Reviewerly/exHarmony huggingface dataset:
A Blind Watermarking Algorithm Based on Wavelet Transform for 3D Model

Abstract In order to protect the visual effect of 3D model, watermarking algorithm should have good invisibility, thus a novel frequency-domain watermarking method is proposed in this paper. Robust regions of 3D model are selected as the watermarking regions in this algorithm, and watermarking regions are transformed into two-dimensional images depending on vertices distribution. Then the two-dimensional images are transformed into wavelet transform images by using wavelet transform algorithm and the watermark is embedded in low frequency coefficients of wavelet transform images. The experimental results show that the watermarking algorithm in this paper has high invisibility, and can resist common attacks such as noise, rotation, cropping and scaling.
DiTenun, Smart Application Producing Ulos Motif

Indonesia is a country which is rich of various traditional cultures and values. One of its representation is traditional woven clothes (well known in Indonesian as kain tenun) which is wide spread throughout Indonesian regions. To support the traditional woven industry, as a relevancy to the industry 4.0 era, we develop DiTenun which is a multiplatform application that is able to produce new motifs of traditional woven intelligently using machine learning approach. The presence of the apps aims to support the growth of traditional weaving industry particularly the small and medium scale ones. The dissemination of the apps is very challenging as traditional woven centers are mostly located in rural area where the digital world has been rarely accessed. In this paper, we present “Ulos” as a case study in the utilization of DiTenun. The implementation of the sustainability of the Ulos industry by DiTenun needs to be adjusted to the development of the industrial era 4.0. Ulos is a traditional woven cloth from Batak tribe, which is located in several rural regions surrounding Toba highland in North Sumatera Utara province. The workflow for producing an item that is marketable is to produce woven fabrics with motifs that have been produced by smart devices. The results of DiTenun can have an impact on the technology produced and on the social life and culture of the weavers. The study shows how DiTenun is designed to support Ulos weavers in creating new motifs of Ulos and to support the economy of relevant small and medium scale industry of Ulos.
Implementasi Sistem Pakar menggunakan Metode Certainty factor Untuk Mendiagnosa Penyakit Herpes Zoster

Herpes Zoster is a skin disease that is very difficult to treat and everyone can certainly experience it, the characteristics of this disease are characterized by unilateral vesicles in groups with pain characterized by radicular around the dermatome. This study aims to establish a Web-based Certainty factor method as a tool to diagnose skin diseases. With this application, it doesn’t need long time to find out what type of herpes zoster is suffering. To use this application is that the patient answers the questions which are provided by the system, then the system will process all the patient answers using the certainty factor method, after that, the system will produce output as the results of the diagnosis of the type of shingles. The system built can help patients to know the type of disease that is being suffered by the patients and in accordance with expert analysis of skin diseases.Keywords:Certainty factor, Herpes zoster, web.
Tutor Support in the Ungraded School of the North

The article describes a model for the formation of ethno-educational competence in primary school teachers, ethno-pedagogization of the educational process and the national civic identity as a condition for the successful implementation of the tasks of the new standard for primary school, and deals with the content and stages of the formation of bachelors’ ethno-educational competencies in future primary school teachers as exemplified by Ammosov North-Eastern Federal University.
Sub-Optimal Local Minima Exist for Neural Networks with Almost All Non-Linear Activations

Does over-parameterization eliminate sub-optimal local minima for neural networks? An affirmative answer was given by a classical result in [59] for 1-hidden-layer wide neural networks. A few recent works have extended the setting to multi-layer neural networks, but none of them has proved every local minimum is global. Why is this result never extended to deep networks? In this paper, we show that the task is impossible because the original result for 1-hidden-layer network in [59] can not hold. More specifically, we prove that for any multi-layer network with generic input data and non-linear activation functions, sub-optimal local minima can exist, no matter how wide the network is (as long as the last hidden layer has at least two neurons). While the result of [59] assumes sigmoid activation, our counter-example covers a large set of activation functions (dense in the set of continuous functions), indicating that the limitation is not due to the specific activation. Our result indicates that "no bad local-min" may be unable to explain the benefit of over-parameterization for training neural nets.
Dual semantic interdependencies attention network for person re‐identification

