Reviewerly/exHarmony huggingface dataset:
Towards High-Efficiency Data Centers via Job-Aware Network Scheduling

Distributed jobs typically facing competition for multiple resources in modern data centers, especially for network. Without effective network scheduling, this competition can cause low efficiency of the data center. Previous work on network scheduling has been focused on reducing flow completion time or improving per-flow fairness. Yet, its effect on improving jobs’ performance is limited by the unawareness of relationships between communication and computation. In this paper, we focus on the problem of scheduling network resources for multiple jobs, with the specific objective to reduce the job completion time (JCT), which also makes the datacenter more efficient. With an in-depth investigation of communication and computation, we identify an opportunity for accelerating jobs in a way that occupies less bandwidth for DAG-based complicated modern jobs. Accordingly, this paper proposes JIT, a job-aware network scheduler that leverages the computational graph to accelerate jobs effectively. To cater to the goal of JIT, we first develop a mathematical model and formulate the scheduling problem as an integer linear programming (ILP) problem. We further prove that it has an equivalent linear programming (LP) problem through rigorous theoretical analysis in order to solve this ILP problem efficiently. Some reasonable simplifications are also adopted to reduce the solving time of JIT to only 1 second. The proposed JIT is simulated and compared against some state-of-the-art designs, and the simulation results demonstrate that JIT can achieve an acceleration of up to 1.55 × , which successfully improves the efficiency of the data center.
Towards accelerated robotic deployment by supervised learning of latent space observer and policy from simulated experiments with expert policies

Up until today robotic tasks in highly variable environments remain very difficult to solve. We propose accelerated robotic deployment through task solving on low-level sensor data in simulation. A simulation allows for a lot of data, which is usually not available in a real world robotic setup due to cost and feasibility. Solving tasks in simulation is safe and a lot easier due to the huge amount of feedback from virtual sensory data. We present a novel sim2real architecture for converting simulated low level sensor data policies to high level real world policies. After solving a task we let the robot complete it a number of times in simulation using domain randomization, while doing so we save the simulated sensor data corresponding to the real robotic setup and actions taken. Given these sensor data and actions a task specific policy can be trained using our architecture. In this paper we work towards a proof of concept by simulating a simple low cost manipulator in pybullet to pick and place an object based on image observations.
Evaluation of Microbiological Contamination of Dummies Used in Cardiopulmonary Resuscitation in Korea

Objectives: In order to prevent infections through dummies used during Cardiopulmonary Resuscitation (CPR) training, we analyzed the microbiological contamination on dummies used in CPR institutions. Methods: A total of 31 dummy samples were collected from 13 different institutions in Korea, and were evaluated for the number of contaminating bacteria and fungi on the surface. PCR and biochemical tests were performed to identify pathogenic bacteria and fungi, including Methicillin-Resistant Staphylococcus aureus (MRSA). Moreover, we further assessed the survival rate of microorganisms on the surface of the dummies. Results: We assessed the total number of microorganisms on the surface to be 77,752CFU/cm 2 (±50,047CFU), which is up to 188 times higher than the required surface contamination level. Grampositive cocci such as Micrococcus spp. and Staphylococcus spp. accounted for the highest proportion (55.3%). Especially, we detected three MRSA strains. Considering the isolated fungi and yeast, Aspergillus spp. and Candidia spp. accounted for the highest proportion. Assessing the contamination level simulation and survival rate on the humanoid surface showed that within two weeks of training, the level of contamination on the dummy’s surface exceeded the standard, and artificially contaminated pathogenic strains on the surface of the dummy survived for at least 40 days. Conclusion: To minimize the possibility of secondary infections during CPR training, there is a requirement for a standardized protocol for proper microbiological management of dummies.
Review on Performance Of SDAE For Historical Usage Data Using Deep Learning

Today, there are huge number of online web-services and size of users have increased in large scaled over the past years. In long-term web services are important to increase the web API economy, how to recommend the long-tail web services efficiently is a primary issue. Moreover, to focus on this problem, the traditional web based recommendation services performs poorly on long-tail side. To overcome the problem of severe sparsity of historical usage data and unsatisfactory quality of description content, we are focusing on Convolutional Neural Network approach to boost the performance of the network and reduce time consumption. A client is an individual who uses the recommendation engine giving his viewpoint about different products and get suggestion about the novel products through the recommender. Generally in recommendation system, user gives input to the recommender. The inputs may be both user defined and built-in inputs. To overcome the sparsity problem and find out 75% accuracy, we use DLSTR methodology by using SDAE.
Web-Based Honorary Teacher Payroll Information System

