Reviewerly/exHarmony huggingface dataset:
The detection of handguns from live-video in real-time based on deep learning

Many people have been killed indiscriminately by the use of handguns in different countries. Terroristacts, online fighting games and mentally disturbed people are considered the common reasons for these crimes. A real-time handguns detection surveillance system is built to overcome these badacts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue,the designed detection system sends an alert message to the supervisor when aweapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgundetection operation. This model has been selected becauseit is fast and accurate in infering to integrate network for detecting and classifying weaponsin images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model bothenhance the accuracy level in identifying the real time handguns detection.
Exploiting Semantic Relations for Fine-grained Entity Typing

Fine-grained entity typing results can serve as important information for entities while constructing knowledge bases. It is a challenging task due to the use of large tag sets and the requirement of understanding the context.We find that, in some cases, existing neural fine-grained entity typing models may ignore the semantic information in the context that is important for typing. To address this problem, we propose to exploit semantic relations extracted from the sentence to improve the use of context. The used semantic relations are mainly those that are between the mention and the other words or phrases in the sentence. We investigate the use of two types of semantic relations: hypernym relation, and verb-argument relation. Our approach combine the predictions made based on different semantic relations and the predictions of a base neural model to produce the final results. We conduct experiments on two commonly used datasets: FIGER (GOLD) and BBN. Our approach achieves at least 2\% absolute strict accuracy improvement on both datasets compared with a strong BERT based model.
Using Python in the Teaching of Numerical Analysis

In this Case Study we describe the rationale, methodology and results of teaching Python as part of a third year optional Numerical Analysis module taken by undergraduate BSc Mathematics students at the University of the West of England, Bristol. In particular we focus on how we have used programming mini-tasks to engage and prepare students for using Python to complete a more significant piece of coursework, taken later in the course. These mini-tasks are marked electronically using the Dewis e-assessment system which provides the students with immediate and tailored feedback on their Python code.
"Iedutech" Mobile Application Development for Information Technology Subjects in Education among TVET Students

Mobile application development is a complex process, it is a heavy responsibility for researcher to produce a mobile application that is developed to meet the quality and achieve its objectives.The use of mobile technology is a way of learning that fits with today's educational needs.This study to develop the Iedutech Mobile Application to facilitate the Teaching and Learning (T&L) process in the classroom for Information Technology in Educational subjects related to Network and Communication topics.Researcher use the ADDIE Model in developing the Iedutech application to provide a systematic work guide in which the phases are (i) Analysis phase, (ii) Design phase, (iii) Development phase, (iv) Implementation phase and (v) Evaluation phase.It also uses Adobe Flash CS6 as a platform for developing the Iedutech application.The respondents involved in the development of this Iedutech applications consisted of 25 Bachelor of Vocational Education (ISMPV) students at Faculty of Technical and Vocational Education (FPTV), UTHM who took the subject of BBD10803 Information Technology in Education.The evaluation of this application was carried out using questionnaires.It was expected that the Iedutech application would be developed for the benefit of users, especially the TVET students and lecturers.
A Detection Method for Phishing Web Page Using DOM-Based Doc2Vec Model

Detecting phishing web pages is a challenging task. The existing detection method for phishing web page based on DOM (Document Object Model) is mainly aiming at obtaining structural characteristics but ignores the overall representation of web pages and the semantic information that HTML tags may have. This paper regards DOMs as a natural language with Doc2Vec model and learns the structural semantics automatically to detect phishing web pages. Firstly, the DOM structure of the obtained web page is parsed to construct the DOM tree, then the Doc2Vec model is used to vectorize the DOM tree, and to measure the semantic similarity in web pages by the distance between different DOM vectors. Finally, the hierarchical clustering method is used to implement clustering of web pages. Experiments show that the method proposed in the paper achieves higher recall and precision for phishing classification, compared to DOM-based structural clustering method and TF-IDF-based semantic clustering method. The result shows that using Paragraph Vector is effective on DOM in a linguistic approach.
Devanagari Text Detection From Natural Scene Images

Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.
Bridging the Gap between Training and Inference for Neural Machine Translation (Extended Abstract)

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context leads to error accumulation among the translation. Furthermore, word-level training requires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but reasonable translations. In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence during training. Experimental results on NIST Chinese-&gt;English and WMT2014 English-&gt;German translation tasks demonstrate that our method can achieve significant improvements on multiple data sets compared to strong baselines.
Dual-Axis Solar Tracking System Efficiency for Hydroponics Pump

The advancement of solar power technology is considered one of many choices for meeting the increasing demand for energy worldwide.Solar energy is radiant light, and use of heat from the sun using technology such as photovoltaic technology which involves the use of semiconductors to directly convert sunlight into electrical energy has become a highly desirable choice.The electrical energy generated by a device for generating photovoltaic power can be used for a wide range of applications.A solar tracking system is the most appropriate technology for enhancing the solar cells performance by tracking the sun.Solar cell with a capacity of 50 Wp solar and battery 7 Ah.Tests conducted for five days, each day of the test starting from06:00 AM to 06:00 PM andusing fourlight dependent resistor (LDR) sensor to track the sun by calculating the highest intensity.Experimental results show that the intensity-based dual-axis tracking system improves the overall efficiency.
A Visual-Based Approach for Manual Operation Evaluation

In order to improve the human-machine interface design and monitor the operational performance, hand behavior detection and analysis are very important. However, there is a lack of effective indicators of hand operation behavior. This paper introduces a vision-based approach for manual operation evaluation. K-curvature is employed to detect the operation key points on hand and the palm center is located combining image region moments and palm maximum circle fitting algorithm. The inter-frame velocity cosine is used as a representation of the motion sequence and we extracted the features of approximate entropy (ApEn) and maximum duration ratio (MDR) from the sequences to distinguish different operation statuses. A digit clicking experiment with different difficulty is designed to study operation behavior characteristics and we found the features of ApEn and MDR had a significant difference between position-determined operation and position-unknown operation. When the operation process is aimless and inconsistent, the ApEn value tends to be larger and the MDR value tends to be smaller. Our approach could effectively describe hand operation and assist manual operation performance evaluation and usability improvement.
Corrections to “A Cooperative Quality-Aware Service Access System for Social Internet of Vehicles”

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Modified Round Function of SIMECK 32/64 Block Cipher

Lightweight Block Cipher's (LBC) various algorithms need to meet the security requirements for all resource-constrained devices and Internet of Things (IoT) technologies.Various LBC addressed such concerns as the SIMON and SPECK block ciphers, which are known for security and performance.SIMECK, a combination of SIMON and SPECK, shows better performance.However, the SIMECK round function remains a concern.This paper aims to present a modification on the SIMECK round function method simulated on its circular and ARX structure.It took at least seven attempts to reach the appropriate structure.The seventh modification had a circular parameter of (3,5) and a change of right-shift on the structure.The results show that the modified round function achieves 54.58% average on the avalanche effect, the runtime performance of 1.3945ms with a randomness p-value of 0.46931 compared with p-value 0.51512 of the original algorithm.The study explores the ARX structure of LBC, which leads to a good venue to implement with other block ciphers.It is recommended that further evaluation of the modified algorithm will be conducted to test its efficiency, like to verify its vulnerability through different attacks.
NFP: A No Fine-tuning Pruning Approach for Convolutional Neural Network Compression

Pruning for Convolutional neural network has proved to be an effective approach to reduce memory and computation. In this paper, we propose a novel pruning approach called NFP(No Fine-tuning Pruning), which calculates the contribution of the pruned convolutional filters to the next convolutional layer and compensates the contribution to the bias of the next convolutional filters. Due to the compensation, the accuracy of pruned network will be almost same as the original network. Therefore, the NFP method can only take a few moment to obtain an accuracy retaining compact network without fine-tuning. We demonstrate the effectiveness of our approach on some CNN models. For ResNet-50, NFP can reduce the number of parameters by 34% and the FLOPs by 37% without loss of accuracy. For object detection task, our approach also achieves an excellent result in YOLO-V3.
Playing Atari with Six Neurons (Extended Abstract)

