Reviewerly/exHarmony huggingface dataset:
Cross Subspace Alignment and Its Applications to Private Information Retrieval and Coded Distributed Computation

Author(s): Jia, Zhuqing | Advisor(s): Jafar, Syed Ali | Abstract: Originating from the construction of the asymptotic-capacity achieving scheme for X-secure T-private information retrieval (XSTPIR), the technique of cross-subspace alignment (CSA) emerges as the natural solution to secure and private information retrieval, secure distributed matrix multiplication, and coded distributed batch computation. Characterized by a Cauchy-Vandermonde structure that facilitates interference alignment along Vandermonde terms, while the desired signals remain resolvable along the Cauchy terms, the idea of CSA is shown to be the essential ingredient in the optimal/asymptotically optimal/state-of-art approaches that minimize the download and/or communication cost of these independently introduced but closely related problems.In this dissertation we will first introduce the idea of CSA to the applications of XSTPIR, XSTPIR with graph-based replicated storage (GXSTPIR) and XSTPIR with MDS coded storage (MDS-XSTPIR). The CSA solution to XSTPIR exploits the Cauchy-Vandermonde structured answer strings that align interference symbols guaranteeing T-privacy and X-security to achieve the asymptotic capacity. The achievability scheme for GXSTPIR reveals a non-trivial generalization of CSA that takes the advantage of a special structure inspired by dual Generalized Reed Solomon (GRS) codes to allow interference alignment for arbitrary storage patterns. For MDS-XSTPIR, we propose a novel scheme based on confluent Cauchy-Vandermonde storage structure and a strategy of successive decoding with interference cancellation. Next, by characterizing a connection between a form of XSTPIR problem known as multi-message XSTPIR and the problem of secure distributed matrix multiplication (SDMM), we characterize a series of capacity regions of SDMM, as well as several of its variants. The idea of CSA serves as an essential component of the construction of several achievability schemes. Given the insights from all these results, next, we construct CSA/GCSA codes based on (confluent) Cauchy-Vandermonde structures for coded distributed batch computation (CDBC) that unify, generalize and improve upon the state-of-art codes for distributed computing such as LCC codes for multivariate polynomial evaluations and EP codes for matrix multiplication. Finally, we study the problem of X-secure T-private federated submodel learning (XSTPFSL), which is a non-trivial generalization of the XSTPIR problem where private writes are needed. The proposed ACSA-RW scheme achieves the desired private read and write functionality with elastic dropout resilience, takes the advantage of the synergistic gain from the joint design of private read and write for low communication costs, and sheds light on a striking symmetry between upload and download costs. Intuitively, private read and write functionalities, as well as their synergistic gain, rely on secure distributed matrix multiplication so that the idea of CSA emerges as the core of the solution.
Utilización de aplicaciones web para optimizar el proceso de enseñanza aprendizaje

The present work that was carried out is about the use of web applications to optimize the teaching and learning process of students. Web applications allow a link between the user and the information, through this the user can access data in an interactive and asymmetric way, in addition, it has certain advantages such as time, space, and a better relationship between teachers and students.
Revisiting Traveling Salesman Problem (TSP): Analysis of GA and SA based Solutions

The purpose of this study to analyze genetic algorithm (GA) and simulated an-nealing (SA) based approaches applied to well-known Traveling Salesman Prob-lem (TSP). As a NP-Hard problem, the goal of TSP is to find the shortest route possible to travel all the cities, given a set of cities and distances between cities. In order to solve the problem and achieve the optimal solution, all permutations need to be checked, which gets exponentially large as more cities are added. Our aim in this study is to provide comprehensive analysis of TSP solutions based on two methods, GA and SA, in order to find a near optimal solution for TSP. The re-sults of the simulations show that although the SA executed with faster comple-tion times comparing to GA, it took more iterations to find a solution. Additional-ly, GA solutions are significantly more accurate than SA solutions, where GA found a solution in relatively less iterations. The original contribution of this study is that GA based solution as well as SA based solution are developed to perform comprehensive parameter analysis. Further, a quantifiable comparison is provided for the results from each parameter analysis of GA and SA in terms of performance of solving TSP.
A Study of Soft Computing Techniques in Chemical Reactions