Attention mechanisms are widely used in re-identification (reID) tasks, but few attention-based architectures have considered integrating local features with their global dependencies, that is the previous works do not model the semantic interdependencies in both spatial dimension and channel dimension. Intuitively, for person feature representations, it is important to model the interdependencies between human body semantics. In this Letter, the authors proposed a dual semantic interdependencies attention module to capture semantic interdependencies in both spatial dimension and channel dimension simultaneously. Besides, they designed a deep supervision branch to directly guide the training of the attention modules and innovatively introduce a channels random dropping mechanism in the training phase to promote the attention modules to capture more discriminative information. Extensive experimental results show that the network merging the above strategies achieves state-of-the-art results on the mainstream reID data sets.
Big Data Storage System Based on a Distributed Hash Tables System

The Big Data is unavoidable considering the place of the digital is the predominant form of communication in the daily life of the consumer. The control of its stakes and the quality its data must be a priority in order not to distort the strategies arising from their treatment in the aim to derive profit. In order to achieve this, a lot of research work has been carried out companies and several platforms created. MapReduce, is one of the enabling technologies, has proven to be applicable to a wide range of fields. However, despite its importance recent work has shown its limitations. And to remedy this, the Distributed Hash Tables (DHT) has been used. Thus, this document not only analyses the and MapReduce implementations and Top-Level Domain (TLD)s in general, but it also provides a description of a model of DHT as well as some guidelines for the planification of the future research.
Comparison of Methods to Tackle Class Imbalance in Binary Classification for IoT Applications

Binary classification has applications in several areas, including the Internet of Things (IoT), and can be achieved by learning a predictive model from a labelled training dataset. A training dataset that is imbalanced leads undesirably to an inaccurate classifier. In this paper, we compare performance of existing methods that tackle class imbalance. These methods are undersampling, oversampling, hybrid, SMOTE, ADASYN, and a method that does not modify the datasets. We choose support vector machine (SVM) as a classifier and estimate classification accuracy, sensitivity, and specificity under each method. The datasets in the comparison cover a wide range of applications and class ratios. The research results indicate that undersampling does not achieve a highest accuracy, sensitivity, or specificity in any benchmark dataset, and is a not a recommended method in general. A method that does not modify the datasets has the most consistent performance; it is statistically most accurate, sensitive, and specific in three, one, and five datasets, respectively, at the significance level of the test at 10%. From the comparison, we find that a suitable method to tackle class imbalance depends on a dataset at hand and that no single method universally performs well across the benchmark datasets. The research results help researchers choose a method to mitigate a detrimental effect of class imbalance, improving classification performance.
A GENE SYNTHESIS REGIME FOR MALAYSIA TO EMULATE IN SECURING FUTURE BIOPRINTED VACCINES

The merger between synthetic biology and bioprinting will someday enable vaccines to be bioprinted utilising genetic material. Unregulated gene synthesis companies may unwittingly supply genetic material to a terrorist if there is no verification of purchasers’ personal identity and affiliation with a legitimate research organisation. This study has the objective of focusing on whether Malaysia regulates and conducts Deoxyribonucleic Acid (DNA) sequence screening among gene synthesis companies which are meant for bioprinting vaccines that can be misused for bioterrorism. This study is qualitative. Gene synthesis guidelines from the United States (US), the International Gene Synthesis Consortium (IGSC), the Nuclear Threat Initiative-World Economic Forum (NTI-WEF) Biosecurity Innovation and Risk Reduction report were referred as examples for changes in Malaysia’s draft National Code of Conduct for Biosecurity [thereafter known as Code]. These soft law documents constitute the regime for gene synthesis and a form of transnational new governance. The findings indicate that in the absence of a specific binding regulation, Malaysia’s draft Code must be amended to incorporate the need to screen customers, genetic sequences and address the cyberbiosecurity of biological life in digitalised form besides the physical biosecurity of laboratories which houses seedstocks from being stolen for malicious intent.
Quantum learning Boolean linear functions w.r.t. product distributions

