Reviewerly/exHarmony huggingface dataset:
Deep Fake Image Detection based on Pairwise Learning

Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.
CzeDLex 0.7

CzeDLex 0.7 is the third development version of the Lexicon of Czech discourse connectives. The lexicon contains connectives partially automatically extracted from the Prague Discourse Treebank 2.0 (PDiT 2.0) and, as a supplementary resource, the Czech part of the Prague Czech–English Dependency Treebank with discourse annotation projected from the Penn Discourse Treebank 3.0. The most frequent entries in the lexicon (131 out of total 218 entries, covering more than 95% of discourse relations annotated in PDiT 2.0), have been manually checked, translated to English and supplemented with additional linguistic information.
Switching-Aligned-Words Data Augmentation for Neural Machine Translation

In neural machine translation (NMT), data augmentation methods such as back-translation make it possible to use extra monolingual data to help improve translation performance, while it needs extra training data and the in-domain monolingual data is not always available. In this paper, we present a novel data augmentation method for neural machine translation by using only the original training data without extra data. More accurately, we randomly replace words or mixup with their aligned alternatives in another language when training neural machine translation models. Since aligned word pairs appear in the same position of each other during training, it is helpful to form bilingual embeddings which are proved useful to provide a performance boost \citep{liu2019shared}. Experiments on both small and large scale datasets show that our method significantly outperforms the baseline models.
Perancangan Informasi Akuntansi Pinjaman Secara Kredit Pada Koperasi Simpan Pinjam Bina Mitra

Information systems have a positive impact on the advancement of life in the technology industry, making it easier for corporate human resources to be fast and precise. For example, companies that currently use applications in their electronic devices or software and drivers to streamline their products and products. All of that is done in order to achieve a goal that is to facilitate the performance of humans, organizations or companies in exchanging data and information and its distribution. Koperasi Bina Mitra Usaha Jatisari is in need of an accounting information system that supports and provides satisfactory services for its members. Koperasi Bina Mitra Usaha Jatisari is a cooperative engaged in several fields, one of which is a loan unit on credit. For this reason, researchers designed the Loan Accounting Information System. Koperasi Bina Mitra Usaha Jatisari method used by the buyer is swot analysis for the design method using unified Modeling language (UML) and blackbox testing system testing methods. This study makes it easy to record loan applications, member loan data, payments, data input, calculations and report generation.
Antisemitism on Twitter: Collective Efficacy and the Role of Community Organisations in Challenging Online Hate Speech

In this paper, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyse the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive fora longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors -i.e. Jewish organisations- had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organisations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organisations in reducing the propagation of cyberhate and increasing trust in SM platforms.
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks

Graph Convolutional Network (GCN) has become one of the most successful methods for graph representation learning. Training and evaluating GCNs on large graphs is challenging since full-batch GCNs have high overhead in memory and computation. In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch. The performance of the model depends largely on the quality and size of subgraphs in the batch-training. Existing sampling approaches mostly focus on approximating the full-graph structure but care less about redundancy and randomness in sampling subgraphs. To address these issues and explore a better mechanism of producing high-quality subgraphs to train GCNs, we proposed the \texttt{Linguine} framework where we designed a meta-model to prune the subgraph smartly. To efficiently obtain the meta-model, we designed a joint training scenario with the idea of hardness based learning. The empirical study shows that our method could augment the accuracy of the current state-of-art and reduce the error incurred by the redundancies in the subgraph structure. We also explored the reasoning behind smart pruning via its visualization.
Democratising Online Content Moderation: A Constitutional Framework

Freedom of expression is one of the cornerstones on which democracy is based. This non-exhaustive statement firmly clashes with the troubling evolution of the algorithmic society where artificial intelligence technologies govern the flow of information online according to opaque technical standards established by social media platforms. These actors are usually neither accountable nor responsible for contents uploaded or generated by the users. Nevertheless, online content moderation affects users’ fundamental rights and democratic values, especially since online platforms autonomously set standards for content removal on a global scale. Despite their crucial role in governing the flow of information online, social media platforms are not required to ensure transparency and explanation of their decision-making processes. Within this framework, this work aims to show how the liberal paradigm of protection of the right to free speech is no longer enough to protect democratic values in the digital environment, since the flow of information is actively organised by business interests, driven by profit-maximisation rather than democracy, transparency or accountability. Although the role of free speech is still paramount, it is necessary to enhance the positive dimension of this fundamental right by establishing new users’ rights in online content moderation based on transparency and accountability of online platforms.
Does Contact Tracing Work? Quasi-Experimental Evidence from an Excel Error in England