This research paper has been described the fuzzy logic and hybrid fuzzy logic-based systems, which is utilize to create knowledge-based frameworks in chemical engineering.  In this proposed study, we have proposed fuzzy logic-based methods such as Fuzzy sets, Fuzzy C-Means (FCM), Subtractive Clustering (SC) and integrated approach of SC and Artificial Neural Network Fuzzy Inference System (SC-ANFIS) for calculating the rate of chemical reaction. After comparing the results of these proposed methods with other exiting methods such as classical fuzzy logic, FCM and subtractive clustering (SC) methods, gives get the better result of SC-ANFIS. The root mean square error (both training and testing data) of SC-ANIFIS is less as compared to existing FCM method.
Enhancing Balanced Graph Edge Partition with Effective Local Search

Graph partition is a key component to achieve workload balance and reduce job completion time in parallel graph processing systems. Among the various partition strategies, edge partition has demonstrated more promising performance in power-law graphs than vertex partition and thereby has been more widely adopted as the default partition strategy by existing graph systems. The graph edge partition problem, which is to split the edge set into multiple balanced parts with the objective of minimizing the total number of copied vertices, has been widely studied from the view of optimization and algorithms. In this paper, we study local search algorithms for this problem to further improve the partition results from existing methods. More specifically, we propose two novel concepts, namely adjustable edges and blocks. Based on these, we develop a greedy heuristic as well as an improved search algorithm utilizing the property of max-flow model. To evaluate the performance of our algorithms, we first provide adequate theoretical analysis in terms of approximation quality. We significantly improve the previous known approximation ratio for this problem. Then we conduct extensive experiments on a large number of benchmark datasets and state-of-the-art edge partition strategies. The results show that our proposed local search framework can further improve the quality of graph partition by a wide margin.
Privacy Budget Scheduling.

Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on this leakage. Each new model trained with DP increases the bound on data leakage and can be seen as consuming part of a global privacy budget that should not be exceeded. This budget is a scarce resource that must be carefully managed to maximize the number of successfully trained models. We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. For example, traditional compute resources are replenishable while privacy is not: a CPU can be regained after a model finishes execution while privacy budget cannot. This distinction forces a re-design of the scheduler. We present DPF (Dominant Private Block Fairness) -- a variant of the popular Dominant Resource Fairness (DRF) algorithm -- that is geared toward the non-replenishable privacy resource but enjoys similar theoretical properties as DRF. We evaluate PrivateKube and DPF on microbenchmarks and an ML workload on Amazon Reviews data. Compared to existing baselines, DPF allows training more models under the same global privacy guarantee. This is especially true for DPF over Renyi DP, a highly composable form of DP.
MODERN FEATURES OF APPLIED PHYSICS TRAINING IN THE PREPARATION OF FUTURE PHYSICS TEACHERS

The article analyzes scientific research on the modern possibilities of teaching the course. Methods, tools, the existing potential for training future teachers of physics, the use of the applied orientation of physics training, the implementation of the applied orientation of teaching in domestic and foreign universities were formulated and defined. In the implementation of the applied orientation of teaching physics, the criteria for the selection of educational material of an applied nature are determined. In addition, various ways of the necessity of applying physical knowledge in practice, the importance of using the process of solving problems to form the applied focus of physics training, are examined. The article analyzes qualitative problems of an applied nature, including the purpose and methods of solving such problems, methods of applying students' theoretical knowledge in practical classes.
Reducing Quantum Cost for Reversible Realization of Densely-packed-decimal Converters

At present, the reduction of circuit design power is a prime research topic. The reversible computation satisfies the criteria of the power consumption reduction compared to the traditional logic design. Thereby, reversible computation is gaining much attention in recent decades. Two reversible design approaches of binary-coded-decimal (BCD) to densely-packed-decimal (DPD) converter (encoder) and two design approaches of DPD to BCD converter (decoder) are proposed in this paper. The designs are carried out through the appropriate selection of the gates and further proper organization of such gates with parallel implementation. The proposed design approaches offer a low quantum cost implementation compared to state-of-the-art design. The cost results analysis of reversible DPD encoder shows appreciable reduction by at least ~23%, and that of decoder by at least ~62% compared to the state-of-art design found in the literature. Furthermore, the structures are decomposed into the primitive-quantum-gates and compressed in compact form for delay calculation
Employee Commitment: The Relationship between Employee Commitment and Job Satisfaction

The aim of the current study is to examine the association between employee commitment and job satisfaction at selected private universities in Kurdistan region of Iraq. Organizational commitment predicts work variables, for example, turnover, hierarchical citizenship conduct, and employment execution. A percentage of the elements, for example, part stretch, strengthening, work shakiness and employability, and appropriation of authority have been appeared to be associated with a laborer's feeling of organizational commitment. This research was carried out at the three different private universities. A quantitative method was used to gather relevant information regarding the relationship between organizational commitment and job satisfaction. The results revealed that all three independent factors are correlated with job satisfaction. The result can be used as a guiding method for the university’s management to increase their employees’ commitment. Job satisfaction has a positive relation with employee commitment if employees are satisfied, they are organizationally committed we found that job satisfaction has a positive impact on employee commitment.
Endomorphisms of Lifted Planning Problems.

