Reviewerly/exHarmony huggingface dataset:
Prospects for the use of educational escape room in training psychologists to provide help in crisis situations

Introduction. Training psychologists to provide help in crisis situations should include the formation of not only a high level of professional skills (hard skills), but also a range of social skills (soft skills), including skills of creativity, critical thinking, effective communication, time-management, flexibility and stress resistance, which requires to implement the advance forms and methods of learning. One of such innovation, which is actively implemented in higher education for the development of flexible skills in specialists in various specialties, is the use of educational escape room (EER) as a new form of game learning. That is why to analyze the feasibility of introducing a new form of education into the training of practical psychologists is very relevant. Aim. To identify prospects for use of EER to train psychologists to provide help in crisis situations, as well as for their professional and personal development. Results. A significant number of publications on the researched problem testifies to the high interest of the educational community in the implementation of new intellectual forms of game learning in higher education institutions. The article reveals the meaning of the concept of "educational escape room", as well as the importance of use EER for development of social skills (soft skills) in future psychologists. The expediency and possibility of using EER to train psychologists to provide help in crisis situations are analyzed. The authors of the article see the expediency and prospects of using EER for professional development of psychologists in such fields as organizational psychology, economic psychology, business psychology, conflict psychology, health psychology, educational psychology and others. Conclusions.The information presented in the article can be useful in the implementation of quest technology for the professional and theoretical training of future psychologists, as well as for the development of their social skills.
Implications of digitalization during the COVID-19 pandemics

The article provides with analysis of the proposal of Regulation on European Digital Identity with regards to processing of personal data, particularly those that concerns health. It presents cross-examination with the Mobile Tracing Application and Digital Green Certificate which were established in order to combat the COVID-19. Lastly, it mentions the main challenges and critical positions, as well as proposals on how to tackle some of them.
Application Information for Forensic Analysis Considerations for Registered Host / Users / Device Name &amp; Bearer Tokens

As the dependence on digital information and the Internet continues to grow, it changes the way of computer crime. The number of computer crimes increases dramatically in recent years and investigators have been facing the difficulty of admissibility of digital evidence. To solve this problem, we must collect evidence by digital forensics techniques and analyze the digital data or recover the damaged data. One place to collect evidence is from application software. This paper seeks to find out how to collect, store and analyze application information for forensic purposes. This is also to determine gaps in current research works and proffer recommendations on what future works relate to application forensics. Keywords: Digital Forensics, Evidence, Application Information, Analysis, Registered Host BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Rosemond O. Addo-Sampong (2022): Application Information for Forensic Analysis (Considerations for Registered Host / Users / Device Name &amp; Bearer Tokens) Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 167-172 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P27
Meningkatkan Minat Baca dengan Membangun Budaya Literasi Pada Anak Panti

Literacy activities are becoming increasingly vital and are being implemented on a large scale. Literacy is defined as the capacity to read and write in general. Literacy, on the other hand, is more than just reading and writing; it also encompasses the ability to utilise knowledge sources in print, visual, digital, and auditory formats. Early childhood is the most essential stage for the creation of children's knowledge and conduct, so literacy activities must begin then. Children have a good recall at this age because their personalities are undeveloped, making it simple for them to get caught up in behaviors that are common in everyday life. As a result, PKL I was carried out at the Sinar Damai Sejahtera Orphanage Foundation in order to help one of the children who lacked interest in reading and learning develop a literacy culture from an early age. The micro intervention method is used in the helping process (case work). As indicated by the rise of children's excitement for learning and interest in reading, the goal of this activity was reported to be met.
Meningkatkan Minat Belajar Dengan Memanfaatkan Youtube Dan Aplikasi Kumpulan Soal

