Reviewerly/exHarmony huggingface dataset:
Using Nonlinear Diffusion Model to Identify Music Signals

In this paper, combined with the partial differential equation music signal smoothing model, a new music signal recognition model is proposed. Experimental results show that this model has the advantages of the above two models at the same time, which can remove noise and enhance music signals. This paper also studies the music signal recognition method based on the nonlinear diffusion model. By distinguishing the flat area and the boundary area of the music signal, a new diffusion coefficient equation is obtained by combining these two methods, and the corresponding partial differential equation is discretized by the finite difference method with numerical solution. The application of partial differential equations in music signal processing is a relatively new topic. Because it can accurately model the music signal, it solves many complicated problems in music signal processing. Then, we use the group shift Fourier transform (GSFT) to transform this partial differential equation into a linear homogeneous differential equation system, and then use the series to obtain the solution of the linear homogeneous differential equation system, and finally use the group shift inverse Fourier transform to obtain the noise frequency modulation time-dependent solution of the probability density function of the interference signal. This paper attempts to use the mathematical method of stochastic differentiation to solve the key problem of the time-dependent solution of the probability density function of noise interference signals and to study the application of random differentiation theory in radar interference signal processing and music signal processing. At the end of the thesis, the application of stochastic differentiation in the filtering processing of music signals is tried. According to the inherent self-similarity of the music signal system and the completeness and stability of the empirical mode decomposition (EMD) algorithm, a new kind of EMD music using stochastic differentiation is proposed for signal filtering algorithm. This improved anisotropic diffusion method can maintain and enhance the boundary while smoothing the music signal. The filtering results of the actual music signal show that the algorithm is effective.
VIRTUAL ELECTRICAL ENGINEERING IN TECHNICAL EDUCATION

The presented paper provides an overview of the possibilities of implementing laboratory teaching in the field of electrical engineering and electronics using an online environment of applications that are freely available to users.The teaching of electrical and electronic subjects are based on the acquisition of basic theoretical and practical knowledge and skills, which pupils or students verify on specific examples and especially in practical laboratory tasks.However, thanks to the ongoing Covid-19 pandemic, these activities have necessitated a relatively fundamental overhaul, not so much in its theoretical basis focused on the practical side, but above all in its form, towards a connectivity approach in the form of tools based on "virtual labs".
Zugzwang: Chess Representation Combining Sonification and Interactive Performance

In this paper we present the design process and the preliminary evaluations of Zugzwang. The system implemented a mixed approach to represent a chess game by combining direct sonification of the moves with instrumental improvisation. The improvisation is guided by a score displayed in real-time that represent the thinking process of the player based on the duration of the moves. To design the system we involved a professional chess player to provide suggestions. We also present a preliminary evaluation of the system with feedback from two chess players.
COVID-19 Economic Tracking and Assistance System (CETAS)

Amidst the current pandemic situation which has been going on for a significant period of time now, although vast numbers of people are safe in their homes, this crisis has rendered millions of people unemployed, mainly the daily wage labourers, etc., who are now struggling with not only the gravity of the coronavirus but also with hunger and unemployment. We are witnessing a rich and poor divide that has crossed boundaries that were never explored before. We are all aware of the hardships of the people who are daily struggling just to manage a day's food for their family, and so as responsible engineers, we plan to develop an Android application which aims on directing financial aid to those who are actually needy by gathering funds from millions of donors across the nation who share the same ideology and wish to help these people out but are unable due to the pandemic related restrictions. People who are deserving of this financial aid and also the people who wish to donate will be able to register themselves on the application, post which verification will be conducted for both the parties after which a designated amount of money will transferred to the needful directly. All cyber security protocols are aimed to be implemented so as there is no presence of any anomaly.
Research on Intent-Slot Recognition Algorithm Based on Knowledge Graph and User Topic

In this article, we propose a joint recognition method of intention and slot. The model can directly obtain the user's intention and slot information through the user's historical interaction information, knowledge graph and current input. In this method, we first use a knowledge reasoning module based on user topic and knowledge graph. The topic model is used to filter the user's historical information, remove meaningless chat information, retain the most important topic information, and further obtain the external information vector from the knowledge graph which is helpful for current intention recognition and slot filling. In addition, we use attention mechanism modules to fuse the historical information, external knowledge vector and sentence features, and suppress the invalid part of external information. Experiments on the data sets show that our method can effectively improve the accuracy of intention recognition and F value of slot filling.
Deep Reinforcement Learning for an Anthropomorphic Robotic Arm Under Sparse Reward Tasks