Classical planning tasks are usually modelled in the PDDL which is a schematic language based on first-order logic. Nevertheless, most of the current planners turn this first-order representation into a propositional one via the grounding process. It is well known that the grounding process may cause an exponential blowup. Therefore it is important to detect which grounded atoms are redundant in a sense that they are not necessary for finding a plan and therefore the grounding process does not need to generate them. This is usually done by a relaxed reachability analysis, which can be improved by employing structural symmetries. Symmetries are bijective self-maps preserving the structure of the PDDL task. In this paper, we introduce a new method which is based on self-maps preserving the structure but which need not be bijective. We call these maps PDDL endomorphisms and we show that they can be used for pruning of redundant objects even if they appear in a reachable atom. We formulate the computation of endomorphisms as a constraint satisfaction problem (CSP) that can be solved by an off-the-shelf CSP solver.
INODE - Intelligence Open Data Exploration.

This article describes the keynote speech on INODE presented at Fourth International Workshop on Systems and Network Telemetry and Analytics (SNTA) which is collocated with International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC) on June 21 in Stockholm, Sweden.
Formación docente en la emergencia: pedagogías del cuidado

Covid-19 pandemic sprang up a process that would drastically change the ways to teach and learn, deepening and accelerating the relationship between technologies and educational processes. The emergency situation challenged Universidad de la Republica (Udelar), which in turn designed and implemented remote teaching devices that allowed the continuity of higher education, minimizing academic impacts. Within this frame of action, the Virtual Learning Environments Program elaborated a Contingency Plan for teaching and learning, with an approach that considered pedagogy of care and critical digital pedagogy. One of the actions implemented was the Teacher Development Course “Teaching Online in Emergency Conditions”. The experience constitutes the first massive online open course in Udelar and it is one of the few of this sort in Uruguay. The article describes this innovative experience in teacher pedagogical development, presents the theoretical and methodological approach, the process and main decisions concerning the course design. Also, it describes main participation profiles and principal achievements of the experience, with the objective of identifying potential points of departure for teaching professional development initiatives based on the MOOC model. MOOCs are still at an initial stage; however, the proposal ended up being an enriching alternative to approach teacher professional development. This experience adds up empirical data, to a necessary accumulation that would in time, allow more generalizable appreciations.
Análise do perfil comportamental do consumidor de automóveis diante do cenário da COVID-19

The Covid-19 pandemic impacted several sectors of the economy and revealed new purchasing habits and priorities. This research aims to analyze the impacts of the pandemic on car consumer behavior, according to the reality of the automotive industries, seeking to answer: What are the impacts of the pandemic on car sales considering the new consumer behavior after Covid-19? The research has an exploratory descriptive character to analyze the theories of consumer behavior, based on the Behavioral Theory of Maslow, followed by a qualitative research to capture the views of managers working in the automotive sector. Through a content analysis presented by Laurence Bardin, a statistical analysis was prepared with the automobile consuming public. At the end of this study, the changes that the consumer presents in the process of purchasing a vehicle were understood and followed by an action plan for companies to resume their sales according to these new expectations of automobile consumers for the coming years. The survey of consumers and industry professionals showed greater concern about the payment options available for the purchase of a vehicle and little change in consumer behavior compared to the period before the pandemic. At the end, there is a proposal for actions to improve the consumer's experience in their purchase journey, in order to increase the sale of new vehicles.
A Machine Learning Approaches on Face Detection and Recognition

Machine learning relies on computer science, which is mainly focused on the construction of algorithms. That may be learning from past work or previous experience. Artificial intelligence, computer vision, biometrics, biological behaviour, and so on are applied to machine learning. This gives the machine the opportunity, without specific code, to continually learn and benefit from previous work or experience. We are working on the basic characteristics of the faces in this model. Those models provided good results in face identification and recognition. Our proposed solution combines a guided transfer learning methodology with a central process of mutual monitoring and centre failure.MobileNet, the recently suggested Convolutionary Neural Network (CNN) model, which has both consistency and speed, is applied both offline and in a real-time environment that allows real-time output to be fast and accurate. Two publicly distributed datasets, Deep Face, JAFFE and CK+, are being analysed. The Deep Face dataset has 97.92 percent accuracy. To ensure that the algorithms perform well this model used our train algorithms and also for research.Keywords: Face recognition system, Convolutional neural network, Random neural network, etc.
CH2 inspired for Air Conditioning System of Inpatient Hospital with Biomimicry Approach

