Reviewerly/exHarmony huggingface dataset:
Sampling Neural Network: A Novel Neural Network Based on Sampling Theorem

In this paper, a new neural network algorithm is proposed, which is based on the signal sampling theorem. A new activation function of neural network in Sa(t) form and a new neuron weight error correction method are proposed. The sampling neural network algorithm (SNN) has the following advantages: clarity in theory, simple in design, reliable in operation, convenient in hardware implementation, low in operations, low in input sample data requirements, and does not fall into local minimum. The specific implementation of the method fully considers the hardware implementation, so the neural network algorithm has certain application prospects and value.
Distributed Learning in Trusted Execution Environment: A Case Study of Federated Learning in SGX

Federated Learning (FL) is a distributed machine learning paradigm to solve isolated data island problems under privacy constraints. Recent works reveal that FL still exists security problems in which attackers can infer private data from gradients. In this paper, we propose a distributed FL framework in Trusted Execution Environment (TEE) to protect gradients in the perspective of hardware. We use trusted Software Guard eXtensions (SGX) as an instance to implement the FL, and proposed an SGX-FL framework. Firstly, to break through the limitation of physical memory space in SGX and meanwhile preserve the privacy, we leverage a gradient filtering mechanism to obtain the “important” gradients which preserve the utmost data privacy and put them into SGX. Secondly, to enhance the global adhesion of gradients so that the important gradients can be aggregated at maximum, a grouping method is carried out to put the most appropriate number of members into one group. Finally, to keep the accuracy of the FL model, the secondary gradients of group members and aggregated important gradients are simultaneously uploaded to the server and the computation procedure is validated by the integrity method of SGX. The evaluation results show that the proposed SGX-FL reduces the computation cost by 19 times compared with the existing approaches.
A Simple and Effective Usage of Self-supervised Contrastive Learning for Text Clustering

Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers, we propose self-supervised contrastive learning (SCL) as well as few-shot contrastive learning (FCL) with unsupervised data augmentation (UDA) for text clustering. SCL outperforms state-of-the-art unsupervised clustering approaches for short texts and those for long texts in terms of several clustering evaluation measures. FCL achieves performance close to supervised learning, and FCL with UDA further improves the performance for short texts.
Outlier Detection and Decision Tree for Wireless Sensor Network Fault Diagnosis

Wireless Sensor Network (WSN) has been used in the industrial world and the household. The increasing number of WSN-based smart home devices requires intensive monitoring and automation. Problems may arise when a fault occurs on these devices that result in misinterpretation of the data received. Existing approaches to fault detection and diagnosis have led to the development of fault diagnosis methods for large-scale data. One of the effective methods for fault diagnosis is the Multi-Scale Principal Component Analysis (MSPCA). This research implements a combination of MSPCA and Decision Tree to detect fault data and diagnose the type of fault cause. The classification of faults is based on significant changes in temperature, humidity, light, voltage, as measured from the Normal Profile extracted by the MSPCA. Experiment results showed that our method was able to determine faults with an accuracy score of 0.913.
Breast cancer histopathology image classification using CNN

The breast cancer is one of the wide spread diseases around the world. Cancer develops in a milk duct and then spreads to the surrounding breast tissues. This initial stage of progression is called invasive ductal carcinomas (IDC). Almost 80% of all breast cancers are invasive ductal carcinomas. If IDC is detected at early stages, the patient can be treated and will have a high survival rate, whereas undetected cancer may spread into other parts of the body, as well as surrounding breast tissues. In this work, the dataset that contains breast cancer histopathology images was used. The objective of this work is to implement a convolutional neural network (CNN) model for accurate IDC classification, by balancing the dataset and tuning hyperparameters. The proposed model achieves an accuracy of 92% for the classification of histopathological images, and outperforms the baseline CancerNet model with accuracy of 86%. Furthermore, our experimental results demonstrate the superiority of our approach over the pre-trained networks, such as VGG16, DenseNet and ResNet18.
Busy Book Media Development In Introducing Early Children's Lampung Language at Tk Bina Pertiwi Katon State Pesawaran

Knowing the local language has been introduced from an early age so that children do not forget their respective regional languages, including the regional language of Lampung. Introducing the Lampung language starting from counting in the Lampung language, singing, and mentioning the simple vocabulary of the Lampung language. The aim of this research is to develop the Busy Book media as a learning medium to introduce the Lampung language for early childhood, see the feasibility of the Busy Book as a learning medium to introduce the Lampung language in child. Research in the form of R &amp; D taken from the development of Brog and Gell, the subject of this research is TK Bina Pertiwi Desa Pejambon, Negeri Katon District, Pesawaran Regency with a questionnaire-based research instrument that is validated by material experts and media experts, and teachers at Bina Pertiwi Kindergarten, Pejambon Village, Negeri District. In Katon Pesawaran District, the type of data produced is qualitative data which is analyzed with new criteria of assessment to determine the quality of the Busy Book being developed. The result of this research is that the Busy Book media as a learning medium for early childhood in Bina Pertiwi Kindergarten is very feasible to use and can also improve Lampung language skills in early childhood.&#x0D; &#x0D;
Face detection based on skin color segmentation and eyes detection in the human face