Abstract The purpose of this research is to design a web-based honorary teacher payroll information system. This research uses descriptive approach method and prototype development method and the system approach used is a structured approach. The results of this study, starting from the design to the implementation stage of the web-based honorary teacher payroll information system, can simplify the work of the treasurer in the payroll process. The treasurer is the most important part in the educational institution, because it handles the employee payroll process including the honorary teacher. But apparently the treasurer is still experiencing various obstacles such as when the process of calculating and recording salaries is still using the manual method. This can be seen in the process of calculating and recording teacher salary data which is still done in a notebook. The coordinator also experienced difficulties when recapitulating teacher absence.
La práctica pedagógica Scout como una comunidad de aprendizaje, desde la educación no formal

espanolEste articulo tiene por objetivo presentar como la dinamica y practica de la ensenanza impartida con el metodo de educacion scout y su pedagogia, se constituye en una comunidad de aprendizaje desde la educacion no formal; se inscribe el estudio dentro de la pedagogia social, en su dimension teorico practica, considerandola en el momento actual, enmarcada en la teoria critica de la ensenanza desde el paradigma comunicativo-critico; en busqueda de una respuesta a la necesidad de resolver carencias que se puedan presentar en la educacion formal. EnglishThis article aims to present how the dynamics and practice of the teaching given with the Scout education method and its pedagogy, constitutes a learning community from non-formal education; the study is inscribed within the social pedagogy, in its practical theoretical dimension, considering it at the present time, framed in the critical theory of teaching from the communicative-critical paradigm; in search of an answer to the need to solve deficiencies that may arise in formal education.
2-Dimensional palindromes with k mismatches

This paper extends the problem of 2-dimensional palindrome search into the area of approximate matching. Using the Hamming distance as the measure, we search for 2D palindromes that allow up to k mismatches. We consider two different definitions of 2D palindromes and describe efficient algorithms for both of them. The first definition implies a square, while the second definition (also known as a centrosymmetric factor), can be any rectangular shape. Given a text of size (n×m), the time complexity of the first algorithm is O(nm(log⁡m+log⁡n+k)) and for the second algorithm it is O(nmlog⁡m+klog⁡k(nm+occ)) where occ is the size of the output.
Statistical Analysis for Customer Product Reviews in Russian Internet Segment Using Text Mining

A growing number of companies begin to realize that data is a valuable resource and declare a data-driven approach to be a fundamental one. In this context certain advantages can be gained by analyzing customer opinions in the form of reviews posted on various web resources. In the paper, we analyze customer product reviews on the base of statistical approaches of Text Mining and machine learning models. The reviews were collected from Russian sites of popular online stores. Data analysis to solve classification problems was carried out in the R environment for statistical analysis. Despite not taking into account word order and relations between words within the bag-of-words model, the results show that a statistical approach to analyzing reviews in the case of classification allows achieving high results irrespective of a vector encoding type of a text. #CSOC1120
Regression Based Clustering by Deep Adversarial Learning

Despite the great success, existing regression clustering methods based on shallow models are vulnerable due to: (1) They often pay no attention to the combination between learning representations and clustering, thus resulting in unsatisfactory clustering performance. (2) They ignore the relationship of data distribution and target distribution such that those methods are noise and illumination-change sensitive. (3) These nonlinear regression methods usually impose the hard constraint to minimize the mismatch between the discrete cluster assignment matrix and latent representations, which leads to over-fitting. In this paper, we utilize deep adversarial regression to tackle these problems and formulate regression based clustering by deep adversarial learning (RCDA). By seamlessly combining with the stacked autoencoder, the proposed model integrates learning deep nonlinear latent representation and clustering in a unified framework. Specifically, RCDA uses a kind of relax constraint between latent representations and continuous cluster assignment matrix to avoid over-fitting, and simultaneously utilizes the t-SNE algorithm and adversarial learning to analyze data distribution and target distribution so that improve representations learning. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than state-of-the-art clustering models in image clustering task.
Automatic Privacy Preservation for User-based Data Sharing on Social Media

Social media networking has turned out to be an essential factor for people wherein from sharing relevant documents to exchanging messages; everything is taken place via these social media sites. However, on social media, when a new user joins the group, (s)he must not be given access to all the previous messages. Hence it is necessary to predict the relationship between the users. This primary aim is to predict the link by taking into consideration using three criteria, which includes classifying sentiments from text messages, recognizing faces from pictures and videos posted on social media to find the relationship between the users. Various algorithms and methods were studies for this purpose wherein neural networks can be used for predicting the relationship between the users.
Designing Lesson Plans For Adaptive Learning Using Moodle LMS Platform

