Reviewerly/exHarmony huggingface dataset:
Path Planning for Autonomous Robot Navigation: Present Approaches

An intelligent autonomous robot is in demand for robotic operations in the fields such as industry, medical, bionics, military. For any machine, designed to follow a precise sequence of instructions, self-positioning, path framing, map architecture, and obstacle prevention are the prerequisites of navigation. This paper presents a survey about the key navigation approaches explored by various authors in the last decade. The survey has a brief insight into the various approaches used for robot navigation concerning to the variable and invariable nature of the vicinity and the obstacle. The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of the discrete classical and heuristic approaches used by the researchers. The research assessment is finally concluded by aggregating the complete knowledge of the various path planning techniques by reviewing the literature.
Design and implementation of thesis exam assessment at Science and Technology Faculty UIN Sunan Gunung Djati Bandung

Abstract Speeding up the calculation process in thesis exam, a web-based thesis exam assessment system is needed, the data of the students would be saved safety on a database. Designing and implementing thesis exam assessment system a web based at Science and Technology Faculty UIN Sunan Gunung Djati Bandung are the purpose. Admin and user are the people who can do at a work principle as a method of this research. User is examiner or counsellor, and admin is operator, usually administration staff. The result would be proven well, after designing and implementing have been processed. All system, such as input text, button and hyperlink in assessment system has been well functioned based on their design, purpose and function.
Programs to Collect Data on Law Enforcement Activities: Overview and Issues [Updated March 11, 2021]

Law enforcement--Technological innovations; Data collection platforms; Law enforcement; Racial profiling in law enforcement
Technical and cultural contexts for measurement systems

This chapter aims to outline the technical and cultural contexts in which measurement systems, as presented in the previous chapter, are designed, set up, and operated. It first introduces the basic proposal that a measurement should produce as result not only one or more values of the property under consideration but also some information on the quality of those values, and discusses the consequences in terms of measurement uncertainty. This proposal is then embedded in the broader context of metrological systems, which help justify the societal significance of measurement results via their traceability to conventionally determined measurement units, so that measurement results can be interpreted in the same way by different persons in different places and times. Finally, we consider the issue of what is measured, i.e., the property of an object, or measurand, which must be somehow defined and identified, an act that is not necessarily related to assigning measurement values. On this basis, the chapters that follow develop and bring further specificity to our analysis and proposals. As with the previous chapter, we believe that the contents of this chapter should be sufficiently uncontroversial to be read and accepted by most, if not all, researchers and practitioners.
iMER-FM: Iterative Process of System Feature Model Extraction from the Requirements

Software product line engineering (SPLE) is a paradigm to promote systematic software reuse. A Feature Model (FM) is a common means to illustrate the commonality and variability of software products in a family. In most existing FM extraction approaches, keywords in the requirement document or certain types of system behavior or external events are considered features. The resulting FM is a combination of user activities and system actions (SAs), making it hard to understand. In this paper, we present an automatic approach to generate a product line FM from multiple requirement documents. We consider user activity and SAs separately in our approach and focus on the expected behaviors of the software system, together with the data being processed. The resulting FM clearly illustrates the expected functionalities of the software system and their variability in the product line. We also compared our approach with existing techniques by processing the same textual documents, and noted improvements in our results.
Conformity assessment of measuring instruments with artificial intelligence

Feedback from industry suggests that AI will soon gain traction in the regulated economic sector known as legal metrology. Driven by innovation, conformity assessment of measuring instruments equipped with machine learning algorithms needs to be standardized to ensure unbiased results. Although there is still no AI legislation, communications from the European Commission already contain fundamental principles and rules for AI development in order to ensure and improve the quality of life for all Europeans. In this context, several classes of use cases for the application of AI in measuring instruments and for conformity assessment are presented and investigated here.
Implementation of Red5 Library for Preaching (Da’wah) live streaming application based on Android

Abstract Mobile-based technology makes it easy to share information. One of the information content that was shared was Preaching (Da’wah). Currently, Da’wah content is spread by mainstream streaming media and social media, so Da’wah content is mixed with other information. Another problem is the lack of verification of Da’wah content. Therefore, a mobile-based application using the Android platform is required to use the Red5 library that works for live streaming. Besides, the function of the Red5 Library is to provide video conferencing services on the android platform, so Da’wah content can be recorded and shared in real-time, fast, and can be accessed by more than 1 user at the same time.
Remote Implementation of Embedded System/Robot Programming Practicesand Verifications of its Effectiveness

