Reviewerly/exHarmony huggingface dataset:
Fast Point Transformer

The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This paper introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel-based method, and our network achieves 129 times faster inference time than the state-of-the-art, Point Transformer, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset.
Brain-like approaches to unsupervised learning of hidden representations -- a comparative study

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is studied using an external linear classifier and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.
Vulnerabilities and Open Issues of Smart Contracts: A Systematic Mapping

Smart Contracts (SCs) are programs stored in a Blockchain to ensure agreements between two or more parties. Due to the unchangeable essence of Blockchain, failures or errors in SCs become perpetual once published. The reliability of SCs is essential to avoid financial losses. So, SCs must be checked to ensure the absence of errors. Hence, many studies addressed new methods and tools for zero-bug software in SCs. This paper conducted a systematic literature mapping identifying initiatives and tools to analyze SCs and how to deal with the identified vulnerabilities. Besides, this work identifies gaps that may lead to research topics for future work.
Efficient Bayesian synthetic likelihood with whitening transformations

Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computationally demanding. Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statistic with a known, tractable distribution -- typically Gaussian -- and then performs statistical inference using standard likelihood-based techniques. However, as the number of summary statistics grows, the number of model simulations required to accurately estimate the covariance matrix for this likelihood rapidly increases. This poses significant challenge for the application of BSL, especially in cases where model simulation is expensive. In this article we propose whitening BSL (wBSL) -- an efficient BSL method that uses approximate whitening transformations to decorrelate the summary statistics at each algorithm iteration. We show empirically that this can reduce the number of model simulations required to implement BSL by more than an order of magnitude, without much loss of accuracy. We explore a range of whitening procedures and demonstrate the performance of wBSL on a range of simulated and real modelling scenarios from ecology and biology.
A Comprehensive Study on Face Recognition Biases Beyond Demographics

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyse FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAADFace attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions.
SaaS Service Combinatorial Trustworthiness Measurement Method Based on Markov Theory and Cosine Similarity

With the rapid progress of information technology, cloud computing and cloud services are widely accepted and applied to all aspects of social life. In the cloud computing environment, SaaS (Software-as-a-Service) services have become the main form of software services. For SaaS services, evolutionary and iterative development methods have become the main methods of software system construction. For systems with high trustworthiness, the independent trustworthiness of each SaaS service has a great impact on the overall status. However, SaaS services with high independent trustworthiness do not always build highly trusted software systems. The combinatorial trustworthiness between SaaS services is as important as the independent trustworthiness of each SaaS service. This paper takes combinatorial trustworthiness between SaaS services as the research object. Combinatorial trustworthiness measurement method based on Markov and cosine similarity theory is proposed. The feasibility and effectiveness of the proposed method are verified through simulation experiments. Applicable scenarios, advantages, and disadvantages of the proposed method are shown through the comparison of different measurement methods. The proposed method provides theoretical and technical support for users to select SaaS services suitable for their application scenarios, build cloud service systems, and monitor the operation status of cloud service systems.
Association Between Spritual Intelligence, Social Support and Mental Health of University Student During Covid-19 Based on Dependency Degree and Ordinal Logistics Regression

The modeling of mental health has been widely studied using statistical approaches such as the chi-square test, regression analysis, and ordinal logistics. However, not many studies apply ordinal logistic regression and dependency degree into this domain, where both of these approaches are good statistical and non-statistical approaches for investigating categorical data. In this study, the researcher is interested in combining the dependency degree and ordinal logistic regression and identifying the mental health factors of students in the Covid-19 era. The research sample was students majoring in Al-Qur'an and Tafsir Study at UIN Suska Riau. The results indicated that a significant relationship between the informative support dimension to the disorder, the instrumental dimension to avoidance and increased self-awareness with an average dependency value between dimensions of 4.13% and 4.53%. Through this relationship, it can be seen that students are more likely to experience stress disorder (PTSD) if they are reluctant to do good than the number of awards received by students.
Identifying Characteristics of Households Recipient of the Government’s Social Protection Program

