Reviewerly/exHarmony huggingface dataset:
PERCEIVED IMPACT OF SCHOOL CLIMATE ON ACADEMIC PERFORMANCE OF SECONDARY SCHOOL STUDENTS IN ONITSHA NORTH LOCAL GOVERNMENT AREA, ANAMBRA STATE

This study examined impact of school climate on academic performance of secondary school students of Onitsha North Local Government Area. The study assessed school climate factors and the level of psychological impact they have on learners. The population of the study was 25,350. The sample of 208 respondents comprising principals, teachers, and students selected from the population through purposive sampling technique was used for the study. The questionnaire used for data collection was structured on a 4-point response scale with mean scores of 2.50 and above as acceptable level. The questionnaire was validated in face and content by experts in Educational Administration. The reliability of this instrument was determined using Cronbach Alpha Coefficient which yielded a coefficient of 0.924. The data collected were analysed using mean scores for the two research questions raised and One Way Analysis of Variance (ANOVA) for the two null hypotheses formulated and tested at 0.05 significant level. Results indicate that, school climate can be improved for better academic performance by maintaining a manageable student-teacher ratio in the classroom; fundamental moral values is promoted; prevention of acts of bullying and when students are treated fairly amongst others. The following recommendations were made: the managers of secondary schools should increase school safety by adopting violence-prevention and conflict resolution programmes; school administrator should maintain a manageable student-teacher ratio in the school amongst others.
Evaluation method of children's learning concentration based on deep learning

In view of the current young parents busy with work, it is difficult to accompany their children to study, and the corresponding supervision methods are relatively scarce. This paper proposes a method for evaluating children's concentration based on deep learning, using improved CenterNet and MobileNet-V2 network cascading to realize children's face region localization and 68 feature points extraction, while maintaining the detection speed, but also taking into account the detection accuracy. According to the extraction of the corresponding feature points, the head posture and fatigue are estimated, and then the AHP analytic method is used to calculate the corresponding weights, and finally the quantified evaluation of children's learning concentration is realized to help children's concentration training. Experiments show that the real-time and accuracy of this method can be used for special evaluation of children's learning.
“Itsyourownfault”: Space omission in a Russian women's support group

Russian online discourse includes phrases in which spaces are omitted deliberately. This phenomenon is a Computer Mediated Communication (CMC) cue: it adds meaning to the verbal message and participates in identity and relationship building in online communities (Herring, 2004). In order to determine the meaning dimension of space omission as a CMC cue and the role of community in its use, an online community with a clearly defined group identity has been chosen as the source of data. Using posts from the women's support group #ščastʹebytʹženoj (happiness of being a wife), this study applies the tools of Computer-Mediated Discourse Analysis (Herring, 2004; Androutsopoulos, 2014) to examine the discourse functions of this cue. The analysis of space omission and its context suggests that, similarly to paralinguistic cues in oral reported speech, this CMC cue contributes to multimodal role-shift (Stec et al., 2016). Moreover, the study concludes that the participants of the group tend to use space omission to report speech that they heard multiple times. This CMC cue can position the original speaker as being a member of the out-group and it demonstrates the typically negative behavior, as viewed by the community. • Space omission belongs to a class of Computer-Mediated Communication cues. • Space omission occurs in reported speech and it serves as a role-shifting signal. • Reported speech with space omission demonstrates the typical negative verbal behavior as viewed by the community. • The use of space omission reinforces the in-group vs out-group boundaries.
Research And Implementation of Path Planning for Mobile Robot in Unknown Dynamic Environment

Unknown environment path planning is a very important technology in the application of robot. In this paper, genetic algorithm, ant algorithm and A*algorithm were researched. First of all, we introduce the principle of each algorithm. On the base of comparing the stability and efficiency of several algorithms in different environments, A* algorithm is selected as the robot path planning algorithm. Based on the simulation verification on upper computer, the C program is written and the actual operation of the lower compute. The running results show the feasibility and practicability of A* algorithm path planning.
Algorithm and Convergence Analysis of Absolute Value Equation Based on Hybrid Network

