Reviewerly/exHarmony huggingface dataset:
TauLiM

With the rapid development of object detection in deep learning (DL), applications on LiDAR point clouds have received much attention, such as autonomous driving. To verify the robustness of object detection models by testing, large amounts of diversified annotated LiDAR point clouds are required to be used as test data. However, considering the sparseness of objects, the diversity of the existing point cloud dataset is limited by the number and types of objects. Therefore, it is important to generate diversified point clouds by test data augmentation. In this paper, we propose a tool for LiDAR point cloud via test data augmentation, named TauLiM. A well-designed metamorphic relation (MR) [1] is proposed to augment point clouds while maintaining their physical characteristic of LiDAR. TauLiM is composed of three modules, namely point cloud configuration, coordinate filtering, and object insertion. To evaluate our tool, we employ experiments to compare the ability of testing between the existing dataset and the augmented one. The result shows that TauLiM can effectively augment diversified test data and test the object detection model. The video of TauLiM is available at https://www.youtube.com/watch?v=9S6xpRbbhtQ and TauLiM can be used at http://1.13.193.98:2601/.
Does E-Governance Reduce Income Inequality in Sub-Saharan Africa? Evidence From a Dynamic Panel

The ICT-income inequality relationship and the governance quality-income inequality nexus have been investigated in recent years. However, the moderating effect of ICT on the governance quality-income inequality linkage has been largely ignored. To fill this gap in the literature, this study examines the moderating effect of ICT on the relationship between governance quality and income inequality for a panel of 42 sub-Saharan African economies over the period 1996-2020. To achieve this goal, the generalized method of moments (GMM) estimation technique has been adopted. The results reveal that while ICT contributes to the improvement of income distribution, governance quality contributes to the exacerbation of income inequality. Interestingly, the results unveil that the promotion of E-governance could contribute to improve social welfare and reduce income inequality. ICT thresholds at which the positive incidence of governance on income inequality is completely nullified is for governance effectiveness and 19.7 for regulatory quality. Policy implications are given based on the findings of this study.
Use of AI/ML to Telecom Service Delivery Model to Change the Customer Perception About Service Performance

With the new technologies like 5G coming to the market, the complexity of the mobile network seems to increase inescapably the launch of new services which earlier were not into being with the older technologies. Technologies like software defined wide area networking (SD-WAN) and network functions virtualization (NFV) got introduced to meet the ever-rising customer expectation, whereas artificial intelligence/machine learning (AI/ML) got introduced to network functions to increase the service provider’s need to increase the intelligence of their network design and planning, optimization, day to day operations, etc. Continuous research is going on AI/ML techniques to make the communication networks and systems more scalable, controllable, and manageable to enable more autonomous operations. With the use of Al/ML into the network automation platforms, like open network automation platform (ONAP) can deliver efficient, time sensitive, and reliable service operations to enhance the service performance and thus resulting to customer satisfaction. However, while choosing the scenario analysis service providers need to be careful to not fall into trap of classical data analytics techniques like big data tools with different statistical analysis and a dose of marketing. Intelligent network is much more than that where intelligent network is able to predict the network outage and can automate the healing, can predict vulnerability threat to the network and can take the smart preventive action based on the type of the threats, etc. In this article, I am going to talk about how AI/ML can be scaled to change the telecom operation scenario in future to reduce the operational cost, increasing revenue, and customer experience.
Design and Implementation of a Lightweight and Fast Tiny Advanced Encryption Standard Algorithm

Cryptographic algorithms are gaining importance due to their relevance and importance in the areas of privacy and security experienced by the Internet of Things (IoT) devices. They improve data privacy and confidentiality by limiting who can decrypt the data to the person who has the key. Advanced encryption standard (AES) is one of the most important encryption algorithms in use. This algorithm uses 10 rounds for each encryption and decryption process and encrypts data starting with 16 bytes, which increases the time needed for encryption and decryption. In order to speed up encryption and decryption while maintaining security levels more than or equal to those offered by the AES algorithm, this paper proposes an algorithm called tiny advanced encryption standard (TAES) with two different scenarios. As the encryption process starts with 4 bytes and goes up to an unlimited number of bytes, TAES features are less complex and more flexible than those of AES and can be quickly implemented, and are characterized by high encryption of images, texts, and sounds. The proposed TAES algorithm is tested on text and images using the MATLAB software. The result is completely distorted images and text. The test results also unveil that the encryption and decryption speeds as well as the throughput of the proposed TAES is much better than those of the original AES algorithm. Moreover, a low signal-to-noise ratio (SNR) values are obtained, indicating a greater degree of image distortion as a consequence of utilizing the proposed TAES algorithm.
Storasi dan Retrivasi Bahasa pada Anak dengan Perkembangan Normal Usia 1-2 Tahun