According to Statistics Indonesia, the number of poor people increased by 1,12 million people in March 2020. In March 2021, the percentage of poor people increased by 0,36 points compared to March 2020. The percentage of poor people in Banten Province has increased in the last three years (2019-2021). One way to reduce poverty by the government is to increase social protection programs. The characteristics of households receiving social protection programs were identified by modeling the classification of households using the random forest technique, obtaining important variables using the permutation feature importance and Shapley additive explanations interpretation techniques, and analyzing the most important variables from the two interpretations methods. Handling the imbalance data on the response variables using SMOTE technique and evaluating the classification model obtained an AUC value of 0,718. The important variables were selected from the permutation feature importance and Shapley additive explanation methods based on a consistent ranking at the top. Shapley’s additive explanation was more consistent than permutation feature importance. Six important, namely capita expenditure, education of the head of household, age of head of household, source of drinking water, floor area, and the number of household members.
PREDICTING THE GROWTH TREND OF DEMAND FOR DISTANCE EDUCATION IN IRAN: A CASE STUDY OF PAYAME NOOR UNIVERSITY

Awareness of the strategic role of higher education has led many countries to pursue long-term goals. The aim of this study is to identify the factors affecting the growth trend of the student in higher education by 2029. The statistical population of the whole is Payame Noor University. The method of this research is documentary-analytical. First, the factors influencing the demand for higher education, especially in open and distance education, were examined and analyzed using a regression model. Research data evaluated using the ordinary least squares regression method. The results show that economic factors have the most critical impact on increasing the demand for distance education. The forecast results show that the growth trend of the student population until 2029, between the two groups of men and women, is equally slightly decreasing.
Design and Build PMB System with Prediction of Prospective Students Accepted or Withdrawal Using Random Forest Algorithm

New Student Admission is one of the essential activities carried out regularly every year or semester. As the operational system of student admissions progresses, student admission data increases yearly. ESA Unggul University (UEU) has not used this data to make strategic decisions, market potential, and consider invitations to enter the academic path. So it is necessary to conduct research whose results can be used by UEU in analyzing prospective students at the time of new student admissions. In this study, data analysis was carried out from 2014 to 2019. This study aims to produce a design using the classification method to predict whether prospective students are accepted or withdrawn. In this study, 19,603 training data and 4,901 test data were used. The results showed the best Random Forest algorithm with an accuracy of 73.61%. The results of this study can be used to support the marketing department in minimizing the number of prospective students who resign.
Desain dan Uji Coba Media Pembelajaran E-learning Berbasis Blog Pada Materi Sistem Periodik Unsur

The average of students and teachers at Islamic Senior High School of Nurul Hidayah Sungai Salak already have Android. However, students tended to use Android just as the communication, entertainment, and social media tools. Even though, it could be used to support them in the learning process, namely applicative learning media. The solution for students’ Android can be utilized optimally by designing an E-Learning learning media in the form of a Blog on priodic system of elements lesson. This research aimed at knowing the levels of validity, practicality, and student response to Blog based E-Learning learning media on Periodic System of the Elements lesson that was developed. It was a Research and Development (R&amp;D) with Borg and Gall development model that 10 steps were reduced to 5. The subjects of this research were a material expertlecturer, a media expert lecturer, a Chemistry subject teacher, and 10 the tenth-grade students atIslamic Senior High School of Nurul Hidayah Sungai Salak. The research findings showed that the levels of validity and practicality of Blog based E-Learning learning media were stated very valid and very practical with the assessment percentages bya material expert, a media expert, and a Chemistry subject teacher continuously 100%, 96%, and 100%. The result of student response overall to Blog based E-Learning learning mediadeveloped was very good. Therefore, it was very peasible to be used as a learning media at school, so that, it would had a positive impact and optimalize the use of Android by student.
Application of Data Mining Using the K-Medoids Algorithm for Poverty Index Clustering