Abstract In view of the shortcomings of structured networks that it is difficult to manage complex shapes and unstructured networks cannot calculate the boundary layer viscous flow, an implicit algorithm based on mixed network points is proposed to solve the problem of successful calculation and high viscosity. Comparing absolute values is a difficult NP problem. It is caused by the interval problem and has a wide range of applications in many practical problems, such as: situation problems, semi-monitored and unprotected classification problems, and bag feasibility problems. When solving linear programming, double matrix strategy, quadratic programming and other problems, they must be converted into linear complementarity problems for processing, and linear complementarity problems can be summarized as absolute value equations. This article mainly introduces the smoothing Newton method of absolute value equation and the conjugate gradient algorithm of absolute value equation. This paper uses a hybrid network to analyze the algorithm and convergence of the absolute value equation, and establishes a potential mathematical model. The model is solved by the smoothing Newton method of the absolute value equation and the conjugate gradient algorithm of the absolute value equation, and the algorithm and convergence analysis results of the absolute value equation based on the hybrid network are evaluated, and the model is revised using historical data to improve The algorithm of the absolute value equation and the accuracy of the assessment of convergence analysis results. The experimental results in this paper show that the smoothing Newton method of the absolute value equation and the conjugate gradient algorithm of the absolute value equation increase the efficiency of the algorithm and convergence analysis of the absolute value equation by 23%, and reduce the false alarm rate and the false alarm rate. Finally, by comparing the experimental data analysis of the management strategy of sports services and the analysis of the reconstruction of sports services, the system illustrates the influence of artificial intelligence on the analysis of sports services’ management strategies.
Design and fabrication of quad copter semi-autonomous drone for medical appliances

A few large number of individuals pass on every day because of the delay taken by the Ambulance administration to arrive at the mishap spot. This occurs because of gridlock, clog in the city. A model of a crisis flying air rescue vehicle or an emergency vehicle drone which can arrive at the genuine cases quicker than a typical emergency vehicle which saves time is planned and it likewise gauges the diverse wellbeing boundaries utilizing its estimating gadgets. At the point when a call is given to the crisis number, the crisis administrator tracks the area and explores utilizing worldwide situating framework. The rescue vehicle drone enters the scene in the moment time and continuous orders are given by the administrator. The robot comprises of a smaller than normal patient observing framework which contains variation sensors which can be advantageously appended to the casualty body and the significant boundaries is estimated and it is promptly shipped off the emergency vehicle just as to the close by clinic utilizing worldwide situating framework and ZIGBEE. The specific outcome assists the paramedics with welling the specialists to assess the circumstance snappier with better demonstrative and restorative decisions. Along these lines, the objective is to build up a generally useful clinical toolbox that can be travelled to any crisis circumstance and it is utilized to give the real time circumstance results to the emergency vehicle and the emergency clinic group so they will be prepared to serve to the requirements of the patients. This model assists with supporting the standard emergency vehicle instead of supplanting the typical rescue vehicle.
Design and Development of an Indian Harmonium with Automatic Bellow Mechanism

Abstract Harmonium is a reed instrument widely used in Indian classical music, utilizing a hand-driven bellow to pump air into the soundbox. The pumped air is released through the metallic reeds producing musical notes. The hand-driven bellow action can be challenging and tiresome for beginners as well as professionals during long practice sessions. This problem can be resolved by automating the bellow. The design process is twofold – 1 st is the synthesis of a motor-driven linkage capable of generating the rocker motion of the bellow, and 2 nd is to obtain the required specification of the motor that can drive the mechanism. A 4-bar crank-rocker linkage has been synthesized using a modified Freudenstein equation, which allows the bellow to compress and expand in one complete rotation of the motor shaft. The novelty of this work is that the nonlinear dynamics of the harmonium bellow has been modeled using a spring-mass equivalence technique. This helps in accurately predicting the operating force for the bellow, the force transmission through the linkage, and estimating the required motor torque. The final design is simple and ergonomic. Based on the design, a working prototype has been developed at the Machine Elements Laboratory of Jadavpur University.
Corpus Development for Dzongkha Automatic Speech Recognition

Speech recognition technology has been very popular and reached an advanced stage in computational linguistics that enables the recognition and transcription of spoken language into text by computers. However, the research on automatic speech recognition (ASR) for Dzongkha is the first of its kind and none has carried out any research and development on Dzongkha language; whereby making the data resource preparation information very limited. More than 11000 Dzongkha utterances of raw data are collected from Dzongkha Development Commission. The data wrangling is carried out on the limited data and the Dzongkha ASR corpus has been developed. In corpus, a total of 10566 usable utterances is prepared which includes Dzongkha text, audio recordings, and transcriptions. The pronunciation dictionary (lexicon) is generated which contains a total of 12605 unique Dzongkha words; and also, a phone list is recorded using Dzongkha phonemic inventory table for the representation of the smallest unit of the sound at the phoneme level. Such speech corpus is the primary requirement for developing the new speech recognition system for Dzongkha language.
A Survey on Multimedia Technology and Internet of Things

