Reviewerly/exHarmony huggingface dataset:
Global Translation of Classification Models

The widespread and growing usage of machine learning models, particularly for critical areas such as law, predicate the need for global interpretability. Models that cannot be audited are vulnerable to biases inherited from the datasets that were used to develop them. Moreover, locally interpretable models are vulnerable to adversarial attacks. To address this issue, the present paper proposes a new methodology that can translate any existing machine learning model into a globally interpretable one. MTRE-PAN is a hybrid SVM-decision tree architecture that leverages the interpretability of linear hyperplanes by creating a set of polygons that delimit the decision boundaries of the target model. Moreover, the present paper introduces two new metrics: certain and boundary model parities. These metrics can be used to accurately evaluate the performance of the interpretable model near the decision boundaries. These metrics are used to compare MTRE-PAN to a previously proposed interpretable architecture called TRE-PAN. As in the case of TRE-PAN, MTRE-PAN aims at providing global interpretability. The comparisons are performed over target models developed using three benchmark datasets: Abalone, Census and Diabetes data. The results show that MTRE-PAN generates interpretable models that have a lower number of leaves and a higher agreement with the target models, especially around the most important regions in the feature space, namely the decision boundaries.
Analyzing Critical Condition of Patients through Augmented Reality Glass

Surgeons are constantly searching for new technologies that will improve their working conditions. Generally, they are the first to adopt and work with new technologies that enable their sector to provide better treatment and experience. As the surgical workplace keeps improving in the digital world, a number of developments have been identified as potentially emerging innovations in the medical field. Augmented reality (AR) is fast-growing technology it is more visible, easy to use, essential and reasonable, it will almost probably be used in health care to improve the medical utilization for taking care of patient's health. We proposed a system in which critical information is displayed on semi-transparent glasses that can be identified and solved by doctors. The aim is to build and develop an assistance system that will allow viewing the patient's health condition by doctors. AR can assist doctors and specialists, providing them with all the necessary information when they're performing surgery. When the sensors are connected to the patient's body and processed information to get the values such as body temperature, Finger detection heartbeat rate, respiratory rate, and oxygen flow in the blood of the patient which is then analyzed, delivered to surgeons through wireless augmented reality glasses, notifying them if any aberrant condition arises. As a result, this prototype assists surgeons in constantly monitoring patients during surgery. Based on the status of health, the doctor takes an action. Once a patient is checked, their medical data are stored in the database and viewed later. Augmented Reality (AR) makes the whole world more engaging by projecting three-dimensional models or simple textual information on top of it.
Designing an Android-Based Peer Assessment E-Rubric

Abstract—Previous research has shown that peer assessment has beneficial effects on students’ learning. Inspired by the need of students to involve peer assessment for gaining some constructive feedback for their works, such as in translation and writing courses, we developed an android-based e-rubric. Android was favored as the assessment platform due to its customizability and affordability. Two classes of English Department with 54 students participated in this research. Interviews and focus-group discussions were conducted to obtain data for needs analysis and collect users’ feedback, whereas the online survey was done to measure the students’ satisfaction. The e-rubric was developed through eight stages: (1) needs analysis, (2) development of rubrics, (3) expert validation, (4) creating the e-rubric app, (5) implementation, (6) users’ feedback, (7) product refinement, and (8) survey of users’ satisfaction. The results show that the developed e-rubric app is useful for supporting collaborative works among students as it allows them to assess each other’s strengths and weaknesses. Moreover, the android platform gives them a handy-and-engaging experience for completing the assessment. In this paper, we review relevant literature justifying the development of the app, outline the formulation steps, and explain how the app can be used in the classroom. Taking the limitations of the app development into account, we propose some recommendations for future research.
Robust Artificial Intelligence Approach to Stabilize and Control Propeller Driven Hybrid UGV

