Reviewerly/exHarmony huggingface dataset:
Strindberg’s A Dream Play and Gumilyov’s Allah’s Child. A mystery plot

Тhe article explores the issue of implementation of a mystery plot in the dramaturgy of the turn of 19th–20th centuries. Within the aspect of the given topic, two plays are considered — A. Strindberg’s drama A Dream Play, and N. S. Gumilyov’s fairy tale Allah’s Child. It is an attempt to demonstrate that in these works one can find the same plotline, which can be traced back to the myth of Persephone. With various interpretations still around as to the content of the ancient Greek mystery, is can generally be acknowledged that the myth comprised the foundation of the sacred action at Eleusis; the debate only concerns its “stage” adaptation. At the turn of 19th–20th centuries, the story of Persephone is already creating its own, new myth about the trials of a wandering soul on the path of knowledge. In Strindberg’s and Gumilyov’s plays, the soul (Persephone) is played by a divine being: in A Dream Play it is the daughter of god Indra of ancient Indian mythology, and in Allah’s Child it is the daughter of a god from the mythology of peoples of Central and Minor Asia. Both heroines go through a hard path of earthly sorrows, and in the denouement return to the divine world. This plot structure, which mirrors the development of an ancient mystery, both playwrights — Swedish and Russian — introduce the Poet as a character. In both A Dream Play and Allah’s Child the Poet becomes an intermediary between the earthly and divine lives, bridging the gap between those worlds, and, in essence, acting as Dionysus. The general trend, whereby the dramaturgy of “the turn” looked back to ancient mythology, speaks to the ongoing process of myth structuring, as it acquires new meaningful layers.
MOOC Coursera Content Post-editing

This paper presents the post-editing features of the machine translation (MT) system Smartling used to translate the learning content of MOOC (Massive Open Online Course) Coursera. Most of the Coursera content is delivered in English, which is one of the reasons for the low uptake of these courses in Lithuania. With the growing demand for online resources, the need to translate courses into Lithuanian has become evident and MT systems are increasingly used for that purpose. This paper describes the results of an experiment carried out with the Smartling MT system. The experiment involved 10 participants, 6 professional and 4 non-professional translators, who post-edited a passage from the Coursera course The Science of Wellbeing. The post-editing process was monitored using the Translog-II tool, which captures the participants‘ keystrokes. The paper presents the classification and frequency of MT errors. One of the most important post-editing features of the Smartling MT system is the splitting of the text into subtitle lines, which is the cause of most grammatical errors. Among the errors not attributable to this text division are those caused by the polysemy of the words, literal translation and the use of pronouns. After the post-editing task, participants filled in a short questionnaire about the functionality of the Smartling system. 7 out of 10 participants rated the performance of this system as satisfactory. The results of the study showed that Smartling is not sufficiently tailored to the Lithuanian language, and that translators have to use a lot of cognitive effort in post-editing.
Directive Speech Acts Used by Teacher Via Zoom Cloud Meeting in English School Indonesia

The purpose of this study is to categorize and describe the varieties, purposes, and tactics of teachers’ directive speech acts via zoom cloud meeting in English School Indonesia. This descriptive qualitative research used “teknik bebas libat cakap (SLBC)” technique or Uninvoled Conversation Observation Technique which is backed by the data collection techniques of keeping records and taking notes. This study makes use of Kreidler’s idea of directive speech act kinds (1998), Bach and Harnish’s (1979) theory of directive speech act functions and Yule’s (1990) theory of directive speech act strategy (1996). Data were analyzed using methodologies of referential and pragmatic distributional. The analysis of the data reveals that: (1) the types of directive speech acts used by teachers are commands, requests, and suggestions; (2) the functions of these speech acts are requestives, requirements, prohibitives, and advisories; and (3) the direct and indirect strategies employed by teachers are for directive speech acts. The type of command is the most dominant directive speech acts used by teachers(48%). With 40% incidence, the requirement function overtakes all other directive speech act functions. With 78% of teachers using it, the direct technique is the most common directive speech act strategy.
Stocks Saving Simulation based on Historical Data Web-based

This study aims to simulate the calculation of saving stocks based on historical data for the past 10 years for the period January 2010 - January 2020, because saving simulations based on historical data are still very rare, so a simulation application for saving stock calculations based on historical data is made that can help customers. investors in simulating the calculation of saving stocks so that it can be used as learning to determine how to save the right way that can produce a good return. This application is created using a waterfall model. From the application made, it is expected to know the good return results of the 10 issuers used in this study, and in making basic applications on user requirements obtained through several respondents through online questionnaires and processed with requirement elicitation techniques. With the application of a stock saving calculation simulation application, it is hoped that it can be a lesson for investors in saving stocks and can also be a lesson for potential investors and can also make it easier for investors to do calculations because the calculation process is computerized.
Feature Enhancement and Coarse-to-Fine Detection for Rgb-D Tracking

