Reviewerly/exHarmony huggingface dataset:
THE ANALYSIS OF GRAMMATICAL ERRORS ON STUDENTS’ ESSAY WRITING BY USING GRAMMARLY

Considering the importance of grammar knowledge in the process of writing essays to students, the researchers aimed to research and explain the grammar errors in the essays of students of the English Department at UNISKA Kediri. The research study is classified into qualitative study. The study analyzed 14 samples English Essays written by UNISKA students. To classify the different types of errors made by students and identify common errors in writing student essays are using triangulation method. The results of this research found types of grammatical errors in the students' essays consist of 7 total errors in the grammar section, including 19,66% were found in articles a/an/the error, 13,68% were found in punctuation errors, 11,97% were found in plural/singular error, 11,97% were found in preposition errors, 5,13% were found in tenses error, 0,85% were found in to + V1 error, and 0,85% were found in pronoun error. Those errors indicated the problems faced by students in writing essays. The most dominant grammatical error found is in the articles a/an/the error including 19,66%. Researcher also found students’ difficulties facing in writing their essays. That found students' questionnaire answers. Researcher found the students’ difficulties are selecting and developing topic, write a well structured sentence and paragraph, grammar, and vocabulary. Based on the findings of grammar errors in student essays, the researcher hopes students to be more aware of writing essays with the correct grammar, aware of the wrong grammar on their Essays and using Grammarly Application to help in writing essays.
Employees Perception on Gender diversity on banking performance in Kathmandu valley

Studying gender diversity in firm performance has been the subject of research request for more than three decades now. Yet, in context of Nepal, study related to the issue is still not abundant. Therefore, this study aims to study the present status of gender diversity on banking performance in Nepalese context that includes several benefits, challenges associated with it and policies and measures that need to be taken to promote gender diversity. The study adopted descriptive cross section research design with the survey questionnaire technique. With the help of convenient sampling technique 300 bank employees were selected purposively from all 27 A grade commercial banks residing Kathmandu valley. The study results that 100% respondents working in commercial banks in Kathmandu valley were aware about gender diversity and most of them have been aware gender diversity through their family and social media. Interestingly, this study also found that employees are not facing any challenges regarding gender diversity signifying that Nepalese commercial Banks have been practicing gender diversity friendly working environment. Similarly, building an inclusive workplace could be one of the best managerial solutions for maintaining and enhancing gender diversity.
LegalLedger–Blockchain in Judicial System

One of the major concerns around the globe is access to justice and with the advancement of technology, there is tremendous scope to make these judicial systems much more efficient and swift. There have been various cases in which a courthouse reopened a case after several years, and it was found that some evidence was either missing or had been tampered with. This is a big issue in countries like India where there is very minimal digitization of such documents, and it has been a great obstruction in strengthening our judicial systems. We thereby propose a system where court proceedings and case-related documents can be stored digitally on a decentralized storage with every record stored on the Ethereum blockchain. Moreover, using blockchain in the legal system will serve as a completely tamperproof solution with easy accessibility and storage for all its users. The evidence files will be stored encrypted in the decentralized storage, so even if the storage is compromised an attacker can never know the content. The private key of the case is also encrypted with an authorized user's public key and stored on the smart contract, so an authenticated user can view the case files. Hence, the objective of this paper is to propose a tamperproof solution for the digitization of court proceedings in a secure storage leveraged by blockchain technology and to also provide accessibility in a secure manner.
Physics-Based Simulation and Automation of a Load-Haul-Dump Operation for an Articulated Dump Truck

Many trucks are used for a class of activities involving a sequence of basic load-haul-dump operations. The repetitiveness of this operation has been an enabler for autonomous vehicle technology in efforts to increase safety and efficiency. In this paper, we present a framework for the automation of the load-haul-dump operation in a mine setting using an articulated dump truck. A simulation environment for the testing of autonomous driving algorithms is developed and a custom mining environment is generated to adapt to our simulation settings. We also present an operational decomposition of the sequence of tasks and develop a finite state machine for high-level decision making based on this decomposition. A path tracking module that considers both bodies of the articulated truck is also developed. The resulting architecture was implemented to achieve autonomy for a load-haul-dump operation in the simulated environment within a fixed path. Experiments show that the proposed FSM-path tracking system can automate the load-haul-dump operation; and that the simulation environment can support the testing and development of autonomous driving algorithms for configurations such as an articulated truck.
Technological tools for virtual teaching and their effect on the satisfaction of online learning

