Reviewerly/exHarmony huggingface dataset:
State-of-the-Art Reformation of Web Programming Course Curriculum in Digital Bangladesh

For last 15 years universities around the world are continuously developing effective curricula for Web Engineering in order to create good opportunities for graduates to cope up with IT-Software industries.From this study we will show the gap between the skill requirements of IT-Software industries and universities' web course curricula.Also, we will provide a balanced and structured web course curriculum for any universities.Nowadays, there is a rapid development in webbased applications everywhere but most of our students are late bloomer in programming.So, to ease their difficulties in web sector we need a balanced web curriculum and effective teaching method.By this curriculum one can achieve an overall idea and a minimum view of web engineering which can be beneficial for them in further Web development.Students get a little knowledge in their university on Web Engineering because of the vastness of the contents and the small duration of semester.Our two-semester web course curricula will help them to overcome this problem.Two-semester web course curricula have a huge impact on achieving the minimum required skill in web development field in IT-Software industries.It will help to obtain most of the area of web related content also it will increase problem solving skill and versatile knowledge of web engineering in undergraduate life.
Certificateless Identity-Concealed Authenticated Encryption Under Multi-KGC

In the certificateless cryptography, users generate their partial private key and the Key Generation Centre (KGC) generates the other partial private key of users. In some certificateless application scenarios, a sender might want to send messages to a receiver which registers with another KGC. Unfortunately, no certificateless authenticated encryption scheme under multi-KGC has been put forward so far. In this work, we propose the first certificateless identity-concealed authenticated encryption scheme under multi-KGC. Our proposed scheme hides the public identity information of both sender and receiver from any third party. We build a security model for certificateless identity-concealed authenticated encryption scheme under multi-KGC. We prove that our proposed scheme is secure under the random oracle model. We also present a variant of our proposed scheme which supports bilateral identity-concealed authentication key exchange.
SPATIAL ANALYSIS OF RADON GAS CONCENTRATION DISTRBUTED AT BAGHDAD CITY USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM TECHNIQUESD

Spatial analysis (spatial statistics) contain systematic techniques, that study actually geographic data have geographic properties such as topological and geometric, spatial analysis involve difference types of technique. Spatial analysis represented new understanding complex topics in form easy by any method or statistical for study area include mathematics and logic. Using function of point density which represented the Radon gas measurement geographic location, and limitation the density area by finding the radii (bandwidth) Kernel Density Estimation (KDE) techniques variation to reach interest regions and hotspot of point’s neighborhood. The aim of the research, applied the spatial statistical method to acknowledge concentrations of radon gas pollution for environmental and human life conservation, and focuses on the region affected by determination and orientation it in this method or tools for control outbreak to be facilitated, this potential possibility aid to avoid research randomly for radon gas pollution regions, this processing consist by extraction the spatial characteristics distribution that depending on distance measurements, by using geographic information system (GIS) technical, and which exploited Global Positioning system to accurate simultaneously the spectral bands of landsate satellite imagery to referencing the study area, and they have been determining the topology objects such as land cover (water, vegetation and soil) and land use, to connected with points measurement of Radon gas in Baghdad city only soils election, thought the soils were caused pollution based for objects type such as water and air, because, the natural formative of layer soils by decay of radioactive isotope Uranium with erosion the rocks and drift process with dissolved water or flood throughout the ages. Doing field the soil samples measurement by RAD7 Mobil detector for 58 point samples election, which represented using soil type such as vegetation land and land use touched with human life.
Real-time Traffic Sign Text Detection Based on Deep Learning

Abstract Aiming at the task of traffic sign text detection in natural scenes, a two-stage cascade detection model based on deep learning is proposed. The proposed model first locates the regions of interest (RoI) of text-based traffic sign by applying improved SSD (Single Shot MultiBox Detector) network. Then a rotation-based text detection network is used to detect text strings in the located RoI. Moreover, the lightweight convolutional neural network MobileNetV2 is combined with the deep learning component in two stages, which reduces network parameters and improves detection speed of the model. On the one hand, the proposed approach takes full advantage of the information of traffic signs. By this way the search area of text detection can be reduced, which makes it possible to simplify the text detector. On the other hand, it can obtain more complete text detection results by using rotation-based text detector. The experimental results show that the proposed method can perform well on different data sets, it can not only keep a high accuracy of detection, but also meets the realtime requirements.
GPU-based efficient join algorithms on Hadoop