Based Program for International Student Assessment ( Bisa) survey, it was released that Indonesia was ranked 72nd out of 77 countries for the student ability category. It is at number 371 for the reading category, and compared to Malaysia which is at number 415. It is at number 379 for mathematics and 396 for science, and compared again with Malaysia, namely at number 440 and 438. Therefore I am motivated to solve problems that arise The problems faced by Rapha'El Simalingkar orphanage children are the lack of interest in learning because they think learning is a boring activity. As well as the target to be achieved, namely increasing interest in learning for the children of the Rapha'El orphanage so that they know how much impact or benefit this learning has. So the solution offered in this field work practice is the provision of motivation and interesting and not boring learning sessions. The Intervention Program used in this Field Work Practice 1 activity is a micro-level intervention (casework) consisting of; Engagement/Intake and Contract, Assessment, Planning, Intervention, Evaluation and Termination.
Fundamental limits of over-the-air optimization: Are analog schemes optimal?

We consider over-the-air convex optimization on a $d-$dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance $\sigma^2$. The codewords satisfy an average power constraint $P$, resulting in the signal-to-noise ratio (SNR) of $P/\sigma^2$. We derive bounds for the convergence rates for over-the-air optimization. Our first result is a lower bound for the convergence rate showing that any code must slowdown the convergence rate by a factor of roughly $\sqrt{d/\log(1+\mathtt{SNR})}$. Next, we consider a popular class of schemes called $analog$ $coding$, where a linear function of the gradient is sent. We show that a simple scaled transmission analog coding scheme results in a slowdown in convergence rate by a factor of $\sqrt{d(1+1/\mathtt{SNR})}$. This matches the previous lower bound up to constant factors for low SNR, making the scaled transmission scheme optimal at low SNR. However, we show that this slowdown is necessary for any analog coding scheme. In particular, a slowdown in convergence by a factor of $\sqrt{d}$ for analog coding remains even when SNR tends to infinity. Remarkably, we present a simple quantize-and-modulate scheme that uses $Amplitude$ $Shift$ $Keying$ and almost attains the optimal convergence rate at all SNRs.
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models

Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions. The integration with an external LM trained on much more unpaired text usually leads to better performance. A Bayesian interpretation as in the hybrid autoregressive transducer (HAT) suggests dividing by the prior of the discriminative acoustic model, which corresponds to this implicit LM, similarly as in the hybrid hidden Markov model approach. The implicit LM cannot be calculated efficiently in general and it is yet unclear what are the best methods to estimate it. In this work, we compare different approaches from the literature and propose several novel methods to estimate the ILM directly from the AED model. Our proposed methods outperform all previous approaches. We also investigate other methods to suppress the ILM mainly by decreasing the capacity of the AED model, limiting the label context, and also by training the AED model together with a pre-existing LM.
Object-Centric Representation Learning for Video Question Answering

Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors. The task demands new capabilities to integrate video processing, language understanding, binding abstract linguistic concepts to concrete visual artifacts, and deliberative reasoning over spacetime. Neural networks offer a promising approach to reach this potential through learning from examples rather than handcrafting features and rules. However, neural networks are predominantly feature-based - they map data to unstructured vectorial representation and thus can fall into the trap of exploiting shortcuts through surface statistics instead of true systematic reasoning seen in symbolic systems. To tackle this issue, we advocate for object-centric representation as a basis for constructing spatio-temporal structures from videos, essentially bridging the semantic gap between low-level pattern recognition and high-level symbolic algebra. To this end, we propose a new query-guided representation framework to turn a video into an evolving relational graph of objects, whose features and interactions are dynamically and conditionally inferred. The object lives are then summarized into resumes, lending naturally for deliberative relational reasoning that produces an answer to the query. The framework is evaluated on major Video QA datasets, demonstrating clear benefits of the object-centric approach to video reasoning.
NorDial: A Preliminary Corpus of Written Norwegian Dialect Use

Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere. There are, however, few available resources to study this variation and its change over time and in more informal areas, \eg on social media. In this paper, we propose a first step to creating a corpus of dialectal variation of written Norwegian. We collect a small corpus of tweets and manually annotate them as Bokm{\aa}l, Nynorsk, any dialect, or a mix. We further perform preliminary experiments with state-of-the-art models, as well as an analysis of the data to expand this corpus in the future. Finally, we make the annotations and models available for future work.
GraphGuess: Approximate Graph Processing System with Adaptive Correction

Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focuses mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.
Identifying and Categorizing Offensive Language in Social Media

Offensive language is pervasive in social media. Individuals frequently take advantage of the perceived anonymity of computer-mediated communication, using this to engage in behavior that many of them would not consider in real life. The automatic identification of offensive content online is an important task that has gained more attention in recent years. This task can be modeled as a supervised classification problem in which systems are trained using a dataset containing posts that are annotated with respect to the presence of some form(s) of abusive or offensive content. The objective of this study is to provide a description of a classification system built for SemEval-2019 Task 6: OffensEval. This system classifies a tweet as either offensive or not offensive (Sub-task A) and further classifies offensive tweets into categories (Sub-tasks B \& C). We trained machine learning and deep learning models along with data preprocessing and sampling techniques to come up with the best results. Models discussed include Naive Bayes, SVM, Logistic Regression, Random Forest and LSTM.
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing

Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual dependency parsing. We train our model on a diverse set of languages to learn a parameter initialization that can adapt quickly to new languages. We find that meta-learning with pre-training can significantly improve upon the performance of language transfer and standard supervised learning baselines for a variety of unseen, typologically diverse, and low-resource languages, in a few-shot learning setup.
Assessing and Supplying the Health of Videos Games via Formal Semantics

Video games, just like any other media have both explicit and implicit messages, and they can have impact on physical and mental health of the users. These impacts can be positive or negative. The impacts, the implications and the meanings which exist in a game can be very widespread, multilayered and complicated. To investigate and guarantee the health of these video games, it is necessary to be able to estimate, assess and determine the implications of video games (from different perspectives). A common approach for studying complicated and multilayered phenomenon is formal semantics. Formal and rigorous methods can help in assessment and supplying the health of video games. In this article, an organizing for this assessment is proposed which is based on formal and rigorous methods and it considers various beneficiaries concerns. Moreover, a technological solution is presented which is based on system compliance to meanings, model checking methods and logical solution. The proposed organizing has several features such as: agility, flexibility, scalability, repeatability of reviews, transparency, adaptation, available details for reviews, assessing various layers and implicit and explicit implications of system of the game, avoiding subjectivity or individual skills, relying on rules and regulations, ability to plan for beneficiaries because of its transparency and employment for specialists.
Text2Chart: A Multi-Staged Chart Generator from Natural Language Text

Generation of scientific visualization from analytical natural language text is a challenging task. In this paper, we propose Text2Chart, a multi-staged chart generator method. Text2Chart takes natural language text as input and produce visualization as two-dimensional charts. Text2Chart approaches the problem in three stages. Firstly, it identifies the axis elements of a chart from the given text known as x and y entities. Then it finds a mapping of x-entities with its corresponding y-entities. Next, it generates a chart type suitable for the given text: bar, line or pie. Combination of these three stages is capable of generating visualization from the given analytical text. We have also constructed a dataset for this problem. Experiments show that Text2Chart achieves best performances with BERT based encodings with LSTM models in the first stage to label x and y entities, Random Forest classifier for the mapping stage and fastText embedding with LSTM for the chart type prediction. In our experiments, all the stages show satisfactory results and effectiveness considering formation of charts from analytical text, achieving a commendable overall performance.
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning

In this work, we propose Behavior-Guided Actor-Critic (BAC), an off-policy actor-critic deep RL algorithm. BAC mathematically formulates the behavior of the policy through autoencoders by providing an accurate estimation of how frequently each state-action pair was visited while taking into consideration state dynamics that play a crucial role in determining the trajectories produced by the policy. The agent is encouraged to change its behavior consistently towards less-visited state-action pairs while attaining good performance by maximizing the expected discounted sum of rewards, resulting in an efficient exploration of the environment and good exploitation of all high reward regions. One prominent aspect of our approach is that it is applicable to both stochastic and deterministic actors in contrast to maximum entropy deep reinforcement learning algorithms. Results show considerably better performances of BAC when compared to several cutting-edge learning algorithms.
Benchmarking Scene Text Recognition in Devanagari, Telugu and Malayalam