In this paper, we describe the remote practice conducted in 2020 as an alternative to the PBL practice on robot programming conducted until 2019. In the remote practice, the disadvantage of the learning environment without real machines is compensated for by simulations and movies. The difference in learning effect between these classes was evaluated by comparing questionnaires. The results show that the remote class students have the same level of understanding as the regular class students in learning topics that would be expected to be lowered due to the disadvantage. Furthermore, 30% more students in the remote class were interested in robotics than students in the regular class. This result shows that the remote learning is effective in the field of embedded systems and robots.
Automatic Reading Algorithm of Pointer Water Meter Based on Deep Learning and Double Centroid Method

AimingLi,Hongqing atWang, Juan theBai, Bing problemLu, Chuang of the reading of the eight-pointer water meter, it is proposed, in this paper, an automatic recognize reading algorithm based on deep learning and double centroid method. The YOLOv3 neural network is used to detect the object of the small dial. Based on the centers of small dial, the gesture of large dial is corrected by perspective transformation. Then, the whole region of pointers is segmented by U-net neural network. According to the segmented region, a new double centroid method of getting the angle of the pointer region is proposed. The angle of the pointer region can accurately be obtained by this method. Finally, in terms of people’s meter-reading method, a new reading rule is established. A large amount of experimental results show the proposed algorithm can guarantee the satisfying effect even when the dial is unclear or the shooting angle is sloped.
Evaluation of the Learning Outcome Using the Rubric in Creative Paper Car Race Education Program

This paper describes a creative education program and its rubric assessment. The program is designed for first-year students in the Department of Mechanical Engineering, Faculty of Engineering, Kindai University. In this course, students work in teams of two to three students on a PBL project to learn the sequence of automotive design from creation of a design concept to drafting and production. The goal is to learn learning practices, creativity, proposal skills, leadership and membership. For beginners, it is often difficult to formulate the “P” (plan) of the PDCA cycle. So, starting with a “C” (check) can help you analyze the problem. In the real world, it’s common to start with an evaluation instead of a “P” . Therefore, we have introduced a one-board paper car that allows for benchmarking. By using rubric assessments and presenting a method of assessment in the learning process, we improve learning effectiveness.
Multidimensional Relevance in Task-Specific Retrieval

Several criteria of relevance have been proposed in the literature. However, relevance criteria are strongly related to the search task. Thus, it is important to employ the criteria that are useful for the considered search task. This research explores the concept of multidimensional relevance in a specific search-task. Firstly, we want to investigate search tasks and the related relevance dimension. Then, we intend to explore the approaches that can be used to combine more than one relevance dimension. The goal of this study is to improve the retrieval system in a specific task.
Background and motivation

This chapter starts with a review of the historical developments of PGMs and their applications in computer vision. It then discusses the motivation and necessity of this book for computer vision researchers. This is then followed by introducing the book's key objectives and the key technical issues for PGMs. This chapter ends with a discussion of the layout of this book, including the topics to be covered in each chapter.
Speech to Sign Language Conversion Using Neuro Fuzzy Classifier

Speech communication is the central interaction between the humans for the procreation and survival. Recently, researchers coordinated toward the advancement of robotized and clever investigation of human expressions. With expanded enthusiasm of human-PC/human–human connections, speech to sign language is an important another research direction. In this paper, we study the speech word to the sign image conversion. Since speech is inconsistent and noisy, fuzzy concept is introduced. The neuro fuzzy classifier is used to increase the classification rate with scaled conjugate gradient.
Real-Time Hashtag based Event Detection Model with Sentiment Analysis for Recommending user Tweets

Recently, the usage of Social Networking Sites (SNS) has increased tremendously. People use online social networking applications like Twitter to specify their opinions and feelings about many topics which they produce an awesome amount of data every day. This generates an unlimited amount of data requiring significant effort to read tweets relevant to the user preference. In this paper, we proposed a model “Real Time Hash tag based Event Detection for Tweet Recommendation” on the level of individual posts. This model recommends Events at various levels from the foremost abstracted to the foremost definite. Firstly, pre-processing techniques like stop word removal, frequent word removal, stemming, lemmatization, lower-case conversation are applied. The proposed HashTag Event Detection (HTED) algorithm is applied to detect events corresponding to the tweets and find the sentiment polarity of each user interested in a particular event. This represents indirectly the interest of the particular user. Finally, the Support vector decomposition (SVD) technique is applied to predict the user preference of events in social media streams using hashtags in posts collected from Twitter. Experiments are performed using our dataset extracted over 1-Lakh tweets of different users. The performance of the HTED system is evaluated in terms of the Receiver Operating Systems curve and mean average recall curve. It is observed that the proposed HTED model performs better than the traditional Random Recommendation and Popularity Based Recommendation for users using hashtags.
Applications and future of machine reading comprehension