The problem of learning Boolean linear functions from quantum examples w.r.t. the uniform distribution can be solved on a quantum computer using the Bernstein-Vazirani algorithm. A similar strategy can be applied in the case of noisy quantum training data, as was observed in arXiv:1702.08255v2 [quant-ph]. However, extensions of these learning algorithms beyond the uniform distribution have not yet been studied. We employ the biased quantum Fourier transform introduced in arXiv:1802.05690v2 [quant-ph] to develop efficient quantum algorithms for learning Boolean linear functions on $n$ bits from quantum examples w.r.t. a biased product distribution. Our first procedure is applicable to any (except full) bias and requires $\mathcal{O}(\ln (n))$ quantum examples. The number of quantum examples used by our second algorithm is independent of $n$, but the strategy is applicable only for small bias. Moreover, we show that the second procedure is stable w.r.t. noisy training data and w.r.t. faulty quantum gates. This also enables us to solve a version of the learning problem in which the underlying distribution is not known in advance. Finally, we prove lower bounds on the classical and quantum sample complexities of the learning problem. Whereas classically, $\Omega (n)$ examples are necessary independently of the bias, we are able to establish a quantum sample complexity lower bound of $\Omega (\ln (n))$ only under an assumption of large bias. Nevertheless, this allows for a discussion of the performance of our suggested learning algorithms w.r.t. sample complexity. With our analysis we contribute to a more quantitative understanding of the power and limitations of quantum training data for learning classical functions.
Time Series Modelling of Academic Employee Commitment of a Sub-Saharan African University

The study objective is to see how human resource management (HRM) could rely on small data evidence-based analytics to gauge employee commitment in a sub-Saharan African University. A 7-point Likert scale questionnaire on academic employee commitment in Kenya Public Universities was designed, validated and pilot tested. Out of around 60 questionnaires administered, only 31 responses were obtained before the Corona Virus (COVID-19) pandemic lockdowns in Kenya. The responses were subjected to the Modeler analyses using the statistical package for social sciences (SPSS version 21) to generate twelve optimal ARIMA (0,0,0) models for further statistical analyses. Results indicate 46.7% of employees want to spend the rest of their career in the organisation, over 61.2% of employees felt alienated and 34.9% were not emotionally attached. Around 59.3%, 64.0% and almost all employees tested on different metrics have difficulty leaving the organisation now. Although 28.9% of employees could leave abruptly, 64.6% of employees felt acculturated and 29.7% would remain at all costs. Overall, add-on effects of willingness to stay and bear with the organisation, emotional attachment, alienation, moral obligation, beneficial to remain, discouragement levels, organisational culture and being sold out to organisation could influence employee commitment levels. Thus, contributing to the HRM field, especially because the twelve-layered cascade of a series-parallel network made up of ladder and lattice structures of shared human and material resources management was used to deduce the Jackson’s theorem. Future research shall consider larger sample sizes to enable us to confirm or refute the conclusions derived in this study.
Improved Gilbert–Varshamov Bound for Entanglement-Assisted Asymmetric Quantum Error Correction by Symplectic Orthogonality

We propose and prove an existential theorem for entanglement-assisted asymmetric quantum error correction. Then we demonstrate its superiority over the conventional one.
Online Exemplar Fine-Tuning for Image-to-Image Translation

Existing techniques to solve exemplar-based image-to-image translation within deep convolutional neural networks (CNNs) generally require a training phase to optimize the network parameters on domain-specific and task-specific benchmarks, thus having limited applicability and generalization ability. In this paper, we propose a novel framework, for the first time, to solve exemplar-based translation through an online optimization given an input image pair, called online exemplar fine-tuning (OEFT), in which we fine-tune the off-the-shelf and general-purpose networks to the input image pair themselves. We design two sub-networks, namely correspondence fine-tuning and multiple GAN inversion, and optimize these network parameters and latent codes, starting from the pre-trained ones, with well-defined loss functions. Our framework does not require the off-line training phase, which has been the main challenge of existing methods, but the pre-trained networks to enable optimization in online. Experimental results prove that our framework is effective in having a generalization power to unseen image pairs and clearly even outperforms the state-of-the-arts needing the intensive training phase.
Multi Receptive Field Network for Semantic Segmentation

Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image. Despite significant progress achieved recently, most existing methods still suffer from two challenging issues: 1) the size of objects and stuff in an image can be very diverse, demanding for incorporating multi-scale features into the fully convolutional networks (FCNs); 2) the pixels close to or at the boundaries of object/stuff are hard to classify due to the intrinsic weakness of convolutional networks. To address the first issue, we propose a new Multi-Receptive Field Module (MRFM), explicitly taking multi-scale features into account. For the second issue, we design an edge-aware loss which is effective in distinguishing the boundaries of object/stuff. With these two designs, our Multi Receptive Field Network achieves new state-of-the-art results on two widely-used semantic segmentation benchmark datasets. Specifically, we achieve a mean IoU of 83.0 on the Cityscapes dataset and 88.4 mean IoU on the Pascal VOC2012 dataset.
Design Information System Accounting Sales Website-Based (Case Study: PT Arbunco Wira Pandega)

