Reviewerly/exHarmony huggingface dataset:
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization

Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset's bias information. As a result, the obtained model would generalize poorly and even mislead the decision process in real-life applications. We propose to remove the bias information misused by the target task with a cross-sample adversarial debiasing (CSAD) method. CSAD explicitly extracts target and bias features disentangled from the latent representation generated by a feature extractor and then learns to discover and remove the correlation between the target and bias features. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. Moreover, we propose joint content and local structural representation learning to boost mutual information estimation for better performance. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches.
Data for the People, by the People

This chapter assesses how citizens can take a more active role in using data for the public good through civic hacking, citizen science, and data justice initiatives. The 'Myq.ie' project, and the other Code for Ireland projects, are an example of civic hacking: citizens linking together practices of civic innovation with computer hacking to address local issues. In the case of Code for Ireland, members of the local community created their own data-driven apps using accessible datasets, and in some cases produced their own data as well, for other citizens to use. The work is altruistic, performed for the good of the community rather than to create a paid service, and is often undertaken over a number of months or years. Closely related to civic hacking is the practice of citizen science. Here, members of the public generate data and undertake analysis on an ongoing basis to better understand a local phenomenon, or to contribute to a wider scientific study.
Heat Pain Threshold Modulation By Experiencing Burning Hands in Augmented Reality

Visual stimuli can modulate the temperature at which people perceive heat pain. However, very little research exists on the potential use of Augmented Reality (AR) to modulate the heat pain threshold (HPT). In this paper, we investigate whether participants’ HPTs can be modulated observing virtual flames on their hands through a video see-through head-mounted display (VST-HMD). In a pilot study (n = 7), we found that rendering virtual flames had a significant effect (p < 0.05) on the HPT. The virtual flames on the participant’s hand led to a decrease in the temperature at which they would perceive pain related to heat. These results indicate that AR-induced stimuli may be an effective way to achieve top-down modulation of the experience of pain.
RetrievalFuse: Neural 3D Scene Reconstruction with a Database

3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to traditional generative learned models which encode the full generative process into a neural network and can struggle with maintaining local details at the scene level, we introduce a new method that directly leverages scene geometry from the training database. First, we learn to synthesize an initial estimate for a 3D scene, constructed by retrieving a top-k set of volumetric chunks from the scene database. These candidates are then refined to a final scene generation with an attention-based refinement that can effectively select the most consistent set of geometry from the candidates and combine them together to create an output scene, facilitating transfer of coherent structures and local detail from train scene geometry. We demonstrate our neural scene reconstruction with a database for the tasks of 3D super resolution and surface reconstruction from sparse point clouds, showing that our approach enables generation of more coherent, accurate 3D scenes, improving on average by over 8% in IoU over state-of-the-art scene reconstruction.
SSE Tech Support: Kinova Gen 3: Vision Module guide: Kinova MATLAB API

This reference guide demonstrates how to use the color and depth camera vision from the Kinova.
Hrvatsko rječotvorje na društvenim mrežama

Since the first decade of the 21st century, when social networks have become a part of everyday life, socially-oriented activities of Internet users have been on the continual rise. By creating a profile on social networks, Internet users cease to be passive Internet content and information consumers, but rather turn into creators, who shape a dynamic space of new media by virtue of their activities and personal contribution. One of the most significant areas in which social network users introduce numerous changes and innovative contents is most certainly communication and language practices. Each and every social network most often ground their recognizability on a fixed set of communication genres available to their members and in that way determine how the members are going to communicate with each other. This has made all the polyfunctional languages, Croatian among them, respond with a rather swift expansion of the existing vocabulary. In my paper, I will be presenting most productive word formation methods in which Croatian language makes up for social networks lexical gaps and shapes the communication style on social networks and instant messaging services. The first is a semantic loan from the English or neosemanticism word formation (e.g. profil, prijatelj, status, zid, dodati, blokirati, notifikacija); the second is a lexical loan word (e.g. lajkati, postati, šerati, atendati, hejtati, trolati, folover, selfi), and the third is creation of abbreviations (pozz, nezz, bmk, jbt, dns, fkt). Emergence of this new and abundant lexical layer in Croatian language requires from us to register a number of new entry units in general dictionaries of Croatian language.
Literary Genre Recognition among Polish Blog Posts

