Reviewerly/exHarmony huggingface dataset:
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation

Machine learning models trained in one domain perform poorly in the other domains due to the existence of domain shift. Domain adaptation techniques solve this problem by training transferable models from the label-rich source domain to the label-scarce target domain. Unfortunately, a majority of the existing domain adaptation techniques rely on the availability of target-domain data, and thus limit their applications to a small community across few computer vision problems. In this paper, we tackle the challenging zero-shot domain adaptation (ZSDA) problem, where target-domain data is non-available in the training stage. For this purpose, we propose conditional coupled generative adversarial networks (CoCoGAN) by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. Compared with the existing state of the arts, our proposed CoCoGAN is able to capture the joint distribution of dual-domain samples in two different tasks, i.e. the relevant task (RT) and an irrelevant task (IRT). We train CoCoGAN with both source-domain samples in RT and dual-domain samples in IRT to complete the domain adaptation. While the former provide high-level concepts of the non-available target-domain data, the latter carry the sharing correlation between the two domains in RT and IRT. To train CoCoGAN in the absence of target-domain data for RT, we propose a new supervisory signal, i.e. the alignment between representations across tasks. Extensive experiments carried out demonstrate that our proposed CoCoGAN outperforms existing state of the arts in image classifications.
Lattice counting problem

We study the error of the number of unimodular lattice points that fall into a dilated and translated parallelogram. By using an article from Skriganov, we see that this error can be compared to an ergodic sum that involves the discrete geodesic flow over the space of unimodular lattices. With the right normalization, we show, by using tools from a previous work of Fayad and Dolgopyat, that a certain point process converges in law towards a Poisson process and deduce that the ergodic sum converges towards a Cauchy centered law when the unimodular lattice is distributed according to the normalized Haar measure. Strong from this experience, we apply the same kind of approach, with more difficulties, to the study of the asymptotic behaviour of the error and show that this error, normalized by $\log(t)$ with $t$ the factor of dilatation of the parallelogram, also converges in law towards a Cauchy centered law when the dilatation parameter tends to infinity and when the lattice and the vector of translation are random. In a next article, we will show that, in the case of a ball in dimension $d$ superior or equal to $2$, the error, normalized by $t^{\frac{d-1}{2}}$ with $t$ the factor of dilatation of the ball, converges in law when $t \rightarrow \infty$ and the limit law admits a moment of order $1$.
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Face Mask Detection using MobileNetV2

The sudden surfacing of the global pandemic, COVID-19 created a commotion on great extent.The pandemic led everyone to take proper measure related to health and sanitation, where face-mask became an inseparable part of any human being.Despite there are violations in rules which adhere to COVID-19 protocols.The proposed research highlights a machine learning model to automate the task of detection of face-mask using MobileNetV2 which is a CNN.The entire process will be a real time detection where, it will check for, is the face masked or not.CCTV or a webcam will be used to for the given purpose.The masked or the unmasked results will be indicated in terms of percentage to the system.
왕안석(王安石)의 서정산문(抒情散文)

Wang Anshi is a politician who led the reform policy of the new law in the Northern Song Dynasty and a poet who represented the literary world at that time. He was one of the greatest writers of all time.The study of the existing Wang Anshi has been focused mainly on assessing the merits and demerits of the new law and reassessing its appearance as a politician, as the image of a reform politician was strongly emphasized.Research on him as a writer focused on his role as a member of TANGSONGBADAJIA and often looked at features differentiated from TANGSONGBADAJIA.As a result of this study, the contents and features of Wang Anshi’s lyric prose can be summarized as follows.First, we could see that Wang Anshi expressed his deep filial piety toward his grandmother, father and mother in prose. Despite his busy job as a bureaucrat, Wang Anshi’s interest in family and filial piety has always been deeply entrenched in his mind.Second, Wang Anshi’s writings to his friend reveal friendships united as one by mutual equal friendship and respect, and heartfelt affection, such as praise and advice for his friend’s philosophy of life.Third, Wang Anshi expressed his sorrow for the revered figures, families and friends with prose in memory of the bereavement.Fourth, The characteristic of Wang Anshi’s lyric prose is the harmonious fusion of lyric sentiment and his thesis.
Presentation of Reed-Solomon codes based on automaton theory

