Reviewerly/exHarmony huggingface dataset:
Language education injustices in Mexican indigenous communities during COVID-19 pandemic

In Mexico, due to the pandemic caused by COVID 2019, most students have received remote education through public television channels and video calls via payment internet services. Although these practices can provide helpful strategies for students to continue with their school training process, the distance education modality began to demonstrate the social inequalities most students find themselves in. This situation has increased students' social and educational disparities in the South of Mexico. In that case, many indigenous communities are good examples of language education injustices before the pandemic, which has become a more critical situation during and after the pandemic. For instance, classes are only in Spanish; some indigenous teachers do not speak the language or community language variety. There has also been a lack of teachers for more than a year in different schools. Based on endangered indigenous languages experiences, in this paper, we aim to reflect on the notion of the school as a space for the democratisation of knowledge. However, in indigenous contexts, one can see critical social injustice conditions for students instead of democratisation of knowledge, as we said before. For that reason, we argue that the COVID-19 pandemic is only an issue and a minuscule particle to show the historical and structural language education injustices for indigenous people. Hitherto, it is necessary to recognise this situation all the time.
Guaranteeing Service Level Agreements for Triangle Counting via Observation-based Admission Control Algorithm

Maintaining guaranteed service level agreements on distributed graph processing for concurrent query execution is challenging because graph processing by nature is an unbalanced problem. In this paper we investigate on maintaining predefined service level agreements for graph processing workload mixtures taking triangle counting as the example. We develop a Graph Query Scheduler Mechanism (GQSM) which maintains a guaranteed service level agreement in terms of overall latency on top of JasmineGraph distributed graph database server. The proposed GQSM model is implemented using the queuing theory. Main component of GQSM is a job scheduler which is responsible for listening to an incoming job queue and scheduling the jobs received. The proposed model has a calibration phase where the Service Level Agreement (SLA) data, load average curve data, and maximum load average which can be handled by the hosts participating in the cluster without violating SLA is captured for the graphs in the system. Results show that for a single host system the SLA is successfully maintained when the total number of users is less than 6.
High Performance Computing in Julia

The book introduced in XXII Escola Regional de Alto Desempenho da Região Sul (ERAD/RS) – event coordinated by Sociedade Brasileira de Computação (SBC) and Comissão Regional de Alto Desempenho do Estado do Rio Grande do Sul (CRAD/RS) with the aim of qualifying professionals from the south region of Brazil – , involves the integration of parallel programing researchers. Written in five chapters, this book is able to provide a grand variety of convergent content, focusing on performance. &nbsp; In its first chapter, entitled “Desenvolvimento de Aplicações Paralelas Adaptativas: Uma Visão de Duas Décadas (2001-2021)”, the authors explain about the evolution of the parallel &nbsp;architectures and the transcription of their uses in dynamic environment. The theme provoques an interesting reflection not only for already-consolidated academics, but also for young students searching for new challenges. In the following two chapters, the main topics are programing languages and technical in parallel computing, named as “Computação de Alto Desempenho em Julia", which refers to modern language, easily used and learn; and "Introdução à Programação com Memória Persistente", which discuss about the motivation and problems related to consistency of processors’ datas with persistent memory (PM) In the fourth chapter, the essential aspects, the evaluation and the differences from the programs aiming to achieve the Gaming section were summarized and discussed. Lastly, the outcome of the book highlights the omnipresent theme from research turned to the processing of high performance (PAD), “Apresentação de Resultados Experimentais para Processamento de Alto Desempenho em R”, which proposes ways to better dispose and reproduc the datas in academic texts and experiments.
Gold and silver minerals in ore facies of Alexandrinskoye VHMS deposit (Southern Urals)

The distribution and composition of gold-silver mineralization in various ore facies of Alexandrinskoye deposit have been studied. In feeder stockwork zone native gold develops as streaks in the contacts between As-containing pyrite and galena. In “black” sphalerite-galena-barite ores on the wedging out of ore body, native gold and hessite associate with galena too. In sulphide chimneys native gold is confined to chalcopyrite-sphalerite encrustations. In clastic ores, subjected to seafloor alteration, the highest contents of gold are confined to roof, and silver – to bornite-containing ores in the base of ore clastic cyclites.
Implementation of API Programming In Company Profile Applications Based On Android Mobile

Company Profile as an image builder and carries a big goal in building university identity. From the point of view of the public and prospective new students, the company profile represents the university in showing various sides that it is proud of and has achieved that will determine how the university develops in the future, considering that other universities see a university from how they build their image. The development of the use of mobile android in the company profile brings new challenges how the capacity of the information produced will be more attractive to prospective students. Narotama University Surabaya as an international-scale campus in education requires this capacity in its company profile so that the company profile not only displays the university profile but can also guide prospective students in various university agendas.
Message from the General Chairs