Robust methods have been proposed for content and topic-based text classification, as well authorship attribution in stylometry. However, the problem of a fine-grained literary genre (style) recognition is much less studied. We present several approaches to the recognition of eight literary genres manually annotated in a large corpus of Polish blogs. Different text representations were combined with neural network classifiers, including deep, recursive neural networks. Very good results were achieved for the representation of blog posts with the help of pre-trained fastText word embeddings and the Bi-GRU recursive deep neural network as a classifier. As the observed good performance of this classifier could be a result of topical bias across genres, experiments on a selected sub-corpus with a reduced dominance of the most frequent topic were also conducted with no significant change observed.
Persepsi Mahasiswa Terhadap Pembelajaran Daring

This study aims to describe students’ perceptions towards online learning. This research uses a quantitative descriptive approach. The data was collected was through questionnaire by adopting the Maulana and Hamidi (2020) instrument which measures three aspects, namely aspects of teaching and learning, capabilities or abilities of lecturers, and infrastructure. The subjects of this study were the Satya Wacana Christian University Guidance and Counseling Study Program students with a sample of 135 students. The results showed that overall in the teaching and learning aspect as many as 77 students or an average percentage of 57% said that in the medium category, the capability aspect (lecturer ability) was 81 students or the average percentage of 60% said it was in good category, and in the aspect of facilities and infrastructure as much as 67 students or an average percentage of 50% said that the facilities and infrastructure for online learning were in the medium category.
Partitioning Cloud-based Microservices (via Deep Learning)

Cloud-based software has many advantages. When services are divided into many independent components, they are easier to update. Also, during peak demand, it is easier to scale cloud services (just hire more CPUs). Hence, many organizations are partitioning their monolithic enterprise applications into cloud-based microservices. Recently there has been much work using machine learning to simplify this partitioning task. Despite much research, no single partitioning method can be recommended as generally useful. More specifically, those prior solutions are brittle''; i.e. if they work well for one kind of goal in one dataset, then they can be sub-optimal if applied to many datasets and multiple goals. In order to find a generally useful partitioning method, we propose DEEPLY. This new algorithm extends the CO-GCN deep learning partition generator with (a) a novel loss function and (b) some hyper-parameter optimization. As shown by our experiments, DEEPLY generally outperforms prior work (including CO-GCN, and others) across multiple datasets and goals. To the best of our knowledge, this is the first report in SE of such stable hyper-parameter optimization. To aid reuse of this work, DEEPLY is available on-line at this https URL.
A Multi-population Coevolution Multi-objective Particle Swarm Optimization Algorithm

In this paper, a multi-population coevolution multi-objective particle swarm optimization(MPCMOPSO) algorithm is proposed. In the proposed algorithm, multiple subpopulations and an inferior population are used to search jointly, which strengthens the utilization of inferior particles and not only improves the efficiency of the algorithm, but also effectively improves the convergence and searching ability of the algorithm. Archive set is used to retain non-dominated paticles to realize information exchange between subpopulations and the inferior population, where non-dominated particles in the archive set are used to replace the inferior particles. Moreover, a strategy to prevent particles from falling into local optima is proposed. Finally, experiment results on ZDTs series functions and comparison with other algorithms are given to validate the proposed algorithm.
IMPLEMENTATION AND ANALYSIS OF CEPH DISTRIBUTED FILE SYSTEM ON CONTAINER ORCHESTRATIONS

