Reviewerly/exHarmony huggingface dataset:
Dual Temporal Memory Network for Video Salient Object Detection

Video salient object detection (VSOD) aims at distinguishing the salient objects from the complex background and highlighting them uniformly in the spatiotemporal domain. One of the fundamental challenges in VSOD is how to make the most use of the temporal information to boost the performance. We propose a dual temporal memory network (DTMNet) which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VSOD. The proposed network consists of two temporal modules including a short-term co-inference learning (SCL) sub-module and a long-range memory learning (LML) sub-module. The SCL is designed for inferencing spatiotemporal interactions between neighboring frames of the current input video clip. The LML aims to satisfy the logical reasoning sequence in timeline and learn the long-time range information between current clip and the previous video clips. Comprehensive evaluations well demonstrate the effectiveness and robustness of our proposed architecture.
FINANCING MANAGEMENT DESIGN IN IMPROVING THE QUALITY OF EDUCATION IN MADRASAH IBTIDAIYAH

The purpose of this study is to describe the implementation of the stages of education financing management to improve the quality of education. This research uses a descriptive qualitative approach, a study of the management of education funding to realize the improvement of education quality. The results showed that 1) planning runs every year and runs well which always begins with several analyses and is determined through joint decisions; 2) Organizing is seen in the structure and has been fulfilled and equipped with tasks, principles, and functions, 3) implementation goes according to plan and is adequately supported by adequate resources, 4) evaluation is carried out according to a predetermined time and runs effectively.
Deep Discriminative Hashing for Cross-Modal Hashing Based Computer-Aided Diagnosis

Massive medical data in multi-modalities emerges with the development of modern medicine, which facilitates the construction of computer-aided diagnosis (CAD) methods. However, most existing CAD methods diagnose diseases only based on the relevant single-modal data, and thus their applications are limited in single modality. To reveal intrinsic connections between heterogeneous modalities and further build multi-modal CAD methods, a novel cross-modal hashing model named Deep Discriminative Hashing (DDH) is proposed. Specifically, semantic labels are encoded to obtain a fixed classifier for the preservation of semantic similarity. Furthermore, benefiting from the classifier, the optimization of hash functions for different modalities is regarded as a classification task that aims to further consider the improvement of discriminability with angular softmax loss. Therefore, DDH projects medical multi-modal data into the common hamming space, and performs multi-modal CAD via cross-modal retrieval. Moreover, since the encoding procedure of different modalities is decoupled, DDH can also execute single-modal CAD based on the medical image retrieval. Experimental results demonstrate the superior accuracy of DDH compared with state-of-the-arts in both medical image retrieval and cross-modal medical data retrieval tasks.
StableNet: Distinguishing the hard samples to overcome language priors in visual question answering

Abstract With the booming fields of computer vision and natural language processing, cross‐modal intersections such as visual question answering (VQA) have become very popular. However, several studies have shown that many VQA models suffer from severe language prior problems. After a series of experiments, the authors found that previous VQA models are in an unstable state, that is, when training is repeated several times on the same dataset, there are significant differences between the distributions of the predicted answers given by the models each time, and these models also perform unsatisfactorily in terms of accuracy. The reason for model instability is that some of the difficult samples bring serious interference to model training, so we design a method to measure model stability quantitatively and further propose a method that can alleviate both model imbalance and instability phenomena. Precisely, the question types are classified into simple and difficult ones different weighting measures are applied. By imposing constraints on the training process for both types of questions, the stability and accuracy of the model improve. Experimental results demonstrate the effectiveness of our method, which achieves 63.11% on VQA‐CP v2 and 75.49% with the addition of the pre‐trained model.
Impact of Electronic Human Resources Management Practices on Employee Commitment in Five Stars’ Hotels in Jordan

The aim of this reaearch was to examine the impact of electronic human resource management practices on employee commitment in five stars’ hotels in Jordan. The study's population was comprised of employees of several hotels. 385 surveys were gathered, with 373 of them being valid for analysis. Multi-regression was employed to test hypotheses. The study findings indicated that there is an impact of E-HRM practices on employee commitment in Jordan's five-star hotels. In light of the findings, the researcher proposes that managers increase their investment in E-HRM and improve e-compensation.
The Role of Blockchain in Transforming the Financial Sector