Training operation skills of a robotic arm using Reinforcement Learning algorithms requires a process of reward shaping, which needs considerable time to adjust. Instead, the setting of sparse rewards makes the tasks clear and easy to modify when the task changes. However, it is a challenge for the agent to learn directly from sparse reward signals because of the lack of reward guidance. To solve this problem, we propose an algorithm based on the DDPG algorithm and add three techniques: Hindsight Experience Replay for improving sample efficiency, Expert Data Initialization for accelerating learning speed in the early stage, Action Clip Scaling for acting stable. For validating our algorithm, we built a simulation environment of an anthropomorphic robotic arm based on the Pybullet module and set up the training interface. There are two tasks trained under sparse reward signals: Push task, Pick and Place task. The experimental results show that the agent can quickly improve the operation skill level in the early stage. It also has a good convergence effect in the later stage, which effectively solving the sparse rewards problem.
Analysis of interdependency of icct underlying technologies and related new research opportunities with special emphasis on cyber security and forensic science

Analysis of interdependency of icct underlying technologies and related new research opportunities with special emphasis on
Information Organization and Information Retrieval in the <scp>LIS</scp> Curriculum

Abstract Within LIS programs, information organization (IO) and information retrieval (IR) serve as foundational topics of instruction. While traditionally represented by required, dedicated courses in LIS curricula, they may now appear as a combined course or in combination with other topics across required courses. The present analysis of 58 IO/IR‐related course syllabi is part of a larger project examining the interrelation of IO and IR instruction across current ALA‐accredited LIS curricula. Preliminary results presented here show significant IO and IR coverage in a range of course types, as well as the heavily applied nature of IR instruction and the movement of IO instruction toward more emergent topics.
An Adaptive Tribal Topology for Particle Swarm Optimization

The success of global optimization rests on the ability for a compatible metaheuristic to approximate global search. Particle swarm optimization (PSO) is one of such heuristics, with the ideal PSO application being one that promotes swarm diversity while incorporating the global progress of the swarm in its performance. In this paper, the authors introduce an adaptive tribal topology within PSO to improve global coverage. Diversity of the swarm population was dynamically managed through an evaluation of a swarm fitness parameter, which takes into account the relative performance a swarm member and its assigned tribe has on finding better objective evaluations. The fitness function simultaneously promotes the breeding of exemplars, and the elimination of swarm members stucks in local minima. The model was evaluated on a series of benchmark problems with unique and diverse search spaces, and the results demonstrate that performance relied on the distribution and scale of the local minima present.
Time-Aware Evidence Ranking for Fact-Checking

Truth can vary over time. Fact-checking decisions on claim veracity should therefore take into account temporal information of both the claim and supporting or refuting evidence. In this work, we investigate the hypothesis that the timestamp of a Web page is crucial to how it should be ranked for a given claim. We delineate four temporal ranking methods that constrain evidence ranking differently and simulate hypothesis-specific evidence rankings given the evidence timestamps as gold standard. Evidence ranking in three fact-checking models is ultimately optimized using a learning-to-rank loss function. Our study reveals that time-aware evidence ranking not only surpasses relevance assumptions based purely on semantic similarity or position in a search results list, but also improves veracity predictions of time-sensitive claims in particular.
Revisiting Lexeme Based Theory’s Word Formation Rules; Counter Evidence From Urdu

The purpose of the current study is to evaluate the application of Lexeme Based Theory on Urdu language, because it claims to engulf, the all aspects of all languages, through its Word Formation Rules (WFRs). In this article, Urdu nouns and adjectives are carefully selected and scrutinized for the processing of these rules. This theory asserts to be universal, because of its rules and declares that morphology enjoys its autonomous position due to these principles, and it does not get crippled to be shouldered by syntax for the building up of meaning. Roots are idiosyncratic and diversification of meaning is confined within the territories of the root, even after inflection and derivation; semantic restriction Isomorphic attributes do not let the root astray from its original meaning. So, on the one hand this theory talks about the integrity of the word (mental lexicon) and on the other side, it says that “phrase like structure do not give unified meaning” Aronoff; 1976. and the word categories (Parts of Speech) never appear like a phrase. They never leave their lexical bases, thus follow No Phrase Constraint (NPC) strictly. This research analysis these two rules and poses these proceeding questions; if Urdu language follows the NPC rule and from where does the Urdu roots inherit their meanings. For this purpose, few of the nouns and adjectives were selected. They were authenticated by the native speakers’ repertoire, and further their word categories and meanings were testified by consulting Urdu dictionaries. The results concluded that Urdu word formation, sometimes do not follow the NPC, thus put the lexical integrity of Aronoff (1976;2010) at stake. Secondly, Urdu language has many polysemic words and their meanings are not self-emergent, unless they are processed in a context (syntax). So, the autonomy of morphology is also defied. This research will be a good addition to many of the languages, where this theory is failed to be fully applicable, in spite of its recent revision.
Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi

The authors evaluate the extent to which a user's impression of an AI agent can be improved by giving the agent the ability of self-estimation, thinking time, and coordination of risk tendency. The authors modified the algorithm of an AI agent in the cooperative game Hanabi to have all of these traits, and investigated the change in the user's impression by playing with the user. The authors used a self-estimation task to evaluate the effect that the ability to read the intention of a user had on an impression. The authors also show thinking time of an agent influences impression for an agent. The authors also investigated the relationship between the concordance of the risk-taking tendencies of players and agents, the player's impression of agents, and the game experience. The results of the self-estimation task experiment showed that the more accurate the estimation of the agent's self, the more likely it is that the partner will perceive humanity, affinity, intelligence, and communication skills in the agent. The authors also found that an agent that changes the length of thinking time according to the priority of action gives the impression that it is smarter than an agent with a normal thinking time when the player notices the difference in thinking time or an agent that randomly changes the thinking time. The result of the experiment regarding concordance of the risk-taking tendency shows that influence player's impression toward agents. These results suggest that game agent designers can improve the player's disposition toward an agent and the game experience by adjusting the agent's self-estimation level, thinking time, and risk-taking tendency according to the player's personality and inner state during the game.
Efektivitas Penggunaan Aplikasi Wordwall Dalam Pembelajaran Daring (Online) Muatan PKN Pada Materi Hak Dan Kewajiban Terhadap Tumbuhan Kelas 4 Di SDN 16 Gunung Tuleh Pasaman Barat Sumatera Barat

This study aims to overcome the boredom of students during distance learning (PJJ) by utilizing the internet network, as well as information and communication technology (ICT). Teachers must package learning into a game. “Games are played primarily for entertainment, fun, but can also function as a means of training, education, and simulation. One of the web-based applications that are easy to use is wordwall. Wordwall can be used to create educational game learning media such as quizzes, matchmaking, pairing, anagrams, word randomization, word search, grouping, and so on. With this application, it is hoped that it will make it easier for students to understand a learning material using online so that the motivation in learning is increasing. From the results of the closing activity for PKN content, the material on rights and obligations to plants in its use of wordwall media, the closing activity was affective, from 20 students it was obtained that 15 people got a complete score, 5 people got an incomplete score on the first test and 17 people got a score completed and 3 people who have not been completed for test II. In the use of Wordwall media, it can be seen that the results of students' learning achievements have been running effectively with students' mastery on the first test of 70% and the results of the second test of 85% (up 15%).
Pre-trained Language Models for Tagalog with Multi-source Data

Pre-trained language models (PLMs) for Tagalog can be categorized into two kinds: monolingual models and multilingual models. However, existing monolingual models are only trained in small-scale Wikipedia corpus and multilingual models fail to deal with Tagalog-specific knowledge needed for various downstream tasks. We train three existing models on a much larger corpus: BERT-uncased-base, ELECTRA-uncased-base and RoBERTa-base. At the pre-training stage, we construct a large-scale news text corpus for pre-training in addition to the existing open-source corpora. Experimental results show that our pre-trained models achieve consistently competitive results in various Tagalog-specific natural language processing (NLP) tasks including part-of-speech (POS) tagging, hate speech classification, dengue classification and natural language inference (NLI). Among them, POS tagging dataset is a self-constructed dataset aiming to alleviate the insufficient labeled resource for Tagalog. We will release all pre-trained models and datasets to the community, hoping to facilitate the future development of Tagalog NLP applications.
Strategy on Disaster Recovery Management based on Graph Theory Concepts

The COVID-19 pandemic has asserted major baseline facts from disaster anthropology during the last three decades. Resilience could be based on the solution to the question: "What is the maximum amount of destruction, if any, that the graph (a network) can sustain while ensuring that at least one of each technology type remains and that the remaining induced subgraph is properly colored?" The concept of a graph's Chromatic Core Subgraph is a solution to the stated problem. In this paper, the pandemic graphs and certain sequential graphs are developed. For these graphs, the Chromatic core subgraph is obtained. The results of the pandemic graphs' Chromatic core subgraph are used to develop a disaster recovery strategy for the COVID-19 pandemic.
University professor: scientific trajectory in the conditions of integration and internationalization