Gebrakan cloud terbaru yang menghasilkan pertumbuhan luar biasa dalam virtualisasi dan sistem file terdistribusi (DFS) membuat klasterisasi komputer menjadi standar baru. Hampir setiap sistem terdistribusi diperlukan untuk mempertahankan tingkat keandalan dan ketersediaan tertinggi. Ini menciptakan permintaan baru dalam layanan penyimpanan yang konsisten untuk mendukung server. DFS seperti Ceph, adalah teknik untuk memungkinkan set komputer terdistribusi untuk berbagi data dan sumber daya penyimpanan dengan menggunakan sistem file umum. Container orchestration seperti Swarm dan Kubernetes, adalah alat yang memungkinkan otomatisasi untuk menarik, menyebarkan, dan mengelola kontainer yang sedang berjalan. Kedua teknologi ini relatif baru dan menjadi tren di komunitas TI. Ceph, Docker dan Kubernetes menjadi kata yang semakin umum. Penelitian ini melakukan implementasi dan analisis Ceph DFS pada container orchestration. Penelitian ini akan melakukan analisis komparatif antara kinerja Ceph di Swarm dengan Ceph di Kubernetes dalam empat skenario yang berbeda menghasilkan dua parameter yang berbeda yaitu throughput dan IO per detik. Setelah hasil yang diperoleh, hasilnya akan diproses lebih lanjut menggunakan metode statistik tertentu. Hasil penelitian ini menunjukkan bahwa Ceph di Kubernetes menghasilkan hasil yang lebih baik terhadap Ceph di Swarm. Pengujian statistik memverifikasi ini dengan menunjukkan tingkat signifikansi statistik dan tren yang lebih cenderung ke arah Ceph di Kubernetes.The recent cloud boom which resulted in tremendous growth in virtualization and distributed file system (DFS) made computer clustering become a new standard. Almost every distributed system is required to maintain the highest level of reliability and availability. This creates a new demand for consistent storage service to support servers. DFS like Ceph is a technique to allow a set of distributed computers to share data and storage resources by using a common file system. Container orchestrator like Swarm and Kubernetes is a tool that enables automation to pull, deploy and manage running containers. Both technologies are relatively new and trending in the IT community. Ceph, Docker, and Kubernetes become more and more common buzzwords. This research conducts an implementation and analysis of Ceph DFS on container orchestration. This research is going to conduct a comparative analysis between the performance of Ceph in Swarm with Ceph in Kubernetes in four different scenarios resulted in two different parameters which are throughput and IO per second. After the results obtained, the results are going to be further processed using certain statistical methods. The results of this research show that Ceph in Kubernetes produces better results against Ceph in Swarm. The statistical testing verified this by showing statistical significance level and trends more inclined towards Ceph in Kubernetes.
The Meaning and Path of Reconstruction of Cultural Value in Rural Schools

In the process of new-type urbanization development, the gradual discard and ignorance of rural cultural characteristics within rural education eventually led to the loss of cultural value of rural schools. Meanwhile, rural schools play the critical role of carrying out the cultural transmission and innovative development, therefore, the reconstruction of the school value also contributing to the realization of cultural value, including the aspects such as the rejuvenation of social education function, the cultivation of cultural perceivers, and the inheritance of outstanding culture. On the basis of rationality carefully examined the meaning of rural school cultural value, it is suggested to revoke the consciousness of rural school culture, enhancing the sense of belonging of rural teachers, and the construction of rural-school cultural community, so as to promote the reconstruction of rural school cultural value.
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

Human-designed data augmentation strategies have been replaced by automatically learned augmentation policy in the past two years. Specifically, recent work has empirically shown that the superior performance of the automated data augmentation methods stems from increasing the diversity of augmented data \cite{autoaug, randaug}. However, two factors regarding the diversity of augmented data are still missing: 1) the explicit definition (and thus measurement) of diversity and 2) the quantifiable relationship between diversity and its regularization effects. To bridge this gap, we propose a diversity measure called Variance Diversity and theoretically show that the regularization effect of data augmentation is promised by Variance Diversity. We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity. An unsupervised sampling-based framework, \textbf{DivAug}, is designed to directly maximize Variance Diversity and hence strengthen the regularization effect. Without requiring a separate search process, the performance gain from DivAug is comparable with the state-of-the-art method with better efficiency. Moreover, under the semi-supervised setting, our framework can further improve the performance of semi-supervised learning algorithms compared to RandAugment, making it highly applicable to real-world problems, where labeled data is scarce. The code is available at \texttt{\url{https://github.com/warai-0toko/DivAug}}.
SmartEYE CCTV - NAZ Company / Nurizatul Amna Shamsul Bahari

NAZ Company have new product to introduce to the customers which is the SmartEYE CCTV. NAZ Company is targeting in supply for CCTV since it is an important tool for security surveillance 24 hours. Nowadays, crime has been increasing in a fast mode and CCTV function are the solution to get rid of the problem since it can be used for security system around us. SmartEYE CCTV is a customer-friendly which is helps people saving time to monitoring for hours. The simple design and new colours of SmartEYE CCTV make it more attractive. The special of this new product is that it has the ability to capture high resolution images of HD coloured and can recorded sound with a good and high quality. Overall marketing strategy will be included specific marketing philosophy and strategy of the company, the value chain and the channel of distribution in the target market. First of all, the target market will be based on the residence in Kuala Lumpur area since NAZ Company was in Kuala Lumpur. For initial intensive selling effort, NAZ Company has set the target customers in other words, types of customer groups that would buy its new product. My target customer is for the working people that are having the middle- and top-income group (M40&T20) since SmartEYE CCTV is a quite expensive product.
A CATEGORY-THEORY APPROACH FOR CONSTRUCTION ONTOLOGIES IN SUBSURFACE MASS TRANSIT

