Reviewerly/exHarmony huggingface dataset:
The Pharmacological Mechanism of the Effect of Plant Extract Compound Drugs on Cancer Pain Based on Network Pharmacology

Objective. We systematically analyzed the mechanism of plant-derived drugs alleviating cancer pain in our hospital through network pharmacology, so as to provide the possibility of further application of traditional Chinese medicine in the treatment of cancer pain. Methods. We used TCMSP, ETCM, and TCMID databases to mine the active ingredients of plant-derived drugs. We combined OMIM, GeneCards, and DrugBank databases to mine and match the common targets of plant-derived drugs for cancer pain. We used the STRING platform and Cytoscape software to analyze and screen out the core targets. We used GO and KEGG methods to analyze the biological processes, molecular functions, cellular composition, and signaling pathways involved in the reduction of cancer pain by plant-derived drugs. Results. We found 153 active ingredients from botanical drugs by TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, TCMSP), ETCM (The Encyclopedia of Traditional Chinese Medicine), and TCMID (Traditional Chinese Medicine Integrated Database) databases, covering 341 protein targets in human body. Combined with OMIM (Online Mendelian Inheritance in Man), GeneCards, and DrugBank databases, we excavated and matched 141 targets of plant-derived drugs and cancerous pain diseases. Through the analysis of the STRING platform and Cytoscape software, 19 core targets including TNF, MAPK1, JUN, and IL-6 were screened out. Go and KEGG enrichment showed that plant-derived drugs alleviated cancer pain processes involving 193 biological processes, 47 molecular functions, 22 cell components, and 118 signaling pathways. By screening genes involved in KEGG signaling pathway, it was found that plant-derived drugs were mainly associated with PI3K-Akt signaling pathway, tumor necrosis factor signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway, and HIF-1 signaling pathway in alleviating cancer pain. Conclusion. These results indicate that botanical drugs can positively affect the expression of inflammatory factors and apoptotic factors in the process of treatment and relief of cancer pain, which is expected to have a potential therapeutic effect on the relief of cancer pain.
Understanding Impacts of Service Robots with the Revised Gap Model

The service quality gap model, which identifies the antecedents of SERVQAUL, reveals that service quality depends on the gap between customer perceived and expected service (Gap 5), which can be caused by other gaps in the service process (Gaps 1–4). The emergence of service robots has affected the quality of services provided; however, little is known about how these impacts happen. Thus, this paper aims to explore the impacts of service robots on service quality by revising the original gap model in the context of robot service. This paper reviews and analyzes the literature related to service robots and develops a revised gap model for robot service by integrating the existing research on the impacts of service robots. By introducing the roles of robots and robot manufacturers into the original gap model, the revised gap model adds three new gaps: Gap 6 (manufacturers’ understanding gap), Gap 7 (technical gap), and Gap 8 (service coordination gap). Based on the revised gap model, the impacts of service robots on service quality are expounded (i.e., Gap 5 can be affected by not only Gaps 1–4 but also Gaps 6–8), and several propositions are introduced. This paper is the first to consider robot manufacturers as part of the service quality gap model, and the first to use a dynamic model to explore the impacts of service robots on service quality. This paper not only supplements the service robot and gap model literature but also provides service companies with a meaningful framework to improve service quality when using robots to provide service.
Boolean Masking for Arithmetic Additions at Arbitrary Order in Hardware

Modular addition is an important component of many cryptographic algorithms such as ARX-ciphers and lattice-based post-quantum secure schemes. In order to protect devices that execute these algorithms against side-channel attacks, countermeasures such as masking must be applied. However, if an implementation needs to be secured against multivariate attacks, univariately secure masking schemes do not suffice. In this work, we focus on hardware architectures for higher-order masked addition circuits. We present and discuss three adder designs that are protected with a provably secure masking scheme. Concretely, we discuss Kogge–Stone, Sklansky and Brent–Kung adders regarding their suitability for high-order masking and their performance in this setting. All architectures are fully pipelined and achieve a throughput of one addition per cycle. In order to achieve multivariate security at arbitrary orders, we use HPC2 Gadgets that satisfy the PINI security notion. Additionally, we apply a first-order secure threshold implementation scheme to the adder variants and compare their performance in the univariate case.
Research on Digital Image Watermarking Algorithm Based on Scrambling and Singular Value Decomposition

