Reviewerly/exHarmony huggingface dataset:
OUTCOME BASED LEARNING: THE EFFECT OF EXPERIENTIAL LEARNING ACTIVITY ON DEEP CONCEPTUAL UNDERSTANDING

The aim of the present study is to investigate the effect of experiential learning activity in deep conceptual understanding of science in comparison with conventional teaching model. In the present experiment quasi experimental and post-test research design is implemented. Totally 80 students participate in the study: 40 girls and 40 boys. The sample is categorized into two: study and the control group. Each group consists of 40 students: 20 boys and 20 girls. The study group is taught “Gases Law” by using experiential learning activities and the control is taught by using the conventional method. Data is collected by using a questionnaire and it consists of 20 multiple choice questions. The collected data is analyzed by using descriptive statistics. The examination of the data illustrates that there is no noteworthy difference in the mean score between the study group and the control group. Independent ‘t-test’ is applied to compare the student’s achievement in post-test. The mean score of the study group, who are exposed to the experiential learning activities, in Science Achievement post-test is 17.35. It is higher than that of (t=6.65; p&amp;gt;0.01) the learners in the control group. The mean of the control group is 14.45. Therefore, it is concluded that the experiential learning activities as a teaching model enhances the deep conceptual understanding of science.
IoT Based Realtime Automatic Headlight Dimmer System

The number of traffic accidents that take place daily have risen annually. Per the records of past years, vehicular accidents are mostly based on the use of wrong beam in different climatic conditions. The use of high beam during situations like fog or rain have caused the most number of accidents in the recent years and have become a rising concern. This paper proposes a prototype of an automatic headlight dimming system, constituting of three major sensors-LDR, rain and fog sensors which provide inputs to Arduino Uno which acts as a microcontroller to provide a control mechanism, that has been developed to toggle the beam from low to high or vice versa for driving scenarios when there is sudden change in climatic conditions or in the visibility(light) in the air. The project can be used in the field of automotive performance improvement or with the help of better experience that maintains the safety of driver as well as the passengers. The project is based upon aims to try to eliminate the human error of beam switching in wrong surroundings hence posing as a major security threat to the driver or the surrounding cars.
Evidence for Monitoring the User and Computing the User’s trust

Cloud computing helps organizations to dynamically increase the resource needs as and when needed, without the need to purchase them. Security is a basic concern in cloud computing, and threats can occur both internally and externally. The basic security protection such as traditional access control (TAC), virus detection, and intrusion detection are unable to manage variety of malicious and network attacks. The number of users might get hacked because of limitation in basic security protection. To implement a secure, reliable, and safe cloud-computing environment, we need to consider the trust issue. A trusted cloud is guaranteed to be safe from user terminals; combined with the concept of a trusted network, it evaluates, forecasts, monitors, and manages the user’s behavior to eliminate malicious datacenter attacks which are performed by unwanted cloud users and hackers; as a result, there is improved cloud security. In this paper, we are addressing trust issue in the cloud computing based on user behavior and what kind of evidence must be considered to implement sufficient model to calculate user’ creditability.
A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams

Today, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources through Apache Kafka and Apache Spark Streaming whose objective is to monitor the state of the system with respect to a variable of interest. For this purpose, the proposed algorithm is a cellular-based multi-objective evolutionary fuzzy system that uses an informed strategy for efficient data processing and a re-initialisation and filtering mechanism to eliminate redundant and low-reliable patterns. The experimental study carried out demonstrates an interpretability improvement of 25% in the extraction of high-interest knowledge by the proposed algorithm, which would make it easier for experts to analyse the problem. Finally, the proposed algorithm is up to five times faster than another proposal on the processing of the same amount of data. In this experimental study, up to 750,000 instances have been processed in approximately four seconds.
Supervised Scene Boundary Detection with Relational and Sequential Information