Inspired by the success of Deep Learning based approaches to English scene text recognition, we pose and benchmark scene text recognition for three Indic scripts - Devanagari, Telugu and Malayalam. Synthetic word images rendered from Unicode fonts are used for training the recognition system. And the performance is bench-marked on a new IIIT-ILST dataset comprising of hundreds of real scene images containing text in the above mentioned scripts. We use a segmentation free, hybrid but end-to-end trainable CNN-RNN deep neural network for transcribing the word images to the corresponding texts. The cropped word images need not be segmented into the sub-word units and the error is calculated and backpropagated for the the given word image at once. The network is trained using CTC loss, which is proven quite effective for sequence-to-sequence transcription tasks. The CNN layers in the network learn to extract robust feature representations from word images. The sequence of features learnt by the convolutional block is transcribed to a sequence of labels by the RNN+CTC block. The transcription is not bound by word length or a lexicon and is ideal for Indian languages which are highly inflectional. IIIT-ILST dataset, synthetic word images dataset and the script used to render synthetic images are available at http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst
Stagnation Detection in Highly Multimodal Fitness Landscapes

Stagnation detection has been proposed as a mechanism for randomized search heuristics to escape from local optima by automatically increasing the size of the neighborhood to find the so-called gap size, i.e., the distance to the next improvement. Its usefulness has mostly been considered in simple multimodal landscapes with few local optima that could be crossed one after another. In multimodal landscapes with a more complex location of optima of similar gap size, stagnation detection suffers from the fact that the neighborhood size is frequently reset to $1$ without using gap sizes that were promising in the past. In this paper, we investigate a new mechanism called radius memory which can be added to stagnation detection to control the search radius more carefully by giving preference to values that were successful in the past. We implement this idea in an algorithm called SD-RLS$^{\text{m}}$ and show compared to previous variants of stagnation detection that it yields speed-ups for linear functions under uniform constraints and the minimum spanning tree problem. Moreover, its running time does not significantly deteriorate on unimodal functions and a generalization of the Jump benchmark. Finally, we present experimental results carried out to study SD-RLS$^{\text{m}}$ and compare it with other algorithms.
Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Cloud-based computing systems can get oversubscribed due to the budget constraints of their users or limitations in certain resource types. The oversubscription can, in turn, degrade the users perceived Quality of Service (QoS). The approach we investigate to mitigate both the oversubscription and the incurred cost is based on smart reusing of the computation needed to process the service requests (i.e., tasks). We propose a reusing paradigm for the tasks that are waiting for execution. This paradigm can be particularly impactful in serverless platforms where multiple users can request similar services simultaneously. Our motivation is a multimedia streaming engine that processes the media segments in an on-demand manner. We propose a mechanism to identify various types of "mergeable" tasks and aggregate them to improve the QoS and mitigate the incurred cost. We develop novel approaches to determine when and how to perform task aggregation such that the QoS of other tasks is not affected. Evaluation results show that the proposed mechanism can improve the QoS by significantly reducing the percentage of tasks missing their deadlines %. In addition, it can and reduce the overall time (and subsequently the incurred cost) of utilizing cloud services by more than 9%.
UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering

Extracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses. The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in this direction by providing a new dataset on which participants train their models to extract meaningful information on measurements from scientific texts. The competition is composed of five subtasks that build on top of each other: (1) quantity span identification, (2) unit extraction from the identified quantities and their value modifier classification, (3) span identification for measured entities and measured properties, (4) qualifier span identification, and (5) relation extraction between the identified quantities, measured entities, measured properties, and qualifiers. We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multi-turn question answering manner. Our best performing model obtained an overlapping F1-score of 36.91% on the test set.