At present, the digital image watermarking algorithm has not embedded the synchronization signal in the image, resulting in the poor performance of the embedded image in terms of, for example, imperceptibility, anti-attack ability, and robustness. Therefore, a digital image watermarking algorithm based on scrambling and singular value decomposition is proposed. The digital watermark is preprocessed by dimensionality reduction and encryption; the digital image is processed in sections and embedded in the synchronization signal. The digital watermark image is embedded in the digital image watermark by using the low-frequency energy ratio technology of sound channel. The watermark image extraction step is designed to extract the watermark image. The transmittance of the degraded image is calculated, the transmittance is refined by relying on the soft matting algorithm and guiding filter, the image contrast in the image frequency domain is enhanced, and the results are mapped to the appropriate visual range to optimize the visual brightness of the image. Finally, according to the uniqueness of singular value matrix, the distributed characteristics of image matrix data are described, the visual effect is enhanced, and the research of digital image watermarking algorithm is completed. The experimental results show that the algorithm can extract the watermark information completely, the image contrast and singular value have been significantly improved, and the algorithm has better anti-attack performance.
Designing Expert System for Centrifugal using Vibration Signal and Bayesian Networks

Centrifugal machines are crucial in the process of making sugar.It requires proper centrifugal maintenance so that the production process runs smoothly.Efficiency and productivity are, therefore, critical factors in producing high-quality sugar.Centrifuge damages may occur, suddenly causing huge losses.Therefore, predictive treatment is essential.To achieve this, vibration analysis is a reliable and easy method to determine the vibration levels of spinning machines such as centrifuges.Simply by attaching the sensor to the outside of the engine, the engine condition will be read.Unfortunately, not all employees understand how to read vibration measurement data.Even experts need time to analyze the vibration signal.Therefore, the purpose of this study was to design an expert system that diagnoses centrifugal vibrations using the Bayesian Network.The vibration analysis process will be employed in the network using a series of complex nodes and trained according to the reference spectrum analysis.The presence or absence of spectral lines is evidence of Bayesian Network input in updating information regarding centrifugal damage.The result shows that the Bayesian network method was successfully applied to diagnose centrifuges based on vibration data.The inputs in the form of 1X and 2X produced an Unbalance probability value of 90%, Misalignment 93.2%, Looseness 57.38%, Bearing 33.79%, Pulley 50.3%, and produce a centrifugal damage probability of 7.86%.Therefore, these values were the actual conditions of the vibration data.
Research and Development of a Unified Methodology for Assessing the Resource Efficiency of International Digital Platform Promotion for E-Learning

Ideas related to the systematization of educational technologies are of great interest. This is due, among other things, to the principles of symmetry. On the basis of their application, the synthesis of well-established facts and principles in international education will be realized. Practical needs that are directed towards the future will also be realized. This paper is devoted to the study and creation of a methodology that makes it possible to evaluate the resource efficiency of an interethnic digital platform. The developed methodology of resource allocation for the promotion of digital educational programs is unified and can be applied in any region or country. Considering the specific legislative acts on the financial support of higher education and the need for professionals in certain areas, this method allows you to give a qualitative assessment and determine the necessary resources for the development and promotion of an interethnic digital educational platform, identifying the most appropriate areas of education depending on the region or country.
Multi-Factor Authentication with Randomly Selected Authentication Methods with DID on a Random Terminal

Background: Information and communication technology development provides services to various fields. In particular, the development of mobile technology has made ubiquitous services possible. However, as technology advances, security is being emphasized more and more. The existing method of user authentication by entering an ID and password is likely to leak sensitive information if the server is attacked or keylogged. Therefore, multi-level authentication is needed to prevent server attacks or keylogging. Methods: Biometric authentication technology has been utilized by smartphones, but it is challenging to apply in the case of a lost device because the central server does not manage biometric data. Encrypting transactions through blockchain technology makes data management more secure because blockchain technology is distributed, and there is no primary target for hackers. Results: The method proposed in this study can become a basis for an authentication method that guarantees both security and integrity due to the synergetic use of biometric and blockchain technologies. Conclusions: The information stored in the service provider does not include sensitive information such as the user’s biometric data but is only a means of verifying the user’s information in the blockchain transaction with guaranteed anonymity. Therefore, users can receive services with confidence and safety.
Privacy-Preserving Collaborative Computation for Human Activity Recognition