This paper presents a research study in developing lesson plans for differentiated learning or adaptive learning using "lesson" on Moodle LMS platform. The lesson are to provide learning paths based on inquiring category from Laurillard Conversational Framework. The design of various learning paths provide teaching methods used in class such as videos, quizzes and reading tasks. This lesson when planned using flowcharts would guide the instructors to link the topics and assessment in any order that would promote students' self-paced and self-learning experience.
Accurate Hierarchical Traffic Measurement in Datacenters Through Differentiated Memory Allocation

A hierarchical flow model provides us the opportunity to monitor network traffic in datacenters, at once, from coarser (aggregate flows) to finer granularity (individual flows). Per-flow traffic measurement based on an idea of counter-sharing is memory-efficient by recording the sizes of all flows in a shared array of physical counters. Through this approach, flows at the same level record their sizes in uniformly sized virtual counter arrays constructed from the shared memory. However, flows at the same level have vastly different sizes. The virtual counter arrays for large flows must be big enough to uniformly distribute errors it contains. However, when large virtual counter arrays are used to record small flow sizes, the arrays accumulate more errors than the recorded flows' sizes, thereby affecting the accuracy of the estimations. The paper introduces the concept of differentiated virtual counter arrays, where the number of virtual counters allocated to each flow is based on its historical size. We derive a mathematical formula for flow-size estimation when recording flow sizes with flexible number of virtual counters and develop a heuristic algorithm to calculate how many virtual counters are assigned to each flow. Through experiments, we demonstrate that our solution is more accurate by up to 75% for aggregate flows and by up to 100% for small base flows compared with the prior art.
CREATEFORUAS: Developing Innovative Technologies for Autonomous UAS

The research project named "CREATEFORUAS" aims at enhancing "trustable" flight autonomy for small Unmanned Aircraft Systems (UAS), by tackling different enabling technologies. The paper briefly describes the main research areas of the project which are relevant to vision-based environment understanding, sense and avoid, robust control, and multi-UAV cooperation. Then, it also provides a snapshot of current developments presenting simulation and experimental results.
Constructing a Learner Model Based on the Latent Cognitive Structure of an Online Course

The structure of tests and tasks for conducting online competitions, the structure of tests and tasks of the online course - all these structures form and/or evaluate the formation of several competencies. And in this case, it is not enough to use a one-dimensional assessment of the knowledge level and existing reliability criteria of the estimates. The paper discusses domain models and student models based on the latent cognitive structure of the course. For modelling the latent cognitive structure authors are using the algebraic lattice of task complication, which is evaluated using student grades from mass online course. Learner model is constructed as overlay model of latent cognitive structures. Models constructed in this way show the patterns of separation of students into score clusters and reveal important properties of the didactic model. Examples of the construction of such models are considered and their applicability for predicting learning outcomes and organizing adaptive learning is investigated. #CSOC1120.
Using Simple Genetic Algorithm for a Hand Contour Classification: An Experimental Study

The area of biometric systems has passed through considerable advancement in the past two decades. Supporting of security provision plays a key role in many branches. There are large amount of the biometrical markers which can be utilized in the person identification process. One of the possible ways is a method which uses a hand shape contour classification. The presented paper solves the problem of hand contours classification with use of a Simple Genetic Algorithm (SGA). The foundations of the SGA were established in 1950’s, but an improvement process of the SGA continues. The hand contour for the classification purposes is obtained from a color image from a biometric scanner. The biometric scanner has fixed pegs to hold the hand, or the hand can be freely placed on the scanning area. A core of the proposed estimator is an Iterative Closes Point algorithm which enables matching of the two point-clouds and expressing their dissimilarity regarding the elected metrics. In the experimental section, a large number of experiments were performed with a different setting of the SGA working parameters. Beside the capability to correctly align/match the hand contours, selected standard benchmark tests were performed with a corresponding number of dimensions. The presented estimator solves the thee-dimensional optimization task. Based on experimental results, it was proven that in the case of identical contours the proposed method, which utilizes the SGA optimizer, provides very accurate results.
Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data

The adoption of natural language processing has become one of the essential part of artificial intelligence. Although, the conventional concept of natural language processing has been researched from more than a decade but still the better results are yet to arrive. Review of existing literatures shows the cases where case specific studies are carried out which still doesn’t address the problem associated with lightweight computational model. Therefore, the proposed study introduces a simplified modeling of natural language processing which is capable of handling the unstructured data unlike existing system without scoring any dependencies on extra resources or cost. The study also introduces an integrated syntactical-based and semantic-based which is quite novel and simplified in its form. The study outcome shows that it offers almost instantaneous response time for all the internal processes.
Creating Emotions Through Digital Media Art