Hybrid Unmanned Ground Vehicle (HUGV) can drive on any terrain including walls and fly as well, using the multi directional thrust force of propellers. In the era of industrial revolution, hybrid UGVs need to be autonomous with intelligent decision making capabilities. During wall climbing of hybrid UGVs, stability is essential and depends on real time feedback from multiple sensors. To increase stability and control, it is proposed that PID control loops should be replaced by AI based algorithms that reduce the decision time and mathematical complexity. For autonomous movement in any terrain using the proposed model, intelligent UGVs can map and localize simultaneously.They can make intelligent decisions about mode of movement i.e. driving on ground or wall, steering on ground or wall, flying and maneuvering by using real time sensor readings. Integration of the proposed AI models with HUGV can be applied to many areas which are hard for humans to access, for instance; inspection of large structures, bio & nuclear hazard environments, planetary exploration & magnetic fields detection.
THE ADAPTATION OF FIRST-YEAR STUDENTS TO THE CONDITIONS OF THE UNIVERSITY

The article deals with the main features of first-year students’ adaptation
Live Streaming Data Analysis using Distributed Stochastic Bi-LSTM Model

In the real world, currently there are more than millions bytes of social media data being created. With the help of modern computational capacity in addition to advanced research in the field of artificial intelligence, a great improvement is shown in natural language processing using deep learning techniques. These AI techniques including machine learning algorithms and deep learning algorithms make it possible to analyze the given tons of data. In addition to it, it also helps us to have a deep dive knowledge in people's mindset towards the current trend in media application. There is another problem of cyber bullying wherein many people start sharing and posting anonymous and fake information to make a significant impact by making riots just as fake heroes. By continuously monitoring these types of online riots will provide a clean and peaceful environment which in turn supports the government in a better way. This article focuses on performing sentiment analysis over a given real time social media data by categorizing it. The effective analysis of live streaming from social media applications is done with word2vec embedding method in natural language processing and trained using bi-directional LSTM model. The performance is compared with the LSTM model, and traditional machine learning techniques like naïve bayes and linear regression model and Bi-LSTM model outperforms the other techniques with 80%-85% accuracy with live streaming unconventional social media data.
Deep Learning on Edge

Deep learning on edge has been attracting the attention of researchers and companies looking to provide solutions for the deployment of machine learning computing at the edge. A clear understanding of the design challenges and the application requirements are fundamental to understand the requirements of the next generation of edge devices to run machine learning inference. This chapter reviews several aspects of deep learning: applications, deep learning models, and computing platforms. The way deep learning is being applied to edge devices is described. A perspective of the models and computing devices being used for deep learning on edge are given, as well as what challenges face the hardware designers to guarantee the vast set of tight constraints like performance, power consumption, flexibility, etc. of edge computing platforms. Finally, a trends overview of deep learning models and architectures is discussed.
Study on handwriting features of ambidextrous persons by using image processing

Handwriting Features of Ambidextrous Persons have been classified as a multi-class classification issue in the deep learning framework, where image recognition task is effectively to test a classifier who can efficiently discriminate each line of paragraph of handwriting. This is very common for a single classifier to be performed on different data sets with a standard deep learning algorithm. It may also be that the same classifying system performs differently with highly differing image instances due to the various manuscript styles of various people on the same numbers. In order to address this problem, it was crucial to develop ensemble learning approaches to enhance overall effectiveness and make the performance highly stable in various data sets. In this text, we are proposing a system involving the CNN-based removal of the data set from an image and algebraic fusion of various words in images to classifies for trained in different sets of edges, which are prepared by means of feature selection applied to the original CNN feature set. Experimental results show that a fusion of classification images can achieve a descriptive word.
Generative magic and designing magic performances with constraint programming

Abstract Professional magicians employ the use of interesting properties of a deck of cards to create magical effects. These properties were traditionally discovered through trial and error, the application of heuristics or analytical proofs. Those proofs come from diverse mathematical areas such as the set, number and graph theory. We discuss the limitations of relying on humans for such methods and present how professional magicians can use constraint programming as a computer-aided design tool (CAD) to search for desired properties in a deck of cards. Furthermore, we implement a solution in Python by making use of generative magic to design a new effect, demonstrating how this process broadens the level of freedom a magician can decree to their volunteers while retaining control of the outcomes of the magic. Finally, we demonstrate that the model can be easily adapted to multiple languages by presenting multiple variations of the effect supporting American English and Brazilian Portuguese.
An Educational Project Based on the YouTuber Phenomenon for the Development of a Minority Language