Poverty index is a term for measuring poverty, this is done by a government agency or commonly referred to as the Central Statistics Agency (BPS). The poverty index or poverty rate is the percentage of the population in a province who is below the poverty line, which is the minimum in obtaining an adequate standard of living. In the government's efforts to reduce the level of poverty in a province, the government often provides special assistance programs for people belonging to the poverty line. Based on the explanations that have been discussed, a conclusion can be drawn. This research can be done using the Data Mining technique to group the total Poverty Index by Province in Indonesia using the K-Medoids Algorithm, then by determining the Clusters randomly. The results of this study are expected to assist the government in providing assistance to the affected population below the poverty line.
Correlation analysis of world cryptovality volatility

In the world there no country, market or economy which is separated, and interconnection is becoming a fundamental feature of almost all social and economic systems. In the case of digital assets, such as cryptocurrencies, the impact of the relationship on their performance and price trajectory increases. The investigation of these phenomena is important for understanding the processes that govern cryptocurrencies. The objective of this paper is to assess the tightness of the relationship between the world's leading cryptocurrencies. In order to achieve this goal, the concepts of «cryptocurrency» and «blockchain», their history and features are considered. The principles of the first cryptocurrency – Bitcoin – are studied and the dynamics of changes in its price from 2015 to 2021 are analyzed. The price of Ethereum, XRP and major financial assets is compared. The best 20 cryptocurrencies at a certain time are identified with their price, capitalization and value changes over the last day and week. Based on the data from the Hackernoon website, research by Larry Chermak and the cryptocurrency publication The Block, it is determined that the strongest cryptocurrencies are interdependent or follow the most important cryptocurrency – bitcoin. Pearson's coefficient is considered. On its basis the correlation strength between eleven leading cryptocurrencies is determined, the part of the data are presented as a confidence interval. It is found that Ethereum and Litecoin have the strongest association with Bitcoin. Coin, Tron, Cardano, Bitcoin Cash, however, have low (negative) correlation between Tether or USDC. The main factors influencing the price of cryptocurrency are identified. It is also determined that alternative cryptocurrencies have lower correlation with bitcoin during periods of price growth, although the differences are not large, and during price decline the strength of bitcoin's correlation with other cryptocurrencies increases significantly, in most cases it reaches even very strong connection. The influence of social networks such as Google+ and Twitter on the price of cryptocurrency is determined as well. The proposed analysis makes it possible to understand the dynamics of cryptocurrency markets and the various processes that affect their efficiency.
Multiplier Effect of Dam Development in Indonesia (A Study on Dam Development during the Leadership of President Joko Widodo)

This paper is a literature study entitled Multiplier Effect of Dam Development Policies in Indonesia with a focus on dam development policies under the leadership of the President of the Republic of Indonesia, Ir. H. Joko Widodo. Library research is part of the type of qualitative research, therefore the characteristics of qualitative research are transformed into the context of library research by moving the field setting to the library room, including changing interview and observation activities into text and discourse analysis. The results of this study indicate that Indonesia's dam construction policy is not limited to the purpose of increasing food security. However, the construction of dams actually has a multiplier effect, such as; as a power generator, flood control, overcoming drought, development of freshwater fish farming, and development of the tourism sector. Where the multiplier impact caused by the construction of dams has proven to be able to open new economic opportunities that stem from increasing the welfare of the community.
COVID-19’S IMPACT ON MACEDONIAN HIGHER EDUCАTION - ARE OUR UNIVERSITIES ENTREPRENEURIAL ENOUGH?