Contact tracing has been a central pillar of the public health response to the COVID-19 pandemic. Yet, contact tracing measures face substantive challenges in practice and well-identified evidence about their effectiveness remains scarce. This paper exploits quasi-random variation in COVID-19 contact tracing. Between September 25 and October 2, 2020, a total of 15,841 COVID-19 cases in England (around 15 to 20% of all cases) were not immediately referred to the contact tracing system due to a data processing error. Case information was truncated from an Excel spreadsheet after the row limit had been reached, which was discovered on October 3. There is substantial variation in the degree to which different parts of England areas were exposed – by chance – to delayed referrals of COVID-19 cases to to the contact tracing system. We show that more affected areas subsequently experienced a drastic rise in new COVID-19 infections and deaths alongside an increase in the positivity rate and the number of test performed, as well as a decline in the performance of the contact tracing system. Conservative estimates suggest that the failure of timely contact tracing due to the data glitch is associated with more than 125,000 additional infections and over 1,500 additional COVID-19 related deaths. Our findings provide strong quasi-experimental evidence for the effectiveness of contact tracing.
DEVELOPMENT OF DIGITAL MULTIMEDIA LEARNING CONTENT MINI SERVER LENTERA

Educational problems in the 3T area (Disadvantaged, Frontier, and Outermost) areas, apart from teaching staff, such as shortage of teaching staff, low qualifications, low competence and mismatches between educational qualifications and fields being managed (mismatched) are also problems of inadequate telecommunications. network facilities, especially internet networks. Lentera (Source of Learning Materials for Remote Areas) is an innovative product in digital learning based on mobile learning applications that can be used without having to be connected to the internet (offline). The learning content is designed in the form of Whiteboard Animation-based learning multimedia or whiteboard animation. Learning content is uploaded to the mini server. The mini server called Lentera can be accessed by teachers and students using smartphones offline or does not require an internet network. The development of digital multimedia learning content with a mini server called Lentera aims to develop innovative digital learning media to support teachers and students who experience difficulties in internet networks to carry out digital learning in the 3T area. The method used in the construction of this mini server is the ADDIE model (Analyze, Design, Development, Implementation and Evaluation). The lantern as a blackboard animation learning medium is very feasible (valid) to be used in learning with an average validation value of 94.87%.
90-th anniversary of the Sci-tech Library of R. E. Alekseev State Technical University in Nizhny Novgorod

The publication coincides with the 90-th anniversary of the Sci-tech Library of Nizhny Novgorod State Technical University. The history of the Library is described; the Library was established as a result of merging several universities, in particular it comprises the collections of Warsaw Emperor Nickolas II Polytechnic Institute. The Library’s collections have been supporting educational and research activities of the University. The continuing process of collection development is based on modern technologies which meets the user demands. The most recent data on library services, collections and users is provided. The library processes are computer-based; the e-catalog and e-library databases are available. The library collections support the learning process in 1,623 courses and disciplines. The role of the library in the educational process and university’s research programs is emphasized. The library environment has become more comfortable for the users. The Library contributes to cultural and social activities and to students’ personal development and social adaptation, humanistic education of the young adults. Innovative activities are described. The author concludes that the Sci-tech library plays a significant role in educational, research and social processes and offers its users various and relevant information products and services.
Recruitment Chatbot using AI