Machine reading comprehension (MRC) endows computers with the ability to read, analyze, and summarize text. With the advent of the information age, the scale of text data is exploding. Therefore MRC can greatly benefit various aspects of society by intelligently automating text processing. For example, search engines can use MRC to better understand user queries and return relevant results; language educators can employ MRC to automatically evaluate essays; doctors can apply MRC to analyze patients’ symptoms and medical history to perform diagnosis and treatment. However, we should also be aware that there are still many unsolved problems in MRC research, such as knowledge, reasoning, and multimodal understanding. This chapter will introduce the applications of MRC in various industries and discuss the existing challenges for future development.
Private and Secure Students' Data Sharing in Educational Systems

Protecting students' private data in the education system is an important issue. However, it is not an easy task. Students' data is important in the process of providing quality education. Different educational regularly bodies issued regulations that requiring securing students' information and making it accessible only by authorized persons. Digital information systems have been a popular solution. However, they introduce some privacy and security concerns. This issue is going to be the focus of our research. We are aiming to design and test a system that allows the sharing of students' data while addressing privacy concerns. Our system is going to restrict access to a student's private information only when the student's parents approve that access. Furthermore, the proposed architecture is believed to protect from data breaches, which has been a huge problem in the past few years.
Extracting Forensic Data from a Device Using Bulk Extractor

Generally, a data or an information of a device will play a key role to find the owner and the things were done in that device from starting day up to the day it was tested were shown in the information which we get from the device. And getting that data was done by data extractors and the problem with the extractor is, for every type of data it needs a different type of extractors to overcome this problem, and this bulk extractor gets all types of data of a device without using any other extractor. This bulk extractor saves the time of testing and gathering information and gets all types of data easily.
Intersection Types for Unboundedness Problems

Intersection types have been originally developed as an extension of simple types, but they can also be used for refining simple types. In this survey we concentrate on the latter option; more precisely, on the use of intersection types for describing quantitative properties of simply typed lambda-terms. We present two type systems. The first allows to estimate (by appropriately defined value of a derivation) the number of appearances of a fixed constant 'a' in the beta-normal form of a considered lambda-term. The second type system is more complicated, and allows to estimate the maximal number of appearances of the constant 'a' on a single branch.
Science and Education for the Future: Trends in Ecologisation

The purpose of the research is to identify the main trends in ecologisation of modern science and education. The article pays special attention to the need for adjusting the subject-content structure of science and education, for changing their worldview foundations. The researcher defines range of the issues necessary for enduing science with ecological functions, considers the features peculiar to a practice-oriented model of ecological education. Scientific novelty of the research lies in specifying content and organisational-methodological aspects of ecologisation of science and education, in justifying the need for changing value and worldview foundations of scientific and educational activities. As a result of the research, the main trends in ecological transformation of science and education are identified and characterised.
Security Smart Farming System – AgriPro

In Sri Lanka many problems have to be dealt with, to be a successful farmer. Poor weather prediction, crop-raiding by wildlife animals and poor agricultural management are some of the main problems the farmers have been dealing with for a long time [1]. We were looking forward to providing a solution for the farmers on the above-mentioned issues. Currently, Farmers cultivate crops based on their past experience. This is turning out to be difficult because of the changes in weather patterns due to industrialization and global warming. We realized this could be overcome by an application that could process the data present in the agricultural and meteorological departments and give farmers insights about weather changes. Crop-raiding is another problem that farmers need to face in Sri Lanka. Farmers use traditional methods as fencing to prevent these attacks. But still, the wildlife intrusions happen over a broken fence [2]. The time taken to notify farmers regarding these problems ensures that the damage has already been done by the time they are informed. As a solution, we are proposing to implement a system to identify the animals and notify the farmer as soon as possible. Poor agricultural management implies a lack of knowledge about diseases, agrochemicals, and poor planning on cultivation [3]. We are planning to improve this situation by gathering information regarding these and provide that information to farmers collectively through an application. The final output of our research would be an application including a weather prediction, wildlife intrusion detection, and a sentimental analytical chatbot together
Analysis on the Coordination Mechanism of the First Class and the Second Class in Higher Vocational Colleges)