Abstract. The construction and expansion of subway systems represents an important step towards better livability conditions in a rapidly urbanizing world. However, underground construction has not benefited from well-established ontologies of semantic and geometric representation, such as Building Information Modelling (which is used for standalone structures) and City Geography Markup Language (which is designed for continuous urban elements). To bridge that gap, this paper proposes a novel and highly flexible means to underpin a relevant ontology. The approach uses the ontology log, or olog, a model of knowledge representation based on Category Theory. In an olog, dependencies between objects are restricted to functional relationships (for every object there is a unique correspondence). This robust mathematical formulation allows for a more flexible, yet also informative and user-readable model of the studied entities. In this paper, the olog’s usability is demonstrated through the ontological representation of common items in the fare-control areas of two New York City metro stations. Ologs are shown to capture similar underlying structures both across different stations and within the same station. Importantly, the olog allows for further generalization to incorporate pre-existing data, as well as being a transferable framework for conceptualizations of other metro systems.
THE EFFECT OF PICTURE SERIES AND STUDENTS LEARNING MOTIVATION ON WRITING COMPETENCY

The purposes of this research are of three folds: first, to closely look into whether there are significant differences in writing achievement; second, the learning motivation of students in writing; third, significant differences in writing achievement between the students with high-learning motivation taught using Picture series technique and those taught by using the conventional technique; and the significant differences in writing achievement between the students with low-learning motivation taught using Picture Series technique and those taught by using conventional technique. This research uses writing achievement test to assess the students’ score in writing. The writing achievement test was administered at the end of teaching learning process to obtain information on the effect of the treatment upon the students’ writing achievement. In this research, it was discovered that there was significant effect of picture series on writing competency as well as the effect of writing competency on high and low learning motivation students who were taught by using guided writing strategy and the conventional strategy.
THE IMPACT OF MOTIVATION OF PRIMARY STUDENTS ON THE IMPLEMENTATION OF DISTANCE LEARNING (SPORTS DAY EXAMPLE)

The article contains the description of the bases and starting points of primary physical education and sports day in Slovenia and other European countries. It presents the motivational point of view of the students at the implementation of distance learning for a sports day. There is a graphic display of the results of an online survey that was carried out among teachers and students of Komen primary school, branch of Štanjel. Fifty students from the 1st to 6th grade and 7 teachers took part in the survey. The purpose of the article is the research about what influences the motivation of students in distance learning. From the collected data it was found out that the ICT and interdisciplinary connection have played the most important role in the implementation of the sports day. The answers of the teachers show that besides the ICT devices, the weather, the family and interdisciplinary connections are the main motivators for the children. The students were very motivated at the implementation of the distance sports day. The teachers, as well as students, have found advantages and disadvantages in the organization of a distance sports day.
Problems Faced By Students Of English Tutorial Program (ETP) In Performing Speaking At Muhammadiyah University Of Surakarta

This study aims at describing the problems faced by students of English Tutorial Program (ETP) in performing speaking. The purpose of this study was to find the problems faced by students in performing speaking, the factors that influence by problem faced on students in performing speaking, and how do the students solve to overcome this problem in performing speaking. It was a descriptive qualitative method. This subject of this research were 9 ETP students. Data collection techniques by questionnaire, online interview, and documentation. The result of this research indicate that there are 5 students problems in performing speaking, including: 1) lack of vocabular, 2) poor in pronunciation, 3) poor in grammar, 4) lack of self-confidence, and 5) anxiety. There are 3 factors that influence by problem faced on students in performing speaking, including: 1) inhibition, 2) low or uneven participation, and 3) nothing to say (lack of motivation). Here are some ways students use to solve this problem in performing speaking, including: 1) self-motivation, 2) memorizing vocabulary every day, 3) learning how to pronounce English through a dictionary, 4) watching movies with English subtitles, and 5) learn grammar.