Reviewerly/exHarmony huggingface dataset:
OpEvo: An Evolutionary Method for Tensor Operator Optimization

Training and inference efficiency of deep neural networks highly rely on the performance of tensor operators on hardware platforms. Manually optimizing tensor operators has limitations in terms of supporting new operators or hardware platforms. Therefore, automatically optimizing device code configurations of tensor operators is getting increasingly attractive. However, current methods for tensor operator optimization usually suffer from poor sample-efficiency due to the combinatorial search space. In this work, we propose a novel evolutionary method, OpEvo, which efficiently explores the search spaces of tensor operators by introducing a topology-aware mutation operation based on q-random walk to leverage the topological structures over the search spaces. Our comprehensive experiment results show that compared with state-of-the-art(SOTA) methods OpEvo can find the best configuration with the lowest variance and least efforts in the number of trials and wall-clock time. All code of this work is available online.
Embraced Separation: Exploring methods of breath attunement in speculative infant swings

This paper explores a speculative design for an infant swing based on breath attunement.There are challenges for working parents to juggle, such as working from home and taking care of an infant, especially when infants want to be physically close to the parent.This design explores how a swing can be adaptable to subtle, dynamic changes in the parent that can soothe the infant while allowing the parent to be more hands-free.These dynamic changes include the use of breath sensors on the parent to change the rhythm of the swing, while also creating shifting amplifications of environmental sounds, as though they were being carried by the parent through the home.This work particularly explores qualities of movement and rhythm as key components of comfort for infants in their parent's arms.Results of this paper include design sketches and a usability evaluation of a smartphone interface, as a speculative approach to designing embodied interactions between parent-child relationships.
Don’t Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models

Event language models represent plausible sequences of events.Most existing approaches train autoregressive models on text, which successfully capture event co-occurrence but unfortunately constrain the model to follow the discourse order in which events are presented.Other domains may employ different discourse orders, and for many applications, we may care about different notions of ordering (e.g., temporal) or not care about ordering at all (e.g., when predicting related events in a schema).We propose a simple yet surprisingly effective strategy for improving event language models by perturbing event sequences so we can relax model dependence on text order.Despite generating completely synthetic event orderings, we show that this technique improves the performance of the event language models on both applications and outof-domain events data.
Cryptographic ledger of blockchain technology in healthcare

Blockchain technology produced a unique nature called Public Health Record (PHR) to shared medical-related data between clients. The big achievement of this technology there is no need for any third party and user can control their personal data. This technology offers encrypted data, integrated data, drug traceability, interoperability and most important thing it gives security regarding patients' data misuse, drug counterfeit. It is distributed framework, maintenance cost, improved our medical sector.
Dynamic Management of Multiple Resources in Camera Surveillance Systems

Distributed camera surveillance systems typically consist of multiple cameras that need to store some fraction of their video streams in a central storage node. The disk space of this node as well as the network between the cameras and this central node constitute shared resources. In the paper the disk space allocation as well as the network bandwidth reservation are solved using techniques normally associated with process control. These include mid-range control and tracking-based control of global shared resources. The approach is evaluated by simulations.
A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)

While word embeddings have been showing their effectiveness in capturing semantic and lexical similarities in a large number of domains, in case the corpus used to generate embeddings is associated with a taxonomy (i.e., classification tasks over standard de-jure taxonomies) the common intrinsic and extrinsic evaluation tasks cannot guarantee that the generated embeddings are consistent with the taxonomy. This, as a consequence sharply limits the use of distributional semantics in those domains. To address this issue, we design and implement MEET, which proposes a new measure -HSS- that allows evaluating embeddings from a text corpus preserving the semantic similarity relations of the taxonomy.
5. Historical and Theoretical Foundations of the Development of Vocal and Choral Culture in Moldova