It is our great pleasure and honor as General Chairs of IEEE BigDataSecurity /HPSC/IDS 2022 to welcome you to the 8th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2022), the 8th IEEE International Conference on High Performance and Smart Computing (IEEE HPSC 2022), the 8th IEEE International Conference on Intelligent Data and Security (IEEE IDS 2022), and the co-located events. IEEE BigDataSecurity /HPSC/IDS 2022 Committees organize these three co-located conferences.
A Tag-Based Post-Hoc Framework for Explainable Conversational Recommendation

Explanations are important for conversational recommendation. They help users to understand how the recommender system works, and elicit user's responses by allowing users to provide informative feedback on them. During the interaction with users, explainable conversational recommender system provides explanations and collects user feedback to further refine the recommendations. Current conversational recommender systems, however, usually make recommendations with black-box prediction models, bringing difficulty in model explainability. Moreover, existing methods for explainable recommendation commonly provide one-shot explanations and fail to leverage user feedback. In this paper, we propose a tag-based post-hoc framework for explainable conversational recommendation (TPECR), which enables black-box recommendation models to provide explanations and refine recommendations based on user preference on tags (e.g., item attributes). Specifically, given the recommendation model being explained, TPECR trains a generation model to construct user embeddings based on their tag preferences. The explanations are provided by utilizing the generation model to estimate the contributions of different tags with respect to each item prediction. Given user feedback, the recommendation at the next turn is refined by tuning the tag preference and generating modified user embedding with the generation model. We instantiate the generation model with conditional variational auto-encoder (CVAE), which reconstructs user embedding conditioned on his tag preference. We conducted experiments by applying TPECR to different models and the results demonstrated the effectiveness of our TPECR on both synthetic and real datasets.
Web 3.0: The Future of Web

Abstract: The current web 2.0 is about connecting peoples during which the social media platforms were invented. Web 2.0 essentially meant the amendment from static to user-generated content and conjointly the expansion of social media where users can interact with each other. The purpose of this study is to outline web 3.0 and discuss the underlying technologies, establish new opportunities and highlight potential challenges that are associated with the evolution to web 3.0 technologies. Keywords: Semantic web, Web technologies, Web 2.0, Web 3.0, Decentralized web
Conclusion to this book

As discussed in previous chapters, we can find out that the integration of these emerging technologies definitely will change the way of living, working in the next decade. But together these technologies get serious challenges like leaking of personal information, habits by IoT devices, require high energy for machine learning (ML) to refine big data, require more storage in cloud computing and many data center to store this data, security of this data by blockchain and providing authentication process to these integrated (ML + blockchain + Internet of Things (IoT)) systems, and many more. Hence, this section discusses few faced critical challenges in the integration of these technologies as:
Introduction to machine learning, blockchain technologies, and Big Data analytics for IoTs: concepts, open issues, and critical challenges

Today a plethora of applications are trying to shift toward using the Internet of Things (IoT) or Internet Connected Things. IoTs can be used in many applications such as Healthcare, Agriculture, Transportation, and Logistics. These devices can help healthcare professionals take care of patients in real time. However, there are several issues around smart devices such as security, privacy, and trust. Blockchain is the most secured technology today. It is a novel concept to provide a decentralized and distributed structure and is based on a distributed ledger concept. This chapter discusses IoT and its uses in smart AI-based applications with the integration of blockchain.
A DISCOURSE ANALYSIS ON STUDENTS SKILL IN INTERPRETING ENGLISH SENTENCES INTO INDONESIAN IN ENGLISH INTERPRETATION CLASS DURING COVID-19 PANDEMICS

The purpose of this research was to know about the students’ difficulties in interpreting English sentences into Indonesia in English Interpretation Class. The type of this research was qualitative research which was the case study to 8 students as the informants. They had been chosen by using purposive sampling. The data were collected by seeing the interview guided. After collecting the data, the validity of the data was tested by using prolonged engagement. Then, it was analyzed by reducing, displaying, verifying, and drawing the conclusion. Based on the result of the research, it can be concluded that there some difficulties found by the students in interpreting English sentences into Indonesian. They are: 1) Being difficult to adjust the meaning of a word with the sentence, 2) Being difficult to find a word in the dictionary, 3) Having lack of vocabulary, 4) Having lack of practice, 5) Time Pressure that contributes to nervousness, 6) Being difficult to read faster the slide show, 7) Having lack of signal, 8) Tending to translate word by word.
Optimal Performance of Security by Fragmentation and Replication of Data in Cloud