In the Basque Autonomous Community (BAC), the “Basic Law on the normalization of the use of Basque” (law 10/1982, of 24 November) establishes that Basque citizens have the right to express themselves in either of the two official languages (Basque and Spanish), and to receive instructions in both languages. Therefore, the Faculties of Education must train future teachers to be able to teach and communicate in the Basque language. However, data from the last VI sociolinguistic survey (2016) tell us that 33.9% of the population aged 16 and over living in the BAC is Basque-speaking, but balanced bilinguals who express themselves with the same fluency in both Basque and Spanish make up only 29.3% of Basque speakers. In a study on linguistic customs in the academic field carried out on a group of future primary education teachers, it was observed that although Basque is the language they will teach in the schools of the BAC, it is not their main language of communication. Given this situation, it was deemed necessary to introduce the use of technology to promote the use of Basque. To this end, a project was designed and implemented, involving the intensive use of the social network YouTube. This paper presents the results of the data generated in this didactic experiment at the university level.
GIU-GANs: Global Information Utilization for Generative Adversarial Networks

Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, because convolutions are limited by spatial-agnostic and channel-specific, features extracted by conventional GANs based on convolution are constrained. Therefore, GANs cannot capture in-depth details per image. Moreover, straightforwardly stacking of convolutions causes too many parameters and layers in GANs, yielding a high overfitting risk. To overcome the abovementioned limitations, in this study, we propose a GANs called GIU-GANs (where Global Information Utilization: GIU). GIU-GANs leverages a new module called the GIU module, which integrates the squeeze-and-excitation module and involution to focus on global information via the channel attention mechanism, enhancing the generated image quality. Moreover, Batch Normalization (BN) inevitably ignores the representation differences among noise sampled by the generator and thus degrades the generated image quality. Thus, we introduce the representative BN to the GANs' architecture. The CIFAR-10 and CelebA datasets are employed to demonstrate the effectiveness of the proposed model. Numerous experiments indicate that the proposed model achieves state-of-the-art performance.
Performance Evaluation of Sequential Rule Mining Algorithms

Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.
Graph-Embedded Online Learning for Cell Detection and Tumour Proportion Score Estimation

Cell detection in microscopy images can provide useful clinical information. Most methods based on deep learning for cell detection are fully supervised. Without enough labelled samples, the accuracy of these methods would drop rapidly. To handle limited annotations and massive unlabelled data, semi-supervised learning methods have been developed. However, many of these are trained off-line, and are unable to process new incoming data to meet the needs of clinical diagnosis. Therefore, we propose a novel graph-embedded online learning network (GeoNet) for cell detection. It can locate and classify cells with dot annotations, saving considerable manpower. Trained by both historical data and reliable new samples, the online network can predict nuclear locations for upcoming new images while being optimized. To be more easily adapted to open data, it engages dynamic graph regularization and learns the inherent nonlinear structures of cells. Moreover, GeoNet can be applied to downstream tasks such as quantitative estimation of tumour proportion score (TPS), which is a useful indicator for lung squamous cell carcinoma treatment and prognostics. Experimental results for five large datasets with great variability in cell type and morphology validate the effectiveness and generalizability of the proposed method. For the lung squamous cell carcinoma (LUSC) dataset, the detection F1-scores of GeoNet for negative and positive tumour cells are 0.734 and 0.769, respectively, and the relative error of GeoNet for TPS estimation is 11.1%.
A SURVEY ON IMPROVING PROCESSING EFFICIENCY OF JOB SCHEDULING AND LOAD BALANCING IN CLOUD BALANCING IN CLOUD USING STANDARD AND DYNAMIC ALGORITHM

In the topic of parallel computing, load balance is being studied.Standard algorithms and dynamic algorithms are the two basic approaches.The technique of allocating a set of jobs among resources with the purpose of improving overall processing efficiency is referred to as load balancing.The multi-variable difficulties are load unbalancing, computer resource efficiency, and performance degradation, and the problem is multi-constrained.The process of load balancing is the distribution of workloads across numerous computing resources.The document management system maximizes resource.availability.DNS load balancing, on the other hand, uses software or hardware to execute the function.
Pengaruh Pembelajaran Daring dan Motivasi Terhadap Hasil Pembelajaran