This essay offers a thorough analysis of the literature on blockchain technology's application in the finance industry. The study looks at how blockchain affects several financial services like lending, trade finance, and payments and remittances. The article outlines the main advantages of blockchain technology, including transparency, security, immutability, and decentralization, and explores how they may revolutionize the financial sector. The assessment also examines the various applications of blockchain in the financial sector globally. The study concludes that although blockchain technology has the potential to disrupt the financial industry, its successful deployment necessitates stakeholder cooperation, regulatory clarity, and a thorough comprehension of its advantages and disadvantages. Policymakers, financial institutions, and other stakeholders who want to use blockchain technology to enhance financial services and advance financial inclusion should take note of the study’s conclusions.
Blockchain Technology and Audit Profession

The technology known as blockchain has gained significant traction as the foundational infrastructure supporting the digital currency known as Bitcoin. Nevertheless, the advantages of this technology extend beyond its role in facilitating cryptocurrencies. Blockchain is a digital ledger that enables the recording of transactions between multiple parties in real time. It functions as a decentralized database where each participant maintains an identical copy of the ledger. The attractiveness of blockchain technology lies in its decentralized peer-to-peer network architecture, coupled with its cryptographic functionalities. The integration of this amalgamation empowers users to carry out transactions in the absence of a reliable third-party intermediary. According to (Alarcon and NG in Pennsylvania CPA J, 26–29, 2018), the potential advantages of blockchain technology in the field of accounting are particularly noteworthy. This is due to the fact that blockchain can facilitate a triple-entry accounting system, wherein all transactions are recorded in real-time, encrypted, and immutable with a time stamp. The objective of this Project is to conduct a comprehensive examination of existing literature pertaining to blockchain technology and evaluate its influence on the field of auditing.
Application of an intelligent evaluation model of online teaching based on improved BPNN

Online teaching quality evaluation is essential for university management, but the current evaluation model of online teaching quality has problems such as subjectivity, high randomness and slow convergence speed. Therefore, the study uses adaptive learning rate with momentum term for optimizing and improving the traditional back propagation neural network, and the improved adaptive back propagation neural network rating model has good application in small-scale low-dimensional datasets, and its computational power is weak in large-scale datasets. Therefore, the study uses deep noise reduction autoencoder to improve the model structure, and then obtains an improved backpropagation neural network. The model shows better performance in a self-made university online teaching evaluation dataset. When the quantity of hidden layers of the improved model is 2, the mean square error value of the model is 0.0026; when the quantity of hidden layer neurons is 25, the mean square error of the model is 0.0012; when the model is made comparison with the shallow model, the mean absolute percentage error, the mean square error, and the root mean square error perform the best with the values of 0.0502, 24.53, and 4.95, respectively. the experimental results verify that the experiment verified that the improved model possesses powerful computational ability, and also proved that it also possesses excellent forecasting convergence, which can better help universities to complete the evaluation of online teaching quality.
Unmasking Hate: A Multimodal Approach to Hateful Meme Detection

Hateful memes are an escalating issue in the digital landscape, demanding innovative solutions for their effective detection and classification. These memes often employ subtlety, sarcasm, and symbolism, presenting formidable challenges for automated detection systems. Moreover, the linguistic and cultural diversity of the internet, transcending geographical and language boundaries, further complicates the task. This research paper presents a comprehensive approach to hateful meme detection, utilizing a Dual Stream Transformer Model, real-world knowledge integration, characteristic detection, and cultural reference understanding. We emphasize the importance of ethics and responsible usage in deploying such technology, underscoring its potential for positive societal impact
The Impact of Integrated Marketing Communications Tools on Achieving Competitive Advantage in Jordanian Universities

The study came to test the impact of the integrated marketing communications on achieving the competitive advantage of private universities in Jordan. The population of this study consists of, administrators and faculty members working in the Department of public relations and Marketing, and employees of the admission and registration department in Jordanian private universities. A random sampling was selected mounted (384). The results indicated that there is an impact of marketing communications on achieving the competitive advantage of Jordanian private universities. According to the results of the study, the researchers recommended all officials and decision makers to in order to increase interest in students, study their characteristics and understand their different motives in order to maintain their share and position among competitors in the market.
Software defect prediction using a bidirectional LSTM network combined with oversampling techniques