Abstract: Outsourcing data to a third-party administrative control, as is done in cloud computing, gives rise to security concerns. The data compromise may occur due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. However, the employed security strategy must also take into account the optimization of the data retrieval time. In this paper, we propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively approaches the security and performance issues. In the DROPS methodology, we divide a file into fragments, and replicate the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker.
Interactive systems for music composition: a new paradigm in music education

The realities of modern music education have forced many educators and learners to look for new solutions to ensure a high-quality learning process. One of them is creating and integrating interactive music composition systems. This study aims to assess the impact of Lumanote, as an interactive music composition system, on piano students’ skill level and motivation for making their own pieces of music. For this, an experiment research with a control group was carried out, with observation, comparison and evaluation as the underlying research methods. The study involved 74 piano students from three universities: Shihezi University, Abai Kazakh National Pedagogical University, and Guizhou Minzu University. The study found that pianists who used Lumanote had a considerably higher level of motivation (2.853) compared to learned who constructed melodies without the help of the software (2.427). Regarding the skill level, a significant difference was present only between the groups’ total means (Z = −2.53; p = .031). The results of this work may be useful to those engaged in the music education industry. According to the empirical data, a new interactive music system can support music composition whenever there are barriers to effective learning. Future research can focus on developing and evaluating the alternative interactive music systems.
Influence of undetected corneal glints on gaze estimation during manual work

Abstract This work is concerned with model-based remote gaze estimation through monocular video oculography using the pupil center and corneal reflections in the context of manual work. Based on simulations, the influence of undetected corneal glints on the estimated direction of gaze is quantified and discussed.
Interpreter

The goal of the Interpreter design pattern is, you guessed it, to interpret input, particularly textual input, although to be fair it really doesn't matter. The notion of an interpreter is greatly linked to Compiler Theory and similar courses taught at universities. Since we don't have nearly enough space here to delve into the complexities of different types of parsers and whatnot, the purpose of this chapter is to simply show some examples of the kinds of things you might want to interpret.
Blockchain Technology In The Process Of Selling Pharmaceutical Products

This article discusses a mathematical model for the implementation of pharmacological products using Blockchain technology. In addition, the current problems of the shadow drug market (corruption, resale) are analyzed and practical solutions are given to eliminate them. Blockchain technology, due to its decentralization, is one of the best solutions today. This is due to confidentiality, the immutability of the hash function, the speed of transactions within the technology. The integration of the hybrid Blockchain will allow using distributed registries more deeply - entering data into the Blockchain about transportation, about the conditions of logistics activities, about expiration dates, etc. Tracking can be done using marking matrix codes (Data Matrix), which are now implemented by the Information Monitoring System drug movement. The operator is the Center for the Development of Advanced Technologies (CDAT), which is already using Blockchain technologies to compile marking codes. In addition, the article presents a schematic representation of the industrial sale of medicines using Blockchain technology. The strengths of its introduction into the sales structure are shown. All this distinguishes Blockchain technology from other current trends.
Beyond Social Distancing: Application of real-world coordinates in a multi-camera system with privacy protection

In this paper, we develop a privacy-preserving framework to detect and track pedestrians and project to their real-world coordinates facilitating social distancing detection. The transform is calculated using social distancing markers or floor tiles visible in the camera view, without an extensive calibration process. We select a lightweight detection model to process CCTV videos and perform tracking within-camera. The features collected during within-camera tracking are then used to associate passenger trajectories across multiple cameras. We demonstrate and analyze results qualitatively for both social distancing detection and multi-camera tracking on real-world data captured in a busy airport in Dublin, Ireland.
Dense correspondence of deformable volumetric images via deep spectral embedding and descriptor learning

Deformable image correspondence plays an essential role in a variety of medical image analysis tasks. Most existing deep learning-based registration and correspondence techniques exploit metric space alignments in the spatial domain and learn a nonlinear voxel-wise mapping function between volumetric images and displacement fields, agnostic to intrinsic structure correspondence. When confronted with high-frequency perturbations of patients' poses and anatomical structural variations, they relied on prior rigid and affine transformations, as well as additional segmentation masks and landmark annotations for reliable registration. This paper presents a data-driven spectral mapping-based correspondence framework to handle the intrinsic correspondence of anatomical structures. At the core of our approach lies a deep convolutional framework that approximates spectral bases and optimizes volumetric descriptors. The multi-path graph convolutional network-based spectral embedding approximation module relieves the computationally expensive eigendecomposition-based embedding of volumetric images. The deep descriptor learning module surpasses the prior hand-crafted descriptors and the descriptor selection. We showcase the efficacy of the core modules, i.e., the spectral embedding approximation and descriptor learning, for volumetric image correspondence and the atlas-based registration on two volumetric image datasets. The proposed method achieves comparable correspondence accuracy with the state-of-the-art deep registration models, resilient to pose and shape perturbations.
IGCE: A Compositional Energy Concept Based Deep Image Generation Neural Network