Abstract&#x0D; PT. Arbunco Wira Pandega does not have a system that covers all activities, one of which is in the sales department and the financial section in making sales reports and financial reports are still manually using Microsoft. Excel so that the media used are still very likely to cause human error so a sales accounting system is needed so that it can produce sales reports and financial reports that are more effective and efficient in the process of making sales reports and financial reports. This research uses the SWOT analysis method, PIECES, system requirements elicitation, and system modeling using UML (Unified Modeling Language) to visualize, which is then implemented with the Hypertext Preprocessor (PHP) programming language with the MySQL-Server database as the database used . With the sales accounting system, it can simplify the sales department and the finance department to produce accurate sales and financial reports in a fast time, so as to create effective and efficient performance, and can support evaluation in internal control for the leadership of the report.
The Psychology Of Space: From The Universe To The Person

Interdisciplinary concepts of space are considered, the essence of the approaches to the concepts of existing space in sociology, philosophy, psychology are revealed. Variants of considering the psychological boundaries that separate a person from the environment are presented. The differences of patterns of functioning and determination of socio-psychological and individual psychological boundaries, their relationship with the processes of socialization and individualization are shown. Revealing the connection of the psychological space with the attitude to the world and other people, the leading role of emotional experience is proved. Such experience individualizes the personal and social space and makes it possible to form an integral form of being in the world. In the focus of attention in this context are the ideas of G.G. Shpet and S.L. Rubinstein, first of all, their approaches to understanding the internal form of language and artwork, as well as the role of culture in the formation of the personal space - exist-sphere. The virtual space of social networks and the Internet is described; possible parameters for the classification and separation of the information space of the information field are presented, as well as the mechanisms of the influence of information on people. The conclusion is made that the idea that everyone needs to build his own space and stand above the social space unites all these concepts. At the same time, virtual space helps to push the outer boundaries of personal space, while culture helps the establishment of the exist-sphere.
How Undergraduate Students experience Distance Learning as Part of Their Information Literacy

Information comes in different forms, and it influences people’s preference in finding and using information. The covid 19 pandemic which has been experienced by Indonesian undergraduate students may have provide new ways on how they looking and using information related to their study. This qualitative study explores the distance learning experience of undergraduate students in one of the University in Semarang during their study from home. It aims to identify how these students have developed their remote study as well as their strategies on searching and using information. A qualitative method was employed by applying semi-structured interviews for collecting data. The data then analyzed by using Thematic Analysis to uncover the pattern of the distance experience of the students. The preliminary findings show that most of the participants have some issues on using optimizing the learning systems, however they finally able to build their own strategies to cope with this new situation. Electronic resources becomes their main information resources because accessing the physical library building is no longer an option. These preliminary findings benefit not only for the next process of the research but also to inform university library about the way undergraduate students experience information resources so that it can be used for the library for developing their information services to students.
Towards Intuitive Reasoning in Axiomatic Geometry

Proving lemmas in synthetic geometry is often a time-consuming endeavour since many intermediate lemmas need to be proven before interesting results can be obtained.Improvements in automated theorem provers (ATP) in recent years now mean they can prove many of these intermediate lemmas.The interactive theorem prover ELFE accepts mathematical texts written in fair English and verifies them with the help of ATP.Geometrical texts can thereby easily be formalized in ELFE, leaving only the cornerstones of a proof to be derived by the user.This allows for teaching axiomatic geometry to students without prior experience in formalized mathematics.
Lessons Learned from the Assessment of Software Defect Prediction on WLCG Software: A Study with Unlabelled Datasets and Machine Learning Techniques

Software defect prediction is an activity that aims at narrowing down the most likely defect-prone software modules and helping developers and testers to prioritize inspection and testing. This activity can be addressed by using Machine Learning techniques applied to software metrics datasets that are usually unlabelled, i.e. they lack modules classification in terms of defectiveness. To overcome this limitation, in addition to the usual data pre-processing operations to manage mission values and/or to remove inconsistencies, researches have to adopt an approach to label their unlabelled software datasets. The extraction of defectiveness data to label all the instances of the datasets is an extremely time and effort consuming operation. In literature, many studies have introduced approaches to build a defect prediction models on unlabelled datasets. In this paper, we describe the analysis of new unlabelled datasets from WLCG software, coming from HEP-related experiments and middleware, by using Machine Learning techniques. We have experimented new approaches to label the various modules due to the heterogeneity of software metrics distribution. We discuss a number of lessons learned from conducting these activities, what has worked, what has not and how our research can be improved.