This paper proposes a novel scene boundary detector by considering different features appropriate for definition changes of scenes according to target services or tasks. In the proposed method, the information in shots is categorized into two groups: relational and sequential information. Relational information is acquired by the multi-layered convolution neural networks by merging and embedding similarity vectors from visual and audio features. Sequential information that contains particular patterns of continuous shots is handled with dual recurrent neural networks. The different definitions of scenes are reflected in the proposed method by supervised parameter estimation with a sampling method. Scene boundaries are rarely observed in video content. Thus, it results in skewed class distribution. The sampling method tries to expand instances in scene boundary using reverse order shots, while it reduces the number of non-boundary shots by variance preserved shot filtering. A focal loss is finally adopted for the training process to lead better parameters from an imbalanced dataset. The proposed method is evaluated with three datasets constructed with real-world movies. We empirically proved that different definitions of scene boundary could affect the performance of scene boundary detection through experiments. The proposed deep neural networks with both relational and sequential information show the ability to handle diverse scene definitions in experiments. With supervised learning, the proposed method can reflect the definition bias in each dataset. As a result, the proposed method shows its effectiveness in handling different types of information and adopting other scene definitions by achieving state-of-the-art performances in two benchmark datasets.
Work-in-Progress: Analysis of the use of Mentoring with Online Mob Programming

Extreme programming (XP) approaches such as pair and mob programming (PP and MP, respectively) have been introduced to the educational sector. However, higher education curricula have started implementing online courses as a method for teaching programming, which poses challenges when conducting XP. Because social factors are vital to learning, we examine a project-based learning method using online MP that introduces a high level of communication. It also maintains the advantages of PP through the “driver” and “navigator” approach. However, there are multiple complications, including discomfort, slow code generation, and interpersonal issues, when using conventional MP. Accordingly, we introduce a mentoring approach to the MP setup and examine the differences compared to conventional MP sessions. We create and analyze co-occurrence networks of codes found via open coding. In this paper, we explain our method and show its advantages based on the results of our analysis.
Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning

Memorising vocabulary is an important aspect of formal foreign language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. These computer-based systems measure learning performance in real time to create optimal study strategies for individual learners. While such adaptive learning systems have been successfully applied to written word learning, they have thus far seen little application in spoken word learning. Here we present a system for adaptive, speech-based word learning. We show that it is possible to improve the efficiency of speech-based learning systems by applying a modified adaptive model that was originally developed for typing-based word learning. This finding contributes to a better understanding of the memory processes involved in speech-based word learning. Furthermore, our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner's pronunciations. Speech-based learning applications are educationally relevant because they focus on what may be the most important aspect of language learning: to practice speech.
Two Phase Image Compression Algorithm Using Diagonal Pixels of Image Blocks

Data compression is the way of reducing the number of bits required to store and efficiently utilize bandwidth during the transmission of data over a network channel. Image compression is also having importance as images contain important binary data to represent as image and that is to be stored and transmitted. The compression of an image must not reduce the quality of the image except in some cases. This paper proposes a compression algorithm with low PSNR, high fidelity images. The algorithm alienates the input image as non-overlapping blocks of size 4*4. Compression is achieved by using diagonally situated pixels and other adjacent pixels of the blocks. The decompression uses a simple approach. The diagonally situated pixels and other adjacent pixels are used to reconstruct images in decompression phase. The proposed algorithm reconstructs images with high fidelity at lower Peak Signal to Noise Ratio (PSNR). The results of proposed algorithm are compared with JPEG. Results have shown that at same PSNR values, reconstructed image quality by the proposed algorithm is better than that of the JPEG algorithm.
A Comparative Study of Vectorization Approaches for Detecting Inconsistent Method Names

Methods (functions) areMinehisa,Tomoya the fundamental components of the software. ProgrammersAman,Hirohisa usually grasp a method’s behavior by looking at the method’sYokogawa,Tomoyuki name. Hence, the name of a method should be a summary ofKawahara,Minoru what the method does. There has been a study utilizing Word2Vec, Doc2Vec, and the convolutional neural network (CNN) to evaluate the consistency between a method’s name and body in an automated way. While the conventional evaluation procedure detects inconsistent method names successfully, its CNN-based vectorization model construction requires costly computations. This paper focuses on such a computational cost and proposes to replace it with another lightweight vectorization approach. The comparative study using four alternative approaches proved that the Sent2Vec, one of the alternatives, can build the vectorization model 14 times faster than the conventional one while maintaining the capability of detecting inconsistent method names.
Mobile learning contribution in improving the understanding of news item text generic structure

Abstract Mobile learning is a technological innovation that packs teaching material so it can be accessed any time with interesting material visualization. The use of interactive learning media has a major contribution to changes in the process of delivering information. This study aimed to represent the contribution of mobile learning in improving the understanding of the generic structure of the news item texts. The research method used in this study was the quasi-experimental method with Non-equivalent Control Group design. Questionnaire data collection techniques and tests were used to determine the effectiveness of Mobile Learning in improving the respondents’ skills to understand the generic structure of the news item texts. The increased understanding of the text could be known by analysing and classifying respondents’ understanding of the main events, the context of the event, and the sources of information contained in news item texts. The results of the study showed that the use of mobile learning was effective in conveying information so as to increase the understanding of respondents in understanding the generic structure of the news item texts. In the digitalization era, mobile learning can be used as a medium to convey knowledge and information in both formal and informal ways.
A Survey on Identification of Blood Group Types and its Hemoglobin Estimation Using Image Processing

It is very crucial to determine human blood groups in an emergency situation. But according to current system, the detection procedure is very slow. At present, human blood groups are determined manually through plate test procedure. It consists of blood collection and mixing with specific reagents in order to determine the blood agglutination. Blood typing is system which basically used to detect specific blood group of human. Investigation of appropriate blood type within short interval of time plays vital role in blood transfusion, donation, accident and other emergency situations. Currently, blood typing tests are performed by technicians in laboratories. But it is monotonous to do with large number of blood samples, require more time to diagnosis and, as these tests carried out manually, it may introduce human error which will be harmful. Hemoglobin (Hb), a very significant parameter for the human body and deficiency of it causes anemia. During pregnancy, menstruation and ICU deficiency of it can be very risky and even caused death. So, it is important to diagnose it continuously. Usually, physicians examine it by conducting a blood test to confirm it is painful, time-consuming and costly. The major concept of this study is to screen Hb levels within a short period of time. This paper focused on recent advances in detection of blood group with hemoglobin, which can classify different blood group within short period of time with great accuracy and calculate its hemoglobin level.
An Arbitrary-Oriented Object Detector Based on Variant Gaussian Label in Remote Sensing Images

Arbitrary-oriented remote sensing object detection is a challenging task because of the periodicity of the angle and large variety of aspect ratios. To address these issues, this letter proposes two simple yet powerful methods: a variant Gaussian label (VGL) generating method and a channel-wise pixel attention (CPA) module. Specifically, VGL is generated to handle the periodicity of the angle and produce various labels to adapt diverse aspect ratios of objects. Then, CPA is utilized to fuse features between channels and pixels, to obtain a global receptive field and extract more robust features. In addition, we collect a dataset, namely, Northwestern Polytechnical University Very-High-Resolution (NWPU VHR-10-R), which is relabeled with oriented bounding boxes based on NWPU VHR-10. To evaluate our proposed method, experiments are conducted on several publicly available benchmark datasets, including the High Resolution Ship Collections 2016 (HRSC2016) and NWPU VHR-10-R. Experimental results show that our method achieves substantial gains compared with baseline approaches.
Analysis of the traditional voting system and transition to the online voting system in the republic of Iraq

Nowadays, technology has developed rapidly, several countries have changed the voting from traditional to electronic voting due to the potential advantages such as reliability, rapidity, etc. This paper analyzes the existing voting system in the Republic of Iraq and explores the main problems associated with its functioning. Our results showed that the existing voting system has security problems and needs to be upgraded using information and communication technologies by international standards. We believe that the transition to online voting in the Iraqi elections is necessary, which provides a new system of trust and security for the user and the government of Iraq and helps to solve the problems that exist in this system. An additional advantage of such a system is the ability of voters, the elderly, and people with disabilities to vote at any time and anywhere via the Internet.
Innovations in civil engineering education at the interdisciplinary and production basis