Existing RGB-D tracking algorithms advance the performance by constructing typical appearance models from the RGB-only tracking frameworks. There is no attempt to exploit any complementary visual information from the multi-modal input. This paper addresses this deficit and presents a novel algorithm to boost the performance of RGB-D tracking by taking advantage of collaborative clues. To guarantee input consistency, depth images are encoded into the three-channel HHA representation to create input of a similar structure to the RGB images, so that the deep CNN features can be extracted from both modalities. To highlight the discriminatory information in multi-modal features, a feature enhancement module using a cross-attention strategy is proposed. With the attention map produced by the proposed cross-attention method, the target area of the features can be enhanced and the negative influence of the background is suppressed. Besides, we address the potential tracking failure by introducing a long-term mechanism. The experimental results obtained on the well-known benchmarking datasets, including PTB, STC, and CTDB, demonstrate the superiority of the proposed RGB-D tracker.
Analyzing The Data Reading Interest Of Tangerang Citizens In Selecting Between Physical Books And E-Books

Reading activities can be done by anyone; children, teenagers, adults, even elderly people. However, these reading activities have much ignored by various circles, especially Indonesian citizens. In an era when technologies have not developed rapidly as today, people used physical books for reading. In practice, physical books are still the main choice, especially for Tangerang people. The reading interest of Tangerang people increased significantly since 2014 to 2019. However, a solution is required to develop literacy amidst modernity in responding to the development of an all-around technology era. E-book is the manifestation of the provision of profound knowledge through digital access with all its strengths and weaknesses. This study aims to identify which one that Tangerang people are more preferred; reading physical books or e-books. This study used qualitative-descriptive method. The data sources used were the result of a 10-day online survey and literature study as the secondary data. Research results indicate that based on the categories of the easiness in obtaining reading materials, and price, e-books are more superior because they are easily obtained and affordable. However, based on the comfort-wise category, physical books still become the preference of Tangerang people because they are clearer, appealing, and prevent eyestrain
IDENTIFIKASI PENYALAHGUNAAN JALUR SMS MARKETING DENGAN KONTEN SMS PREMIUM PADA PT DIAN PRIMA JAYARAYA DENGAN ALGORITMA NAIVE BAYES

PT Dian Prima Jayaraya is an SMS broadcast delivery service company in Indonesia, currently operators in Indonesia separate SMS channels into marketing and premium. The marketing channel is used for Promo/Marketing SMS and the Premium line is used for SMS that has a time period such as OTP. But in reality, many companies send premium content such as OTP in the marketing channel due to a lack of awareness of the content sent. The operator will impose sanctions on SMS that are misdirected, and the sanctions will be forwarded back-to-back to companies that use the services of PT Dian Prima Jayaraya. To avoid abuse, a classification model will be built using the Naives Bayes Algorithm, so that companies can identify misplaced content early to avoid fines from operators.
History, Features, Challenges and Critical Success Factors of ERP in the Era of Industry 4.0

This paper uses a state-of-the-art review system with a purpose to review and synthesize the latest information on the possible integration of potential disruptive technologies into the future development of ERP. Different software, such as Material Requirement Planning (MRP), Manufacturing Resource Planning (MRPII), and Computer Integrated Manufacturing (CIM) have been found to be the base of existing versions of ERP. Potential disruptive Industry 4.0 technologies that can be integrated into ERP are found to be artificial intelligence, business intelligence, internet of things, big data, blockchain technology, and omnichannel strategy. Notable Critical Success Factors of ERP are top management support, project team, IT infrastructure, communication, skilled staff, training & education, and monitoring & evaluation. Moreover, cybersecurity has been found to be the most challenging issue to overcome in future versions of ERP. This paper could help the future ERP researchers and respective stakeholders contribute to integrate newer features in the future versions of ERP.
Aplikasi Presensi Kehadiran Online pada Karyawan PT. Bringin Karya Sejahtera dengan Metode Location-Based Service Menggunakan Android Studio dan MySQL