This paper explores the identification of human face and eye in still frontal color pictures. First; the appropriate pre-processing stage is done. It requires image refresh, RGB to grayscale conversion, image binarization, noise reduction and removal of small artifacts. The proposed algorithm is then extended to the position the face by detecting the edges the face using the detection of pixel color variations in the binary image. Finally, a normalized cross-relation is applied to determine the precise location of the eyes within the localized region of the face. proposed algorithm is robust enough to achieve approximately 94% accuracy and can locate a face in single face images.
Mind Maps sans Frontières

Collaborative development is becoming more and more important and popular in everyday life. Development of any system or project, idea, design, model, or realization by teams of several experts promises high performance during development and ensures a high quality of the developed system. For this purpose, there is a need for advanced and elaborated tools for collaborative design and development. This paper presents the tool CMCM (Collaborative Mind-and Concept-Mapping), which is used for collaborative modeling of Mind Maps to show the feasibility of tool collaboration provided by model differencing. CMCM manages Mind Maps through models conforming to a self-developed modeling language for Mind Maps. The CMCM modeling language differentiates between abstract, diagram and concrete syntax following the DD (Diagram Definition) standard of the OMG. To support collaborative modeling, instances of CMCM exchange model differences conforming to the operator-based Difference Language (DL) via the CoMo-X (Collaborative Modeling &#x2013; eXtend) server. Macro and micro versions of models are equally described with DL and enable asynchronous as well as synchronous collaboration. Using a self-reflective Mind Map of collaborative Mind Mapping as a use case, the functionality of CMCM is demonstrated, the concepts behind CMCM are explained, and CMCM is evaluated. The underlying approach is shown to be both effective and efficient, as well as transferable to other modeling domains and languages.
A Text-to-Image Generation Method Based on Multiattention Depth Residual Generation Adversarial Network

In this paper, we focus on generating realistic images from text descriptions. The current methods mainly use stacked networks, and there are two main problems. (1) The text information is relatively complicated, and it is difficult for the convolutional layer to extract deep-level text features. So the text features often can’t guide the generation of images effectively, which leads to a lower degree of matching between the generated image and the text; (2) The quality of the generated images is poor, and the authenticity, vividness and diversity of the images need to be improved. In this article, we propose a multi-attention depth residual generation adversarial network (MADR-GAN) based on the DM-GAN model to generate high-quality images. The network proposes a deep residual self-attention mechanism (DRSAM) in the low-resolution image generation stage, which is utilized to lift deep-level text information features and improve the quality of initial generated images. In the high-resolution image refinement stage, the CBAM attention mechanism is introduced to enhance the quality of high-resolution image generation. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200 dataset. Experimental results demonstrate that our MADR-GAN model performs favorably against the state-of-the-art approaches.
Visual Metamodeling with Verification Based on Surrogate Modeling for Adaptive Computing

The issue of creating a methodology for designing computing systems for space probes is discussed in this paper. According to the authors, it is the modern paradigm of metamodeling that makes it possible to reduce labor costs when creating software and hardware systems for deep space scientific missions. The paper proposes a semantic approach, based on metamodeling. The approach makes a conceptually new low-level metamodel by combining two well-known IDEF0 graphic symbols and a flowchart notation. This solution makes it possible to practically eliminate the disadvantages inherent in each of the methodologies separately. A new methodology and rules for its application based on the semantic element have been obtained. An example of a metamodel is presented. The proposed approach allows designing and simulating the operation of on-board computing systems of a space probe. We assume that this virtually eliminates the shortcomings of the initial models. In our opinion, this will also reduce labor costs in the design of various on-board computing systems, increasing the quality and unambiguity of work. The ideology of metamodeling has a peculiarity. The created model can fully comply with the rules, but not provide the required parameters. Surrogate modeling can be the solution to the problem. An iterative process to achieve the required metamodel parameters is proposed.
Slavery and Britain in the 19th century