The object of research is the processes of error-correcting coding in telecommunication and computer systems. The main attention is paid to Reed-Solomon codes, which belong to the very widespread error-correcting codes. Despite the 60-year existence of these codes, the complexity of their decoding still remains a problem. This problem is mainly due to the use of an algebraic approach to their description.The article proposes to use the theory of linear finite-state machine (LFSM) for RS codes as a mathematical basis, which is a combination of the theory of digital filters and finite automaton over nonbinary Galois fields. In the course of research, 12 types of LFSMs are considered for the first time: the recursive LFSMs of 8 types and the non-recursive LFSMs of 4 types.The recursive LFSMs are used for systematic encoding and form a circuit for dividing of polynomials, and the non-recursive LFSMs are used for non-systematic encoding and form a circuit for multiplying of polynomials. All types of LFSMs give the same result for encoding and decoding, but with different complexity, which is important for practical implementation.The automaton representation is the most suitable for PC codes, since it takes into account the cyclicity property and other features of these codes to the maximum. In contrast to algebraic methods, automaton decoding methods have a simple software and hardware implementation and high performance. With the help of automaton-graphical models, it can accurately estimate the corrective capability of the code. Automaton representation combines known methods of representing Reed-Solomon codes (polynomial, matrix, algebraic) and provides mutual transitions between them.The article attention is spare to the fact that automaton methods for encoding and decoding (n, k)-codes of RS using quantum computers give a gain in time n times.
Generating and Analyzing Collective Behavior in a Robotic Swarm by the Use of Deep Reinforcement Learning and Deep Neuroevolution

This study proposes a method to apply deep neural networks to controllers of robotic swarms. In a typical approach to design controllers, the designer has to define the features extracted from sensory inputs in advance. By applying deep neural networks with convolution layers, it can automatically extract features from sensory inputs. We applied two methods to train the deep neural networks, i.e.,deep reinforcement learning and deep neuroevolution. The controllers were tested in a path-formation task using computer simulations. Compared with deep reinforcement learning, deep neuroevolution was able to generate collective behavior even in sparse reward settings.
How Weave's Smartphone App Can Help You Manage Phones in Unforeseen Circumstances

If you run a clinic, hospital, office, or any other kind of small business, you know that you need to be available and on top of things almost 24/7. ...
A Review and Catalog of Security Metric during the Secure Software Development Life Cycle

Background: With the advancement in the field of software development, software poses threats and risks to customers’ data and privacy. Most of these threats are persistent because security is mostly considered as a feature or a non-functional requirement, not taken into account during the Software Development Life Cycle (SDLC). Introduction: In order to evaluate the security performance of a software system, it is necessary to integrate the security metrics during the SDLC. The appropriate security metrics adopted for each phase of SDLC aids in defining the security goals and objectives of the software as well as quantify the security in the software. Methods: This paper presents a review and catalog of security metrics that can be adopted during the distinguishable phases of SDLC, security metrics for vulnerability and risk assessment reported in the literature for secure development of software. The practices of these metrics enable software security experts to improve the security characteristics of the software being developed. The critical analysis of security metrics of each phase and their comparison are also discussed. Results: Security metrics obtained during the development processes help to improve the confidentiality, integrity, and availability of software. Hence, it is imperative to consider security during the development of the software, which can be done with the use of software security metrics. Conclusion: This paper reviews the various security metrics that are meditated in the copious phases during the progression of the SDLC in order to provide researchers and practitioners with substantial knowledge for adaptation and further security assessment.
A NEW APPROACH IN WEBSITE FRAUD DEDUCTION BY CLASSIFYING FEATURE SELECTION METHOD

This paper is used to detect the website fraud by using popup window. Because Website Site Fraud Detection is a crucial point that applies to numerous ventures including the monetary areas, banking, government organizations, protection, and law requirement, and other industries also. In spite of battles with respect to the grieved associations, countless amounts are squandered to misrepresentation every year. Pop-up windows are mostly generated by the websites. Web traffic is measured in visits, sometimes called sessions, and is a common way to measure an online business effectiveness at attracting an audience. Website traffic refers to web users who visit a website. By using pop-up window and website traffic we are going to detect the fraud which are occurring in website.
SISTEM INFORMASI PENJUALAN HANDPHONE ONLINE DI TOKO BBENK SHOP BATAM BERBASIS SMARTPHONE ANDROID

Increasing technological developments encourage human nature to utilize and use technology as well as possible. The emergence of a mobile operating system called Android that has performance and decent performance with a smartphone for mobile phones that use the operating system. BBENK shop is a store that serves sales of mobile phones in sales or systems that exist today is still using social media delivery is still very simple so that the marketing of products and services has not been maximized. With the existing problems then needed an information system online smarthphone android phone based online sales. This application contains information about BBENK batam shop, and there is an online sales medium that can be used by users to order mobile products in BBENK batam.
Bivariate beta mixture model with correlations

