Reviewerly/exHarmony huggingface dataset:
CHaNAS: coordinated search for network architecture and scheduling policy

Automatically design an efficient DNN solution for a given deep learning task on the target hardware mainly decided by the neural network architecture and the schedule mapping strategy, where the two goals are closely coupled with each other to fully exploit the advantages of the underlying hardware. Prior hardware-aware Neural Architecture Search (NAS) methods mostly ignore the impacts of different scheduling policies (e.g., graph-level optimization, loop transformations, parallelization, etc.) on network candidates being evaluated in the search process. Thus, they may miss the true-optimal architecture that can only be discovered by trying-out different scheduling policies. This work proposes a NAS framework (CHaNAS) that searches for not only the network architecture but also the dedicated scheduling policy, as the optimal co-design solution on target hardware that fully exploits the advantages of the underlying hardware. We propose to use a block-based pre-scheduling methodology to reduce the co-design search space, and enable the automatic generation of the optimal co-design, including the network architecture and the tensor programs that practice the scheduling policy. We evaluate CHaNAS on Imagenet on different hardware back-ends against the state-of-the-art hardware-aware search method MobileNet-v3. Experimental results show that the co-design solutions obtained by ChaNAS show up to 1.6x, 1.9x, and 1.7x performance boost on NVIDIA P100 GPU, Intel Xeon 8163 CPU, and Samsung Note 10 Mobile, respectively, over the baselines of the same-level accuracy.
Automated Blood Group Determination Using Image Processing Techniques With Integration of Raspberry Pi

Blood group detection is a vital procedure during conducting any transfusion of blood during critical transplants or emergencies. Currently, blood group determination tests are performed under standard laboratory conditions by experienced technicians. A novel method employing the technique of Image processing is used in this research paper to deduce blood group with less consumption of time and elimination of personnel error. Clear pictures of the blood samples which are tested are taken from the laboratory. These pictures of the blood samples are captured after the addition of commercial antigen reagents, and are processed using image processing techniques such as Preprocessing, Thresholding, Morphological operations, HSL Luminance plane extraction and Quantification. The blood group is determined by looking for agglutination in the sample image on reaction with a specific reagent, and calculating the intensity variations of the pixels. This procedure can find its application as a self-testing device that does not require any technician supervision, thus reducing the clinical exposure of patients, and reducing human-to-human contact which is of utmost significance in situations where these could be further detrimental to the health of the patient like in the ongoing COVID-19 pandemic.
Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.
Design of motor online monitoring system based on ARM

Abstract In order to monitor the operation status of motors accurately in real time, and realize the early warning of abnormal operation of motors, a motor online monitoring system based on ARM is designed. The system uses STM32F103ZET6 microcontroller based on ARM Cortex–M3 core as the control core to monitor the motor temperature, vibration, environmental temperature and humidity and other related parameters, which communicates with the upper computer through wireless transmission, and the upper computer completes data display, storage, processing and alarm. The results show that the motor online monitoring system based has the advantages of strong real-time performance, high measurement accuracy, high stability and long transmission distance.
Optional English Speech Teaching Method Based on Recognition Emotion Mining and Deep Learning Algorithms

Speech recognition technology and speech evaluation technology is the core of computer-aided speech learning. Of the two, speech recognition technology is particularly critical and plays a vital role. This paper analyses the application of emotion mining and deep learning algorithms in the recognition and teaching of English speech. Emotional visualization can enable teachers and curriculum managers to be more intuitive when sensing emotional changes in studentsâ€™ learning process, and will assist teachers to provide personalized teaching and intervention. The experimental results show that the model presented in this paper for English phonetics teaching, speech recognition and evaluation is reasonable and valid. It can give learners timely, accurate and objective evaluation and feedback guidance, and can help learners identify the differences between their own pronunciation and the standard pronunciation. In addition, this paper optimizes the English language model by means of the sub-word modelingmethod, which alleviates the problem of sparseness and robustness of the traditional whole-word language model brought by the very large vocabulary of adhesive words.Keywords: Speech recognition; Deep learning; Emotion mining; DNN algorithmCite AsX. Zhang, H. Li, N. Wang, R. Shi, Optional English Speech Teaching Method Based on Recognition Emotion Mining and Deep Learning Algorithms, Engineering Intelligent Systems, vol. 27no. 3, pp. 141-150, 2019.
INTEGRATION OF BAYESIAN MODEL AND ADAPTIVE CLUSTERED SAMPLING INTO CONTACT TRACING TO CURB THE SPREAD OF COVID -19 CASES