Research on language restoration and retrivation is still rare. Moreover, underestimating the eyes of children under 5 years of age because the resulting data on language storation and retrivation are few. This condition raises the suspicion of investigating the Language Storation and Retrivation in Children with Normal Age Development of 1-2 Years. The specific objectives of this study were (1) to describe the forms, words, and sentences that children aged 1-2 years old are capable of being corrected; (2) describe the meaning of the sentence in context. This type of research is a case study with a descriptive qualitative approach. Two research subjects were taken based on the consideration that the subjects were classified as children with normal age development of 1-2 years. The results of the study: (1) Subject 1, vocal fon / i /, / e /, / Ə /, / a / can do all ranks, while fon / u /, / Ɛ /, / o / still cannot be clearly restored. Subject 2 is able to transmit fon / i /, / e /, / Ə /, / Ɛ /, / a / in all positions, while the vocal fon / u /, / o /, /ᴐ/ still cannot be clearly pronounced. (2) Subject 1 has not been able to retrivation consonant fon / f /, / l /, / q /, / r /, / s /, / v /, / x /, / z /, while Subject 2 has not been able to transmit consonant fon / f /, / g /, / q /, / s /, / v /, / x /. (3) Subjects 1 and 2 are able to retrivation word classes. (4) Subjects 1 and 2 have not been able to interpretation sentences based on context because they have not been able to interpretation sentences. Conclusion: Research subjects 1 and 2 are able to retrivation vocal and consonants, although not as a whole; retrivation of holophrastics category words; retrivation of diphthongs and clusters in word usage; not able to pronounce the sentence; does not have problems in the acquisition and development of the language.
The Meat and Potatoes of SQL Server

Several years ago I was preparing for one of the famous “Conor and Bob” shows for the PASS Summit keynote. I created this elaborate idea involving machine learning, analytics, and other types of new features. I showed this to Conor, and he simply said, “Bob, I think we should stick to the meat and potatoes for our show.” Being from Texas I knew what he meant. Highlight the core engine capabilities of SQL Server.
Class-Based Machine Learning for Intelligent Reservoir Characterization Over the Life Cycle of a Field in the North Sea

A consistent approach to evaluate the mineralogy and petrophysical attributes throughout the extent and life of the field is essential. This ensures proper assessment of the reservoir potential and provides deeper insights about the sedimentological and depositional environment. However, different logging technologies with varying capabilities and applications are often used during field development phases, introducing differences and discrepancy in the assessment. In a study from North Sea, only few wells had high-end spectroscopy measurements to decipher complex mineralogy. Due to the lack of similar measurements in other wells, we explore the application of Intelligent Reservoir Characterization using Class-based Machine Learning (CbML) to provide a reliable and consistent evaluation. During training phase of CbML, key wells with complex mineralogy evaluated using advanced wireline spectroscopy logs are used. First training data is automatically reduced into interpretable facies using a novel Petrophysical Data-Driven Classification (PDDC) methodology. Then facie-wise learning models, including 1)outlier detection, 2)prediction of target complex mineralogy from input spectroscopy, 3) feedback QC loop of reconstructing spectroscopy from learnt mineralogy, and 4)corresponding uncertainties, are developed. During prediction phase, learnt models are applied to basic spectroscopy data in non-key wells to automatically identify outliers, assign facies, predict target complex mineralogy with uncertainties and reconstruct spectroscopy for a comprehensive QC. The procedure takes advantage of traditional petrophysical workflows as well as machine learning algorithms to quickly assess and deploy an intelligent reservoir characterization application. The methodology was used over a blind well where the predicted mineralogy showed a good match with that of an expert driven assessment using high-end spectroscopy data. In the non-key well, the mineralogy predicted from the CbML application was used to determine different petrophysical attributes like porosity, intrinsic permeability, and elemental concentrations, which matched well with the conventional core analysis and XRF data. Overall, the uncertainties estimated from the workflow were very low, establishing further the robustness and reliability of the results. While it is desired to have all the necessary measurements to address the formation complexity, the well logging program is mostly constrained by the logging environment or planned borehole trajectory. The case study above shows how the CbML workflow can be used to develop a fit-for-purpose intelligent solution as an alternative to traditional interpretation. Once a learnt model is developed, it can be shared among experts and applied to any new well to provide near-instant, consistent quantities of interest, such as mineralogy, grain density and elemental concentrations as in our present case.
Utilization of Blockchain Technology in Making Police Record Certificates (SKCK) Using an Enterprise Architecture Approach