Seeking job can be a mentally frustrating and emotionally draining experience.The idea of building a chatbot came from a moment when understood that the recruiting process is filled with manual, redundant, and time and sequence-dependent tasks that can be automated with a recruitment chatbot. Adding a recruiting chatbot into your recruiting workflow can be very easy and very effective. Another major time-dependent task is interview scheduling. Simply calling candidates isn’t very effective anymore. Candidates aren’t answering calls from unrecognized phone numbers. Calling candidate in the middle of their current job is inconvenient, and playing the back and forth “what time works for you” is a miserable waste of time for everyone. Recruiting Chatbots are great atthis and making it easy for recruiters.Artificial IntelligenceMarkupLanguage(AIML) isused tobuild chatbot artificially intelligent. Inthispaper,wegiveabriefreview of chatbotwhich use AIML. Using Natural Language Processing (what happens when computers read language. NLP processes turn text into structured data), the machine converts this plain text request into codified commands for itself. Keywords: AIML,Chatbot,Artificial Intelligence, technology, NLP Cite this Article: Lokesh Katta, Balaji Katkar, Akshay Maske, Kirti Mhamunkar. Recruitment Chatbot using AI. Journal of Mobile Computing, Communications & Mobile Networks. 2020; 7(2): 5–10p.
Applications of quantum computing in IT security

Observing the developments in the quantum computing, our current encryption-decryption systems such as the RSA are insubstantial. Therefore, to maintain the security and protection of data, quantum cryptography through the understanding of quantum key distribution is the future of secured user-data.
Penggunaan Android Kotlin Untuk Pembuatan Aplikasi Lelang

The making of this auction aplication is about to accommodate people who want try auction transaction. The auction itself has a process people need to bid a item from the application. This auction transaction nowadays started by many people to try get item which the item has many request but the item just produced a few. Because of that, this application is made to help people get auction item that they want and help the process of the auction so no more mistake or bid and run. This application is running in android mobile, because many people more using android.
معاییر جودة خدمة الموقع الإلکترونی للجامعة وعلاقتها بتحقیق رضا الطلبة

تهدف هذه الدراسة إلى التعرف على مدى توافر معايير جودة خدمة الموقع الالکتروني للجامعة القاسمية وعلاقتها بتحقيق رضي الطلبة. ولتحقيق هدف الدراسة تم تطبيق الاستبانة کأداة قياس، بعد التحقق من الخصائص السيکومترية لها، وقد طبقت على عينة من الطلبة بلغ عددهم 100 طالب وطالبة تم اختيارهم بطريقة عمدية، وقد توصلت الدراسة إلى مجموعة من النتائج أهمها وجود انطباع ايجابي لدي عينة الدراسة على کل من معيار جودة النص، وجودة التصميم، وجودة المحتوي، وجودة الابحار، وجودة سهولة التعامل، في حين أظهرت النتائج وجود انطباع متوسط – إن لم يکن سلبي -  لمعيار جودة المعلومات الأکاديمية. ولذا أوصت الدراسة بضرورة الاهتمام بهذا المعيار، کما أوصت بضرورة احتواء الموقع على عناصر تتيح التفاعلية. Abstract: This study aims at identifying the availability of quality standards for the university’s website service and its relationship to achieving student satisfaction. To achieve the goal of the study, a questionnaire was applied as a measuring tool to a sample of Qasimia university students after checking its psychometric properties. The number of people in the sample reached 100 students who were deliberately selected. The study reached a set of results, the most important of which is a positive impression of the study sample on both the text quality standard, design quality, content quality, navigation quality, and ease of handling. While the results showed an average (if not negative) impression of the standard of academic information quality. Therefore, the study recommended the necessity of paying attention to this standard, as well as the necessity for the site to include elements that allow interactivity.
Geomorphological Modelling and Mapping of the Peru-Chile Trench by GMT