&lt;span&gt;The objective of the research is to analyze the satisfaction of the online learning of the applied electricity subject, when implementing technological tools for virtual teaching. The development of the research determines a high level of student satisfaction, finding the perception of reliability with 93.05%, that of security with 93.2%, that of answer’s capacity with 90.73% and empathy with 82.87%. Satisfaction with the technological tools of virtual teaching is related to the adequate and accessible use of simulation software during online learning, which allowed compliance with the syllable. In addition to the security and confidence when the teacher is willing to help him in the use of the simulation software, responding to it appropriately and quickly. Satisfaction of online learning of the applied electricity subject using virtual teaching tools is related to the teacher's sample of concern towards students regarding their academic needs and their expressed interests.&lt;/span&gt;
Adaptive Moving Ground-Target Detection Method Based on Seismic Signal

Moving ground-target detection system is widely used to monitor illegal activities of pedestrians and vehicles. However, existing detection methods are restricted by the power consumption in hardware and are usually based on some single feature of the seismic signal, which leads to low detection accuracy and false alarms. To address these issues, we propose a new moving ground-target detection method for detecting the weak seismic signals generated by distant moving ground targets. This method combines an adaptive strategy and support vector machines (SVMs). Both time- and frequency-domain features of seismic signals are considered in the detection method. Additionally, we carry out field experiments to evaluate the performance of the proposed method. The results show that the proposed moving ground-target detection method can detect distant moving ground targets and avoid false alarms as many as possible, which indicates good performance.
Time-Memory Tradeoffs for Large-Weight Syndrome Decoding in Ternary Codes

We propose new algorithms for solving a class of large-weight syndrome decoding problems in random ternary codes. This is the main generic problem underlying the security of the recent Wave signature scheme (Debris-Alazard et al. 2019), and it has so far received limited attention. At SAC 2019 Bricout et al. proposed a reduction to a binary subset sum problem requiring many solutions, and used it to obtain the fastest known algorithm. However —as is often the case in the coding theory literature— its memory cost is proportional to its time cost, which makes it unattractive in most applications. In this work we propose a range of memory-efficient algorithms for this problem, which describe a near-continuous time-memory tradeoff curve. Those are obtained by using the same reduction as Bricout et al. and carefully instantiating the derived subset sum problem with exhaustive-search algorithms from the literature, in particular dissection (Dinur et al. 2012) and dissection in tree (Dinur 2019). We also spend significant effort adapting those algorithms to decrease their granularity, thereby allowing them to be smoothly used in a syndrome decoding context when not all the solutions to the subset sum problem are required. For a proposed parameter set for Wave, one of our best instantiations is estimated to cost $$2^{177}$$ bit operations and requiring $$2^{88.5}$$ bits of storage, while we estimate this to be $$2^{152}$$ and $$2^{144}$$ for the best algorithm from Bricout et al..
THE ROLE OF PERSONAL DEVELOPMENT IN STUDENTS ADAPTING TO THE ACADEMIC LIFE

In this article we describe the results obtained in an activity of evaluation of a personal development program addressed to students in the first year of study. It was carried out within a project financed by the Scholarship Scheme for Universities, concluded between the Ministry of National Education through the Management Unit of Externally Funded Projects and the Faculty of Electrical Engineering, Energy and Applied Informatics, “Gheorghe Asachi” Technical University of Iasi. The specific objectives of the project pursued three intervention components: motivation (M), networking (R) and workload (V). These have been directly correlated with other identified aspects (individual failure, academic failure) that lead to school dropouts and low academic performance. In order to achieve the objectives, the project presented a proposed activity plan for a target group of 80 students for one year of implementation). The activities proposed within the project represent punctual educational interventions on the factors that influence school dropout and academic performance. The evaluated program, the results of which we present here, was subordinated to the objectives related to the motivation of the students and their relationship skills and pursued personal development through creativity and socio-emotional education in students. This dimension of the programme was chosen because a major need existing on the labour market is the need to educate young people to have the attitude of a winner and to find solutions to problems .The behavioural acquisitions of the students, within the personal development programme through creativity and social-emotional education were: flexible, generative thinking in solving problems; acquiring coping strategies; optimisation of personal and academic life.
Public Integrity Auditing of Shared Encrypted Data within Cloud Storage Group