Abstract The origins of the national choral art are based on Church singing, which has been widely developed since the founding of the Putnyansky and Nyametsky monasteries. In the further development of the centuries, Church singing intertwined with folk choral singing, which was reflected in the works of such conductors and composers as G. Muzichesku, M. Berezovsky, A. Kristya, M. Byrke, V. Popovich. Special attention is paid to the creative and conducting activities of Gavriil Muzichesku, all of whose initiatives were innovative and progressive, later becoming the leading ones in the national choral activities of Romania and Moldova. This article identifies and systematizes the main trends related to the process of creating vocal and choral works and their application in the system of Romanian and Moldovan musical education. Their analysis shows that the composer worked on the theoretical generalization of his own experience, turned to the best ideas of domestic and foreign pedagogy, including Russian, and thanks to this he was able to bring his knowledge and experience into a fairly coherent and integral system of musical education and education through vocal and choral culture.
Persepsi Mahasiswa terhadap Learning Management System (LMS): Pengaruh Lokasi, Perangkat dan Analisis Kepuasan Mahasiswa

LMS is one of the many online learning models that are applied as online learning media in universities. This model is believed to be able to overcome the high cost of internet packages incurred by students when using online meeting applications. This study aims to measure the level of student satisfaction and perceived with the Ilearn application, with the Moodle platform, which is used by Andalas University. Student perceptions were measured using 6 categories, namely: (a) student interaction with lecturers; (b) student interactions with other students; (c) internet devices and access; (d) motivation to learn); (e) participation during lectures; and (f) perception and absorption of material. Measurement of the level of satisfaction is calculated using descriptive statistical methods. These variables were analyzed based on gender, study period, location of residence, and the instruments used using the one way ANOVA method. This study concluded that the level of satisfaction felt by students as a whole was quite low, especially for the items of internet access, focus and concentration of lectures, and active participation in lectures. It was also concluded that there was no significant effect of gender, location, and the tools used on the level of satisfaction and uptake of recovery materials. Meanwhile, the study period had a significant effect on the level of satisfaction felt by students
Role and Impact of Software Engineering in Mobile and Wireless Application

In this world, smart devices are infiltrating with such a high speed that we can find them everywhere.Because of the widespread usage of the smart devices, software developers and engineers must be aware of the fascinating characteristics of mobile phones and familiarize their formative methods to meet the requirement of the software used in mobile devices.Due to the simple application transferability, distinctive sensors, and high processing, the designers and developers of autonomous and commercial systems have established their new operational ground within the frame of agile devices.However, we discover that the computer programmers who are responsible for the fast-paced growth of this modern stage are required to update themselves.The paper will discuss four main challenges faced by mobile software engineers for a variety of applications, as well as potential research opportunities.We would also be examining at the different issues given: A) Creating a User Interface for disabled people B) Development of platform Independent Application C) Development of context-aware application, and D) To deal with ambiguous requirement.
A Modular Approach for Romanian-English Speech Translation

Automatic speech to speech translation is known to be highly beneficial in enabling people to directly communicate with each other when they do not share a common language. This work presents a modular system for Romanian to English and English to Romanian speech translation created by integrating four families of components in a cascaded manner: (1) automatic speech recognition, (2) transcription correction, (3) machine translation and (4) text-to-speech. We further experimented with several models for each component and present several indicators of the system’s performance. Modularity allows the system to be expanded with additional modules for each of the four components. The resulting system is currently deployed on RELATE and is available for public usage through the web interface of the platform.
Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients

Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too large if we want to deploy them on memory-limited devices such as mobile phones, smart watches and IoT devices. To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. First, we propose an efficient way to generate compact binary embedding of the data which can preserve the kernel similarity. Second, we propose a simple but effective algorithm to learn a linear classification model with binary coefficients which can support different types of loss function and regularizer. Our algorithm can achieve better generalization accuracy than existing works on learning binary coefficients since we allow coefficient to be -1, 0 or 1 during the training stage and coefficient 0 can be removed during model inference. Moreover, we provide detailed analysis on the convergence of our algorithm and the inference complexity of our model. The analysis shows that the convergence to a local optimum is guaranteed and the inference complexity of our model is much lower than other competing methods. Our experimental results on five large real-world datasets have demonstrated that our proposed method can build accurate nonlinear SVM model with memory cost less than 30KB.
Case Article—Production and Distribution Optimization of Beach Equipment for the Marinero Company