Blockchain is one of the products born from the rapid development and growth of technology which is common in Industry 4.0. However, Indonesia has not implemented blockchain in the government sector until now, even though this is an important thing to create a safe and transparent system for its citizens and to improve technological development in Indonesia so that it can always keep up with the times. Even though Blockhain technology can be used in the design of any system, including the creation of a Surat Keterangan Catatan Kepolisian (SKCK) because the use of blockchain in SKCK can help secure the data of every resident. The research method used is descriptive analysis of the results of the various literatures studied. The purpose of this research is to design a system for making Police Record Certificates (SKCK) by utilizing blockchain technology using an Enterprise Architecture approach. From here it is analyzed using several diagrams such as Use Cases and Flowcharts to be able to describe the needs and business processes of an SKCK system. The conclusion of this research is that Blockchain implementation is very necessary because Blockchain implementation can make SKCK data and information more secure and verifiable using immutable hashes.
To Discover Novice Expert Paradigm: Sequence-Based Time-Domain and Graph-Based Frequency-Domain Analysis Method of Eye Movement

Eye movement data can show the cognitive process in performing tasks to a certain extent. The existing researches on eye movement analysis are usually based on statistics, and it is difficult to show the correlation between the information associated with the scene. Other probabilistic algorithms usually focus on user feature recognition based on eye movement representation. In this paper, the concept of time-domain and frequency-domain analysis of eye movement area of interest is proposed, within which, the frequent pattern mining method and visual cognitive graph model are constructed to mine the relationship between the areas of interest. Finally, some application examples of this model in the novice expert paradigm are presented.
Feature Analysis of Electroencephalogram Signals Evoked by Machine Noise

Recently, people who have been working in mines for a long time are affected by the noises of high-power coal mining machines. The noises of machines for a long time may greatly influence people’s physical and mental health. To further understand the psychological state of workers working in the mine, and to extract features and analyze the change rule of EEG signals induced by machine noises stimulation. This paper uses the improved singular value decomposition and reconstruction algorithm to process the EEG signal artifact. The improved signal-to-noise ratio is increased from 9.462 to 11.063, the root mean square error is increased from 4.435 to 4.232, and the correlation coefficient is improved to 0.813. Through experiments, the signal generated by the human brain stimulated by machine noises is collected and analyzed after filtering. The results show that the electroencephalography (EEG) of F3, F4 and other electroencephalography (EEG) sensitive electrode potentials tend to be adaptive to a stable state when the frequency of machine noises is different.
Complex Valued, Multi-Layer Hopfield Neural Network

Based on a Complex Valued Neural Network (CVNNs) with the neurons placed in multiple layers, several interesting associative memories are proposed and their dynamics is investigated. Specifically, the synaptic weight matrix was chosen to be Block Hermitian, Block Jacobi, Block Toeplitz and Block Hankel. It is reasoned that in the case of such structured multilayer CVNNs, an interesting Convergence theorem holds true which enables that utilization of such an Artificial Neural Network as an associative memory. It is expected that the CVNNs proposed in this research paper have biological significance.
Automatic Speech Recognition for Qur’an Verses using Traditional Technique

Deep learning is the one of approaches of machine learning that uses algorithms for building a model based on complex unstructured data. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, a traditional method to build a robust Qur’an versus recognition is proposed. The MFCC is used to extract features. These features are adapted using minimum phone error (MPE) as a discriminative model. The acoustic model was built using the deep neural network (DNN) model. We present an n-gram language model (LM). The dataset of Qur’an verses is used for training and evaluating the proposed model, consisting of 10 hours of .wav recitations performed by 60 reciters. The Experimental results showed that the proposed DNN model achieved a significantly low character error rate (CER) of 4.09% and a word error rate (WER) of 8.46%.
Credit card fraud detecting using machine learning classifiers in stacking ensemble technique

Credit cards have been a primary target for scammers across the globe due to the low risk and high reward nature of this type of fraud. Credit card fraud detection datasets are mostly unbalanced because, in day-to-day life, only a handful of transactions are fraudulent. Most credit card transactions are normal and the fraudulent ones are very similar to normal transactions. In our paper we describe how we designed a model to work on unbalanced datasets by using an ensemble technique of stacking classifiers; we chose RF, XGB, KNN and MLP to be the classifiers in level 0. Level 0 prediction will come to the level 1 meta classifier. The meta classifier in our model was logistic regression. By applying different experiments to this stacking classifier, we achieved a model that gives more accurate results than those shown in previous literature.
PERANCANGAN MANAJEMAN PROMOSI MTs NEGERI 2 PESAWARAN BERABSIS WEBSITE

MTs Negeri 2 Pesawaran is located H. Subeki Village Gunungrejo Kecamatan Waykhilau Kabupaten Pesawaran Provinsi Lampung, 35367. Currently, MTs Negeri 2 Pesawaranstill uses the manual approach in sharing the information, not using Internet or website. So it is limited in the transmission of information and it needs a computer based information system. This study was to design the building of an information system MTs Negeri 2 Pesawaranthat are capable of storing data and presenting in the form of web base mobile information, so it makes it easier for people who want to get the information that are accurately and fastly about MTs Negeri 2 Pesawaranand accessible to broader areas. The method of research used in this study is waterfall. And the method of data collection uses observation methods, dokumentation, interview, and library studie. By using the information systems on MTs Negeri 2 Pesawaran based web mobile, so the information access is no longer limited, people can access information at any time without having to come directly to MTs Negeri 2 Pesawaran.
Semi-supervised Bolt Anomaly Detection in Haphazard Environment

