Reviewerly/exHarmony huggingface dataset:
Relational Reasoning Model Based on Evidence Sentences for Document-level Relation Extraction

Document-level relationship extraction (DRE) aims to extract relationships between entity pairs in a document. Different from sentence-level relation extraction (SRE), there may be cross-sentence relationships with multiple sentences around two entities in DRE. In most cases, only some of the sentences involve the relationship information, we call them evidence sentences, which are very important when the context of entity pairs is complicated and multi-hop relational reasoning is required. Although the current works related to DRE have achieved good results, they mostly ignore the information of evidence sentences. They usually use only the sentences containing entities, which leads to errors because of noisy sentences. To solve this problem, this paper proposes the Evidence sentence detection and Relational reasoning Neural Network (ERNN) model. ERNN is a multi-task joint model that can detect evidence sentences and extract relationships based on evidence sentences. ERNN first uses the evidence sentences to construct a reasoning path, and then classifies the relationship. The experiments on the DocRED dataset shows that ERNN performs better than the existing models in extracting relationships that require complex inference processes.
Video Captioning using Deep Learning and NLP to Detect Suspicious Activities

In recent years, computer vision has been getting a lot of attention. Big data is being generated from surveillance videos. Analysis of this video data in real-time helps in getting the gist of events occurring. Video captioning is the task of generating the descriptions of the video. The transformer model has shown remarkable accuracy on machine translation tasks. Visual transformers have shown great accuracy in the classification of videos. Using the existing method to extract the entity, motion, and textual features from a given video. These extracted features are later used for generating the captions. The custom dataset used here is based on the military domain. These captions are further analyzed to detect suspicious activity in real-time. This paper is the implementation of the video captioning model for the military-specific domain. The generative model is based on a transformer model combined with PWC (pyramid, wrap, cost volume) architecture pre-trained on the Kinetic dataset and pre-trained Glove embeddings. We will be using custom military-specific captions. The detailed results from the video captioning experiment are presented here. The captions generated from the test image are evaluated. The BLEU (bilingual evaluation understudy) and METEOR scores are evaluated.
Flood Early Warning System Using River Water Level Prediction with Artificial Neural Network (Case Study: Jakarta City)

The problem of flooding is still an important topic to be solved in Indonesia. In 2021, floods were the most common disaster. So, this research proposes one way to deal with flood disasters: by predicting the arrival of the flood by predicting the river’s water level to know the alert status in each river so that residents can anticipate the arrival of floods. In this study, the prediction of river water height will use one of the machine learning algorithms, Artificial Neural Network (ANN). The machine learning model that has been created will be presented using the web in making a machine learning model using 1096 historical data on river water levels and weather. In the experiments carried out, we also carried out comparisons using data that the Synthetic Minority Over-Sampling Technique has carried out with Gaussian Noise (SMOGN) process and data that SMOGN has not processed. The results show that using 125 nodes can produce a smaller prediction error value. For experiments using SMOGN, the results of the experiment are still inconsistent, and some observation points produce larger MAE values.
Low Illumination Image Enhancement Based on Improved RetinexNet Network

In the case of insufficient lighting conditions, the images taken by the equipment have problems such as loss of details and presence of noise, which affect people’s visual perception seriously, and will also reduce the performance of advanced visual tasks using normal lighting images. At present, most of the research on image enhancement is based on RGB model, and there is a problem that the correlation between channels is too strong. Therefore, this paper makes use of the relatively independent characteristics of each channel in the HSV model to research. At the same time, adding attention module to the enhancement network reduces the noise effectively.
Security Framework for Hosting Systems on the Cloud: Case Study of Jordan E-Government Websites