Use of multimedia devices has been increasing day by day with exponential growth. This growth has greatly influenced human life in about all the paradigm of life. Though it has facilitated to improve the quality of life, it created new dimensions of problems also, primarily on privacy issues. In this article, we are going to provide a survey work of IoT and then we discuss its characteristics. After that we shall discuss different issues involved with each of these fields. Multimedia data generated by IoT system are very huge in size and need a lot of bandwidth to communicate from one system to another. We shall also discuss architecture of these different multimedia IoT systems and the scope of improvement in these systems so that it can be stored, communicated and used for prediction of system behavior.
Upper Bounds for Particle Location Variance Convergence Measures in the Stochastic Model of Particle Swarm

Properties of the Particle Swarm Optimization method depend on the parameters configuring the behavior of single particles. In the stochastic particle stability analysis, there were proposed recurrent formulas of the expected value and the variance of particle location. Recently, a particle variance convergence time measure was defined, and for a selected set of configurations, a simple explicit formula for particle location variance under the stagnation assumption was derived. We propose an explicit formula for the upper bound of the particle variance convergence time for the same selected set of configurations. Furthermore, we propose an explicit formula for the upper bound for the earlier defined measure of the particle location variance stasis time. We visualize both upper bounds and verify them in simulations.
Interaction of Two-Level Atoms with a Single-Mode Quantized Radiation Field

We have studied the statistical and squeezing properties of the cavity light generated by a two-level laser. This optical system contains N two-level atoms available in a cavity coupled to a single-mode vacuum reservoir. They are pumped to the top level from the bottom level by means of the electron bombardment. Applying the steady-state solutions of the equations of evolution of the expectation values of the atomic operators and the quantum Langevin equation, we obtained the global and local photon statistics of the single-mode light beam. We have found that, for the two-level laser operating well above the threshold, the uncertainties in the plus and minus quadratures are equal and satisfy the minimum uncertainty relation. In view of this, we have identified the light generated by the laser operating well above threshold to be coherent. On the other hand, the light generated by the laser operating at threshold is found to be chaotic. From the obtained results, we have also observed that a large part of the local mean photon number, the local photon number variance, and the local quadrature variance are confined in a relatively narrow frequency interval.
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising

Deep neural networks have achieved state-of-the-art performances on named entity recognition (NER) with sufficient training data, while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem, we propose a novel data augmentation method based on pre-trained language model (PLM) and curriculum learning strategy. Concretely, we use the PLM to generate diverse training instances through predicting different masked words and design a task-specific curriculum learning strategy to alleviate the influence of noises. We evaluate the effectiveness of our approach on three datasets: CoNLL-2003, OntoNotes5.0, and MaScip, of which the first two are simulated low-resource scenarios, and the last one is a real low-resource dataset in material science domain. Experimental results show that our method consistently outperform the baseline model. Specifically, our method achieves an absolute improvement of 3.46% $$F_1$$ score on the 1% CoNLL-2003, 2.58% on the 1% OntoNotes5.0, and 0.99% on the full of MaScip.
One Solution of Memory Communication within Camera Framework

As performance requirements in the automotive domain have increased in recent years, engineers have had to respond to new challenges. Autonomous vehicles, which have the ability to revolutionize the model of mobility in the global world, are equipped with new electrical and mechanical devices for environment perception, communication, and computing. Such systems require the use of multi-core platforms, that are able to support complex performance demands. These modern systems typically use multiple cameras simultaneously, and as camera resolution and color depth increase, it is clear that the way data is handled has a key impact on the available memory bandwidth in the system. Therefore, it is very important to optimize communication to achieve fast and efficient data transfer, whether it is a communication within local host or a remote host. In this paper, one solution of a camera framework is presented. This camera system architecture enables efficient and reliable frame exchange between producers and consumers, both locally and remotely, relying on the shared memory concept. For evaluation, we demonstrated the efficiency of the implemented producer component, by measuring the time from fetching the frame to the delivery of the message to the consumer.
Quantum convolutional neural networks on NISQ processors

Growing interest in quantum machine learning has resulted into very innovative algorithms and vigorous studies that demonstrate their power. These studies, although very useful, are often designed for fault-tolerant quantum computers that are far from reality of today's noise-prone quantum computers. While companies such as IBM have ushered in a new era of quantum computing by allowing public access to their quantum computers, quantum noise as well as decoherence are daunting obstacles that not only degrade the performance of quantum algorithms, but also make them infeasible for running on current-era quantum processors. We address the feasibility of a quantum machine learning algorithm on IBM quantum processors to shed light on their efficacy and weaknesses to design noise-aware algorithms that work around these limitations. We compare and discuss the results by implementing a quantum convolutional filter on a real quantum processor as well as a simulator.
Segmentation and classification using image processing and supervising learning framework for mitosis detection in breast cancer mammographic images

