Reviewerly/exHarmony huggingface dataset:
Multimodal music emotion recognition method based on multi data fusion

In order to overcome the problems of low recognition accuracy and long recognition time in traditional multimodal music emotion recognition methods, a multimodal music emotion recognition method based on multiple data fusion is proposed. The multi-modal music emotion is decomposed by the non-negative matrix decomposition method to obtain the multi-modal data of audio and lyrics, and extract the audio modal emotional features and text modal emotional features respectively. After the multi-modal data of the two modal emotional features are weighted and fused through the linear prediction residual, the normalised multi-modal data is used as the training sample and input into the classification model based on support vector machine, so as to identify multimodal music emotion. The experimental results show that the proposed method takes the shortest time for multimodal music emotion recognition and improves the recognition accuracy.
Image correlation, non-uniformly sampled rotation displacement measurement estimation

A rotation invariant image correlation algorithm is described which measures and locates the rotational error of individual spatial frequency components. This information may then be interpreted to track signal dependent signatures, analyse spatial frequencies with much higher bandwidths and form optimal matched and generalised correlation filters. The technique is based on image re-sampling and a non-uniform sampling interval that is adjusted depending on its distance from the origin of the polar map. A nearest-neighbour polar interpolated grid scheme, comparable to linear interpolation error, achieves accuracies of 0.1% of a degree. Preliminary measurements based on images containing natural and rigid structure are presented. The algorithm also has potential applications for data-driven image registration and deformation analysis with small variations.
Impact of Financial Development and Economic Growth on Poverty in Middle Income African Countries

This study examined the impact of financial development and economic growth on poverty reduction in middle-income African countries using panel data from 1990 to 2021. The study was limited to Botswana, Kenya, Nigeria, South Africa and Tunisia. Im, Pesaran and Shin unit root test and Pedroni cointegration test approach were used for data analysis. Also, data were analysed using Panel ARDL model based on MG and PMG estimators proposed by Peseran and Smith (1995) and Peseran, Smith and Shin (1999). Results of the unit root test shows the variables are stationary of mixed order, while cointegration test indicates that the variables are linearly cointegrated in the long run. The study revealed that financial development proxy by broad money as share of GDP and broad money as ratio to reserve contributed to poverty reduction in the long the-run. On the other hand, Economic growth proxy by gross domestic product per capita also contributed to poverty reduction in the long run. On the contrary, financial development proxy by domestic credit to private sector as share of GDP had detrimental impact of poverty. To reduce poverty, the study recommended the need for the middle-income countries to implement long run sustainable financial and growth policies. Also, there is need for the implementation of sustainable financial policies that will encourage more credits to private sector at single digit interest rate to promote inclusive financial system, grow the economy and increase income redistribution and reduction in poverty.
The Impact of Internal Marketing on Achieving Organizational Innovation: A Field Study in the Departments of the Ministry of Culture, Tourism, and Antiquities in Iraq

Purpose: The study aimed to investigate the impact of internal marketing on achieving organizational innovation through a field study conducted in the departments under the Ministry of Culture, Tourism, and Antiquities in Iraq, numbering twelve. The researchers employed a purposive sampling method to study the population, with a study sample consisting of 387 employees holding positions such as Assistant Director-General, Department Head, Assistant Department Head, and Section Head in the departments of the Iraqi Ministry of Culture.&#x0D; &#x0D; Theoretical Framework: The underlying concept of this study revolves around internal marketing and its influence on achieving the objectives of the Iraqi Ministry of Culture. In the current era, the special focus on internal marketing is imperative to attain organizational innovation. It is considered a necessary paradigm for achieving the desired progress and effectiveness in government departments and business institutions, as supported by various studies such as those by Muqimah &amp; Aseemi (2023) and Nasef &amp; Abouraia (2023). These studies explore the practice and effectiveness of internal marketing behavior, advocating the adoption of this approach.&#x0D; &#x0D; Design/Methodology/Approach: This study falls under the category of field research and adopts a descriptive and analytical approach. It relies on two types of sources to obtain information and data (primary and secondary sources) and employs questionnaires as a means to collect data and information. Several statistical methods within the SPSS software were utilized for data analysis.&#x0D; &#x0D; Findings: The study has yielded several key findings, notably that internal marketing has a significant impact on achieving organizational innovation, with the dimensions of internal marketing collectively scoring at a high level. The results also demonstrate the importance of internal marketing within the departments of the Iraqi Ministry of Culture for fostering innovation.&#x0D; &#x0D; Practical, and Social Implications of the research: The study recommends a focus on training and the establishment of a fair incentive and reward system directed at all employees to encourage them to embrace the principle of risk-taking.
Face Recognition approach via Deep and Machine Learning

Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to identify with conventional facial recognition technologies, and the recognition rate is lower. The paper present proposed model combined between deep wavelet scattering transform network regarding the extraction of features and machine learning for classification purposes. The proposed model consists four stage: obtaining images, performing pre-processing, extracting features, and then applying classification techniques. using both SoftMax classifier (part of deep learning model) and Support Vector Machine classifier (SVM). We used property collected dataset called MULB dataset. The experimental result shows that SVM classifier provide better results than SoftMax classifier. The results from the experiments conducted on the MULB face database showcased the efficacy of the suggested face recognition approach. The proposed method achieved an outstanding recognition accuracy of 98.29% with SVM classifier and 97.87% with SoftMax classifier.
To the Manifesto!

Work done differently requires leadership done differently. The authors of the Agile Manifesto recognized that the work of software development was a different kind of work. The purpose of the Manifesto was to guide this work, enable this work, and assist the people doing this work—the software developers—to do the work to the best of their abilities.
Detect Any Shadow: Segment Anything for Video Shadow Detection

Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to consider shadows as background and therefore does not perform segmentation on them. In this paper, we propose ShadowSAM, a simple yet effective framework for fine-tuning SAM to detect shadows. Besides, by combining it with long short-term attention mechanism, we extend its capability for efficient video shadow detection. Specifically, we first fine-tune SAM on ViSha training dataset by utilizing the bounding boxes obtained from the ground truth shadow mask. Then during the inference stage, we simulate user interaction by providing bounding boxes to detect a specific frame (e.g., the first frame). Subsequently, using the detected shadow mask as a prior, we employ a long short-term network to learn spatial correlations between distant frames and temporal consistency between adjacent frames, thereby achieving precise shadow information propagation across video frames. Extensive experimental results demonstrate the effectiveness of our method, with notable margin over the state-of-the-art approaches in terms of MAE and IoU metrics. Moreover, our method exhibits accelerated inference speed compared to previous video shadow detection approaches, validating the effectiveness and efficiency of our method. The source code is now publicly available at https://github.com/harrytea/Detect-AnyShadow.
Accurately Estimating Frequencies of Relations With Relation Privacy Preserving in Decentralized Networks

Abundant valuable knowledge can be obtained by learning frequencies of relations in a decentralized network, which benefits various further complex tasks, such as range query and commodity recommendation. Nonetheless, counting frequencies in original networks can reveal sensitive data and pose a risk to individual privacy, specifically relation privacy. Current privacy notions do not fully preserve both the privacy of relations' values and existence. In this paper, we introduce an enhanced privacy notion, relation local differential privacy (relation LDP), which provides comprehensive preservation in relation privacy. However, a significant amount of noise in perturbed networks often leads to severe errors in the accuracy of relation frequencies. To obtain accurate frequencies, we propose a framework called frequency estimation based on combination (Fest-C), which decomposes relation frequencies into two independent parts, the total frequency of relations and relative proportions. Binning relations into hyper-relations, Fest-C reduces errors of total frequency and relative proportions, respectively, and then estimates frequencies by logically multiplying them. Finally, we rigorously prove that Fest-C satisfies relation LDP. Our experiments on various datasets confirm the high accuracy of Fest-C in estimating relation frequencies with a much lower time overhead compared to competitors.
How Double Standard Nurtures Widespread Corruption that Entrenched Poverty in Africa

Using secondary sources of data or information such as articles, reports, research papers, and working papers obtained from the internet, this study conceptually explicates how double standard nurtures widespread corruption that entrenched abject poverty in Africa. The paper argues that while the West including its media has been conspicuous for taking punitive actions (sanctions, freezing accounts) on corrupt African leaders, it has also been conspicuously silent on the role played by Western financial institutions and corporations that facilitate the transfer and laundering of stolen funds from Africa. The paper also argues that the tens of billions of dollars that leave the shores of Africa could have been used to address or tackle poverty in Africa. As such, the paper opined that the failure of the West to institute penal action against Western financial institutions and corporations that facilitate the transfer and laundering of stolen funds from Africa validates hypocrisy and the double standards of its stance of fight corruption in Africa. Therefore, the paper concludes that Africa reliance on the West to help fight against widespread corruption responsible for abject poverty is not only an illusion but must be viewed with serious skepticism.
Social Media and Electoral Law Violation in Cameroon: The Case of the 2018 Presidential Election