Absence is one important performance appraisal. Absences made by each worker can affect the net monthly salary PT Bringin Karya Sejahtera is a service provider engaged in the business sector or providing services for building cleaning workers, building and building technicians. But there are some problems in his absence in the field of cleaning ATMs. Employees must come to the ATM and take selfies to take attendance. The purpose of this study is to map locations where ATM officers are located so that employees can take attendance at BRI branch offices using GPS technology, and barcodes on Android by calculating the distance to the location using the Haversine Formula method. This study also discusses absence from work due to leave, permission, sickness and overtime calculation. The development method used in designing android-based attendance applications is Rapid Application Development (RAD). The system design uses the Unified Modeling Language (UML). System coding is done using Java, PHP and MYSQL. The results of this study are the design of an android-based attendance application for employees to attend attendance at BRI branch offices.
Development of Client Server-Based Queuing Applications at The Samsat Gowa Office

The government has established a SAMSAT (One-Stop Manunggal Administration System) office which has duties in the motor vehicle tax service. Queuing activities make people spend their time waiting. Therefore, it is necessary to create a queuing system that can inform the estimated queue time remotely. The queuing system does not require users to wait physically, so the waiting time needed to queue can be used by taxpayers to carry out their own personal and work activities that are more useful. Queuing application development aims to develop queuing service features from previous applications which only provide queued information on taxpayers. Features developed in the form of queuing data processing with different activities, monitoring information on time and queue status periodically. The research method used is SDLC (Software Development Life Cycle) with a prototype model. The test results using the Gray Box Testing method show that queuing applications in government agencies providing motorized vehicle tax services have been successfully developed.
Optimality in High-Dimensional Tensor Discriminant Analysis

Tensor discriminant analysis is an important topic in tensor data analysis. However, given the many proposals for tensor discriminant analysis methods, there lacks a systematic theoretical study, especially concerning optimality. We fill this gap by providing the minimax lower bounds for the estimation and prediction errors under the tensor discriminant analysis model coupled with the sparsity assumption. We further show that one existing high-dimensional tensor discriminant analysis estimator has matching upper bounds, and is thus optimal. Our results apply to tensors with arbitrary orders and ultrahigh dimensions. If one focuses on one-way tensors (i.e., vectors), our results further provide strong theoretical justifications for several popular sparse linear discriminant analysis methods. Numerical studies are also presented to support our theoretical results.
Law Enforcement Leadership, Tenure &amp; Jail Overcrowding

Calls for criminal justice reform have become commonplace, as issues ranging from ethnic and racial bias in policing to prison overcrowding have taken center stage in many policy discussions. The demand for change in the criminal justice system largely falls on the shoulders of leadership within the criminal justice system who might have their own preferences toward criminal justice reform. In this work we examine the issue of sheriff tenure on jail occupancy rates, as jails are directly managed by the sheriff and capacity issues in jails have resulted in not only safety and security problems for inmates and staff, but also fiscal stress. Utilizing a unique institutional feature - that 192 county sheriffs are limited to no more than two terms in the position - we employ an instrumental variable identification strategy to examine the impact of sheriff tenure on jail occupancy rates. Our results show that longer sheriff tenure is associated with higher jail occupancy rates. Exploring heterogeneous effects we find that politically competitive Democratic and politically uncompetitive Republican counties are the largest contributing group to our main effect. This work sheds insights into the agencies that are contributing to higher jail occupancy rates, as well as the conditions that are fertile for jail overcrowding.
Combining Eap and Oli (Online Language Instruction) to Benefit Student Learning: An Explanatory Case Study

Students whose native language is not English attending EAP courses at English-medium universities and colleges are expected to develop both academic writing and the skills needed to produce coherent and effective language. An additional challenge may be the limited instructional hours. Online instruction can offer a solution. This large private, non-for-profit higher education institution in southern Greece offers a “stand-alone” EAP Program. This EAP Program redesigned its most advanced course making it a blended one, leveraging educational technology to maximize face-to-face and online instruction. Each week has a dual focus: a) help learners develop and apply the necessary academic writing skills and b) develop and reinforce all other advanced-level academic skills. While increasing student written production, this course has also enhanced in-class instruction. Benefits and drawbacks of this blended course will also be discussed.
S-Shape Activation Function with Adaptive Saturation Rate for Neural Networks

Activation function plays a vital role in the ability of artificial neural networks to learn complex functional mapping from data. Currently, the most successful and widely-used activation functions are designed by mimicking the models of biological neurons or proposed from the perspective of machine learning. In this work, motivated by two testimonies in neuroscience that the sigmoidal function is a common stimulus-response function and the response of a neuron varies from trial to trial even when the same sensory stimulus is repeatedly delivered, we propose an s-shape activation function with adaptive saturation rate, referred to as AFAS, through adding a trainable parameter α into the traditional sigmoid function. In this approach, α controls the saturation rate of activation function to characterize the variation in the neuron’s response. Meanwhile, we limit the minimum value of α to alleviate the vanishing gradients problem that may occur in deep neural networks. The effectiveness of the proposed AFAS is comprehensively substantiated by five different tasks with various types of network architectures.
Fusion: Computer Science and Dance