The growing data have brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up nested loop join, hash join and theta join, combining Hadoop with GPU, which is also the first to use GPU to accelerate theta join. At the same time, after the data pre-filtering and pre-processing, using MapReduce and HDFS in Hadoop proposed in this paper, the larger data table can be handled, compared to existing GPU acceleration methods. Also with MapReduce in Hadoop, the algorithm proposed in this paper can estimate the number of results more accurately and allocate the appropriate storage space without unnecessary costs, making it more efficient. Experimental results show that comparing with GPU-based approach without Hadoop, our approach increases the speed by 1.5–2 times, and comparing with the Hadoop-based approaches without GPU, our approach increases the speed by 1.3–2 times.
Finding 1-Day Vulnerabilities in Trusted Applications using Selective Symbolic Execution

Trusted Execution Environments (TEEs) constitute a major building block for modern mobile devices' security architectures.Yet, the analysis tools available to researchers seeking to examine these critical components are rudimentary compared to the vast range of sophisticated tools available for other execution contexts (i.e., Linux or Windows userland).We see the primary reason for the lack of tools is originating from the closed-source nature of TEEs.Specifically, the analysis of Trusted Applications (i.e., userland applications executed in a TEE) is of vital importance, since they account for the largest attack surface.However, hardware primitives (i.e., ARM TrustZone) prevent access to this high-privileged context and thwart any form of dynamic analysis.In this paper, we present our approach to investigate 1-day vulnerabilities using selective symbolic execution of real-world Trusted Applications (TAs).Our system, SimTA, is based on angr and emulates the TA's execution environment.We build SimTA based on insights gained from manual static analysis of a commercially and widely deployed closed-source TEE by using an exploit on a physical device.In our evaluation, we elaborate on how SimTA facilitates the binary-diff-guided analysis by replicating the analysis of a known critical vulnerability.Additionally, we reveal two further issues, an authentication bypass and a heapbased buffer overflow, that have quietly been introduced by the vendor.
Video Genre Classification using Convolutional Recurrent Neural Networks

A wide amount of media in the internet is in the form of video files which have different formats and encodings.Easy identification and sorting of videos becomes a mammoth task if done manually.With an ever-increasing demand for video streaming and download, the Video Classification problem is brought into foresight for managing such large and unstructured data over the internet and locally.We present a solution for classifying videos into genres and locality by training a Convolutional Recurrent Neural Network.It involves feature extraction from video files in the form of frames and audio.The Neural Networks makes a suitable prediction.The final output layer will place the video in a certain genre.This problem could be applied to a vast number of applications including but not limited to search optimization, grouping, critic reviews, piracy detection, targeted advertisements, etc.We expect our fully trained model to identify, with acceptable accuracy, any video or video clip over the internet and thus eliminate the cumbersome problem of manual video classification.
Examining the Influence of Saliency in Mobile Interface Displays

Designers spend more resources to develop better mobile experiences today than ever before.Researchers commonly use visual search efficiency as a usability measure to determine the time or effort it takes someone to perform a task.Previous research has shown that a computational visual saliency model can predict attentional deployment in stationary desktop displays.Designers can use this salience awareness to co-locate important task information with higher salience regions.Research has shown that placing targets in higher salience regions in this way improves interface efficiency.However, researchers have not tested the model in key mobile technology design dimensions such as small displays and touch screens.In two studies, we examined the influence of saliency in a mobile application interface.In the first study, we explored a saliency model's ability to predict fixations in small mobile interfaces at three different display sizes under free-viewing conditions.In the second study, we examined the influence that visual saliency had on search efficiency while participants completed a directed search for either an interface element associated with high or low salience.We recorded reaction time to touch the targeted element on the tablet.We experimentally blocked high and low saliency interactions and subjectively measured cognitive workload.We found that a saliency model predicted fixations.In the search task, participants found highly salient targets about 900 milliseconds faster than low salient targets.Interestingly, participants did not perceive a lighter cognitive workload associated with the increase in search efficiency.
Diversity regularized autoencoders for text generation

