Reviewerly/exHarmony huggingface dataset:
한국어 학습자의 구어 발화에 나타난 어휘 발달 양상 분석 -중국인 학습자를 중심으로-

This study investigates lexical complexity in spoken Korean produced by Chinese students learning Korean by measuring lexical density and diversity. This study intends to examine the developmental differences in lexical complexity according to different groups and tasks. 136 Chinese students have been divided into three groups: basic group, intermediate group and advanced group. The results of this study reveal that the lexical diversity of the three groups shows a statistical significance, while the lexical density of the three groups does not show a statistical significance. Lexical diversity is statistically significant among the three groups in all tasks. However, lexical density was statistically significant among the three groups in some tasks. Also, the lexical diversity of the divergent tasks was higher than the convergent tasks. The result of this study would not only provide the validity of measurement tools of lexical complexity in spoken Korean, but also present basic information of measuring learners’ spoken production. (UNSW & Yonsei University)
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes

Context is of fundamental importance to both human and machine vision -- an object in the air is more likely to be an airplane, than a pig. The rich notion of context incorporates several aspects including physics rules, statistical co-occurrences, and relative object sizes, among others. While previous works have crowd-sourced out-of-context photographs from the web to study scene context, controlling the nature and extent of contextual violations has been an extremely daunting task. Here we introduce a diverse, synthetic Out-of-Context Dataset (OCD) with fine-grained control over scene context. By leveraging a 3D simulation engine, we systematically control the gravity, object co-occurrences and relative sizes across 36 object categories in a virtual household environment. We then conduct a series of experiments to gain insights into the impact of contextual cues on both human and machine vision using OCD. First, we conduct psycho-physics experiments to establish a human benchmark for out-of-context recognition, and then compare it with state-of-the-art computer vision models to quantify the gap between the two. Finally, we propose a context-aware recognition transformer model, fusing object and contextual information via multi-head attention. Our model captures useful information for contextual reasoning, enabling human-level performance and significantly better robustness in out-of-context conditions compared to baseline models across OCD and other existing out-of-context natural image datasets. All source code and data are publicly available this https URL.
Anticipating Gaze-Based HCI Applications with the Tech Receptivity Interval: Eye Tracking as Input

HCI researchers have repurposed diagnostic eye tracking technology as a mode of user input. Existing applications are numerous, but primarily address severe motor disability, with a recent increase in gaming enhancement. As noted by cognitive psychologist Nadiya Slobodenyuk, gaze-based HCI represents a fundamental change to the human-computer relationship if adopted for general interaction and information design purposes. A gaze-responsive system can make inferences on a user's mental state and respond rapidly without explicit user commands. The implications of such a system are significant, and are difficult to imagine and anticipate. We introduce the tech receptivity interval (TRI) as a framework to guide speculative design investigations that imagine potential applications of nascent technology. TRI distinguishes infancy and maturity conditions of receptivity, emphasizing the need for users to adapt to technologies before technological affordances can be fully realized. We provide case reports on gaze-based interaction, using TRI and conducted in an academic design studio. The case reports suggest applications not yet addressed in the literature. The case reports also suggest gaze-responsive changes to information structures in the form of temporal hierarchy and temporal text, which break from the long tradition of language representation in static lines and paragraphs.
Class imbalanced detection of sarcasm in social networking websites based on minority up-weighting

Over the past few decades, the evolution of machine learning has been tremendous, which gave solutions to many real-world problems and still making lives more comfortable as well as innovative. One of the applications of machine learning would be to detect sarcasm in social media like Twitter, Facebook etc., which will, in turn, give us insight into a topic trending in social networking websites. Thus this mining of people’s opinions would be of use to make decisions in many ways. But due to the highly imbalanced classes of social media data, it is often difficult to get the accuracy that is desired. So, to deal with this type of dataset, in this study, synthetic minority oversampling based methods are proposed. The main focus of this article is minority up-weighting, hence, two methods namely KMeansSMOTE and BorderlineSMOTE algorithm are used along with classifiers like Bernoulli Naive Bayes (BNB), Multilayer Perceptron (MLP) and Decision Tree Classifier (DTC) to get better accuracy while dealing with imbalanced dataset. Thus this article contains an analysis of imbalanced classification in the detection of sarcasm in social media through minority up-weighting techniques.
Bahasa &amp; Perkembangan Literasi pada Anak Usia Dini: Sebuah Studi Literatur

Language is one of the domains of individual development. Language development is related to literacy skills, both from the beginning of life as well as in later times. Individual literacy abilities are related to academic achievement. Early childhood in the early stages of pre-school is the basis for the introduction and understanding of basic children's literacy. Therefore the concept of development and also efforts to maximize the potential for children's development is in accordance with their uniqueness. Because of its importance, this article will discuss developments related to language and literacy in children and the methods used in efforts to develop children's language skills and literacy.
Generic Structure of Literature Reviews in Research Articles: Iranian and International Journals (Research Paper)

Investigating the rhetorical patterns of research article (RA) sections elucidates the complex nature of academic literacy, especially, for non-native researchers. Although the literature review (LR) has a central role in establishing the foundation of the study, less clarity is available concerning its unique complexities. This paper thus explores the generic structure of the literature review section (LR) of 122 RAs published in Iranian journals (IrRAs) and international journals (IntRAs) using Swales’ (1990) Create A Research Space (CARS) model. The IrRAs (n=61) were selected from seven established Iranian journals and the IntRAs (n=61) were chosen from seven professional journals with an impact factor greater than 1. Two coders identified the moves and steps of several RAs via negotiated coding. The results indicated that both groups had approximately similar patterns regarding the moves and steps. In both groups, ‘establishing a territory’ (M1) and ‘occupying a niche’ (M3) were the most and least used moves, respectively. Likewise, both groups used M1 and M3 to start and finish LR, respectively. A tendency for employing the irregular move patterns in IntRAs and the regular move patterns in IrRAs was also observed. These findings help novice researchers to better tackle the challenges of LR writing.
Machine-checked ZKP for NP-relations: Formally Verified Security Proofs and Implementations of MPC-in-the-Head.

MPC-in-the-Head (MitH) is a general framework that enables constructing efficient zero-knowledge (ZK) protocols for NP relations from secure multiparty computation (MPC) protocols. In this paper we present the first machine-checked implementations of MitH. We begin with an EasyCrypt formalization that preserves the modular structure of the original construction and can be instantiated with arbitrary MPC protocols, and secret sharing and commitment schemes satisfying standard notions of security. We then formalize various suitable components, which we use to obtain full-fledged ZK protocols for general relations. We compare two approaches for obtaining verified executable implementations. The first uses a fully automated extraction from EasyCrypt to OCaml. The second reduces the trusted computing base (TCB) and provides better performance by combining code extraction with formally verified manual low-level components implemented in the Jasmin language. We conclude with a discussion of the trade-off between the formal verification effort and the performance of resulting executables, and how our approach opens the way for fully verified implementations of state-of the-art optimized protocols based on MitH.
Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models

As Machine Translation (MT) has become increasingly more powerful, accessible, and widespread, the potential for the perpetuation of bias has grown alongside its advances. While overt indicators of bias have been studied in machine translation, we argue that covert biases expose a problem that is further entrenched. Through the use of the gender-neutral language Turkish and the gendered language English, we examine cases of both overt and covert gender bias in MT models. Specifically, we introduce a method to investigate asymmetrical gender markings. We also assess bias in the attribution of personhood and examine occupational and personality stereotypes through overt bias indicators in MT models. Our work explores a deeper layer of bias in MT models and demonstrates the continued need for language-specific, interdisciplinary methodology in MT model development.
New Developments In Teaching Academic Course In Russian National System Of Innovations

The paper studies suggestions to improve training offered to postgraduates within the course in ‘National System of Innovations in Russia’. Authors suggest a system of training promoting proactive inclusion of each postgraduate in discussion of problematic issues, organized distribution and assignment of tasks and functions within the course, followed by writing and discussion of synopses and reviews by postgraduates with further publication in the form of online chapters. The type of NSI determines forms of its development, with development of NSIR following the lines similar to those of bigger national economy and being regarded integral of the national economy. NSI promotes proliferation of concept of Russia as a single and united nation, establishing similar standards of living in any of its parts or territories based on development of regional systems of innovations. NSI contributes to preservation of various resources and NSI promotes increased levels of technological development in various industries and sectors of the Russian economy based on the effect of multiplication.
The Case for Peele’s Authorship of <i>Titus Andronicus</i> 4.1: Cross-Examining Attribution Methods in a Disputed Scene

Abstract William Weber (2014) argues that Shakespeare authored Titus Andronicus 4.1 as evidenced by verbal collocation data and auxiliary investigations into the vocabulary, rhetoric, and classical content of the scene. Anna Pruitt (2017) corroborates this view using n-gram testing, and critical opinion has shifted against the older orthodoxy assigning the scene to George Peele. Yet despite efforts to select a limited set of Shakespearean works for their experiments, the datasets compiled by Weber and Pruitt from the LION database are invalidated by the imbalanced canon sizes of the two authorship candidates. This oversight compromises their quantitative arguments, as does their neglecting The Troublesome Reign of King John. This article demonstrates that when equitable canons are implemented 4.1’s vocabulary is strongly indicative of Peele’s hand, in clear opposition to the current consensus view. Additionally, the scene contains classical allusiveness that is distinct from that of Shakespeare with regard to sources, effect, and scope, further reinforcing the case for Peele’s authorship. The article then revisits the un-Shakespearean metrical properties of 4.1 and testimonial evidence in support of an earlier Titus play, arguing that Peele’s ownership of the scene can greatly inform our model of Shakespeare’s timeline and alleged innovations in the revenge tragedy genre during his earliest career phase. Recent critiques of automated searching in attribution studies show why integrated methods that unite quantitative and qualitative techniques remain indispensable, and the 2014 and 2017 studies’ erroneous conclusions provide a cautionary tale for future research in the field.
Conceptual Foundations Of Education For Sustainable Development In Overcoming Transformational Socio-Technogenic Processes

The technocratic liberal-economic vector of development of modern society leads to the increasing destruction of natural environment – the biosphere. The role of education in addressing this problem has been repeatedly emphasized during a number of UN conferences on environment and development. However, to date, the issues of forming a worldview and skills of socially- humane economic activity that allow preserving biosphere life remain unresolved. Therefore, there is an urgent need to correct the educational strategy. In our research, we rely on a systematic socio-natural approach. The analysis of theories of sustainable development shows that they have significant gaps associated with a lack of understanding of the essence of the socio-technological processes occurring on our planet, associated with the change in the evolution of life from natural to artificial. These theories do not analyze the human transformations that have arisen as a result of the changed environmental conditions, the transition of life to the urban technosphere. All this does not allow us to develop a full-fledged philosophic strategy for the development of education based on the theories of sustainable development, which could help a person not only to act without destroying the biosphere world and life, but also to adapt to the conditions of changing development. In solving the given tasks, social pedagogy can provide effective assistance. On the basis of this scientific discipline, it is possible to develop a practically oriented strategy for the socialization of the younger generation based on the values of ecological culture.
Services and features available with pfSense to secure your Organizational Network

This is a white paper that would help the readers and the System Administrator to get some knowledge on the pfSense, open-source software that can be easily deployed in any of the network systems. The paper presents the reader with important aspects of using the pfSense in the network and its benefits. The pfSense contains different varieties of features that can be installed, configured, and implemented in any scale of the network. The paper briefly explained the features and advantages of using pfSense from user credentials to authenticate and protect the resources in the network in terms of its confidentiality, integrity, and availability of the services to the user. The pfSense provides an easy way to set up any features required for an organization. Most features are available for free use, however, if users want to have their network a more secure and complex one, there are features the user can subscribe and download module and plugins.
Problems Of Distance Learning In Higher Education

Currently, all universities are actively using digital technologies in educational activities. Such requirements are imposed on the system of higher education by the state policy of the Russian Federation. Thus, competitive universities must meet a number of criteria, where informationization and digitalization of education become the determining factors in the development of the university. Digital education is actively involved in the educational environment. And its use in this situation is justified. In our age of rapidly developing technologies, it is difficult now to imagine education in the traditional form in which Ya.A. Kamensky, at one time. It is difficult to imagine education today without the use of information and communication technologies, distance learning, etc. And in the light of recent events (COVID - 19), it was digital resources that turned out to be the only technology possible in a situation of population distancing. In addition, these resources have established themselves as the most effective and efficient in building educational activities at all levels of education, including higher education. The article is devoted to the problem of using digital resources (distance education) in the educational process of the university. The publication analyzes the advantages and disadvantages of using distance learning in higher education. It is shown that, although this form of education is very actively used in the modern educational space of the university, however, it is still not the only and optimal means for increasing the effectiveness of the educational process in the higher education system.
Increasing Use of Microtargeting and Geofencing in Political Campaigns

A paper flier or banner is non selective in its approach to the audience, even TV and radio ads aren't as selective as digital media advertisements. all these modes of advertising are targeting users irrespective of their age (whether they are eligible to vote or not), nationality, gender or other attributes. (Bennett #) While designing an offline political advertisement might begin with a design process, design of a digital political campaign usually starts with segmentation of the target audience. A Digital political ad, designed and run for a specific segment of the voting population can be made visible to only that specific segment, other segments of the same population are not aware of any such campaign and this segmentation is most crucial to digital political campaign.
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection.

In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data. This has motivated research in Unsupervised Domain Adaptation (UDA) algorithms for detection. UDA methods learn to adapt from labeled source domains to unlabeled target domains, by inducing alignment between detector features from source and target domains. Yet, there is no consensus on what features to align and how to do the alignment. In our work, we propose a framework that generalizes the different components commonly used by UDA methods laying the ground for an in-depth analysis of the UDA design space. Specifically, we propose a novel UDA algorithm, ViSGA, a direct implementation of our framework, that leverages the best design choices and introduces a simple but effective method to aggregate features at instance-level based on visual similarity before inducing group alignment via adversarial training. We show that both similarity-based grouping and adversarial training allows our model to focus on coarsely aligning feature groups, without being forced to match all instances across loosely aligned domains. Finally, we examine the applicability of ViSGA to the setting where labeled data are gathered from different sources. Experiments show that not only our method outperforms previous single-source approaches on Sim2Real and Adverse Weather, but also generalizes well to the multi-source setting.
Serverless skeletons for elastic parallel processing

Serverless computing is an emerging cloud computing paradigm with the goal of freeing developers from resource management issues. As of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other. These workloads benefit from on-demand and elastic compute resources as well as per-function billing. However, it is still an open research question to which extent parallel applications, which comprise most often complex coordination and communication patterns, can benefit from serverless computing.In this paper, we introduce serverless skeletons for parallel cloud programming to free developers from both parallelism and resource management issues. In particular, we investigate on the well known and widely used farm skeleton, which supports the implementation of a wide range of applications. To evaluate our concepts, we present a prototypical development and runtime framework and implement two applications based on our framework: Numerical integration and hyperparameter optimization - a commonly applied technique in machine learning. We report on performance measurements for both applications and discussthe usefulness of our approach.
AI Smart Factory Model for Integrated Management of Packaging Container Production Process

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, onsite PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.
Unusual Activity Detection in Surveillance Video Scene: Review

Abnormal activity may indicate threats and risks to others. An anomaly can be defined as something that deviates from what is expected, common, or normal. Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary, which can monitor human actions in real-time and categorize them as ordinary or exceptional, as well as create an alarm. Human activities in public and sensitive regions such as bus stations, airports, railway stations, malls, banks, universities, car parks, roads, and other regions can be monitored using visual surveillance to prevent crime, theft, terrorism, vandalism, accidents, and other suspicious activities. ​This makes video surveillance a key to increasing public security. The main objective of event discovery is to discover the occurrence of events and categorize them into normal or abnormal actions. This discovery requires identifying and tracking objects and then learning what is going around those tracked objects. Recent research is based on one of two technologies: handcrafted features and deep learning models. Handmade features are based on extracting low-level features, and their strength is based on selecting the best features, that produce the best results. After the success of deep learning techniques for classifying images, the researchers examined the ability of deep learning techniques to detect, which bypasses the manual step of feature extraction and works directly with images. This paper presents a survey of both handmade and deep learning models to detect abnormal events.