Human Activity Recognition (HAR) enables computer systems to assist users with their tasks and improve their quality of life in rehabilitation, daily life tracking, fitness, and cognitive disorder therapy. It is a hot topic in the field of machine learning, and HAR is gaining more attention among researchers due to its unique societal and economic advantages. This paper focuses on a collaborative computation scenario where a group of participants will securely and collaboratively train an accurate HAR model. The training process requires collecting a massive number of personal activity features and labels, which raises privacy problems. We decentralize the training process locally to each client in order to ensure the privacy of training data. Furthermore, we use an advanced secure aggregation algorithm to ensure that malicious participants cannot extract private information from the updated parameters even during the aggregation phase. Edge computing nodes have been introduced into our system to address the problem of data generation devices’ insufficient computing power. We replace the traditional central server with smart contract to make the system more robust and secure. We achieve the verifiability of the packaged nodes using the publicly auditability feature of blockchain. According to the experimental data, the accuracy of the HAR model trained by our proposed framework reaches 93.24%, which meets the applicability requirements. The use of secure multiparty computation techniques unavoidably increases training time, and experimental results show that a round of iterations takes 36.4 seconds to execute, which is still acceptable.
A STUDY ON LISTENING PROBLEMS FACED BY STUDENTS OF HIGHER EDUCATION

The purpose of this study is to describe listening comprehension problems faced by students when taking the TOEFL-like test. This research is a descriptive research. This research is also categorized as a survey, the broad area of survey research encompasses any measurement procedures that involves asking questions of respondents. Furthermore, the researcher gave questions to the sample in form of questionnaire to get the data about listening problem faced by students taking TOEFL-like. In this research, the researcher used the questionnaire to collect the data. After discussion in the previous chapter, finally leads the researcher to make conclusion from what have been already discussed. The conclusion is among the twenty indicators, all the indicators came from various sides, such as: students' side, TOEFL organizer, material, listening equipment and another supported thing that related to the activity on TOEFL test especially listening skills.
Blockchain Technology Implementation for Medical Data Management in Malaysia: Potential, Need and Challenges

This study highlights several challenges along with the potential and need for blockchain technology implementation in the Malaysian healthcare industry context. The systematic review unearths potential technological, organizational, environmental, individual-level acceptance concerns and challenges associated with deploying blockchain to support electronic health records. As such, this research serves as the starting point for a chain of studies aiming at detecting, analyzing, and responding to such demands. The study is expected to guide policy and decision-making procedure of a secure and resilient health information exchange for healthcare stakeholders of developing nations such as Malaysia.
A Three-stage Filtering Approach for Face Recognition

Face recognition has become a crucial topic in recent decades, which offers important opportunities for applications in security surveillance, human-computer interaction, and forensics. However, it poses challenges, including uncontrolled environments, large datasets, and insufficiency of training data. In this paper, a face recognition system is proposed to iron out the above problems with a new framework based on a hashing function in a three-stage filtering approach. At the first stage, candidate subjects are chosen using the Locality-Sensitive Hashing (LSH) function. We employ a voting system to select candidates via disregarding a large number of dissimilar identities considering their local features. At the second stage, a robust image hashing based on Discrete Cosine Transform (DCT) coefficients is used to further refine the candidate images in terms of global visual information. Finally, the test image is recognized among selected identities using other visual information, resulting in further accuracy gains. Extensive experiments on FERET, AR, and ORL datasets show that the proposed method outperforms with a significant improvement in accuracy over the state-of-the-art methods.
New Frontiers

Research on online harassment has become increasingly important as more aspects of daily life rely on the internet. Currently, large gaps in knowledge of how online harassment affects adults exist, in part because of the difficulties inherent in defining and measuring online harassment. This review traces the history of the definition of online harassment in order to suggest more useful approaches. The historical analysis reveals that portions of bullying definitions that have been carried over to online harassment definitions should be discarded. Instead, the workplace and sexual harassment literatures provide a better model to evaluate and compare existing measures and definitions of online harassment. Therefore, we propose the definition of online harassment as "interpersonal aggression or offensive behavior(s) that is communicated over the internet or through other electronic media." We also examine the benefits of different measurement time frames as well as the use of direct query or behavioral checklists. Finally, we present a new measure of online harassment, the Online Harassment Experience Questionnaire (OHEQ). The OHEQ is composed of eight online harassment items, categorized based on traumatic stress theory and selected after a review of the current online harassment literature. Each item’s frequency is measured on a 6-point Likert scale.
ACADEMIC INTRINSIC MOTIVATION AND LEARNING ENGAGEMENT IN MATURE STUDENTS IN PRIVATE HIGHER EDUCATION INSTITUTIONS IN THE SOUTH OF ENGLAND