The method of clustering is a probabilistic model based on clustering technique. The clustering method is often based on the assumption that data comes from a mixed model. One such mixture model is the beta mixed model. This mixed model can be used for the case of one variable or multiple variables. However, for the mixed beta model of the double variable, each variable is assumed to be independent. In this article, we propose a mixed beta model with correlated variables. The parameter estimation method uses the MLE method via the EM algorithm. While determining the optimal number of clusters using the ICL-BIC criteria. Monte Carlo simulation is used to see the performance of the model.
Binary Sequences Derived from Differences of Consecutive Primitive Roots

Let $1<g_1<\ldots<g_{\varphi(p-1)}<p-1$ be the ordered primitive roots modulo~$p$. We study the pseudorandomness of the binary sequence $(s_n)$ defined by $s_n\equiv g_{n+1}+g_{n+2}\bmod 2$, $n=0,1,\ldots$. In particular, we study the balance, linear complexity and $2$-adic complexity of $(s_n)$. We show that for a typical $p$ the sequence $(s_n)$ is quite unbalanced. However, there are still infinitely many $p$ such that $(s_n)$ is very balanced. We also prove similar results for the distribution of longer patterns. Moreover, we give general lower bounds on the linear complexity and $2$-adic complexity of~$(s_n)$ and state sufficient conditions for attaining their maximums. Hence, for carefully chosen $p$, these sequences are attractive candidates for cryptographic applications.
Improving reading comprehension skill through Listen-Read-Discuss (LRD) learning strategy

The problem in this research is the students' reading comprehension skills in English. The purpose of this study to investigate the improvement in reading comprehension skills of VIII grade students of SMP Negeri 5 Manda in Maros Regency. This type of research is experimental research. The technique of collecting data is done by using a test technique. The test instrument is in the form of multiple-choice questions tested before and after being treated with learning strategies. The data analysis technique used the t-test. Based on the calculation of the difference test, the average value of the data in the t-test for the equations of means column has a significance value of 0.025&gt; 0.000, so H0 is rejected, so it can be concluded that the Listening-Read-Discuss (LRD) Learning Strategy can be used to improve reading comprehension skills in students.
LibGuides: Identifying Sources in Works Cited Lists and Bibliographies: Example of Journal Finder Results Screen

Works cited lists are very effective ways to find relevant sources for your research. In order to be able to make use of the information in works cited lists, you must be able to identify the kind of source it is: is it a book, a chapter in a book, a jour
Methods Of Studying Louis De Broglie Wave And Corpuscular-Wave Dualism In The Course Of Physics In Academic Lyceums

The analysis of the textbooks of the physics course of academic lyceums shows that the content of the teaching materials in the department of quantum physics is sufficiently complex that it is briefly, vaguely described by the authors of the textbooks. Despite the fact that in recent years in the Russian Federation and Uzbekistan in this area have defended several dissertations, published textbooks and suggestions for improving teaching methods, the content of textbooks remains very shallow. Quantum physics is the basis of modern physics, a great contribution to the development of physics and technology. adds. It is well known that microcosm phenomena are subject to statistical laws, macrocosm, or physical phenomena studied on the basis of classical physics are mainly subject to dynamic laws, but systems consisting of many particles studied on the basis of classical physics, such as gaseous and liquid properties, are also subject to statistical laws.The phenomena of the microworld are studied on the basis of quantum physics, in contrast to the phenomena of the macroworld, the state of each particle in the microworld has a statistical character. Thus, the phenomena studied on the basis of quantum physics are based on probabilistic-statistical laws [3.4,5]. In the physics course of lyceums the basic ideas of teaching methods of the department of quantum physics with the correct formulation of the content of teaching materials and the application of probabilistic-statistical laws are presented. Currently, in the physics textbooks of secondary schools and academic lyceums in the Republic and the Russian Federation, the department of quantum physics is mainly limited to a detailed study of the photoelectric effect and Compton effect, a brief description of the corpuscular-wave dualism. [10,11,12] For this reason, students' perceptions of quantum physics remain shallow.The authors teach the experimental foundations of quantum physics, ie the laws of radiation of an absolutely black body, the concept of quantum, photoeffect, Compton effect, De Broglie waves and its probabilistic-statistical interpretation, wave function, are invited to add topics. [9,20]Pedagogical experiments were conducted on the proposed content of quantum physics in the areas of in-depth study of physics in academic lyceums, resulting in a sharp increase in students' interest in learning the basics of modern physics [7,9,14]. The presented article is devoted to the study of De Broglie waves and their possible statistical interpretation topics.
한국의 국제평화활동 30주년 평가와 발전방안