Covid-19 is a communicable virus that causes serious illness (Severe acutepiratory syndrome (SARS)) and middle east respiratory syndrome (MARS)). İts outbreak started in Wuhan, China on December 8, 2019. Fever, cough, tiredness are its signs and symptoms and appear between two to fourteen days after exposure. The severity of COVID-19 can include complications; pneumonia, heart problems, acute kidney injuries. Covid-19 careers should be identified in order to curb the spread of the virus within a population. In this regards, contact tracing is the current technique in use to identify and track the Covid-19 carriers. The aim is to curb the spread of the virus within the population. In order to achieve this goal effectively, appropriate technique is required in the identification of Covid-19 carriers and Modeling. It is known that Covid-19 carriers are hidden, clustered and very difficult to identify in the population. At this point, the Adaptive Cluster Sampling, which is a specialized sampling for identification of hidden and clustered event and Bayesian Model, comes to the practice. Therefore, in this study, Adaptive Cluster Sampling which is capable of tracking hidden and clustered events and Bayesian Model are integrated in contact tracing, and the application on how this technique is used is included
Adopting the UAT Scrum Framework for Systematic Quality Assurance

Currently, competition in the software organization business is rapidly increasing especially in systems that are essential instruments and which have a direct effect on an organization's survivability. Software companies must place importance on quality assurance activities in order to ensure the quality of products and information. The tests must focus not only on software products, but also on the accuracy of information shown. The aim of this study is to introduce the UAT Scrum framework, which is an application of a Software development method called Scrum together with UAT methodology called the PEF framework. In addition, “User stories” were used to express the “Definition of Done” in UAT testing to ensure all stakeholders understand the goals. The User stories were used to complete the outline of the tasks for the entire team. Three Nursing Colleges within Thailand were selected as test sites for this research. Twenty-nine nurses, five administrators and ten members of the QA and software development team were included as a sample. The average result of the experiment was 3.97. Participants were satisfied with the UAT Scrum framework for the UAT testing process. The end users and the UAT team improved their communication. User stories on the Kanban board provided comprehensive information on the work for which they were responsible and showed completeness of the information clearly. Utilizing the Scrum UAT framework also vastly improved the QA team and software developers’ working processes, relieved time constraints, and assisted in producing software of a higher standard.
Strategi Modeling The Way untuk Meningkatkan Keterampilan Berbicara dan Hasil Belajar Peserta Didik di SD Negeri 3 Candisari Purwodadi Kabupaten Grobogan

This study aims to apply the Modeling The Way strategy to improve speaking skills and learning outcomes in learning Theme 9 for fifth grade students of SD Negeri 3 Candisari. This research is a classroom action research conducted in three cycles. The subjects of this study were the fifth grade teachers and the fifth grade students, totaling 14 people. Collecting data through interviews, observation, and documentation. As for the validity of the data using source triangulation and techniques triangulation. The results showed an increase in speaking skills, namely: 1) In the first cycle the first meeting reached 35.71%, 2) the second meeting increased to 42.85%, 3) in the second cycle the first meeting increased to 50%, 4) the second meeting increased to 57.14%, 5) in the third cycle the first meeting increased to 71.42%, 6) the second meeting increased to 85.71%. For learning outcomes also increased, namely: 1) In the first cycle the first meeting reached 35.71%, 2) the second meeting increased to 42.85%, 3) in the second cycle the first meeting increased to 42.85%, 4) the second meeting increased to 42.85% the second meeting increased to 50%, 5) in the third cycle the first meeting increased to 78.57%, 6) the second meeting remained 78.57%. The conclusion of this study is that the Modeling The Way strategy can improve speaking skills and learning outcomes in fifth grade students. SD Negeri 3 Candisari in 2010/2021 academic year.
Sentiment Analysis on Big Data Using Machine Learning Algorithms