The pandemic of COVID-19 has posed a severe challenge to the traditional on-site centralized development projects; people therefore have to share data in a group by the cloud storage server and develop projects at home. The cloud server is untrustworthy, although it supplies the powerful computing capability and abundant storage space; so far wide research has been proposed to verify data integrity. Therefore, how to leverage the cloud server and ensure the integrity of the data (especially the encrypted data) stored on the remote cloud devices remains an issue for the clients. To address this issue, we utilize the technique of homomorphic hash function to implement reencryption ciphertext blocks and introduce a certificateless signature scheme for the integrity verification of encrypted data shared within a group. A detailed challenge-and-response game represents that the proposed scheme can preserve encrypted data blocks integrity against the internal/external attacker and malicious cloud service servers. We give the theoretical and experimental performance analysis of the scheme and exhibit that the scheme is efficient and practical.
Automatic inverse treatment planning of Gamma Knife radiosurgery via deep reinforcement learning

Several inverse planning algorithms have been developed for Gamma Knife (GK) radiosurgery to determine a large number of plan parameters by solving an optimization problem, which typically consists of multiple objectives. The priorities among these objectives need to be repetitively adjusted to achieve a clinically good plan for each patient. This study aimed to achieve automatic and intelligent priority tuning by developing a deep reinforcement learning (DRL)-based method to model the tuning behaviors of human planners.We built a priority-tuning policy network using deep convolutional neural networks. Its input was a vector composed of multiple plan metrics that were used in our institution for GK plan evaluation. The network can determine which tuning action to take based on the observed quality of the intermediate plan. We trained the network using an end-to-end DRL framework to approximate the optimal action-value function. A scoring function was designed to measure the plan quality to calculate the received reward of a tuning action.Vestibular schwannoma was chosen as the test bed in this study. The number of training, validation and testing cases were 5, 5, and 16, respectively. For these three datasets, the average scores of the initial plans obtained with the same initial priority set were 3.63 ± 1.34, 3.83 ± 0.86 and 4.20 ± 0.78, respectively, while they were improved to 5.28 ± 0.23, 4.97 ± 0.44 and 5.22 ± 0.26 through manual priority tuning by human expert planners. Our network achieved competitive results with 5.42 ± 0.11, 5.10 ± 0. 42, 5.28 ± 0.20, respectively.Our network can generate GK plans of comparable or slightly higher quality than the plans generated by human planners via manual priority tuning for vestibular schwannoma cases. The network can potentially be incorporated into the clinical workflow as planning assistance to improve GK planning efficiency and help to reduce plan quality variation caused by interplanner variability. We also hope that our method can reduce the workload of GK planners and allow them to spend more time on more challenging cases.
PREDICTORS OF BREAST SELF EXAMINATION AMONG FEMALE UNDERGRADUATES IN LAGOS STATE, NIGERIA

Breast self-examination is a cheap and easy to practice method of detecting mad cells in the breast tissue, as early detection has been proven to increase the chance of survival. The study assessed the Breast Self-Examination (BSE) knowledge and practice among female undergraduates in Lagos State. A cross-sectional survey was conducted among two hundred and thirty female undergraduates in Lagos State. The respondents were drawn from Lagos State University and the University of Lagos using a simple random sampling technique. A self-developed breast self-examination questionnaire (BSEQ) was used to elicit information on knowledge and practice of BSE among the participants. Chi-square test of association was used to test the association between the variables. The result of this study revealed that there was a significant association (χ2 =18.14, P-value = 0.234&amp;lt; 0.05) between knowledge of BSE and practice of BSE, but there exist no statistically significant association (χ2 = 15.10, P-value = 0.009˃0.05) between the age of the respondents and the practice of BSE. Family economic status does not have a significant association (χ2 = 13.15, P-value = 0.02˃0.05) with the practice of BSE. Also, the number of years spent in the school by the respondent does not have any significant association (χ2 = 6.33, P-value = 0.007˃0.05) with their practice of BSE. Having a close relative diagnosed with breast cancer encouraged (χ2 = 11.05, P-value = 0.114&amp;lt; 0.05) the practice of breast self-examination. This study concluded that awareness of BSE and practice was high among the respondents; however, only a minor proportion practiced BSE properly and regularly. Therefore, there is a wide gap between knowledge and proper practice of breast self-examination, hence there is a need for health educators to rise up to their responsibility as the town crier through strategic advocacy using the media and other available means.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0944/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
A real-time auto calibration technique for stereo camera

Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision. Usually, to get accurate 3D results the camera should be manually calibrated accurately as well. This paper proposes a robust approach to auto calibration stereo camera without intervention of the user. There are several methods and techniques of calibration that have been proven, in this work, we exploiting the geometric constraint, namely, the epipolar geometry. We specifically focus to use seven techniques for features extraction (SURF, BRISK, FAST, FREAK, MinEigen, MSERF, and SIFT), however, tries to establish the correspondences between points extracted in stereo images with various matching techniques (SSD, SAD, Hamming). Then we exploit the fundamental matrix to estimate the epipolar line by choosing the perfect eight-point algorithms (Norm8Point, LMedS, RANSAC, MSAC, and LTS). A large number of experiments have been carried out, and very good results have been obtained by comparison and choice of the perfect technique in every stage.
Automatic Decision-Making Style Recognition Method Using Kinect Technology

In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.
Expert System for Diagnosis of Sexual Diseases (Paraphilia) Using Method Dempster Shafer

Sexual behavior disorder (paraphilia) is sexual behavior that is unacceptable in society. The lack of knowledge and the many obstacles to getting information about early and independent detection of the community about sexual deviations and ignorance of the community in responding to deviations are also factors that cannot be handled early The main symptoms of this paraphilia disease are repeated childhood traumatic, coming from a family that is too restrictive or too disruptive to children in sexual behavior, and has a disorder in sexual relations since childhood. There are several types of sexual disorders (paraphilia) including Voyeuristic, Exhibitionistic, Frotteuristic, Sexual Masochim, Sexual Sadism, Pedophilic, Fetishistic and Transvestic. The Dempster Shafer method provides space for the expert in providing the value of trust in his knowledge. Based on the problems above, a Web-based system will be built that functions to help the community conduct independent consultations about disturbances that can be accessed with a wide range and unlimited time. The system built using PHP and MySQL programming as a database. The method used for tracing is the dempster shader. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of the rules about the disorder suffered and information about possible treatments that can be done.
Iterative Rotations and Assignments (IRA): A shape matching algorithm for atomic structures

<h2>Abstract</h2> IRA is a Fortran library that solves the shape matching problem for atomic structures, stored as sets of points representing the atomic positions. In the case of exact- and near-congruence, IRA provides the optimal rigid transformation between the structures, given by the atomic assignments, the rotation/reflection matrix, and the translation vector. IRA is also able to operate on structures containing a non-equal number of atoms, i.e. matching a structure to any of its fragments. Any application that requires the solution of a shape matching problem could benefit from IRA.
Policy Gradient From Demonstration and Curiosity

With reinforcement learning, an agent can learn complex behaviors from high-level abstractions of the task. However, exploration and reward shaping remain challenging for existing methods, especially in scenarios where extrinsic feedback is sparse. Expert demonstrations have been investigated to solve these difficulties, but a tremendous number of high-quality demonstrations are usually required. In this work, an integrated policy gradient algorithm is proposed to boost exploration and facilitate intrinsic reward learning from only a limited number of demonstrations. We achieved this by reformulating the original reward function with two additional terms, where the first term measured the Jensen-Shannon divergence between current policy and the expert's demonstrations, and the second term estimated the agent's uncertainty about the environment. The presented algorithm was evaluated by a range of simulated tasks with sparse extrinsic reward signals, where only limited demonstrated trajectories were provided to each task. Superior exploration efficiency and high average return were demonstrated in all tasks. Furthermore, it was found that the agent could imitate the expert's behavior and meanwhile sustain high return.