One of the important areas in the hospital building is inpatient care, where inpatient care is used throughout the day. In hospitalization, users consist of healthy users and sick users, so prevention of airborne infection management and air conditioning that supports patient recovery is important. However, even though the air conditioning in the inpatient area of the hospital has standards that must be met, there are still many problems with ventilation conditions in the inpatient room, first problems with the inpatient supervision system, both related and assistive devices to support the running system. Both air comfort and air quality do not meet the standards to support patient care. Third, the use of ventilation systems in inpatient services that support energy savings. With a descriptive qualitative method using a case study on the House Council 2 building. Council House 2 which uses natural inspiration as a solution to the air conditioning problem in the building can be role-modeled in the conditioning system in inpatient buildings which has similar space layout and duration of use. The results of this study are that some ventilation system components are used passively in the room following the problems that hospital nurses have such as vegetation, building thermal mass management, ventilation piles, protective facades, ceiling panels, and beam panels, and spray water.
ИННОВАЦИОННЫЕ ТЕХНОЛОГИИ ПРИ РАБОТЕ С ДЕТСКИМ ХОРОМ

The article deals with innovative technologies in working with children's choir. These innovative forms and methods of work in the classroom, of course, expand the possibilities of the modern educational process, so they need to be actively implemented in the practice of teaching in the school, but do not forget about the importance of education school of academic singing in the spirit of the best traditions of choral culture.
How does Attention Affect the Model?

The attention layer has become a prevalent component in improving the effectiveness of neural network models for NLP tasks.Figuring out why attention is effective and its interpretability has attracted a widespread deliberation.Current studies mostly investigate the effect of attention mechanism based on the attention distribution it generates with one single neural network structure.However they do not consider the changes in semantic capability of different components in the model due to the attention mechanism, which can vary across different network structures.In this paper, we propose a comprehensive analytical framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines a series of indicators to capture the impact of attention on sequence semantics.Through a series of experiments on various NLP tasks and three representative recurrent units, we analyze why and how attention benefits the semantic capacity of different types of recurrent neural networks based on the indicators defined in the proposed framework.
Segmentation of digital images with various edge detection techniques using OpenCV

In the domain of computer vision or image segmentation, Edge detection is the most widely used technique to determine and outline the presence of lines or objects in an image appropriately. The main aim is to segment the image data so that the complexity of the image is reduced also the amount of data to be processed in further steps of advanced algorithm is reduced. In simple language, we can say that edges are the sudden and significant changes in the intensity of the image which occur at the boundary of an object in the image. In this paper, the Sobel operator technique, the Laplacian technique, and the Canny technique are used and compared with one another for edge detection in an image. This paper represents the detailed analysis of the performance of these edge detection tools by using the input images with different types of backgrounds for the computer vision or image segmentation or any other advanced algorithm. Further, we have done the comparison among these techniques in presence of different types of noise by using the OpenCV library and PythonSoftware.
Sintesis Ferri Salen-Taeniolit Sebagai Katalisator Pada Reaksi Pembentukan Monomer Poli Karbonat

The researcher has succeeded in synthesizing the Ferri Salen-Taenolite complex as a clay based catalyst and applying it to the reaction of glycerol carbonate formation. Lewis acidity of the catalyst complex can increase through the immobilization process in addition, the activity and selectivity of the catalyst also increase. The formation of salene ligands is shown by the appearance of the peak C=N at the wave number around 1640 cm-1 and the O-H peak around 3400 cm-1. The formation of the Fe(salen)NO3 complex is indicated by the reduction of the vibration intensity O-H at the wave number of about 3400 cm-1 and the shift of the peak C=N in the area around 1640 cm-1 to 1690 cm-1. The complex [Fe(salen)]NO3 has been synthesized with physical properties in the form of brown powder and effective magnetic moment by 2,197 BM. The complex [Fe(salen)] + was successfully immobilized into the LiTN as much as 9.12 mmol/g LiTN. Based on the catalytic activity test, the catalyst was active in the reaction of polycarbonate monomer formation which is indicated by a change in vibration of the urea carbonyl (CO amine) group at wave number 4242 cm-1 to vibrate the carbonyl group (CO ester) at wave number 1786 cm-1 which indicates that glycerol carbonate has been formed.