Data Analysis which means the data which is obtained that is converted into useful information. Data analysis begins with data through its raw state and turns it into a format that is more readable in the form of graphs, records, plots, etc. allowing it the state and meaning to be understood. After the data is obtained, it reaches the stage of data planning. The level at which raw data is cleaned up and prepared for the next stage of data processing is data planning, also referred to as pre-processing. Raw data were diligently reviewed for any faults during planning. This move are meant to remove bad data. Therefore the clean data is entered into it and converted into a language it can comprehend. The first step in which raw data starts to take the form of functional information is data entry. The data inserted into the machine in the previous stage is actually analyses for analysis during this step. Machine learning algorithms are used to process the process, but the process itself can differ slightly depending on the data source being processed. The step of output / interpretation is the stage at which data will ultimately be used by non-data researchers. It's accessible and translated.
Suboptimal coverings for continuous spaces of control tasks

We propose the {\alpha}-suboptimal covering number to characterize multi-task control problems where the set of dynamical systems and/or cost functions is infinite, analogous to the cardinality of finite task sets. This notion may help quantify the function class expressiveness needed to represent a good multi-task policy, which is important for learning-based control methods that use parameterized function approximation. We study suboptimal covering numbers for linear dynamical systems with quadratic cost (LQR problems) and construct a class of multi-task LQR problems amenable to analysis. For the scalar case, we show logarithmic dependence on the breadth of the space. For the matrix case, we present experiments 1) measuring the efficiency of a particular constructive cover, and 2) visualizing the behavior of two candidate systems for the lower bound.
The Usage of Forensic Science in the Criminal Justice System

Justice is not something that will come to a person easily. To achieve justice a long and tedious procedure has to be followed and it may take even years for justice to be served in the existing judicial setup of India. The common citizens usually get misguided by the various Hollywood movies and series where the cases in the courts just get solved within an hour. But if we look into the real world it is quite the opposite. In reality, justice requires a huge amount of time, money, patience, and energy. A person needs to complete a huge process which includes filing an FIR, to court proceedings, and then at the end seeking court judgments. For providing judgments the judges play a great role and are considered to be a vital part of providing justice. As time has passed there is a huge development that has taken place in the judicial system. And there is a remarkable technological advancement taking place as technology is now used to solve crimes. Forensic science is the new helping hand. The article tries to analyze the usage of forensic science in the criminal justice system.
LibGuides: Government Publications: Finding Government Publications

Government Publications is a collection of materials put on deposit by the federal and state government for all citizens use free of charge and located on the Third Floor of the James E. Brooks Library. We encourage you to browse our collection whenever
Recent Advances in Qubit Allocation Algorithm for NISQ Architectures

In recent years, the advancement of quantum computers has been improving, with its use increasing in practical applications, including noisy intermediate-scale quantum (NISQ) technology. However, an obstacle that limits quantum computer usage is the connectivity restriction between logical and physical qubits. For performing quantum entanglement on NISQ machines, two logical qubits must be mapped to physical qubit pairs that much up to the connectivity constraint. This mapping process must insert additional operations into an initial quantum circuit. Many powerful algorithms have been proposed to solve the mapping problem, which also mitigate circuit size or depth and improve the fidelity of the quantum state. In this review paper, we present various studies for solving qubit mapping or allocation problems and analyze their time complexity and methods.
QR CODE AND IT'S EFFECTUALITY TOWARDS LIBRARY SERVICES

In this study the paper relates to the concept of Quick response (QR Code), and how it works to disseminate information in quick way. The main objective of this paper is to amend the basic concept of QR Code, their structure, advantageous nature and its activities. This research paper mainly focuses on the effectiveness of QR Code & its applications on library services in the present scenario also has discussed other services in which people are dependent during covid19 pandemic and how it placed as a position of successful tool in this technical age. © 2021. All Rights Reserved.
Scrambling Keypad for Secure Pin Entry to Defeat Shoulder Surfing and Inference Attacks

Personal identification number (PIN) is a common user authentication method widely used especially for automated teller machines and point-of-sales devices. The user's PIN entry is susceptible to shoulder-surfing and inference attacks, where the attacker can obtain the PIN by looking over the user's shoulder. The conventional keypad with a fixed layout makes it easy for the attacker to infer the PIN entered by casual observation. This paper proposes a method of authentication to address these challenges. The paper develops a prototype numeric keypad with a layout akin to the conventional keypad, with the keys randomized for each PIN entry. The shuffle algorithm, Durstenfeld shuffle algorithm, is implemented in an application developed using JavaScript, which is a prototype-based object-oriented programming application that conforms to the ECMAScript specification. The prototype is implemented on three computing platforms for evaluation. The test proves the effectiveness of the system to mitigate shoulder-surfing and inference attacks.
LibGuides: Digital Literacy and Public Service: Advanced