Abstract Detection of the area withholding the mitotic cell growth is a vital marker in breast cancer detection. This paper aims to fabricate an automatic Computer-Aided Detection (CAD) [2] model that helps to locate the region of mitotic cell growth and signify the type of breast cancer found: benign or malignant. Contrary to the legacy literature [1], which uses partially supervised learning models applied to histopathology images, we devise a model which involves a fully supervised convolution model applied to mammographic images. The model trained with image datasets: benign and malignant breast cancer images. This model exploits the MIAS database and datasets collected privately from hospitals, consisting of mammographic images available as samples for breast cancer detection. Applying image segmentation techniques to the datasets, we highlight the region of interest, and thereby using classification methodologies, we separate the results as benign or malignant. The developed model equips us to yield results with an accuracy of 97.96% on the dataset.
Computing Plans that Signal Normative Compliance

There has been increasing acceptance that agents must act in a way that is sensitive to ethical considerations. These considerations have been cashed out as constraints, such that some actions are permissible, while others are impermissible. In this paper, we claim that, in addition to only performing those actions that are permissible, agents should only perform those courses of action that are _unambiguously_ permissible. By doing so they signal normative compliance: they communicate their understanding of, and commitment to abiding by, the normative constraints in play. Those courses of action (or plans) that succeed in signalling compliance in this sense, we term 'acceptable'. The problem this paper addresses is how to compute plans that signal compliance, that is, how to find plans that are acceptable as well as permissible. We do this by identifying those plans such that, were an observer to see only part of its execution, that observer would infer the plan enacted was permissible. This paper provides a formal definition of compliance signalling within the domain of AI planning, describes an algorithm for computing compliance signalling plans, provides preliminary experimental results and discusses possible improvements. The signalling of compliance is vital for communication, coordination and cooperation in situations where the agent is partially observed. It is equally vital, therefore, to solve the computational problem of finding those plans that signal compliance. This is what this paper does.
Learning and Logic for Formal Synthesis

Author(s): Caulfield, Benjamin Vincent | Advisor(s): Seshia, Sanjit A | Abstract: Program synthesis is the use of algorithms to derive programs that satisfy given specifications. These specifications are usually given in some form of computer-understandable logic. Specifications are usually much easier to write than the programs themselves. By ‘filling in the details’ given by the specification, program synthesis opens the possibility of creating simple programs to both lay people and non-programming domain experts.Recent work in program synthesis has used techniques from ‘exact active learning’, where learning algorithms are able to pose queries to oracles. In the case of program synthesis, these oracles are implemented by checking potential programs against the given specification or asking a user for more inputs.Another recent development in the field is Syntax-Guided Synthesis (SyGuS), where the space of potential programs is given by a tree-grammar (or context-free grammar). Specifications are given in the logic of SMT (satisfiability modulo theories) problems.This thesis further develops the theory behind exact active learning, program synthesis, and their intersection. We provide upper and lower bounds, including undecidability results, for SyGuS problems defined on various SMT theories. We introduce the subject of actively learning equational theories and show how it can be used to learn constrained EUF formulas. We expand the problem of learning symbolic automata to use different types of symbolic counterexamples. We study the problem of exact active learning of concepts that are comprised of independent components, andshow when the knowledge that components work independently can significantly reduce learning complexity. Finally, we introduce new methods for SyGuS solving with respect to cost, where the goal is to find the minimal cost program satisfying a specification.
The Research on the Influencing Factors of Students' Advanced Mathematics Score Based on Hierarchical Multiple Regression Model

This paper takes students from an applied undergraduate university in Jiangsu Province as the research object, the advanced mathematics score as the dependent variable, 19 factors such as gender and birthplace as independent variables by hierarchical multiple regression and stepwise regression. Experimental results: eleven factors such as gender, birthplace, family economic, and career planning will have a significant impact on students' advanced mathematics score. Psychological assessment level, class size, and counselors have no effect on advanced mathematics score. Conclusion: the advanced mathematics scores of unretaken Jiangsu girls with a clear career plan but whose family was difficult financially were higher. At last, this paper makes suggestions in three aspects: student management, class management, and course teaching.