In this paper, we propose a simple yet powerful text generation model, called diversity regularized autoencoders (DRAE). The key novelty of the proposed model lies in its ability to handle various sentence modifications such as insertions, deletions, substitutions, and maskings, and to take them as input. Because the noise-injection strategy enables an encoder to make the latent distribution smooth and continuous, the proposed model can generate more diverse and coherent sentences. Also, we adopt the Wasserstein generative adversarial networks with a gradient penalty to achieve stable adversarial training of the prior distribution. We evaluate the proposed model using quantitative, qualitative, and human evaluations on two public datasets. Experimental results demonstrate that our model using a noise-injection strategy produces more natural and diverse sentences than several baseline models. Furthermore, it is found that our model shows the synergistic effect of grammar correction and paraphrase generation in an unsupervised way.
Prediksi Ketepatan Waktu Lulus Mahasiswa Menggunakan Algoritma C4.5 Pada Fakultas Dakwah Dan Ilmu Komunikasi UIN Raden Intan Lampung

Student graduation data is very important for universities because it is used in the accreditation process. Data continues to grow and is ignored because it is rarely used. Data of graduating students can provide useful information if processed optimally. This study processes data using data mining to obtain information in the form of a prediction of student graduation punctuality. The method used is the C4.5 algorithm. The criteria used are gender, regional origin, type of school origin, ranking and entry point. In its application, the C4.5 algorithm can be used in predicting student graduation times with a precision value of 70.70%, 60.4% recall, and 58.2% accuracy. In measuring the performance of the algorithm in pattern recognition or information retrieval it is recommended to use a minimum of two parameters namely precission and recall to detect bias, therefore in this study the F-Measure calculation is used. From the calculation of the F-Measure obtained a value of 71% which means that the C4.5 algorithm is considered good in classifying and predicting students who graduate on time
Aspect level sentiment classification with unbiased attention and target enhanced representations

Aspect-level sentiment classification aims at inferring the sentiment polarities of opinion targets for a given sentence. As a sentence might contain multiple sentiment-target pairs, extracting relevant information concerning the given target entity is the main challenge of this task. We try to overcome the challenge from two aspects. First, the attention mechanism is able to focus on the relevant part of the given entity and is well suited for this task. However, previous attention-based models still suffer from the problem of paying too much attention to some sentiment words that are irrelevant to the target. We call this as attention bias problem. To alleviate the biases, in this work, we introduce an adversarial training method to get unbiased attention. Second, we try to enhance the impact of the target from the perspective of word representations. Thus we propose an Embedding-Preserving Gating (EPGating) Mechanism. The mechanism dynamically incorporates target-related features into word representations as well as retains original word information. The experimental results on SemEval datasets demonstrate the effectiveness of our model.
Abstract Summarization Based on the Combination of Transformer and LSTM

Summary is the task of compressing a piece of text into a short version with the main information of the original text. While previous architecture choices revolve around Convolutional Neural Networks (CNN) and long shortterm memory (LSTM) recurrent neural networks, recently self-attention and transformer have been used for the text generation task and achieved very good results. However transformer lacks local attention information and uses simple calculations as address vectors. In our article, we put forward a new seq-to-seq model for generating text summary called LTABS (Abstractive summarization on LSTM and Transformer). This model makes transformer be more suitable for summary generation tasks. For the encoder part of the model, we make use of the hidden layer result of LSTM as the location information. At the same time, transformer is used as the attention matrix of LSTM. This design allows the transformer to obtain location information and enhance local attention. We add copy mechanism to the new network to finish off the summary tasks OOV (out of vocabulary) problem. We apply our model to CNN and Daily Mail and Xsum datasets, the test outcome shows that the LTABS framework is superior to the most advanced model in semantic and syntactic structure, and has achieved competitive results in manual language quality assessment.
Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.
Components of Mathematical Problem Solving Competence and Mediation Effects of Instructional Strategies for Mathematical Modeling