Art has a power different from all other human actions; it can produce a variety of human emotions like nothing else. The main purpose of this chapter is to study the relation between media arts and emotions. Virtual environments are increasingly being used by artists; the use of immersive environments allows the media art artist to go further than express himself, allows that through contemplation and interaction the participant also becomes part of the artistic artefact. Immersive environments can induce emotional changes capable of generating states of empathy. Considering an immersive environment as a socio-technical system, where human and non-human elements interact, establishing strong relationships, the authors used actor-network theory as an approach to design an immersive artifact of digital media art. The use of neurofeedback mechanisms during the participant's exposure to immersive environments opens doors to new types of interaction, allowing to explore emotional states to generate empathy.
Akurasi Penjurusan Bidang Keahlian Teknik Komputer dan Informatika Menggunakan Metode SAW

Many students choose majors because of joining friends, because of the popularity of majors and other reasons even though the chosen majors are not in accordance with their talents. This research is expected to provide solutions to students regarding the majors they will choose. If this problem can be overcome then it will greatly affect both students and schools, the impact on students will be increased enthusiasm and enthusiasm for learning, for schools the impact will be the development of every department there is because the output produced is competent students because of the knowledge delivered right on target. This research is expected to minimize the mistakes in the selection or determination of student majors by applying the SAW method, can determine what variables determine the accuracy of the majors separation by applying the SAW method. The data tested on this system is the data of Bengkulu 1 Vocational School students as many as 100 student data on the Computer and Information Engineering skills program. he data that is processed in this system is the data of Bengkulu 1 Vocational School students as many as 204 student data on the Computer Engineering and Information Technology expertise program. The method of decision making in the system is the Simple Additive Weighting method. The results of testing this method from 204 test data performed for an accuracy of 94,61%. The high accuracy obtained from testing the data so that this system is very suitable to be used to determine the majors of students of SMKN 1 Kota Bengkulu in the field of Computer and Information Engineering expertise.
Information System Services Wenow Clean Franchise

Abstract WENOW CLEAN is a franchise business formed by WENOW. WENOW CLEAN is a business that is engaged in shoe washing services. The service process is still done manually, where transactions are still carried out through memorandum, and making reports that are still not computerized. The aim of this research is to provide an application to make order services at WENOW CLEAN easier for the customer and easy to monitor transactions at all outlets easier by WENOW. The method used in this research is a descriptive method while the system development method used the prototype method. The system-approved method used is object-oriented with the Unified Modeling Language (UML) design tool. The expected results of this franchise information system can facilitate service and service improvement at WENOW CLEAN, as well as ease of supervision and inspection of the WENOW CLEAN franchise so that franchise data can be integrated and monitored by WENOW.
Parameters of geochemical effect equation for lanthanides and their geochemical significance for a series of metamorphic coals

Abstract The "geochemical effect of lanthanides" is a new concept proposed by the authors during the past decade. This concept reflects lanthanide shrinkage in elemental geochemistry, and it is statistically quantifiable. However, the geological significance of the various parameters of the equations obtained following quantization is not entirely clear. Cooperation and discussions from scholars in related research fields of rare earth elements in geology are required. In the present paper, from the perspective of coal geochemistry, the metamorphic coal seam of C2 series in Fengfeng Mine of Handan Coalfield in Hebei Province, China was used as an example. The geochemical significance of parameters in geochemical effect regression equation for lanthanides was evaluated, and two new formulas (regression equations) that characterized the geochemical behaviors of lanthanides were proposed. On this basis, concepts related to the geochemical effect of lanthanides, such as “individual parameters”, “parameters in common”, “two-sided parameters”, and the “deviation value” of lanthanides, were proposed. In this study, it was proved that the goodness of fit for all types of function regression equations for lanthanides and the radii of their trivalent ions, and the “deviation value” of lanthanides, were all “individual parameters” that could indicate the post-modified geological environment of C2 coal seam, such as the influence from magmatic-hydrothermal fluids. A covariant figure was constructed according to these individual parameters and other indexes, and the C2 coal seam in Handan was effectively divided into two different metamorphic series of A (C2 coal seam uninfluenced or slightly influenced by magmatic-hydrothermal fluids) and B (C2 coal seam strongly influenced and evidently changed by magmatic-hydrothermal fluids). Consequently, the scientific significance of all the parameters for lanthanides in an identifying series of metamorphic coals within the geochemical effect regression equation was further clarified.