Deep reinforcement learning applied to vision-based problems like Atari games maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it. By separating image processing from decision-making, one could better understand the complexity of each task, as well as potentially find smaller policy representations that are easier for humans to understand and may generalize better. To this end, we propose a new method for learning policies and compact state representations separately but simultaneously for policy approximation in reinforcement learning. State representations are generated by an encoder based on two novel algorithms: Increasing Dictionary Vector Quantization makes the encoder capable of growing its dictionary size over time, to address new observations; and Direct Residuals Sparse Coding encodes observations by aiming for highest information inclusion. We test our system on a selection of Atari games using tiny neural networks of only 6 to 18 neurons (depending on the game's controls). These are still capable of achieving results comparable---and occasionally superior---to state-of-the-art techniques which use two orders of magnitude more neurons.
Counting Query Answers over a DL-Lite Knowledge Base

Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the LOGSPACE case, we have devised a novel query rewriting technique into first-order logic with counting.
CREATIVITY DEVELOPMENT BASED ON THE IDEAS OF KI HAJAR DEWANTARA

Penelitian ini bertujuan untuk mengetahui pengembangan kreativitas dengan menggunakan gagasan Ki Hajar Dewantara yaitu niteni, nirokke dan nambahi yang dikenal dengan istilah Tri-N. Metode yang dipilih adalah deskriptif kualitatif dengan studi literatur. Data dihimpun dengan menggunakan literasi yang sudah ada, baik dari sumber primer maupun sekunder, kemudian dianalisis dan disajikan secara deskiptif. Hasil kajian menunjukkan bahwa Konsep niteni, nirokke dan nambahi merupakan sebuah proses yang di dalamnya terkandung pembentukan kreativitas. Niteni adalah awal dari munculnya gagasan atau ide yang kemudian disusul dengan aktivitas nirokke atau menirukan sebagai wahana mengasah keterampilan dengan menambahkan makna pada contoh-contoh yang sudah tersedia, dan nambahi merupakan muara proses yang padanya terlihat jelas bagaimana sebuah produk dari kreativitas mampu menjawab permasalahan dengan menggunakan berbagai macam cara. Menumbuhkan kreativitas, dengan demikian dapat dilakukan dengan menggunakan gagasan niteni, nirokke dan nambahi yang dilakukan secara prosedural. &#x0D; Ki Hajar Dewantara has many ideas and have been applied in the Indonesian education. One of his most well-known ideas is the concept of niteni, nirokke and nambahi as known as Tri-N. Translated into Indonesian the three words mean pay attention, imitate, and add. These three elements have characteristics that are very compatible with the development of creativity. Referring to this, this study was conducted with the aim to find out the development of creativity by using the idea of ​​Ki Hajar Dewantara namely niteni, nirokke and nambahi. Using descriptive qualitative study of literature, data was collected by using existing literacy from both primary and secondary sources and then analyzed and presented descriptively. The results of the study showed that the concept of niteni, nirokke and nambahi is a process which contains the formation of creativity. Niteni is the beginning of the emergence of ideas, followed by nirokke, activities or imitating as an effort to improve skills by adding meaning to the existing examples, and adding is the ultimate process that show clearly how a product of creativity is able to answer problems by using various ways. Thus, growing creativity can done by using the idea of ​​niteni, nirokke and nambahi as a procedure.
Optimization of UAV Flight Control Algorithm and Flight Simulation in Two-dimension