The article considers the concept and the basis of the legal status of a professor of a Russian university. Comparisons with a similar position in France and Germany are given. According to the results of a comparative analysis of legal acts of Russia, Belarus, Armenia, Kazakhstan, Kyrgyzstan, significant differences in scientometric indicators for applicants for the degree of Doctor of Law were revealed. The differences in the criteria for awarding the academic title of professor in the member states of the Eurasian Economic Union are shown. The opinion is expressed about the negative impact of this differentiation on labor mobility and academic mobility. It is concluded that the achievement of scientific indicators is necessary throughout the active creative life of the professor. The tendency of “internationalization” of publication activity is revealed, since the requirements for the availability of publications indexed in international databases are present in legal acts regulating various aspects of the professor's activity. It is recommended to take this factor into account when building the scientific trajectory of a modern professor.
Density Estimation of Heterogeneous Crowd in Mass Religious Gatherings Using Image Processing and Denoising Filter

Crowd density is an essential parameter of crowd dynamics that can be used to manage the crowd, predict pedestrian movement patterns, and minimize crowd risk situations. Inadequate crowd control measures in a mass gathering can eventually lead to crowd disaster. A potential solution to avoid the risk is to predict high-density crowds and prevent them before they turn fatal. Bombay Dharamshala in Ujjain is one such crowded place, where pilgrims usually choose to halt during Kumbh Mela—the world’s largest mass religious gathering. For this study, CCTV footage of Bombay Dharamshala collected on 8-May-2016 during Kumbh Mela is used. The motive of this study is to assess the performance of a deep-learning-based Image Processing Module (IPM) combined with a denoising filter in estimating pedestrian density in the video footage of a highly heterogeneous crowd. The pedestrian count as predicted by the IPM is fed into the denoising filter for noise reduction. The model used for detecting pedestrians is unique, accounting for the crowd’s heterogeneity to benefit Indian mass gathering scenarios. The combined Pedestrian Count Model (PCM) shows an accuracy of 87.28%.
Task-based support in search engines

Darío Garigliotti is a postdoctoral researcher at Aalborg University, Denmark, where he works in collaboration with Prof. Katja Hose and Assoc. Prof. Johannes Bjerva, in the areas of information extraction, information retrieval and natural language processing. He completed his doctoral degree in Computer Science at University of Stavanger, Norway, under the supervision of Prof. Krisztian Balog. The website at https://dariogarigliotti.github.io/ contains more information about Garigliotti's work. The research conducted by Garigliotti focused on utilizing and extending methods and techniques from semantic search within an information access paradigm that aims to support users in achieving their tasks. More specifically, to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The work presented in this thesis is organized in three grand themes: entity type information for entity retrieval, entity-oriented search intents, and task-based search. Alongside the theoretical and empirical contributions, a number of resource contributions were developed, including several corpora and test collections, and a knowledge base of entity-oriented search intents.
Analyzing Deep Learning Models’ Generalization Ability Under Different Augmentations on Deepfake Datasets

Deepfakes allow users to manipulate the identity of a person in a video or an image. Improvements on GAN-based techniques also generate more realistic and hard to detect fake faces. This threatens individuals and decreases trust in social media platforms. In this work, our goal is to report eight different models’ learning ability on, by far, the largest fake face dataset - DFDC. The models’ generalization ability was tested on the DFDC test set and Celeb-DF-v2 dataset. Effect of the various cut-out like augmentations to the learning was also reported.
Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets

Imaging spectroscopy (hyperspectral imaging) has been used to successfully map minerals at the outcrop, deposit, district, and regional scale. This contribution presents spectral-based mineral maps of the Battle Mountain mining district, Nevada, USA, generated using multi-scale airborne imaging and ground-based point spectrometers. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and AisaFENIX 1K imaging spectrometer data were processed using Atmospheric and Topographic Correction (ATCOR-4) software with an empirical correction multiplier derived from field data. Data were used to generate spectral-based mineral maps with spatial resolutions of 13.5 and 1.8 m. A comparison of the various radiative transfer models used to convert radiance data to reflectance indicated that the ATCOR4 rugged model performed best for these datasets. These mineral maps were then used to spectrally characterize two potential porphyry mineral targets in the district.
Digital Contact Tracing in the <scp>EU</scp>: Data Subject Rights and Conflicting Privacy Governance

Abstract The wide‐spread use of digital exposure notification systems, often called contact tracing apps, raises questions about privacy and efficacy of technosolutionism to address COVID‐19. As part of a broad study of global contact tracing apps, this poster explores the governance and privacy implications of European digital contact tracing solutions. We explore legal justifications for data collection in these apps and data subjects' rights with respect to the General Data Protection Regulation (GDPR) and national laws throughout Europe.