Image generation (IG) aims to generate new images from existing dataset. Generation quality, diversity and stable training are three primary challenges in image generation task. This paper proposes a novel compositional energy concept-based network, IGCE, to simultaneously overcome the three challenges. To ensure high generation quality, we propose an energy image decomposition mechanism to map image into new representation space. Our method is able to generate a full-size new image with complicated details. For diversity, we propose a latent representation method to support IGCE with adaptive learning ability. Furthermore, we propose an energy generation loss to constrain IGCE with stable performance in various datasets. Experimental results on public released datasets DIV2 and CelebA datasets show that images generated by our proposed IGCE imply accurate similarity with original datasets. The generated images reveal potentials on applications like security communication, a target forgery biometric identification etc.
Learning Robust Latent Space of Basketball Player Trajectories for Tactics Analysis

Tactic analysis of trajectory data plays an important role in the field of sports science. However, the tactical labels of trajectories are usually insufficient, which limits the deep models to learn the general concept of tactics. In this work, we combine the recurrent variational autoencoder with attention module to learn a robust latent space of the basketball offensive trajectories without the need of labeled data. The learned latent space can be further applied to advanced tasks such as supervised tactics classification, unsupervised tactics clustering, and defensive trajectory generation. The experimental results show that our proposed model achieves better or competitive performance than the previous methods. Furthermore, our proposed model is the first one that focuses on zero-shot tactics clustering and the performance even outperforms the previous methods with supervised and unsupervised settings, which shows the promise of tactics analysis for trajectories in the case of limited labeled data.
Learn Effective Representation for Deep Reinforcement Learning

Recent years have witnessed an increasing application of deep reinforcement learning (DRL) on video games. While deeper and wider neural networks have played a crucial role in computer vision and natural language processing, such capacity remain under-explored in most DRL works. Under the fact that feature propagation together with large networks contributes to learning a good representation, we propose an end-to-end Large Feature Extractor Network (LFENet) that uses large neural networks with dense connections to train a high-capacity encoder. Even though the increased dimensionality of input is usually thought to result in poor performance for RL agents, we introduce the information bottleneck to alleviate the problem. Finally, we combine LFENet with Proximal Policy Optimization (PPO) algorithm. Through numerical experiments on Atari 2600 video games, we demonstrate our method matches or outperforms state-of-the-art algorithms.
Metode Bayesian untuk Estimasi Parameter Distribusi Eksponensial pada Data Tersensor

Parameter is a value that describe the characteristics of a population. But the parameterof a real data, the value is unknown. To estimate the value of the parameter,there are several methods, which are maximum likelihood estimation method (MLE)and Bayesian parameter estimation method. In Bayesian method, the prior informationis applied to update the current data. The prior is determined based on the informationin the data. This mini thesis is using censored data with exponential distribution, andusing the conjugate prior. Followed by squared error loss function (SELF), the estimatedvalue function ot the λ parameter is ˆλ =α+Σ_{i=1}^{n}δ_{i}β+Σ_{i=1}^{n}t_{i}with α and β are hyperparameters,Σ_{i=1}^{n}δ_{i} is the number of objects that experienced the event and Σ_{i=1}^{n}t_{i} is the numberof the survival time. When the function was applied on Stanford heart transplant data,the value of ˆλ = 0.00089, which means the patient’s failure (death) probability is lowand the patient’s probability to survive is high.
Integrating the Fairlight CVI into the video workflow

The Fairlight Computer Video Instrument (CVI) is one of the earliest video synthesisers, released in 1984. Over time the aesthetic of its effects has evolved from revolutionary to retro. Although MIDI was developed at around the same time, the CVI is controllable via RS232 rather than MIDI. This paper reviews its history and applications in live and studio-based video production environments. A method is outlined for controlling the CVI that allows sequencing of control data, effect automation, and integration with a digital audio workstation. Results are presented from a system using Logic Pro X for sequencing and Processing for MIDI-serial conversion.