Abstract The traditional classroom teaching mode cannot stimulate students’ interest and initiative in learning. Introduce the second classroom to complement and extend the first classroom. It will improve the comprehensive quality of students. This article analyzes how to achieve synergy between these in order to improve the contribution of the second classroom in talent training.
User Preference Translation Model for Next Top-k Items Recommendation with Social Relations

Recommendation systems are used to predict the interests of users through the analysis of historical preferences. Collaborative filtering-based approaches usually ignore the sequential information and sequential recommendation usually focus on the next item prediction. In this work, we would like to determine the next top-k recommendation problem. We propose User Preference Translation Model (UPTM) with item influence embedding and social relations between users. In addition, we will also solve the cold start problem in UPTM.
Deep Semantic Entity Linking

Named entity linking systems are an essential component in text mining pipelines, mapping entity mentions in the text to the appropriate knowledge base identifiers. However, the current systems have several limitations affecting their performance: the lack of context of the entity mentions, the incomplete disambiguation graphs and the lack of approaches to deal with unlinkable entity mentions. The PhD project will focus on solving the aforementioned challenges in order to develop a NEL model which outperforms state-of-the-art performance in Biomedical and Life Sciences domains.
Enabling Buildings’ Comfort Using Context-Aware Technologies: A Systematic Review of the Literature

In the study reported by this paper a systematic review was performed to identify relevant applications supported by context-aware technologies with impact in the comfort of the buildings’ occupants. Sixteen studies presenting context-aware systems with diverse aims were retrieved: visual comfort (six articles); thermal comfort (three articles); and multiple perspective comfort (seven articles). The context-aware components are diverse and include a broad range of sensing devices (i.e., to collect the identity, location and activity of the occupants and the environment conditions), reasoning mechanisms, and devices to generate proper reactions to optimize the buildings’ comfort.
DSMER: A Deep Semantic Matching Based Framework for Named Entity Recognition

The task of named entitiy recognition(NER) is normally regarded as a sequence labeling problem. However, this kind of NER framework does not utilize any prior knowledge. In this paper, we propose a novel framework called DSMER, which stands for Deep Semantic Matching based Framework for Named Entity Recognition. DSMER is a two-phase framework: 1) detect the boundary and extract candidate span, 2) calculate the distance between candidates and entity type. Meanwhile, the representation of each entity type is encoded from its corresponding annotation rules and example set. Since the combination of various textual data, DSMER has the ability to integrate informative prior knowledge. Additionally, we introduce the Word Mover's Distance to measure the similarity between sequences of different lengths. We conduct experiments on CoNLL 2003 and OntoNotes 5.0 dataset. Experimental result shows our approach achieve state of the art performance, and demonstrates the effectiveness of the proposed framework.
The effectiveness and learning outcomes of mobile learning integrated with ethnopedagogy on metacognitive ability of students in North Tapanuli

This research aims to find out about the effectiveness of mobile learning media integrated with ethno- pedagogy to improve the metacognitive ability of students on the acid-base material. To find out about the effectiveness of media integrated with ethno-pedagogy, quasi-experimental, quantitative-method research is done through the field- tested with one control class and one experimental class of 35 students in each class. The metacognitive ability was measured by a validated inventory questionnaire test, while the acid-base learning outcomes were measured by an analytical test. The metacognitive ability was analyzed by SPSS 22 application through the Independent t-Test and the acid-base learning outcomes through the Paired Sample t-Test with 0.05 level of mistake. The result of this research showed that there was a difference in student metacognitive ability after mobile learning media integrated with ethno- pedagogy has been applied compared to conventional learning with an average of 16.80 and 14.40. The result of this research also shows that there was a difference that the learning outcomes of mobile learning media integrated with ethno-pedagogy are more inclined compared to conventional learning with an average of 70.62 and 62.22. This research concludes that the mobile learning media integrated with ethno-pedagogy is effective to be used as a learning media to improve the metacognitive ability of students on the acid-base topic, obtained by the metacognitive correlation result and the acid-base learning outcomes in the amount of 0.585 with a moderate correlation category.