This study is aimed at investigating the relationships between the components of problem solving skills (four procedural components and modeling competence) and the impact of instructional strategies (collaborative learning and problem posing) on students’ mathematical modeling competence. A survey on students’ mathematical problem solving competence, that was comprised of 40 items, was administered to 1,224 students in Korea and their responses were analyzed quantitatively (exploratory factor analysis, confirmatory factor analysis, and path analysis). The results indicated that students’ competencies regarding procedural components of mathematical problem solving positively affected their mathematical modeling competence. Additionally, instructional strategies utilizing collaborative learning and problem posing mediated and synergized the effect in terms of procedural components of mathematical problem solving and mathematical modeling competence. The study also discusses its contributions to and implications for mathematics educators and teachers.
3D point cloud reconstruction from a single 4D light field image

Obtaining accurate and noise-free three-dimensional (3D) reconstructions from real world scenes has grown in importance in recent decades. In this paper, we propose a novel strategy for the reconstruction of a 3D point cloud of an object from a single 4D light field (LF) image based on the transformation of point-plane correspondences. Considering a 4D LF image as an input, we first estimate the depth map using point correspondences between sub-aperture images. We then apply histogram equalization and histogram stretching to enhance the separation between depth planes. The main aim of this step is to increase the distance between adjacent depth layers and to enhance the depth map. We then detect edge contours of the original image using fast canny edge detection, and combine linearly the result with that of the previous steps. Following this combination, by transforming the point-plane correspondence, we can obtain the 3D structure of the point cloud. The proposed method avoids feature extraction, segmentation and the extraction of occlusion masks required by other methods, and due to this, our method can reliably mitigate noise. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. In this way, we used the LOD (Level of Detail) to compare the number of points needed to describe an object. The results showed that our method had the highest level of detail compared to other existing methods.
Development of a Simple and Low-cost Smartphone Gimbal with MPU-6050 Sensor

T his paper discusses about design and development of gimbal smartphone, which are made to be simple and less money consuming by utilizing microcontroller and MPU 6050 sensor. Recently, the development of videography and image processing is inseparable from the rapid advance in smartphone technology. One of the most popular features is camera. Hand movements and shocks cause fewer maximum results. To improve the quality of camera captures and video, it is necessary to have a stabilizer for camera position. Thus, it is expected that the outcomes of this paper are able to give contribution for cheap gimbal smartphone. Design and implementation of gimbal uses acrylic as the material, with 5 mm of thickness. This MPU 6050 sensor is optimized to detect sway in axis X, Y, and Z or roll, pitch, and yaw. Gyroscope and accelerometer provide input to the microcontroller, which will process for output on 3 servomotors that function to maintain the camera’s position at a specified set point. The results show that MPU 6050 sensor can response angle reading error of 1.34° of roll, 0.25° of pitch, and 0.78° of yaw. Error in maximum servomotor movement is 1.5°. Thus, it can be concluded that gimbal can work optimally with less money consuming as well as low error. It is expected that the next research will add other suitable and accurate control, that is, PID or fuzzy.
Object Recognition Through Artificial Intelligence Techniques

This paper describes a methodology for object recognition categorized as polyhedron and non-polyhedron. This recognition is achieved through digital image processing combined with artificial intelligence algorithms, such as Hopfield networks. The procedure consists of processing images in search of patterns to train the system. The process is carried out through three stages: i) Segmentation, ii) Smart recognition, and iii) Feature extraction; as a result, images of objects are obtained and trained in the designed neuronal network. Finally, Hopfield's network is used to establish the object type as soon as it receives one. The proposed methodology was evaluated in a real environment with a considerable number of detected images; the noisy images recognition uncertainty was 2.6%, an acceptable result considering variable light, shape and color. The results obtained from this experiment show a high recognition level, which represents 97.4%. Out of this procedure, we can assume that it is possible to train new patterns, and it is expected that the model will be able to recognize them. Potentially, the proposed methodology could be used in a vast range of applications, such as object identification in industrial environments, grasping objects using manipulators or robotic arms, tools for blind patients, among other applications.