The paper gives theoretical view of impact of Covid -19 on higher education in Macedonia.Covid -19 outbreak has essentially brought to the surface all the anomalies and strengths of the universities' work up to now and the management of their resources.Analyzing the case study -Goce Delchev University, as a positive and successful example, we can conclude that the entrepreneurial orientation in management of universities is crucial for successful coping with the crisis.By March 2020, more than 4,400 students and 360 lecturers were actively using Microsoft 365 tools to access, learn, and share content, making Goce Delchev, one of the universities in Macedonia shift to a remote educational environment, aligning students with the demands of increasingly digitized world.The paper instigates мany questions for higher education: What are the new teaching methods, tools and formats that have been emerging so far?Which of these will remain after the pandemic?How are universities counterbalancing this "loss" and how will internationalization be reshaped in the months and years to come?Can universities turn this into an opportunity?
The Online Teaching System as a Sustainable Way of Learning

The COVID-19 pandemic, in addition to the sometimes-dramatic effects in the health sector, presented a wide range of challenges for the various sectors of the national economies. This article refers to the effects of the pandemic in the university field, especially for the period of 2021 until now. The period corresponds to the end of the pandemic overlapping with the post-pandemic return to normal. Although at the beginning of the pandemic the effects seemed terrible, being widespread and long-lasting, some effects were still useful and motivational for the “real world”. The effects in question are those of compulsory online teaching, so on a large scale, of all subjects from all disciplines. The authors modeled and implemented a database system that includes several types of tests to generate representative samples from the university population. In the content of the article, two such tests are exemplified for a single one-semester discipline: “Computer programming and programming languages II”. The experience of the authors revealed, by comparison, practical ways to teach the theory provided in the states of functions in the real world, using exclusively the online or the mixed environment. Finally, we provide a brief conclusion and ideas for future work.
Deep Learning based DFWF Model for Audio Spoofing Attack Detection

One of the biggest threats in the speaker verification system is that of fake audio attacks. Over the years several detection approaches have been introduced that were designed to provide efficient and spoof-proof data-specific scenarios. However, the speaker verification system is still exposed to fake audio threats. Hence to address this issue, several authors have proposed methodologies to retrain and finetune the input data. The drawback with retraining and fine-tuning is that retraining requires high computation resources and time while fine-tuning results in degradation of performance. Moreover, in certain situations, the previous data becomes unavailable and cannot be accessed immediately. In this paper, we have proposed a solution that detects fake without continual-learning based methods and fake detection without forgetting in order to develop a new model which is capable of detecting spoofing attacks in an incremental fashion. In order to retain original model memory, knowledge distillation loss is introduced. In several scenarios, the distribution of genuine voice is said to be very consistent. In several scenarios, there is consistency in distribution of genuine voice hence a similarity loss is embedded additionally to perform a positive sample alignment. The output of the proposed work indicates an error rate reduction of up to 80% as observed and recorded.
Research on Legal Regulations of Information Automated Decision-making from the Perspective of Private Law

Information automated decision-making has been widely applied and developed in private field. Being in the trend of a mighty rise, it brings convenience to life, meanwhile its own nature attribute of the “black box” has triggered various legal challenges which are increasingly prominent, such as the issues of transparency, discrimination and interpretation power. The legal regulations of automatic information decision-making from the perspective of private law need to analyze the causes of legal challenges and the limitations of relevant legal regulations. Exploring the root causes of interest conflicts and putting forward suggestions on the right structure of data subjects and the definition of obligations of decision-making subjects, as well as being based on the status quo of private law of information automated decision-making in China, this paper proposes legal regulations from the perspectives of legislation, institutional framework, multiple governance mechanism and judicial accountability.
Big Data Application Technology and Prospect Analysis in Smart Distribution Network

Intelligent power distribution network system main application technology including the communications, computer and network, the modern electronic technology, etc., adapter power automation system main components for the master station, terminal automation monitoring system and communication system, has formed a complete collection, transmission and processing in the integration of information system, to realize the remote management of distribution network. The application of big data technology in smart distribution network makes it more intelligent and ADAPTS to the needs of social development. This paper analyzes the application technology and prospect of smart distribution network big data.