Most presidential elections in Africa, especially in the central African sub-region have always ended up in some form of violence. Ever since the social media became a key tool in elections including those organize in African countries, it has been used as a platform to either decrease or escalate election violence, given its characteristics. The social media is not regulated, the law governing its use is difficult to enforce, everybody can create contents, it has a wider reach and it can be used to spread information instantly. The 2018 presidential election in Cameroon was marked by violence. The violence was blamed partly on the way the social media was used by different actors during the 2018 presidential election. This paper examines how the social media was used by actors of the election to violate the electoral code. The theoretical assumptions of this research are based on agenda setting which deals with the prioritization of stories or issues, their treatment and their place in the media. The paper used a quantitative research approach. A total of 176 questionnaires were distributed to voters. They were based in Douala and Buea. Key the findings reveal that social media was used to violate the electoral code during the 2018 election as 103 (58.5%) respondents received or read campaign messages before the official campaign. The findings equally reveal that the social media was used to spread fake news, and misinformation which played a role to incite and facilitate violence. The implication of this finding is that it will get election stakeholders to understand the dangers of the social media especially during elections and see what needs to be done to make the social media a better tool for election management.
A Meta Analysis Study of the Student Teams Achievement Divisions (STAD) Method in English Teaching and Learning

This study aims to analyze the effectiveness of the STAD method towards English teaching and learning. The method use in this research is meta-analysis. Data collection is carry out using coding data sheets in which researcher code information from research articles in journals about the use of the STAD method in English teaching and learning. The data are analyze by calculating the effect size. The results of the study reveal that overall the STAD method is effective in influencing English learning as much as 1.53 or is in the category of large effect. The STAD method also has an impressive impact in terms of areas with a magnitude of 1.31 in Indonesia. In addition, based on education level, the result of the large effect is 0.9 at the junior high school level and 1.9 at the high school level. On the other hand, the effect on students' English proficiency is 1.64. STAD affects reading, speaking, and writing skills in English with a large effect value of 1.05, 0.81, and 3.07. From these results, it can be concluded that the STAD method is effective to be applied in learning English because STAD has a great influence in various regions in Indonesia, in education levels, and can improve students' English skills in various aspects.
Estimation of Ordinary Kriging Method with Jackknife Technique on Rainfall Data in Malang Raya

Geostatistics is a science that focuses on spatial data. In geostatistics, there is an estimation method to handle variables whose values â€‹â€‹vary with the change in location or place, which are called regionalized variables. The estimation method used to handle regionalized variables is called the kriging method. In the ordinary kriging method it is necessary to take into account the semivariogram. Rain is a process of falling water from the clouds to the earth. Rain is measured through rainfall. The purpose of this study was to determine estimation of the ordinary kriging method on normally distributed data and abnormally distributed data, and determine the best semivariogram. The data used is monthly rainfall data in Malang Raya for the period January 2016 to December 2016. From the monthly rainfall dataset, the data are normally distributed in January, February, March, April, May, June, August, September, October, November and December 2016, while the data are not normally distributed in July. Ordinary kriging with Jackknife method can be used to analyze data with normal distribution and data with abnormal distribution.
Cognitive A Priori and Amodal Perception in the Focus of Experimental Philosophy

The article discusses the prospects of neurophenomenology as a substantiative theory with respect to the results of neurocognitive research. Some relevant ideas and theoretical findings of the phenomenology of Edmund Husserl, which are of great importance for the interpretation of experimental data from neuroscience, are consistently presented and analyzed. In particular, the emphasis is made on the procedure of analogizing apperception (appresentation), based on an even deeper pairing mechanism. In so doing, I consider and trace the evolution of these ideas in Husserl's works of different years and periods. As an example, that clearly demonstrates the universal a priori rock-bottom role of appresentation in cognition and perception, the procedure of amodal completion is chosen. Amodal completion is the process of perception by which an object is apprehended as a whole while some parts of it are occluded by other objects. These research seems to open up wide opportunities for neurophenomenology as the theoretical basis of the sciences of consciousness, mind and brain.
Jörg Renner, Editor-in-Chief, GJI

Abstract Sue Bowler talks to Jörg Renner at the end of his term as Editor-in-Chief of Geophysical Journal International, about Earth physics, surprises and what publishing really means for academics.
ANALISIS SERANGAN CYBER MENGGUNAKAN HONEYPOT PADA WEB BERBASIS CLOUD