Online learning is a system that is applied to the world of education with a distance learning process with teaching methods applied through the internet network. However, in the practice of implementing online learning, there are many obstacles, including there are still many students and also teaching staff who still do not understand or master the use of the learning media used. Learning outcomes are a benchmark for achievement in the learning process carried out by teaching staff and students. The benchmark for learning outcomes is student scores. Good learning outcomes are obtained when students are able to fully understand learning well. Motivation is an impulse that must be possessed by humans to get something. The purpose of the study was to see whether online learning and the use of learning media had an influence on students' learning motivation. Where in this study using quantitative research methods by testing using SPSS. The results showed that H1 was accepted, which means that online learning has an influence on student learning outcomes. While H1 is accepted, which means that the motivation possessed by students has an impact on student learning outcomes. Overall, the variables have a stimulant effect on student learning outcomes, which can be seen from the H3 received
Influence of remuneration on firm performance of telecommunication firm in Airtel Head Office, Rwanda 

Employee Empowerment is picking up energy through management to make employee voice heard and to increase a firm performance.This study seeks to investigate the influence of employee empowerment on performance at Airtel head office, Rwanda.More specifically it focused on the following objective; to establish the influence of remuneration on performance at Airtel head office, Rwanda.It was also to establish the relationship between employee empowerment and how it impacts performance.Research used a descriptive research survey design, the study used a quantitative method.The target population was 189 employees of Airtel head-office, Rwanda and a sample size was 128 employees who are working at Airtel head office Rwanda.A stratified random sampling method was applied.The study used the structured questionnaire for data collection.Statistical package for social science (SPSS) software vision 20 was used for data analysis process.The cronbach alpha coefficient formula was used to find out the reliability and validity.The findings were presented and interpreted by using the frequencies and the tables.After that, the results showed that employee empowerment influences a performance.On remuneration factor; Out of 128 respondents, 85.2% agreed that their salaries were proportionate with the effort employee put in their obligations and it means that almost of them were empowered with their salary, only 14.8% disagreed; 96.9% agreed that the benefits were reasonable and empowered them on their positions, only 3.1% disagreed; 79.7% agreed that the structure of salary and benefits motivated and empowered them but the results showed that some strongly disagreed, disagreed and others undecided; 100% agreed that their firm recognized and awarded employees through annual awards celebrations/bonuses and/or other benefits which empowered them to successfully contribute on performance of firm.The results showed that there was a significant relationship between remuneration and performance with value r=0.564.The study recommended that other telecommunication firms should successfully use and modernize the employee empowerment methods that give enough knowledge/ skills to employees in order to empower them and increase a performance in firm.By conclusion, the results showed that Airtel head office, Rwanda use the proper methods to empower their employees in purpose of achieve on their goal of giving the clear telecommunication services to the customers.
Research and Analysis of Different Cloud Storage Databases

This is a study of the evolution of the cloud technologies in full stack development for storage purpose due to advancement of technology. It helps give you a better understanding of what full stack development was and how full stack development evolved, it shows a take on the fact that full stack development is not dead and has just evolved to better accommodate the user's needs.
Research on the Image Description Algorithm of Double-Layer LSTM Based on Adaptive Attention Mechanism

Image text description is a multimodal data processing problem in the computer field, which involves the research tasks of computer vision and natural language processing. At present, the research focus of image text description task is mainly on the method based on deep learning. The work of this paper is mainly focused on the imprecise description of visual words and nonvisual words in the description of image description tasks in the image text description. An adaptive attention double-layer LSTM (long short-term memory) model based on coding-decoding is proposed. Compared with the algorithm based on the adaptive attention mechanism based on the coding-decoding framework, the evaluation index BLEU-1 is improved by 1.21%. The METEOR was 0.75% higher and CIDEr was 0.55%, while the indexes of BLEU-4 and ROUGE-L were not as good as those of the original model, but the index was not different. Although it cannot surpass all the performance indicators of the original model, the description of visual words and nonvisual words is more accurate in the actual image text description.