It has been 30 years since the Korean military began international peace operations. This paper analyzed the results and lessons learned of the Korean military''s international peace operations over the past 30 years and suggested ways to develop Korea''s peace operations.The United Nations is actively engaged in peacekeeping operations for global peace. The UN changed its Force Generation policy and PCRS in 2015 to ensure rapid and dynamic mission performance. Recently, amid the shrinking of international peace operations due to the threat of COVID-19 pandemic, it is developing new concepts and contents of peace operations. The ROK government has sent more than 56,800 troops over the 30 years.The ROK military has established a forbidden tower for peace operations as well as enhancing the status of the country through exemplary UN PKO, Multi-national Forces Peace operations(MNF PO) and Defense Cooperation Activities(DCA). The Korean military has accumulated practical experience, created a model for civil-affairs operations, and achieved extension of diplomatic power.However, there are many things to develop, such as changing the paradigm of the deployment policy, improving organizations related to overseas dispatch, and overhauling related laws. In particular, it is time to prepare for international peace operations in line with the Post COVID-19 era.
Towards Real World Federated Learning

Federated Learning, also known as Collaborative Learning, is a relatively new Machine Learning field of study, born in 2015 to address critical matters such as data privacy, data security and data access. In this scenario, a central server model is trained exploiting data stored locally on multiple devices (i.e. the clients). Unlike the standard machine learning setting, here the model has no direct access to the data themselves: a fundamental requirement for any application where the user's privacy must be preserved (e.g. medical records, bank transactions). The server model is asynchronously sent to the clients which train it using their own local data. Then, the parameters of the locally updated models are sent back to the server and its central model is updated accordingly. In this way, the clients' data never leave their device, preserving privacy. This technique raises several challenges. Among them, federated learning models usually rely on two main assumptions. The first one is that the central model can work efficiently across several users but may not hold actually, since distinct clients might be employed on different input distributions (e.g. different speakers). The second assumption is that each client locally stores supervised data but that is easily violated, since labelling is costly and it is not possible to annotate each datum sample automatically for every task (e.g. object recognition). The aim of this thesis is to build a machine learning model in a Federated Learning scenario addressing those two issues. As for the former, we merge federated and graph learning, a deep learning technique that exploits graph-structured data as feature information. On the server side, graphs are used to model user specificity, where each node captures the characteristics of a different subset of the clients (i.e. domains). Clients and server keep training multiple models, each referring to one node of the graph, thus producing a final domain-specialist one. At test time, the new user is compared with the nodes of the graph and the initial parameters of his models are set according to the most similar domains. As for the second issue, we cope with the possible lack of supervision on the clients by using semi-supervised training objectives. All the results are compared with the current state-of-the-art Federated Learning algorithms on the LEAF benchmark.
Machine Learning Based Classifier for Breast Cancer Prediction

Machine learning, the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. The majority of practical machine learning uses supervised learning. Machine learning has been used in various fields of medicine. Decision support tools based on machine learning models can be developed for breast cancer prognosis. Such models can contribute to early diagnosis and treatment and thus improved outcomes. With comprehensive electronic medical records and advanced machine learning technologies, a breast cancer prediction model can be used as a decision support tool for care intervention. Breast cancer is the fifth leading cause of cancer mortality worldwide. Early detection of recurrence risk is critical in improving prognosis. Breast cancer occurs in breast cells, the fatty tissue or the fibrous connective tissue within the breast. Breast cancer is malignant tumors tend to become progressively worse and grow fast leading to death. Factors such as age and a family history of breast cancer can increase the risk of breast cancer. Two types of tumours: Benign: this tumor type is not dangerous for a human body and rarely causes human death. Malignant: this tumor type is more dangerous and causes human death, it is called breast cancer. The aim of this study is developing and evaluating models based on multiple parameters for breast cancer prediction.
A Simple Disaster-Related Knowledge Base for Intelligent Agents.

In this paper, we describe our efforts in establishing a simple knowledge base by building a semantic network composed of concepts and word relationships in the context of disasters in the Philippines. Our primary source of data is a collection of news articles scraped from various Philippine news websites. Using word embeddings, we extract semantically similar and co-occurring words from an initial seed words list. We arrive at an expanded ontology with a total of 450 word assertions. We let experts from the fields of linguistics, disasters, and weather science evaluate our knowledge base and arrived at an agreeability rate of 64%. We then perform a time-based analysis of the assertions to identify important semantic changes captured by the knowledge base such as the (a) trend of roles played by human entities, (b) memberships of human entities, and (c) common association of disaster-related words. The context-specific knowledge base developed from this study can be adapted by intelligent agents such as chat bots integrated in platforms such as Facebook Messenger for answering disaster-related queries.