This study uses a corpus of just under two billion words from one historic British newspaper, the Liverpool Mercury, to explore shifting attitudes to slavery in Britain in the nineteenth century in the context of a port city that benefitted from the trade. In doing so, we explore three methodological issues – how to explore concepts in large corpora, how to do this over time and how to deal with poor quality data. Our approach to the study of concepts through time uses a new approach to looking at word usage change over time, Usage Fluctuation Analysis (McEnery, Brezina & Baker 2019). Our exploration of the issue of poor quality data is motivated by the variable quality of the OCR texts which constitute our nineteenth-century newspaper corpus data. Problems in data quality bedevil work on large-scale text collections of historic material. In this paper we will show that collocation, the core technique of UFA, can be used on such data if appropriate settings are chosen that minimise the problems arising from poor quality electronic text, permitting the exploration of corpus data at scale.
Guaranteed Convergence Sine Cosine Algorithm

We are still under the way in finding new optimization algorithms including their improvements to conquer the complicated problems and find their solutions. Considering the updating equation in the sine cosine (SC) algorithm, the positions in the next iteration would be an direct addition operation of the current positions and their random weighted, sine or cosine functionalized, absolute distance to the position of the global best candidate, which would slow down the approaching rate when the individuals were near the global optima, or decrease the capability when the individuals were trapped or faraway from the global optima. To conquer this shortcomings, the guaranteed convergence method is introduced to increase the SC algorithm in this papaer. Simulation experiments are carried out and qualitative, intensification, diversification analysis are made. Results verify the capability in superiority of the improved algorithm in optimizing traditional symmetric benchmark functions, either unimodal or multimodal. However, both of them lack the capability in optimzing non-symmetric benchmark functions, although the guaranteed convergence SC algorithm still performed overall better.
FPGA-Implemented Fractal Decoder with Forward Error Correction in Short-Reach Optical Interconnects

Forward error correction (FEC) codes combined with high-order modulator formats, i.e., coded modulation (CM), are essential in optical communication networks to achieve highly efficient and reliable communication. The task of providing additional error control in the design of CM systems with high-performance requirements remains urgent. As an additional control of CM systems, we propose to use indivisible error detection codes based on a positional number system. In this work, we evaluated the indivisible code using the average probability method (APM) for the binary symmetric channel (BSC), which has the simplicity, versatility and reliability of the estimate, which is close to reality. The APM allows for evaluation and compares indivisible codes according to parameters of correct transmission, and detectable and undetectable errors. Indivisible codes allow for the end-to-end (E2E) control of the transmission and processing of information in digital systems and design devices with a regular structure and high speed. This study researched a fractal decoder device for additional error control, implemented in field-programmable gate array (FPGA) software with FEC for short-reach optical interconnects with multilevel pulse amplitude (PAM-M) modulated with Gray code mapping. Indivisible codes with natural redundancy require far fewer hardware costs to develop and implement encoding and decoding devices with a sufficiently high error detection efficiency. We achieved a reduction in hardware costs for a fractal decoder by using the fractal property of the indivisible code from 10% to 30% for different n while receiving the reciprocal of the golden ratio.
An Overview of Requirements Elicitation Techniques in Software Engineering with a focus on Agile Development

In software requirement engineering, main focus is on eliciting information and extracting the main requirements of user. For extracting the requirement of the user, selection of appropriate tool/technique for specific situation is mandatory. Requirement elicitation is a difficult phase of requirement engineering. Elicitation includes traditional, collaboration, cognitive, innovative and contextual techniques. According to the nature of project and situation, one elicitation technique is not sufficient. This paper inspects that how requirement elicitation and its different tools/techniques work and what are the drawbacks of tools/techniques on the basis of particular situation that are demanded by the client. Agile model is the most common model of software development. Therefore, in this study tools/techniques of elicitation that are used in agile development phase have also been discussed in detail. This paper helps the analyst and project managers to select the appropriate tool and technique for a particular situation.
An Empirical Study into the Role of Quality Factors in Influencing the Effectiveness of the E-learning System: An Academic Staff Perspective

COVID-19 has caused the closure of schools around the world. According to the Global World Forum, more than 1.2 billion students have dropped out of classrooms worldwide. As a result, education has transformed dramatically with the extensive rise in e-learning where teaching is delivered remotely through technological solutions and platforms. Even though successful transitions among many universities have taken place, the users’ expectations of e-learning systems are still poor due to this rapid movement. Therefore, e-learning systems need to be investigated from a perspective of quality in order to ensure its effectiveness. The aim of this study is to empirically investigate the role of quality factors in influencing the effectiveness of e-learning systems from the perspective of academic staff. A quantitative approach was adopted using a web-based questionnaire. A total of 129 valid questionnaires were obtained through simple random sampling techniques. The results of the study indicated that all examined factors, namely, System Quality (SQ), Information Quality (IQ), Service Quality (SEQ), Technical Support Quality (TSQ), Course Design Quality (CDQ), and Learner Quality (LQ), influenced E-learning System Effectiveness significantly. The proposed factors explained a significant percentage with 90.8 percent of the total variance of E-learning Effectiveness. Furthermore, the results of multiple regressions using stepwise analysis indicated that the top three contributing factors were Service Quality, Learner Quality, and System Quality respectively. Moreover, the results revealed that Attitude partially mediated the relationship between Service Quality and E-learning System Effectiveness. Several implications and recommendations were proposed for future research.
Identifying Cyberbullying Post on Social Networking Platform Using Machine Learning Technique