Nowadays database systems become more and more critical in every domain of our lives. Websites are becoming more dynamics, they being more than browsing content and pages through internet. They are being more interactive and keep users comfort as they can read, browse, send and receive critical information through their interfaces. Recently websites are being hosted in larger datacenters owned by large hosting companies. Most technologies trends move towards Cloud computing technologies that leads them for global datacenters rather that local ones. Even though this technology will provide a lot of services, there are many concerns related for securing and maintaining the cloud environments safe. Many techniques are being done to handle these issues in the literature but they are provided separately; they are not dedicated for specific applications and targeted issues. Hosting websites in secure datacenter is a major concept. There are many layers should be introduced to provide access to secure websites and applications published on the internet. In this paper we introduce complete framework to host a dynamic website on cloud datacenters, to emphasis the security requirements for hosting secure websites and database services. We have analyzed the attacks targeting the hosting services and find that the application and network levels have a significant part in the security framework design. Also, best practices and recommendations were also provided.
Generating Complex Questions from Knowledge Graphs with Query Graphs

Question Generation from Knowledge Graphs (KGQG) is a task that aims to generate natural language questions from subgraphs within the given Knowledge Graph. Previous research has discussed numerous KGQG approaches, generating simple and complex questions from triples and queries based on the template-based and Deep Learning-based models. However, the current KGQG research could be identified three unique challenges: determining how to represent the queries, mapping different query structures to different compositional structures in the natural language questions, and generating questions with high language variety. In this paper, we propose a novel framework to conduct the KGQG task, which consists of two stages: query graph construction and graph-to-question generation, to deal with the three identified challenges. Firstly, we propose a query graph structure that specifies the entities and relationships within a SPARQL query addition with the characteristic of each entity in order to explore an appropri-ate query graph representation. We then propose a Graph-to-Sequence (Graph2Seq) model that utilizes Gated Graph Neural Network (GGNN) to encode the constructed query graphs and an attention decoder with a Long Short-Term Memory (LSTM) network to generate natural language questions. To make an objective evaluation, we validate our framework on two compli-cated datasets designed for complex questioning and answering, namely LC-QuAD 2.0 and KQA Pro. Experimental results show a dramatic improvement in our framework compared with the Sequence-to-Sequence (Seq2Seq) baselines. The robustness of our graph structure and the Graph2Seq model in the KGQG tasks are all confirmed, accompanied by a case study to highlight the effectiveness of our proposed framework.
Text Analysis And Classification Using Topic Modeling

A large amount of data is generated and collected daily to understand the trends. Searching and analysing the data becomes complex as more information becomes available. Therefore, to search through, sort, analyse, and comprehend huge amounts of data, we need sophisticated methods and tools. The topic modeling tools that can help organize, comprehend, and summarize a significant amount of textual information are examined in this work. With topic modeling, it is possible to organize, search, and summarize texts by annotating documents with hidden patterns that are present throughout the textual data. This paper uses the Latent Dirichlet Allocation (LDA) in topic modeling for text analysis and classification of data extracted from medical equipment installation logs. When a healthcare product-based company installs their product in a hospital they pay the engineer based on their hourly service. It becomes a crucial concern if these service hours are hiked. To understand the reason for the service hour hike, an analysis is required. This paper aims in classifying the product installation text, entered by field personnel based on their daily activities until the completion of the installation in the high/medium/lowcost category. After classification one can analyse the text that belongs to the high-cost category only to understand the challenges faced during field installation. The analysing word space is drastically reduced while analysing the text by application of LDA.
Character Identifier Spotting Based on Deep Learning in Video Surveillance Images

Nowadays, surveillance cameras have been widely deployed. These cameras generate a large number of surveillance images everyday. The time and location character identifier are usually overlaid on the images by the surveillance system. It is very important for managers to obtain these identifier information. Based on deep learning, a character identifier spotting method is presented for video surveillance images. The process consists of character identifier detection, recognition, and post-processing. The YOLOv3 combined with CTPN is designed to detect the bounding boxes of character identifier in surveillance images. It achieves the recall of 0.8667 on the testing data set. CRNN is used to recognize the character identifier in the cropped bounding boxes images. It achieves a system accuracy of 0.8180. A simple but effective post-processing method is proposed to correct the recognized character identifier. The system accuracy is increased by nearly 10%(i.e. 0.9139) after post-processing.
A Large-Capacity Coverless Steganography Based on Two-MSB and Artificial Immune System