We present a mixed integer nonlinear mathematical programming model, covering a broad range of operations research (OR)-related topics. The case is designed to allow students to use knowledge acquired from OR and management science classes to model, analyze, and provide concrete solutions for the considered problem. Because of its high practicality, this exercise is an ideal tool to make the OR domain more accessible and to learn how to balance a problem’s complexity with the availability of algorithms for its solution. The case has been proposed as a competitive challenge and has been assigned both to students pursuing a bachelor’s degree in management engineering and students pursuing a master’s degree in industrial engineering. Students were grouped into working teams, and all teams competed against each other to get the best solution to win the challenge. Both the work-in-team and the challenge settings have been enjoyed by the students. During three lectures of 90 minutes, a brief review of OR-related tools and a detailed description and analysis of the case study have been provided to the students. Successive periodic debriefing meeting sessions have been scheduled to engage and monitor students during project development. Online supplemental files are available at https://doi.org/10.1287/ited.2018.0202ca .
Qos Correlation with Constraint Based Optimal Service Selection in Composition

In a service-oriented architecture, service composition plays an important role as a key technology for integrating complex web applications. The service selection as part of the service composition process attracted towards Quality of Service as mandatory criteria for optimal selection of services for the composition process. While fulfilling the non-functional requirements from the user among a functionally similar list of services is a very challenging task. Web services are treating Software as a Service for building web applications. The Quality of Service correlation of services with constraints for selection of service is proposed in this paper. Experimental results of correlation without constraint method and proposed method show that our method outperforms over existing approach.
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games

We extend the formats of explanations in interpretable NLP with the proposed entity-centric reasoning chains for multi-hop question answering.We also propose a cooperative game approach to learn to recover such explanations from weakly supervised signals, i.e., the question-answer pairs.We evaluate our task and method via newly created benchmarks based on two multi-hop datasets, Hot-potQA and MedHop; and hand-labeled reasoning chains for the latter.The experiments demonstrate the effectiveness of our approach.
Preparing educators for work in an inclusive educational environment of a preschool educational organization

The article considered the essence of training educators to work in an inclusive educational environment of a preschool educational organization. The problems and contradictions of inclusive practice in the conditions of a preschool educational organization are considered, which is confirmed by a SWOT-analysis of the educational activities of a preschool educational organization in conditions of inclusion. This method is one of the most effective and promising methods of scientific-analytical and reflective research, which is especially important in the framework of pedagogical science. The authors reveal the content of the components of the readiness of educators to implement inclusive practice in a preschool educational organization. In addition, a significant segment of the research is devoted to the consideration of the structure, components and levels of readiness of educators to work in an inclusive educational environment of a preschool educational organization. Based on this, an exhaustive conclusion is made, which is reflected in the final part of the article.
Comprehension and Knowledge

The ability of an agent to comprehend a sentence is tightly connected to the agent's prior experiences and background knowledge. The paper suggests to interpret comprehension as a modality and proposes a complete bimodal logical system that describes an interplay between comprehension and knowledge modalities.
Revisiting Dominance Pruning in Decoupled Search

In classical planning as search, duplicate state pruning is a standard method to avoid unnecessarily handling the same state multiple times. In decoupled search, similar to symbolic search approaches, search nodes, called decoupled states, do not correspond to individual states, but to sets of states. Therefore, duplicate state pruning is less effective in decoupled search, and dominance pruning is employed, taking into account the state sets. We observe that the time required for dominance checking dominates the overall runtime, and propose two ways to tackle this issue. Our main contribution is a stronger variant of dominance checking for optimal planning, where efficiency and pruning power are most crucial. The new variant greatly improves the latter, without incurring a computational overhead. Moreover, we develop three methods that make the dominance check more efficient: exact duplicate checking, which, albeit resulting in weaker pruning, can pay off due to the use of hashing; avoiding the dominance check in non-optimal planning if leaf state spaces are invertible; and exploiting the transitivity of the dominance relation to only check against the relevant subset of visited decoupled states. We show empirically that all our improvements are indeed beneficial in many standard benchmarks.
Perancangan Board Game Kolaboratif. Studi Kasus: Legenda Gunung Tondoyan