Abstract Large-scale Unmanned Aerial Vehicle (UAV) groups flight simulation in two-dimensional planes is commonly applied to UAV cluster mission planning algorithm design. In this paper, an UAV control algorithm optimized for two-dimensional planes is designed. The tandem structure of closed loops, as well as the control laws of L1 and Total Energy Control System (TECS), are transplanted to an UAV model that is simplified to a moving particle of a two-dimensional space, and UAV flight simulation is performed based on the flight control algorithm. Compared with the traditional 3D space flight simulation, it can save hardware resources and improve the simulation efficiency. Compare with the flight simulation based on the geometric method, on the premise of maintaining the dynamics basis, the trajectory and dynamics curves are closer to the actual flight results in 3D space, and the dynamics data onto the entire flight can be recorded. It has been verified that 50 UAVs need only 0.8s to perform 100 square-area snake-like search simulation experiments, and the fitting degree of the UAV’s flight curve to the three-dimensional space is greater than 80%. The two-dimensional plane flight control algorithm and flight simulation proposed in this paper provide a new simulation method for the design of UAV cluster mission planning algorithms, therefore can help promote the development of the subject.
An Optimized LOF algorithm Based on Tree structure

LOF (Local Outlier Factor) is a classical local outlier detection algorithm, widely used in network intrusion detection and processing of classification benchmark data. However, the algorithm spends too much time on computing, searching and querying data objects, which leads to high time complexity and poor scalability of the algorithm, and cannot complete outlier detection for large-scale data sets. In order to solve the above problems, this paper proposes an improved LOF algorithm based on tree structure, which is called ILOF (Increment Local Outlier Factor), and uses a tree structure with a more efficient way of building and querying. The results of the experiment indicate that ILOF not only ensures the detection accuracy, but also improves the efficiency of outlier detection and has good scalability.
Biometric and RFID based authentication system for exam paper leakages detection using IoT technology

&lt;p&gt;Education is the soul of a community; it goes from one generation to another.&lt;br /&gt;Exam is the prime responsibility of an educational framework. The reason&lt;br /&gt;for an examination is to select talented applicants for multiple positions.&lt;br /&gt;Exam is an important aspect of the education system to test students&amp;amp;#39; abilities&lt;br /&gt;online and in oral papers. Every year we receive deferred / canceled exam&lt;br /&gt;messages due to paper leakage. Therefore, we have come up with a&lt;br /&gt;affordable and concise outcome, and we have decided to design and&lt;br /&gt;implement the &amp;amp;quot;Exam Paper Leak Protection Framework&amp;amp;quot;, which will be a&lt;br /&gt;much more secure structure depending on the controller. Together with&lt;br /&gt;RFID Reader, Finger prints sensor, Buzzer, LCD, and Wi-Fi module. First,&lt;br /&gt;the university will send a selection sheet to an educational institution&lt;br /&gt;&amp;amp;quot;electronic sealed box&amp;amp;quot; called the &amp;amp;quot;electronic control box&amp;amp;quot;. Electronic control&lt;br /&gt;box is a prototype that can be proposed using controller, and the RTC to&lt;br /&gt;display the current date and time. If anybody tries to intrude the box the&lt;br /&gt;buzzer will beep and alert message will be send to the university.&lt;/p&gt;
Design of Mobile Game-Based Learning Application for Children with Dyslexia

A game-based learning system is a common approach to improve the learning process of the different types of learners such as dyslexic learners.The researchers investigate first the factors or components of a game-based approach that affect the learning of the learner from a variety of related literature and identify which factors and principles that shall apply and significant in the development of a game-based learning system to improved the learning of the dyslexic user.The proposed framework consists of the different components that meet the dyslexic users learning needs and provide a guide for designing a game-based learning system for children with dyslexia.The proposed framework was implemented in this study by developing an application for dyslexic users.The respondents of this study moderately agreed with the usability, efficiency, and portability of the application developed in this study.Therefore, the application is applicable, useful, meets and satisfied the needs of the dyslexic children in terms of the learning process of the basic reading in Filipino language and shall be used as learning tools and materials for reading.