This guide is a resource for public librarians who want to educate their patrons on digital literacy skills.
An Early Evaluation of the Privacy Impacts of the COVID-19 Pandemic

At the time of this writing in mid-2020 the COVID-19 pandemic has gripped the world and frustrated health experts. In the interest of “ﬂattening the curve” of new cases, state and local government ofﬁcials have implemented a variety of legal measures including stay-at-home orders, social distancing requirements, and mandates to wear masks in public. These legal responses to the pandemic have created both new sources of data about people and new avenues for accessing existing data that may have been difﬁcult to access before the pandemic. This survey will address four common scenarios related to these data streams that these policies have fostered. The impact of these data streams on people’s privacy is just starting to be understood. The term “privacy” in this survey refers to an individual’s ability to control disclosure of her personally identiﬁable information. This survey explores some early answers to the question of how the COVID-19 pandemic has impacted people’s privacy in the U.S context.
Mobile Learning for Mathematics II Module (FGE1123) to Improve Teaching and Learning Method at UC TATI: A Pilot Study

In this new decade, technology has become an essential in daily life. Most of the activities in our life will use technology such as communication, purchasing, online payment and banking. In education, teaching and learning method also has gone through the phase of changes. Nowadays, the traditional method in teaching and learning need to be improved or changed as the students are more interested in technology. Furthermore, with the introduction of the internet, there is a need to enhance teaching and learning methods through the utilization of the technologies. Hence, this study aims to develop a mobile app for Mathematics II (FGE1123) module and to improve teaching and learning method through the utilization of technology. Methodology for this study consist of three phases. The first phase is mobile app development, the second phase is design and developing the app and the last phase is to test the app. Based on this study, the Mathematics II app is successfully developed and the students satisfied and found the apps is interesting to use. This result shows that mobile learning is good and effective teaching strategies to improve students’ interest in learning.
The Negative Impact of Excessive Use of Technology during COVID-19 on University Students: A post Crisis Perspective

: Technology has made our lives better and easier, and we cannot now imagine our life without it, despite the objection of some because it may affect the human mind. Technology has played a crucial role in overcoming the difficulties the world faced due to COVID-19. E-education via modern programs and applications, played a major role in reducing the negative repercussions on students as a result of school interruption during the crisis. Despite the many advantages that have been achieved through e-education, this system has drawbacks that may extend even after crisis is over, such as low motivation of students, poor practical skills, as well as limited participation of students in scientific research. Therefore, it is important for the concerned authorities in educational institutions, especially the university, to compensate for this deficiency, despite the burden it poses for both the student, the teacher, and the educational institution.
Improvement based on the proof-of-work consensus algorithm

Abstract Among the Bitcoins using Proof-of-work consensus algorithm, handling capacity is seven second each sum, which is less inefficient. But the Proof-of work depends on the computer function. It is beneficial to the higher-counting point, but unfair to he normal point. Make the computing power centralized. Aiming at the above-mentioned the waste and centralization of the Proof-of-work synchronic arithmetic, as well as the transaction-processing efficiency, we do the research and improvement.
STATISTICAL ANALYSIS OF THE AMBIGUITY OF CIPHERTEXT RECOVERY DEPENDING ON ITS LENGTH

This article provides a statistical analysis of the ambiguity that occurs when restoring a previously encrypted text, using a simple replacement cipher, depending on its length, and studies this issue at different text lengths.
Acciones implementadas por Instituciones de Educación Superior en México durante el COVID-19

Higher Education is fundamental for the development of countries; in Mexico, according to ANUIES, in the 2019-2020 school year, 4,061,644 students were enrolled in the National Education System in the public and private sectors in the school-based modality, which allows us to measure the impact of education at a national level. The health contingency caused by COVID-19 forced Higher Education Institutions (IES) to make changes in their teaching modalities, since all institutions have restricted attendance. In view of this situation, SEP and ANUIES applied a questionnaire to 582 public and private institutions all over the country, with the aim of identifying the actions implemented to maintain the educational services before COVID-19. In this work, a documentary analysis is carried out for the characterization of the phenomenon, through an exploratory and descriptive research, the actions implemented by the IES during the pandemic are evaluated and a categorization of them is built; the strategies implemented, the decision-making processes within the IES, the challenges and the way in which they were faced are analyzed, as well as an analysis of the continuity of the educational activity.