The non-traditional experience for mature students can sometimes be challenging as they connect to the college/university later in life. A new avenue for career growth achieved through higher education can be advantageous for those at a career transition phase. The present research aimed to study the academic intrinsic motivation and learning engagement of mature students in private higher education institutions. The contemporary literature has found a higher intrinsic motivation for learning in mature students than the regular traditional students. Also, student engagement is correlated to success factors such as increased retention, high impact and lifelong learning, social and personal growth and development and student wellbeing. A survey research design was employed to collect primary data (N=300) from full-time undergraduate mature students studying in the private higher education institutions in the South of England, aged 21 years and above. The data was analysed using SPSS version 26.0 and AMOS version 23.0. The bivariate analysis results showed a positive correlation between intrinsic academic motivation and different learning engagements. The non-traditional students who were intrinsically motivated tend to participate more in academic-related activities in private higher education to experience efficacious learning. Furthermore, the result of exploratory factor analysis yielded a three-factor solution for the 15-item study motivation questionnaire with a reliability of 0.93. The result of confirmatory factor analysis confirmed the hypothesised three-factor model structure of the 15-item university student engagement inventory on mature students with a reliability of 0.85. Hence, both scales confirmed high psychometric properties. The academic needs of mature students must be considered. Colleges/universities should organise non-traditional programmes to enhance their motivation for personal and professional growth through higher studies later in life.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0980/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Using Repeated- Reading and Listening –While- Reading via Text-To- Speech APPs. in Developing Fluency and Comprehension

One of the challenges in teaching a foreign language is: finding appropriate ways to enable students to develop their reading fluency and comprehension. Repeated reading and listening-while-reading are two significant strategies that enhance students’ fluency and comprehension. This study aimed to develop fluency and comprehension of EFL college students. During the treatment, the teacher trained the students to use some free Text to Speech apps that support oral repeated reading RR and listening while reading LWR activities. Pre-post tests were used to assess students’ reading fluency and comprehension. Data obtained from the tests were analyzed statistically through SPSS software. Results indicated development in students’ reading fluency and comprehension. Conclusions suggested the use of RR and LWR through Text to speech apps to assist the reading skills of higher education students. It is recommended that TTS Apps are promising tools that can be integrated into reading instruction.
SUPER-RESOLUTION IMAGES ON MOBILE SMARTPHONE AIMED AT 3D MODELING

Abstract. In these last years have been calculated several general algorithms to process the images acquired by mobile devices. Nevertheless, they don't always work at their full effectiveness due the numerous constraints and external issues. The latest generation smartphones, pushed by higher user expectations, have increasingly performing camera functions, in particular way, about the always increasing resolution, with a large number of pixels to process or two cameras for stereo view. Considering that pixels in an image sensor synthetizes the number of incoming photons, taking a photograph with a digital camera means to applying a low-pass filter to a scene where the tiny textures, such as characters that measure a few pixels, are observed blurry. This happens when using digital zoom or when the visibility is compromised or during the night artificial lights. For all these reasons the conventional image converter resolution algorithms (as bilinear interpolation algorithm) don’t work with the high-frequency information of a scene once lost. All these aspects are more relevant if we are taking photos to carry out a 3D scenario. Indeed, the 3D model will have an higher geometric accuracy if the image resolution will be higher. Super-Resolution algorithms (SRa) are classified into two categories: (1) approaches that reconstruct a high-resolution image from itself and (2) approaches that register multiple low-resolution images to interpolate sub-pixel information. In this paper we verify the geometric accuracy of a 3D model, when using the Morpho Super-Resolution™ algorithm, also in critical condition. This algorithm doesn’t require pixel shift, indeed, some cameras have a functionality named pixel shift, which captures multiple images while shifting the image sensor.
Pattern capacity of a single quantum perceptron

Abstract Recent developments in quantum machine learning have seen the introduction of several models to generalize the classical perceptron to the quantum regime. The capabilities of these quantum models need to be determined precisely in order to establish if a quantum advantage is achievable. Here we use a statistical physics approach to compute the pattern capacity of a particular model of quantum perceptron realized by means of a continuous variable quantum system.
Sand Dust Images Enhancement Based on Red and Blue Channels

The scattering and absorption of light results in the degradation of image in sandstorm scenes, it is vulnerable to issues such as color casting, low contrast and lost details, resulting in poor visual quality. In such circumstances, traditional image restoration methods cannot fully restore images owing to the persistence of color casting problems and the poor estimation of scene transmission maps and atmospheric light. To effectively correct color casting and enhance visibility for such sand dust images, we proposed a sand dust image enhancement algorithm using the red and blue channels, which consists of two modules: the red channel-based correction function (RCC) and blue channel-based dust particle removal (BDPR), the RCC module is used to correct color casting errors, and the BDPR module removes sand dust particles. After the dust image is processed by these two modules, a clear and visible image can be produced. The experimental results were analyzed qualitatively and quantitatively, and the results show that this method can significantly improve the image quality under sandstorm weather and outperform the state-of-the-art restoration algorithms.