Abstract: Dance may not seem like it has much to do with computing but there are some clear links between dance and computing. Basically, a dance is just a series to follow. To dance we have to follow the steps exactly in a defined and repeated order. The same loop we need to execute again and again. This same concept we implement in algorithms. Step by step formation of instructions leads to an algorithm in the programming languages of computer science. A dance is just an algorithm executed by people that is enjoyable to watch. Writing a program and choreographing a dance is very similar to each other. Dance and computing could be a great fusion in coming future for the one who is technical by profession and dancer by passion or vice versa. The primary objective of this study is to understand relation between dance and computing, understanding its practical implementation and obstacles if any.
Simplifying Subgraph Representation Learning for Scalable Link Prediction

Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally expensive, and not scalable to large-scale graphs due to expensive subgraph-level operations. To unlock the scalability of SGRLs, we propose a new class of SGRLs, that we call Scalable Simplified SGRL (S3GRL). Aimed at faster training and inference, S3GRL simplifies the message passing and aggregation operations in each link's subgraph. S3GRL, as a scalability framework, accommodates various subgraph sampling strategies and diffusion operators to emulate computationally-expensive SGRLs. We propose multiple instances of S3GRL and empirically study them on small to large-scale graphs. Our extensive experiments demonstrate that the proposed S3GRL models scale up SGRLs without significant performance compromise (even with considerable gains in some cases), while offering substantially lower computational footprints (e.g., multi-fold inference and training speedup).
Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response Generation

Ensuring that generated utterances are faithful to dialogue actions is crucial for Task-Oriented Dialogue Response Generation. Slot Error Rate (SER) only partially measures generation quality in that it solely assesses utterances generated from non-categorical slots whose values are expected to be reproduced exactly. Utterances generated from categorical slots, which are more variable, are not assessed by SER. We propose Schema-Guided Semantic Accuracy (SGSAcc) to evaluate utterances generated from both categorical and non-categorical slots by recognizing textual entailment. We show that SGSAcc can be applied to evaluate utterances generated from a wide range of dialogue actions in the Schema Guided Dialogue (SGD) dataset with good agreement with human judgment. We also identify a previously overlooked weakness in generating faithful utterances from categorical slots in unseen domains. We show that prefix tuning applied to T5 generation can address this problem. We further build an ensemble of prefix-tuning and fine-tuning models that achieves the lowest SER reported and high SGSAcc on the SGD dataset.
Credible, Optimal Auctions via Blockchains

Akbarpour and Li (2020) formalized credibility as an auction desideratum where the auctioneer cannot benefit by implementing undetectable deviations from the promised auction and showed that, in the plain model, the ascending price auction with reserves is the only credible, strategyproof, revenue-optimal auction. Ferreira and Weinberg (2020) proposed the Deferred Revelation Auction (DRA) as a communication efficient auction that avoids the uniqueness results from Akbarpour and Li (2020) assuming the existence of cryptographic commitments and as long as bidder valuations are MHR. They also showed DRA is not credible in settings where bidder valuations are $\alpha$-strongly regular unless $\alpha > 1$. In this paper, we ask if blockchains allow us to design a larger class of credible auctions. We answer this question positively, by showing that DRA is credible even for $\alpha$-strongly regular distributions for all $\alpha > 0$ if implemented over a secure and censorship-resistant blockchain. We argue ledgers provide two properties that limit deviations from a self-interested auctioneer. First, the existence of smart contracts allows one to extend the concept of credibility to settings where the auctioneer does not have a reputation -- one of the main limitations for the definition of credibility from Akbarpour and Li (2020). Second, blockchains allow us to implement mechanisms over a public broadcast channel, removing the adaptive undetectable deviations driving the negative results of Ferreira and Weinberg (2020).
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining

While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text and studio-quality audio data. This paper proposes a method for zero-shot multilingual TTS using text-only data for the target language. The use of text-only data allows the development of TTS systems for low-resource languages for which only textual resources are available, making TTS accessible to thousands of languages. Inspired by the strong cross-lingual transferability of multilingual language models, our framework first performs masked language model pretraining with multilingual text-only data. Then we train this model with a paired data in a supervised manner, while freezing a language-aware embedding layer. This allows inference even for languages not included in the paired data but present in the text-only data. Evaluation results demonstrate highly intelligible zero-shot TTS with a character error rate of less than 12% for an unseen language.