Cloud computing has a fairly high-security system, however as it can be accessed from anywhere via the internet network, it does not rule out the possibility that the system is safe from cyberattacks, such as Port Scanning, Brute Force Attacks, Malware Attacks, and other types of cyberattacks, T-Pot Honeypot is an all in one system from Honeypot which is an additional security system to detect, trap attacks not to be able to enter the main system. Based on the research results, the implementation of this T-Pot Honeypot can detect attacks and successfully trap attackers by providing false information such as a list of open ports that are the target of the attacker's search. The log data of the detected attack results are processed by the Honeypot system and forwarded into graphs and diagrams that can be seen through the Kibana Dashboard, making it easier for administrators to monitor attack anomalies carried out by attackers so that they can be used to improve security further.
Conflict Management Effectiveness Degree of Educational School Principals at Jerusalem District and its Relationship to Teachers’ Morale

The study aimed to evaluate the conflict management effectiveness degree of educational school principals at Jerusalem district and its relationship to teachers’ morale. The study sample consisted of (384) school teachers selected using stratified random method. To achieve the study objectives. The correlational descriptive methodology was used. A questionnaire was used to collect data. The study showed that the degree of conflict management effectiveness among the principals of education schools in Jerusalem district from teachers’ point of view was moderate. It also showed that the degree of morale of teachers from their point of view was moderate. The study revealed a positive significant correlation between the degree of conflict management effectiveness among the school principals and the degree of morale of teachers in the education schools in Jerusalem district.
Pendampingan Belajar Bahasa Inggris Melalui Kegiatan English Class Bagi Siswa MAN 2 Situbondo

English is a language that must be master by learners. One of the important components in learning English is mastering vocabulary well. In addition, the right method is also needed to be able to develop an English skill, these are 4 skills that need to be developed, namely listening, reading, writing and speaking. In this case, the students of MAN 2 Situbondo learn together by "English Class" with the guidance of tutors from the students of English education study program, Ibrahimy University. English learning activities are carried out using the collaborative learning method which can help the students to be easier for learning English in the school environment, especially in the field of communication. In addition, the students also give a positive response by learning English together with their friends into doing a conversation and also perform mini drama. It also enhance the students motivation and being more confidence in learning English.
Klasifikasi Tingkat Kerusakan Sektor Pasca Bencana Alam Menggunakan Metode MULTIMOORA Berbasis Web

During 2020-2021, 10,152 disasters occurred in Indonesia, significantly impacting the affected sectors. The recovery of these sectors needs to be done as quickly as possible to maintain human survival. This study aims to analyze the factors that affect sector damage after natural disasters in Indonesia and measure the classification accuracy. The data used in this research is data from the Regional Disaster Management Agency of Malang City in 2020. This study developed a web-based Decision Support System (DSS) using The Multiplicative Form Integrated MOORA (MULTIMOORA) method. This method is the result of the development of the MOORA method by adding a complete multiplication form to the MOORA method. In this study, the MULTIMOORA method was used to classify the level of damage to sectors after natural disasters. The results showed that using the MULTIMOORA method in this DSS resulted in an accuracy rate of 84% and was included in the good enough category.
TUBERCULOSIS CASE MODEL USING GCV AND UBR KNOT SELECTION METHODS IN TRUNCATED SPLINE NONPARAMETRIC REGRESSION

The nonparametric regression approach is used when the shape of the regression curve is not known. The advantage of nonparametric regression is that it has a high degree of flexibility. The truncated spline is a method in the nonparametric regression approach, which can overcome changing data patterns at certain sub-intervals with the help of knot points. The purpose of this research is to obtain the best truncated spline nonparametric regression model estimates based on the GCV and UBR knot point selection methodsThe data used in this study came from the publications of the Indonesian Ministry of Health and BPS Indonesia. The response variable used is the percentage of successful treatment of tuberculosis patients in Indonesia with predictor variables namely the percentage of people who smoke over the age of 15 years, the percentage of households that have access to proper sanitation, the percentage of poor people, the percentage of food processing establishments that meet the standard requirements , national health insurance membership coverage and percentage of accredited hospitals. The results showed that the best model came from the GCV method using three knots. This model produces an MSE value of 3.65 with value of 97.04. The value indicates that the predictor variable used in this study affects the response variable by 97.04% while the other 2.96% is influenced by other variables that are not included in this study.