. The total cost of the algorithm satisfies the Heisenberg-limited scaling O~(ϵ−1). As a result, our algorithm may significantly reduce the circuit depth for performing phase-estimation tasks on early fault-tolerant quantum computers. The key technique is a simple subroutine called quantum complex exponential least squares (QCELS). Our algorithm can be readily applied to reduce the circuit depth for estimating the ground-state energy of a quantum Hamiltonian, when the overlap between the initial state and the ground state is large. If this initial overlap is small, we can combine our method with the Fourier-filtering method developed in [Lin and Tong, PRX Quantum 3, 010318, 2022], and the resulting algorithm provably reduces the circuit depth in the presence of a large relative overlap compared to ϵ. The relative-overlap condition is similar to a spectral-gap assumption but it is aware of the information in the initial state and is therefore applicable to certain Hamiltonians with small spectral gaps. We observe that the circuit depth can be reduced by around 2 orders of magnitude in numerical experiments under various settings.1 MoreReceived 6 December 2022Revised 21 February 2023Accepted 24 April 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020331Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum simulationQuantum Information
A Framework for Recognition of Facial Expression Using HOG Features

Recognition of facial expressions has been an important topic of study over the last several decades, and despite the advancements that have been made, it is still difficult to do because of the significant intra-class diversity. The handcrafted feature is used in traditional methods to address this issue. This feature is then preceded by a classifier that is trained using a database of pictures or videos. The majority of these works do quite well on datasets of photographs that were recorded in a controlled environment. However, they do not perform as well on datasets that are more difficult to work with since they include greater image variance and partial faces. The Histogram of Oriented Gradient (HOG) descriptor is the foundation for the strategy that is suggested in this study. During the initial step of the procedure, the input picture is pre-processed in order to locate the datum region, which assists in the extraction of the most relevant characteristics. After that, the Random Forest (RF) algorithm was employed as a classifier for facial expressions. The Japanese Female Facial Emotions Database (JAFFE) is used to assess our technique. The experimental findings demonstrated that the suggested method is accurate and effective in identifying facial expressions.
Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms, and our results demonstrate that the neural network-based classifier yields an accuracy as high as 92% for the RSNA dataset which is a new dataset that provides two views and additional features such as age. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.
Analysis of teachers in the use of digital resources in online teaching and assessment in COVID times

The study considered the use of online learning applications for instructional delivery and assessment purposes in higher institutions of learning that stimulates a new atmosphere where lecturers could utilize these facilities to promote learning. The study also determined the gender difference between lecturers use of online learning applications for instructional delivery and classroom assessment in a post-covid context. The digital applications analyzed in this study include Google classroom, Email, Moodle, Canvas, Google, Microsoft Team, Zoom, Google Meet, Cisco Webinar, Facebook groups, Open Educational Resources (OERs), Google docs, Google slide, WhatsApp and Telegram Channel. At present, however, opinions are divided over the extent to which lecturers utilize these online learning applications in instruction and assessment. A descriptive survey research design was adopted with 152 participants (n = 88 males and n = 64 females) for the study and the instrument used for data collection was Utilization of Online Leaning Applications in Classroom Instruction and Continuous Assessment Questionnaire (UOLACICAQ). On December 2021, the participants completed the Questionnaire. The internal consistency of the instrument was determined using Cronbach’s Alpha and the reliability index of .93 was obtained. Results of the study show a low extent of utilization of online learning applications in classroom instruction and continuous assessment (Canvas, Microsoft Team, Google Meet, Cisco Webinar, OERs, Google slide, Telegram Channel, Facebook groups, Moodle, Google Classroom, learning management systems, Google forms and Google sheet). There was however, a significant difference between male and female lecturers' use of online learning applications for continuous assessment. Lecturers’ utilization of online learning applications in instruction and assessment of learning will expose them to different online learning applications used in delivery instructions and assessing students’ learning. The research supplies good idea of the instrument for measuring those staked learning outcomes in higher institution. However, lecturers do not differ in terms of the extent to which they use online learning applications while delivering instructions in the classroom based on gender.
Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification.