Reviewerly/exHarmony huggingface dataset:
REPRESENTATION OF CRISIS OF MEANING IN MODERN PSYCHOLOGICAL SCHOOLS

A review of representation of the concepts of «meaning» and «crisis of meaning» in the thesaurus of main branches of psychology is conducted in the article. The scientific works of psychodynamic, existential, humanistic and subject activity psychology were analyzed. The main components of the systems of meaning and the processes of crisisare described as well as their influence on activity and adaptability of personality. Based on this models of the development of crisis of meaning are created.The given model include: the conflict of intrapsychic and extrpsychic drives and their role in subconscious reflection of gained experience that result in rigid models of behavior and subjective interpretations ofthe events in personal life; the process realization of Major Concerns as the main aspects of human existence in the development of existential conflict with concepts of «finality of being», «existential freedom», «existential isolation» and «meaninglessness» which result in the developmentof noogenic neurosis; the functions of major life motives, values and aspirations; the wholesome narrative system that unites the individual life path in the categories of «past», «present» and «future» bothin critical and stable periods of life.
Solving SCAN Tasks with Data Augmentation and Input Embeddings

We address the compositionality challenge presented by the SCAN benchmark.Using data augmentation and a modification of the standard seq2seq architecture with attention, we achieve SOTA results on all the relevant tasks from the benchmark, showing the models can generalize to words used in unseen contexts.We propose an extension of the benchmark by a harder task, which cannot be solved by the proposed method.
MRF-Chat: Improving Dialogue with Markov Random Fields

Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others. While neural models have shown reasonable results, modelling the cognitive processes that humans use when conversing with each other may improve the agent’s quality of responses. A key element of natural conversation is to tailor one’s response such that it accounts for concepts that the speaker and listener may or may not know and the contextual relevance of all prior concepts used in conversation. We show that a rich representation and explicit modeling of these psychological processes can improve predictions made by existing neural network models. In this work, we propose a novel probabilistic approach using Markov Random Fields (MRF) to augment existing deep-learning methods for improved next utterance prediction. Using human and automatic evaluations, we show that our augmentation approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents.
Studi Long Storage Sebagai Upaya Mengatasi Kekeringan Di Daerah Cinta Manis Sumatera Selatan

This study aims to conduct a long storage study as an effort to overcome drought in the Cinta Manis area, South Sumatra. The research data used is the maximum daily rainfall data from 2009 - 2018 which was obtained from the Palembang Climatological Rain Station. From the research results obtained hydrological analysis data and hydraulic analysis. For hydrological analysis, statistical parameter methods are used to determine the planned rainfall. The results of this method were analyzed in harmony with the Chi Square test and the Smirnov-Kolmogorov test and the distribution type used was the Log Pearson III distribution. To calculate the water holding capacity in long storage, the cumulative storage volume analysis is used to analyze the volume that is bounded by a certain contour, with a simple formula published by the Manual of Sediment Control Dam Construction of the Ministry of Public Works. Based on the research results, it can be seen that the potential for long storage storage obtained from rainwater is 95771.18 m³ and water storage capacity in long storage which is 137334.43 m³ is able to serve 40.87 ha. The water storage capacity in long storage is able to irrigate the sugarcane fields in Rayon 1B and as a storage place for water reserves so that there is no drought when the dry season arrives. Keywords: long storage, rain, analysis
Recommender System for Enterprise Resource Planning

This paper deals with a major scientific and technical problem for many research and production enterprises that are faced with the need to carry out logistics activities. The authors describe combined algorithms for recommender systems, that take into account different ways to evaluate objects: content-based and collaborative filtering and knowledge-based approach as well. Such implementation allows to carry out efficient and exact ranking in order to make rational recommendations for users. In this research we propose four different kinds of criteria that could be obtained with the use of ERP system database and other data sources available via Internet connection. Developed algorithms are invariant to the subject area and criteria itself, thus designed software can be applied in different modern scientific problems.
Design a Web Security Testing Mechanism by Using Semantic Comparision Method to Prevent Cross Site Scripting Attacks

There are two faces of every coin, so is true for the web applications also. With the growing demand for online services web applications have become pervasive and with such exponential growth has increased the exploitation of the web application vulnerabilities also. JavaScript, which is the backbone in supporting a dynamic client side behavior, is often exploited for evil intentions. Malicious scripting code is injected into the application which are executed in the end user’s computer thereby revealing the confidential parameters and compromising the security of the application. Cross Site Scripting (XSS) is certainly standing quite ahead in the hierarchy of the most dangerous and frequently used tactics executed by the attackers. Several defensive mechanisms are employed to prevent XSS attacks and reinforce the applications against them, however the attackers also are quite innovative and come up with newer attacking mechanisms. In this paper we try to apply Static Analysis to identify the XSS exploits. Although static application security testing mechanism comprehensively covers the entire code and is quite authentic in its vulnerability identification but, such good record is marred by a high number of false positives. Thus, we try to amalgamate static analysis and other algorithms to improve upon the cross-site scripting detection results.
Multivalent Entailment Graphs for Question Answering.

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailment graphs for this purpose. We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. We show that directional entailment is more helpful for inference than non-directional similarity on questions of fine-grained semantics. We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.
A study on improvement points for sustainable management through the analysis of the ISO management system audit report

Purpose The purpose of this study is to analyze the PDCA cycle aspect through empirical analysis of the ISO certification audit report that evaluates the organization''s process and overall system management based on ISO 9001, ISO 14001, and ISO 45001, and to find improvement points for the company''s sustainability management. Method An empirical analysis was conducted focusing on the ‘observation’ presented in 424 audit reports prepared during the ISO management system certification audit and follow-up management audit conducted by Certification Body A from 2018 to 2020. Results The requirements for improvement in each ISO management system include resources(7.1), production and service provision(8.5) in ISO 9001, actions to address risks and opportunities(6.1) and emergency preparedness and response(8.2) in ISO 14001, ISO 45001 identified competence(7.2), operation planning and control(8.1), and emergency preparedness and response(8.2). Conclusion It is very meaningful to explore the points and directions for building a foundation for sustainable management and improving it based on the audit findings presented in the existing ISO management system audit report. Based on this point, it is judged that it will be possible to clearly recognize the core goals pursued by each ISO management system and to develop efficient management activities that can increase the value of the management system.
SIGACT News Complexity Theory Column 110

My deepest thanks to Beatrice and Carlo for their fascinating article, Quantum Finite Automata: From Theory to Practice. Regarding the extent to which their article brings to life both parts of its subtitle... wow! And I think Section 4 is an absolute first for this column; please don't miss it!
Geologic Time

Geologists first unraveled the geologic time scale by relative age-dating, discussed in the last chapter. Once geologists sorted out the order of rock units, subsequent advances in methodologies, detailed in this chapter, by chronometric and numerical means based on radioisotopes, other atomic measures, and quantitative techniques, were employed to measure time. Many minerals and rocks have “clocks” within them that can be used to pin down the actual age of the particular geologic sample or the age of boundaries between formal units of the geologic time scale. This chapter explains how geologists decipher those clocks and determine the ages of rocks by numerical age-dating. The history of radioisotopes is tracked, starting with Ernest Rutherford and Pierre and Marie Curie. The modern geologic time scale is depicted and expanded upon, along with why it is essential for geologic maps and how the time scale can help with people-sized problems and challenges faced on the Earth.
Using Memes to Carve a Space for Undocumented Latino/a Visibility and to Tackle Fossilized Myths

This article considers the impact of memes shared among Millennial and Generation Z–oriented Latino/a social media outlets during the years 2014–17, and proposes reading memes as viable microliterary texts. Through the examination of many dozens of memes and hundreds of Facebook posts from the nonprofit organization UndocuMedia, I have identified two themes that reoccur with notable frequency: (in)visibility and knowledge. As expressed within the memetic platform, these themes have cultural functions beyond superficial banter: humor detracts from political absurdity, arguing points permits one to assume defensive and protective postures, and connecting with friends expands the network of allies. I first define memes and explain how they might be read as socially conscious microliterary texts. I then examine selected meme examples to illustrate how they are shared with the intent to challenge the social and political marginalization that has long plagued the undocumented Latino/a demographic in the United States and to debunk long–held fossilized myths. I conclude by discussing the role of accompanying hashtags and emoji in the process of transplanting online activism to the offline world.
On the robustness of intent classification and slot labeling in goal-oriented dialog systems to real-world noise

Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy data. We collect and publicly release a test-suite for seven common noise types found in production human-to-bot conversations (abbreviations, casing, misspellings, morphological variants, paraphrases, punctuation and synonyms). On this test-suite, we show that common noise types substantially degrade the IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models. By leveraging cross-noise robustness transfer, i.e. training on one noise type to improve robustness on another noise type, we design aggregate data-augmentation approaches that increase the model performance across all seven noise types by +10.8% for IC accuracy and +15 points for SL F1 on average. To the best of our knowledge, this is the first work to present a single IC/SL model that is robust to a wide range of noise phenomena.
Effects of email users' behaviour and demographics on respond to each step of a phishing attack

Phishing is a process in which attackers send emails to Internet users and try to convince them to click on a link to steal their sensitive information or open an attachment to compromise their account, computer, organisation systems, etc. Users' behaviour, such as their risk-taking preference and decision-making style, can influence a phishing attempt's success. However, studies did not profoundly investigate the effects of the behaviours on each step of a phishing process (e.g., opening the email, clicking on the link, and submitting sensitive information on the phishing webpage). This study demonstrated the effects of risk-taking level and decision-making style, gender, age, and education level on the users' respond to each selected step of a phishing attempt.In this real-world study, we measured the behaviours of 135 participants from academia using psychological scales and tests. We then tested their phishability level by sending them simulated phishing emails. The regression analysis results showed that the general risk-taking preference and gender of the users could predict their phishability in the second step, i.e., clicking on the phishing link (p<0.05). We could not find any significant relation between their decision-making style and other demographic factors with the users' phishability level in the second step of the phishing. We also could not find any relations between the measured behaviours, age, gender, and education level of the users and their phishability level in the first and second steps (i.e., opening the phishing email and submitting sensitive data to the phishing website).The results of this study can help us develop proper mitigation actions to minimise phishing success in different steps. Organisations can use this approach to identify risky users and focus on decreasing their phishability level, for instance by providing more training to them or changing the behaviour (if possible). The developed model can be used as a comprehensive framework to investigate other behaviours’ effects in each step of phishing.
MONETARY UNION FEASIBILITY IN THE EAST AFRICAN COMMUNITY: EVIDENCE FROM GPPP

This study utilises the generalised purchasing power parity (GPPP) to assess the feasibility of a monetary union in the East African Community (EAC) region. Time series and panel vector error correction models (VCM) were used to analyse monthly data from 1996-2016 for the five EAC countries. The cointegration results support the existence of a long-run relationship between the tested variables, providing evidence for the optimum currency area (OCA) and the feasibility of monetary union in the EAC region. The VECM results indicate some differences in the size of the coefficients, suggesting that any change/shock of real exchange rate in the region may cause unintended currency flow from one country to the other in the short run, and this may constrain the possibility of an effective and efficient monetary union. Therefore, member countries should harmonise their monetary policies well ahead of the implementation of the monetary union the region.Keywords: East African Community, Monetary Union, GPPP, Optimum Currency Area.JEL Classifications: E42, F15, F31, F36, F45DOI: https://doi.org/10.32479/ijefi.11846
An Analysis of Novice Teacher’s Questioning Types During the Microteaching Classroom

The only thing that is expected in classroom learning activities is the achievement of learning objectives. One of the efforts to achieve the learning objectives is to always communicate with students, such as asking questions. This study aims to determine the query used by novice teachers during their micro-teaching practice in teaching Senior High School students. The theory of Lockhart (1994) is used in this research. Researchers used qualitative methods in processing data. The data obtained from the results of classroom observations with video zoom recording and documentation. The data sources for this study were taken from 10 novice teachers at one of the universities in Indonesia. The author took the data to find out what the novice teacher used during they were doing micro teaching classroom. As a result, the types of question that the teacher used are procedural question, convergent question, and divergent question. Then, the type of question that frequently uses is convergent question. The questions that novice teachers in teaching often use do not require high thinking.
A MACHINE LEARNING APPROACH FOR DIABETICS PATIENT DETECTION USING ENHANCED ANN

In this research work present a hybrid model that is combination of linear regression model and cascaded feed forward network with back propagation training. The detection of the diabetic type 2 patient shows better accuracy as compare to other previous methods. In the different previous method analysis got the drawbacks of previous method that is over come in the proposed method that is shown in the result discussion. The outcome proposed method shown that can be detected with an awfully high accuracy, up to 96.54 % in the different diabetic patient datasets. The proposed method also shows lower complexity and better timing due to regression model soft threshold values. In the proposed hybrid model is the combination of CFFNN and Linear Regression method. Cascade-forward networks are similar to feed-forward networks, but include a connection from the input and every previous layer to following layers. As with feed-forward networks, a two-or more layer cascade network can learn any finite input-output relationship arbitrarily well given enough hidden neurons. For greater accuracy on low-dimensional through medium-dimensional data sets, fit a linear regression model. There are major parts of proposed work that is describe in this thesis work. The accuracy of the proposed methods comparison of different methods such as Discrim, MLP, Logdisc, SMART, Bayesnet, Naive bay, Random Forent, J48, SGD, SMO, Backiprop, RBF, LMT nad proposed hybrid model shows better outcome as compare to other previous methods, that is shown in simulation and result.
Research on the Communication Effect of Emoji Derived from Hot Topics on Social Media

Emojis in the social media era have become a vital part of people's online social activities, and their development has continuously enhanced people's demand for online communication experience.This article sorts out the hot topics that appear on Weibo in 2020, selects the hot topics in the two major areas of business and daily life, and analyzes the use of Emojis and the communication effects achieved under hot topics in different fields.This paper first uses Octopus data collector to collect blog posts and blog comments under the three major topics, then uses ROST CM 6.0 to perform word frequency statistics and semantics analysis on the collected data, and discusses the communication effect of Emoji of hot topics through qualitative methods.Companies in the commercial sector use Emojis to deepen the personification image of brands.In people's daily lives, Emojis provide a window for netizens to express themselves and seek empathy.
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension

Machine reading comprehension (MRC) is one of the most challenging tasks in natural language processing domain.Recent state-ofthe-art results for MRC have been achieved with the pre-trained language models, such as BERT and its modifications.Despite the high performance of these models, they still suffer from the inability to retrieve correct answers from the detailed and lengthy passages.In this work, we introduce a novel scheme for incorporating the discourse structure of the text into a self-attention network, and, thus, enrich the embedding obtained from the standard BERT encoder with the additional linguistic knowledge.We also investigate the influence of different types of linguistic information on the model's ability to answer complex questions that require deep understanding of the whole text.Experiments performed on the SQuAD benchmark and more complex question answering datasets have shown that linguistic enhancing boosts the performance of the standard BERT model significantly.
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation

Pre-trained language-vision models have shown remarkable performance on the visual question answering (VQA) task. However, most pre-trained models are trained by only considering monolingual learning, especially the resource-rich language like English. Training such models for multilingual setups demand high computing resources and multilingual language-vision dataset which hinders their application in practice. To alleviate these challenges, we propose a knowledge distillation approach to extend an English language-vision model (teacher) into an equally effective multilingual and code-mixed model (student). Unlike the existing knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model learns and imitates the teacher from multiple intermediate layers (language and vision encoders) with appropriately designed distillation objectives for incremental knowledge extraction. We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages. Experimental results and in-depth analysis show the effectiveness of the proposed VQA model over the pre-trained language-vision models on eleven diverse language setups.