Article is devoted to the problem of modernization of engineering education in modern economic conditions. This is necessary to provide qualified personnel for the construction industry. The authors considered innovative approaches to the organization of the educational process. They are based on interdisciplinary links of training of engineering and construction contingent in technical universities. And also on the introduction of digitalization in the teaching of disciplines of a natural science profile. We discussed the combination of traditional forms of training with production activities at existing enterprises of the industry. The work justified the relevance of modernization of engineering education, analysed factors that justify its need and ways to achieve the objectives. Studies have been carried out to assess the effectiveness of combining educational processes at the university with production activities at existing enterprises in Rostov-on-Don. To combine training and production processes in the training of qualified civil engineering personnel the structural division of the enterprise was allocated -the construction laboratory. The laboratory fully meets the tasks set by functional characteristics. Special working conditions of factory laboratories at small and medium-sized enterprises were noted. This is due to the difficult modern economic conditions. The main requirements for the personnel composition of factory laboratories of the new formation have been determined. These include depth and versatility of knowledge, non-standard thinking, flexibility in decision-making, interchangeability, readiness to improve knowledge and professional skills. The practical results of the experience of four years of combining innovative modernization of the training process of civil engineering personnel with production activities are presented. At the existing enterprises of the construction industry in Rostov-on-Don. The results confirm the high effectiveness of the proposed events.
Semantic Segmentation of Substation Scenes Using Attention-Based Model

Inspection robots are widely used in transformer substation for inspection tasks. In order to complete the task, the robots need to understand and recognize road scenes. However, the transformer substation contains lots of similar and small equipment, which are difficult to recognize. To address this problem, we propose a semantic segmentation model to recognize road scenes. Specifically, our proposed model discriminates the equipment by leveraging multi-view method. We use attention mechanism to strengthen the relationship between pixels that belong to an individual category. Compared with previous methods, our approach achieves a good performance in a new dataset from a transformer substation and shows robustness in recognizing equipment of transformer substation.
Building Trust and Companionship in e-Coaching Through Embodiment

E-coaches are promising tools for health interventions, being scalable and relatively low costs compared to human coaches. Nevertheless, they cannot compare to humans when it comes to trust and social companionship. Hence, many researches were held around the importance of tangible user interfaces in building trust and companionship during human-machine interaction. Co-design, in particular, is the act of creating with stakeholders within the design development process to ensure the results meet their needs and are usable. This chapter describes a design process method and the rationale for building an embodiment for a companion and a trustable e-coach. We also shed lights on the co-design process held in the NESTORE project, which was specifically conducted with older adults to build a vocal assistant that supports user’s behavioral change, as well as to increase the social bonding with the NESTORE e-coach.
Optical Packet Header Recognition System Based on Parallel Reservoir Computing and Compatible with IPv6 Protocol Addresses

Aiming at the 128-bit routing addresses in IPv6 protocol, the parallel recognition of three-channel 128-bit optical packet headers in optical packet switching network is successfully realized utilizing the parallel reservoir computing system, which greatly improves the signal processing speed of optical packet switching nodes in IPv6 network. The simulation results show that when the signal-to-noise ratio (SNR) of the input signal is 40 dB, the optimal normalized root mean square error (NRMSE) of 128-bit optical packet header recognition is in the range of 0.1, and the optimal word error rate (WER) is 0%.
Image Steganography Using GANs

Secure transmission of information across networks is of esteem importance to meet confidentiality and privacy goals and therefore is a huge concern to modern society. To prevent information from being compromised during communication, many steganography or data hiding algorithms have been introduced in recent years. Steganography refers to hiding secret information into cover files like text, images, audio etc., to secure communications. The term Image Steganography is used to describe steganography processes where images are used as cover files. Conventional steganography algorithms are observed to impose high risk of being deciphered by intelligent systems. This paper proposes a system that eliminates such a risk by hiding secret information inside stego images generated using Generative Adversarial Networks (GANs) and then safely reproducing it using an Extractor model. The experimental results of the proposed system demonstrated an accuracy of 92.34% achieved by the extractor model while retrieving the hidden information from the stego images and an overall steganography capacity of 93.75e−3 bytes/pixel for the entire system. Thus, the proposed system reduces the probability of any external intelligent system intercepting the communication and stealing information, like in the case of systems following traditional algorithms.
Hungarian Traffic Sign Detection and Classification using Semi-Supervised Learning

Semi-supervised learning is a special way to improve the classification performance of a model where labeled data are not available. By using unlabeled observations and handling them as training data in a transfer learning buildup, we get a structure often referred to as self-supervision. In case of traffic sign detection and classification the task is complicated to the large number of tables and the different representations from country to country. While a number of public datasets are available, these might not give satisfying performance; to deal with this issue, a semi-supervised method is presented where frames of dashcam recordings are automatically annotated and reused as training samples.