Grease injection is one of the most important parts of high-speed rail maintenance, and whether the bolts on related facilities contain anomaly plays a crucial role in the safety of the entire grease injection process. Bolt anomaly detection is typically performed by certified inspectors. However, this task is not only hazardous for the personnel but also time-consuming. The decisions that depend entirely on the individuals’ experiences are subjective. Therefore, this article presents an efficient bolt anomaly detection framework based on a semi-supervised technique. We first train a novel U-Net-based neural network with anomaly-free samples, which can simultaneously predict the positions of all bolts and segment areas that contain anomaly-free bolts. Subsequently, we build a novel variance distribution model to extract the prior knowledge of areas that contain anomaly-free bolts based on the segmentation results and predicted position results from the network. Finally, we utilize the pre-built model to generate anomaly response maps of test images, which intuitively visualize the areas containing abnormal bolts. The experimental results demonstrate that our proposed bolt anomaly detection framework achieves satisfactory accuracy and efficiency, which can meet the requirements of practical engineering detection.
The New Right Limitation and Exception Rules in Chinese Copyright Law

Through the third amendment to the Copyright Law of the PRC, the legislator intends to adopt the three-step test while introducing an open-ended list of limitations and exceptions to constitute a China-specific “two-step test”. Court decisions in China frequently apply the fair use doctrine of US copyright law, neglecting to consider its peculiar context of the US common law tradition and thus, unduly expanding the Chinese courts’ discretionary power. The rules of “copyright limitations and exceptions” in the digital era are the landmark of determining copyright legitimacy and balance the tradeoff of interests among the copyright holders, users and the public. This chapter concludes that China should enact judicial interpretative guidelines under ripe conditions that reshape the “first step” of the three-step test, by “purposefulness” and “foreseeability,” and increase its openness. Also, it is suggested that the Article 24, paragraph 1, item 13 Chinese Copyright Law (2021) shall be revised in line with the proper interpretation of “the first step” of the three-step test and the four standards of fair use shall be adopted to extend and enrich the “second” and “third” steps and introduce “the principle of proportionality” to support its equilibrium.
Pengembangan Media Youtube dalam Pembelajaran "Speaking" di SMAN 1 Buyasuri. Lemabata, NTT

This research aims to develop Youtube media for teaching speaking and to measure the feasibility of the final product as the media used for teaching speaking at senior high school. Design of the research is R &amp; D modified into 6 Steps : 1) potential and problem, (2) data collection, (3) product design, (4) design validation, (5) product revision, (6) product trial. The final product of this research is a youtube link for teaching speaking of tenth graders. The results of the feasibility assessment of youtube as learning media are "Very Good" category and the aspect of final product , the students obtain score of 587 with an average score of 39.13. In conclusion, youtube learning media is "appropriate" to be used as one of the learning media for teaching speaking at SMA Negeri 1 Buyasuri, Lembata, NTT.
Impact of Islamist Uprising on Human Capital and Demographics: Evidence from Mali

We investigate the impact of Islamist uprising on regional human capital and demographic by exploiting the rise and fall of Islamists in northern Mali in 2012. The Islamist uprising and occupation in northern Mali resulted in a decreased level of educational attainment of women who spent their school age under the Islamists compared to women who spent their school age outside of the Islamists’ influence, especially in rural areas. However, the impact on male educational attainment was smaller. The women who spent their school age under Islamist influence tended to have more children and had their first child at a younger age. There was no significant impact of the Islamists’ influence on family cultural norms proxied by attitudes towards domestic violence. Possible mechanisms are discussed in this paper. Given the stronger presence of Islamist militant groups in sub-Saharan Africa and the importance of human capital for economic development, these groups can be an obstacle in achieving development goals.
Security state monitoring method for perception node in the power internet of things based on a low rank model

To overcome the problem of low precision and recall in the current power internet of things security monitoring results, a low rank model based security monitoring method for power internet of things sensor nodes is proposed. This method constructs the security monitoring platform of the power internet of things sensing node, designs the adaptive sensing mechanism of edge node data types under counting bloom filter, and realises the adaptive recognition of sensing node data fields. The normal observation data is described according to the low rank part, and the abnormal data is described according to the sparse part. The augmented Lagrangian method is used to optimise the objective equation and realise anomaly detection. The experimental results show that the method has high accuracy and recall, and reliability.