Collaboration is one of the soft skills needed in the 21st century. One way to advance collaborative skills is to use a board game. It is common today to find board games for learning purposes. The design of this collaborative board game uses qualitative methods, namely literature studies, brainstorming, physical prototypes, software prototypes, and design documentation. With this research, it is hoped that it will help understand how to design a collaborative board game.&#x0D; Keywords: board game; collaborative; prototype
Real Time Face Mask Identification using AI-Deep Learning Neural Network

The COVID - 19 pandemic is devastating mankind irrespective of caste, creed, gender, and religion. Contribution of each individual to constrain the expansion of the corona- virus. Is a primary objective/Fundamental duties as a responsible individual to Use a face mask can undoubtedly help in managing the spread of the virus. COVID - 19 face mask Detector uses or owns Facemask net, deep learning techniques to successfully test whether a person is with wearing a face mask or not. In this project we are working on “FACE MASK IDENTIFICATION USING AI DEEP LEARNING NEURAL NETWORK”. The end of 2019 witnessed the outbreak of Corona virus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2020. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in-person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. We are using a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a faster region-based Convolution Neural Network (FRCNN) architecture capable of detecting masked and unmasked faces and can be integrated with preinstalled CCTV cameras. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.
Machina Ex Machina Artificially Intelligent Systems as Inventors under Polish Legal Framework

Abstract Not only do advanced artificially intelligent (AI) systems play an increasingly important role in modern society, but they also significantly enhance industrial and economic development. AI systems are already capable of generating outputs, which, had they been created by humans, would be eligible for patent protection. Polish patent regime has yet to determine how it will address inventive computational results. This paper aims at addressing a question whether AI-generated outputs can be considered patentable inventions under Polish legal framework and if so, who would be recognized as the inventor. The author draws conclusions de lege lata and briefly outlines de lege ferenda observations. The author argues that vesting the inventor status in one of the persons who contributed to the AI-generated result offers a reasonable incentive to actors involved in the innovation process and, at the same time, leaving aside vexed problem of computational personhood, does not undermine established legal paradigms, in particular the traditional notion of human creator (inventor).
Literature Review on Differential Evolution Algorithm

Differential evolution algorithm is one of the most efficient metaheuristic approaches. In this paper, a review and analysis is presented in order to help for future research in differential evolution algorithm. It covers an analysis of about 142 papers of the previous work in the modifications of the algorithm including the main parameters of the classical steps of the algorithm and hybridization with other algorithms. The analysis also shows the applications that optimized using the differential evolution algorithm.
DEVELOPING AUTOPLAY BASED READING MATERIALS FOR SENIOR HIGH SCHOOL STUDENTS

This study was intended to develop autoplay-based Reading learning media that meet the valid, practical, and effective criteria. The method used in this research is research and development method with ADDIE development design (Analyze, Design, Development, Implementation, and Evaluation). Data collection techniques in this study used questionnaires and tests. Based on the results of the research that has been described, it can be concluded that the autoplay-based reading learning media developed has met the valid criteria, because the average expert validation results are 90.67 (valid) including good criteria. In addition, autoplay-based reading learning media also meets practical criteria, because student responses to the use of autoplay-based reading learning media are 85%. The score is included in the good category, namely the practical autoplay-based reading learning media used in the learning process. Autoplay-based reading learning media also meets the effective criteria, because the average value of student learning outcomes is 87. This value exceeds the KKN set by the school, which is 75.
USE OF CLOUD TECHNOLOGIES IN EDUCATION

The article describes the advantages of the use of cloud technologies in training and theoretical researches of the scientific literature. "Cloud technology" appeared in a result of evolution of information technology over the last decade. The term "cloud technology" frequently found in scientific and technical literature. This term denotes a model of network access on-demand to a common set of configurable computing resources (network, servers, storage, applications, services) that can be quickly prepared for work; wherein the part of the user requires minimal interaction with the service provider, just having a computer device having access to the Internet. The idea of using "cloud technology" in the course of training, transfer of educational-methodical documentation, theoretical and practical exercises in the Internet is literally "in the air". Cloud computing has a great future, so all who are close to IT should master this technology