Online social networking platforms have become a common choice for people to communicate with friends, relatives, or business partners. This allows sharing life achievement, success, and many more. In parallel, it also invited hidden issues such as web-spamming, cyberbullying, cybercrime, and others. This paper addresses the issue of cyberbullying with machine learning models. Three classifiers namely naive bayes, logistic regression, and the random forest were used to develop the model. The experimental outcome confirmed that the random forest classifier is a better choice for the said problem which yielded 93% accuracy for the best case.
Bitcoin: an alternative currency to pay for goods and services or a useful investment tool?

Research background: Bitcoin is defined as digital money in a peer-to-peer decentralized payment network, an amalgam hybrid between fiat and commodity currency without a real value. This digital currency is also independent of any government or currency administration. Purpose of the article: This article explores whether bitcoin works as a medium of exchange or relates to assets, focusing on its current use and future utility regarding its characteristics. Methods: Analysing bitcoin statistical features, we found no connection with traditional asset categories such as stock, bonds and commodities either in intermediate time, or periods of financial crises. Findings &amp; Value added: The study suggests that investors’ abiding interest in bitcoins can have a positive impact on their liquidity in the real time.
Video-Based Dynamic Authentication System

In any industry, whether it may be a multi-national company or a developing start-up, there are a number of people who frequent the building premise. These people may include the employees of the industry or business partners, seeking to form a rapport with that industry. At times it may also include malicious people who may try to break in for unethical reasons. In such cases, if a break-in occurs and important data are lost, the blame ultimately falls onto the security system of that industry and the security personnel in charge. So, there is a requirement for the security to have an automated system which handles the entry of the employees, and in case if it is an unknown person, in that situation alone, to request the assistance of the security. Once a person is approved by the security they should be allowed during the other times when they come by the building again. This should be an open-source application, scalable and easy-to-use for the sake and ease of the security. Further, a record of the entry and exit time of each person to frequent the building should also be maintained. This can be preferred over the maintenance of CCTV footages, which require high space to maintain the large volume of videos that can be generated over the day. In the proposed system the input is obtained from the CCTV footages. And then the data which is retrieved from the footage will be pre-processed, using HOG (Histogram of Gradients) the features to be consider from the data will be extracted. The proposed system uses SVM (Support Vector Machine) classifier for classification of input data with the database. This further action could be to directed the person to the security. According to the security’s decision, this person is given permission to enter the company. The main defining feature of this system is that the newly accepted person’s features can be dynamically added to the system. Finally, the entry details, i.e., the name and time of the person is stored in a CSV file. This is particularly preferred over maintaining large-sized footage from CCTV because of the dramatic reduction of size when compared to CSV files.
Comment on paper by Villaseñor, A.B., and Olóriz, F. “Mexican Kossmatia - Historical review and proposed revision. Journal of South American Earth Sciences (2019), 102105, doi: https://doi.org/10.1016/j.jsames.2019.05.011”

Villaseñor and Olóriz (2019) present a comprehensive review of latest Jurassic to earliest Cretaceous ammonites from Mexico, which previously were ascribed to the genus Kossmatia, and provides evidence that these fossils should be referred to other genera. However, descriptions of two new genera provided by Villaseñor and Olóriz (Burckhardtieia and Aguilerites) are lacking indication of their type species that make these genera unavailable following to ICZN articles 13.3 and 67.4.1. Discussed paper also includes figures of the new species Burckhardtieia westermanni, which is lacking formal description, although illustrated by few figures and its holotype is clearly indicated. The genus Burckhardtieia Villasenor and Oloriz can be considered as a junior synonym of the genus Fierrites Cantu Chapa. The latter genus is suggested to be invalid by Villaseñor and Olóriz (2019), but without strong background in support of this statement. Here the author is follow the proposal by Enay (2009) and consider the genus Fierrites as a correct name, which can be used for all American “Kossmatia”. Discussed article is also lacking detailed biostratigraphic data concerning the range of the Burckhardtieia, although clear indication of its range (and ranges of species included in this taxon) seems to be necessary.