Abstract Software defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting software defects is a vital aspect of ensuring software quality. This can significantly impact both saving time and reducing the overall cost of software testing. During the software defect prediction (SDP) process, automated tools attempt to predict defects in the source codes based on software metrics. Several SDP models have been proposed to identify and prevent defects before they occur. In recent years, recurrent neural network (RNN) techniques have gained attention for their ability to handle sequential data and learn complex patterns. Still, these techniques are not always suitable for predicting software defects due to the problem of imbalanced data. To deal with this problem, this study aims to combine a bidirectional long short-term memory (Bi-LSTM) network with oversampling techniques. To establish the effectiveness and efficiency of the proposed model, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR) and mean square error (MSE). The average accuracy of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) was 88%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average accuracy by 6 and 4% compared to the original datasets. The average F-measure of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) were 51%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average F-measure by 43 and 41% compared to the original datasets. The experimental results demonstrated that combining the Bi-LSTM network with oversampling techniques positively affects defect prediction performance in datasets with imbalanced class distributions.
Impact of Social Media Marketing on Electronic Word of Mouth: A Study of Jordanian Private Universities

The objective of this research aims is to identify impact of social media marketing on EWOM. The study population consisted of students at Jordanian private universities. Smart PLS software was used to test the study hypotheses. The results of the study showed an impact of Social Media Marketing on E-WOM. The researchers recommend managers to activate marketing via social media and the electronic spoken word by enhancing interaction and effective communication between the online store and the customer and listening to their suggestions and opinions.
An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction

The development and utilization of solar energy has become an important strategic decision for the sustainable development of many countries. Short-term variations in solar irradiation have an impact on the safety and stability of photovoltaic and solar thermal power plants, therefore, the development and accuracy of solar irradiance prediction models have received much attention. This paper proposes a short-term irradiance prediction model based on mixed intelligent optimization algorithm and deep learning algorithm that integrates features of various forms of information. First, the sequence containing the picture attributes as well as the color and texture characteristics are recovered from ground-based cloud images, historical irradiance and meteorological feature information is decomposed and reconstructed by singular spectrum analysis (SSA). Secondly, the bidirectional short term to long term memory (BiLSTM) network is optimized for model training and finally assessment using an enhanced chaotic sparrow search method. The technique exceeds benchmark methods and a number of sophisticated individual algorithms in forecasting ultra-short-term global horizontal irradiance (GHI), according to experimental data, while also offering extremely high accuracy and resilience.
K-Nearest Neighbor and Naive Bayes Classifier Methods for Expedition Service Comparison Analysis of User Sentiments

&#x0D; &#x0D; &#x0D; &#x0D; Depending on the service chosen, expedition is one of the freight forwarding companies that operate in the domestic market. The availability of expedition services can make it easier for traders to transfer items to purchasers who conduct online transactions, as well as encourage shipping businesses to collaborate with online dealers. The JNE, JNT, and Pos Indonesia excursions were utilized in this study. The goal of this project is to develop an analytical model that will make it simpler for online merchants to find collaborators for effectively and securely transporting their goods. Based on user sentiment on the social networking site Twitter, this study uses sentiment analysis. With the keywords "JNT, JNE, and Pos Indonesia," this study compares the accuracy results using the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. According to this study, testing accuracy for the NBC method was 80% and training accuracy was 83%. While the accuracy of the KNN approach is 68%. According to public opinion, the JNE expedition is the best one for distributing products, scoring 68.58% in favor of it and 30.64% against it.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
The Impact of Creative Behavior on Achieving Marketing Ambidexterity: A Field Study in Commercial Banks in Jordan

The objective of the research is to examine the impact of creative behavior on achieving marketing ingenuity. The study population was limited to a category (branch manager, department head, division head and administrator) of the commercial banks workers, and the number of questionnaires valid for statistical analysis was (356) questionnaire. (PLS-SEM) was used to analyze the hypotheses. The results of the study concluded that creative behavior has an impact on marketing ingenuity. The study recommends to enhancing Originality in the work of their employees by enabling employees to complete the tasks assigned to them in a renewed manner.
The Impact of Diversity of Human Resource on the Quality of Work-Life