The author presents a geospatial analysis of the Peru-Chile Trench located in the South Pacific Ocean by the Generic Mapping Tool (GMT) scripting toolset used to process and model data sets. The study goal is to perform geomorphological modelling by the comparison of two segments of the trench located in northern (Peruvian) and southern (Chilean) parts. The aim of the study is to perform automatic digitizing profiles using GMT and several scripting modules. Orthogonal cross-section profiles transecting the trench in a perpendicular direction were automatically digitized, and the profiles visualized and compared. The profiles show variations in the geomorphology of the trench in the northern and southern segments. To visualize geo- logical and geophysical settings, a set of the thematic maps was visualized by GMT modules: free-air gravity anomaly, geoid, geology and bathymetry. The results of the descriptive statistical analysis of the bathymetry in both segments show that the most frequent depths for the Peruvian segment of the Peru-Chile Trench range from -4,000 to -4,200 (827 recorded samples) versus the range of -4,500 to -4,700 m for the Peruvian segment (1,410 samples). The Peruvian segment of the trench is deeper and its geomorphology steeper with abrupt slopes compared to the Chilean segment. A comparison of the data distribution for both segments gives the following results. The Peruvian segment has the majority of data (23%) reaching 1,410 (-4,500 m to -4,700 m). This peak shows a steep pattern in data distribution, while other data in the neighbouring diapason are significantly lower: 559 (-4,700 m to -5,000 m) and 807 (-4,200 m to -4,400 m). The Chilean segment has more unified data distribution for depths of -6,000 m to -7,000 m. This paper presents GMT workflow for the cartographic automatic modelling and mapping deep-sea trench geomorphology.
ACTIVITIES OF UNIVERSITY TEACHERS ON THE DEVELOPMENT OF FUTURE TEACHERS’ RESEARCH THINKING

Elaborating on the previously conducted studies into polar model of sense-making, the authors propose a trialectic model of the way meanings are born.The purpose of this article is to provide theoretical grounds for transition from the dual to trinal model of sense-making strategies and their description.The sense-making strategy determines the direction and quality content of the generated image-meaning.The authors find it expedient to consider trialectic level of the system of senses for more adequate modeling of possible strategies that reflect the key traits of a personality.The above mentioned triad contains hypo-adaptive, hyper-adaptive and pre-adaptive sense-making strategies.The proposed approach enables to identify a personal profile that acts as a multifactor model of possible states of value-notional sphere of an experiencing subject.In terms of modeling behavior patterns, a personal profile of such kind reveals the main direction and principal features of sense-making regulation.
Reflection on Human and Automatic Image Description in Elearning Context

Images have a strong presence in educational contexts, particularly in online environments. When images add vital information for the understanding of a given subject, this information must be perceived by all students, including those who have a visual impairment. Although most web tools and platforms have a field for the description or alternative text, most images do not contain this information. Since 2016, some platforms and web services have been providing features, based on Artificial Intelligence, which present a brief description of the images to screen readers. Are these descriptions effective for people with visual impairment, in an eLearning context? Are they enough for a blind person to create a mental image? How do they differ from the description performed by humans? To answer these questions, it is necessary to have an idea of how we perceive images and how they make sense in our brain, according to our values and culture. Thus, in this work we present a reflection related to these questions, using examples of descriptions obtained by the google chrome feature “Get image description” and descriptions made by people in the various editions of the MOOC “Image description in web context”.
PENGARUH BIMBINGAN BELAJAR DI LUAR SEKOLAH TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS IV SDN SEKECAMATAN PEJAGOAN TAHUN AJARAN 2019/2020

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Abstract: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;The study aimed to prove the difference between students taking tuition and students who did not take tuition on mathematics learning outcomes of fourth grade students in public elementary schools at Pejagoan sub-district in academic year of 2019/2020.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;The research was quantitative comparative method. Type of sampling was cluster random sampling technique with the help of Statistical Product and Service Solution (SPSS) version 16. The samples were 263 students in ten public elementary schools at Pejagoan sub-district. Data analysis used independent sample t-test, with a significance level of 0.05.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;In the test of homogeneity, the result was sig = 0,000 (sig &amp;lt; 0&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt;05). It meant that there was significant difference between students taking tuition and students who did not take tuition on mathematics learning outcomes. The data of student mathematics learning outcomes was homogeneous.&lt;/em&gt;&lt;strong&gt; &lt;br /&gt;&lt;/strong&gt;&lt;/p&gt;
When Law Frees Us to Speak

A central aim of online abuse is to silence victims. That effort is as regrettable as it is successful. In the face of cyber harassment and sexual privacy invasions, women and marginalized groups retreat from online engagement. These documented chilling effects, however, are not inevitable. Beyond its deterrent function, law has an equally important expressive role. In this article, we highlight law’s capacity to shape social norms and behavior through education. We focus on a neglected dimension of law’s expressive role—its capacity to empower victims to express their truths and engage with others. Our argument is theoretical and empirical. We present new empirical research showing cyber harassment law’s salutary effects on women’s online expression. We consider the implication of those findings for victims of sexual privacy invasions.