This research aims to test the effect of managing diversity in human resources on the quality of work life. It was focused on telecommunications companies in Jordan. A random sample of (1066) respondent. Structural equation modeling (SEM) to test hypotheses. The results indicated an effect of the diversity of human resources in the telecommunications companies on the quality of work life. Researchers recommend managers to spread the culture of diversity and difference among employees in telecommunications companies due to the many advantages that these companies obtain as a result of the different experiences, knowledge, and skills.
NCCNet: Arbitrary Neural Style Transfer with Multi-channel Conversion

Arbitrary style transfer aims to create a novel image from a content image and a style image, stylizing the content image with the style of the style image. However, recent algorithms are prone to unnatural output due to focusing on the statistical distribution of deep features and ignoring the relationship between channels in feature maps. In this paper, we devise a novel Arbitrary Neural Style Transfer Network called NCCNet, which is useful for artistic style transfer. Specifically, NCCNet creates good visual effects by fusing different layers of textures. Moreover, we design an Effective Channel Conversion Modules (ECCM) which uses a multi-channel matrix to rearrange style feature maps to the appropriate range by analyzing the relationship between channels. Then, we can obtain a covariance based on the rearranged style feature map. At the same time, we design a decoder named Self-Adaptive Decoder (SA-Decoder) that conforms to our algorithm. Moreover, we apply our algorithm to the video style transfer and get good results. Qualitative and quantitative evaluations demonstrate that NCCNet performs well in arbitrary video and image style transfer tasks.
Science, Dependency and Africa

The production of science globally is a matter to be examined regularly to study its impact on human life. This chapter sets the background for studying science in an important continent, Africa. Indicators such as publications in science, the share of the GDP to R&D, the number of researchers and science policies come in handy when assessing the status of science. Apart from these, factors such as the scientific partnerships that Africa forged with the world, funding it received for conducting research in crucial areas, and the locations of science production such as universities and institutions that provided leadership in science production are relevant. Due to the diversity of Africa on several fronts science presents dissimilar presence and features. The chapter elaborates on these topics to see the extent of self-reliance and dependency of African science.
Development of an AR Application for Learning Traditional Patterns

Traditional Japanese patterns, widely used in clothing, architecture, and crafts, are designs that convey various wishes expressed in the form of motifs which depict plants and living things. Moreover, these traditional patterns express the aesthetics of Japanese culture, which are highly appreciated both at home and abroad. In this study, we developed an augmented reality (AR) application that allows people around the world to learn the meaning and history behind these traditional patterns through AR contents by capturing images of the patterns on ceramics in real space.
Visual Realism Assessment for Face-Swap Videos

Deep-learning-based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also important as a quality assessment metric to compare different face-swap methods. In this paper, we make a small step towards this new VRA direction by building a benchmark for evaluating the effectiveness of different automatic VRA models, which range from using traditional handcrafted features to different kinds of deep-learning features. The evaluations are based on a recent competition dataset named DFGC-2022, which contains 1400 diverse face-swap videos that are annotated with Mean Opinion Scores (MOS) on visual realism. Comprehensive experiment results using 11 models and 3 protocols are shown and discussed. We demonstrate the feasibility of devising effective VRA models for assessing face-swap videos and methods. The particular usefulness of existing deepfake detection features for VRA is also noted. The code can be found at https://github.com/XianyunSun/VRA.git .
A Terminological and Semiotic Review of the Digital Object Concept

The expression “digital object” is used in different initiatives dealing with the effects of exchanging and reusing information in scenarios with multiple standards and sources. However, born in different communities with distinct requirements, models, and vocabularies, the definitions of what is a digital object are usually incompatible, aggravating the interoperability problem they are trying to solve. As a contribution, this article reviews the historical and contemporary concepts of object, eliciting similarities and distinctions to result in a better understanding of the main framework proposals. Furthermore, we seek help in the Peircean concept of the semiotic sign to better understand how information flows in digital artifacts and media, and which are the relevant roles played by different agents, either human or not. We argue that, because computers can only deal with digital representations, it is irrelevant to the architecture of digital objects whether their data refers to real or virtual entities. Finally, we claim that a multipurpose definition should be broad enough to embrace new contexts and interpreters that cannot be prescribed a priori, in contrast to the usually rigid schemas found in object-oriented programming environments and relational database management systems.