With the development of information hiding technology, coverless steganography, which provides non-embedding and distortion-free hiding by constructing a mapping relationship between the secret information and the cover image, has attracted more and more attention. This paper proposes a large-capacity coverless steganography based on two-MSB (MSB and second MSB) and Artificial Immune System (AIS). In order to use the two-Msbto represent feature information, the cover image is first divided into blocks and the pixel value of each block is averaged. Then, randomly scramble blocks to obtain the feature sequence. Next, according to the hidden ratio, a diffusion factor is introduced and optimized by an artificial immune algorithm to generate the optimal feature sequence. Finally, the mapping relationship between the optimal feature sequence and the secret information is established, and an error map is generated in the process. Experimental results show that the proposed method has good robustness and can resist attacks such as noise and filtering. Compared with existing coverless steganography algorithms, this method has higher hiding capacity and security.
Metamorphic Cryptography Using AES and LSB Method

Now a days, internet users has led to the significant growth of cyber attack incidents. Therefore, it is essential to secure sensitive data before transmission to guarantee the secrecy of information and prevent unauthorized data alteration or loss. This issue can be solved using cryptography and steganography techniques. This paper uses an LSB-based image steganography algorithm to hide the message. Before being put into the cover image, the secret message is encrypted using the AES cryptographic algorithm while the secret message can be a text string, pdf, image, audio, or video. As a result, only the authorized user with the decryption key can extract the actual message from the stego object. Thus, it is still challenging to get the secret message when an attacker discovers the message. It uses Python version 3.9.10 to implement the proposed algorithm.
Analysis of Centralized vs Decentralized Electronic Voting

Some developed countries, such as America, Switzerland, and Norway, have not implemented an e-voting system. However, several countries have implemented an e-voting system, Estonia by implementing a centralized e-voting application. Now, the e-voting system can be implemented in a decentralized manner by utilizing blockchain and smart contracts. This paper presents a comparative study of centralized and decentralized e-voting applications. This study gives an overview of the features offered by each application and analyzes the performance, security, usability, and user experience of each application. The analysis was obtained from simulations and questionnaires for each application carried out by two groups of respondents. The questionnaires were distributed using a simple randomization technique and then analyzed using tools provided by SUS. The results from security testing show that voting data in decentralized e-voting applications cannot be modified, while centralized ones can. Therefore, a decentralized e-voting application indicates the existence of a guarantee of the integrity of the stored data. However, there are still performance issues that are likely to be fixed in a near future.
Using Genetic Algorithm for Wide yet Even Scattering of Game Objects: Applications on Irregular Levels and Involving Multiple Objects

The rising costs of game development necessitate algorithmic and automatic methods for creating game content, also known as procedural content generation or PCG. Among many types of content, game levels are increasingly generated with PCG. The literature has recorded various level generation methods for specific games or contexts, yet no general-purpose one. We have previously addressed this gap by proposing a genetic algorithm-based method that scatters objects widely yet evenly on a level. The algorithm satisfies the player’s needs for exploration. Our previous work acquired good results when applying the method on a simple and rectangular level. This paper presents two follow-up case studies that reflect real games: one with irregular levels and another with multiple object types. Our results show that our method fits the first case study; however, the irregular level’s layout may yield unexpected results, such as the tendency of objects to appear along its edge. Meanwhile, we also find that objects of different types are best scattered separately, each under a specific parameter, to optimize their wide yet even distributions. We then discuss our results’ implications for PCG research and how to generalize our method to generate other types of game content.
Obstacle Avoidance, Drive Wheel Synchronization, and Line Tracking Capabilities in an Autonomous Robot

Autonomous Robots may prove useful in the industry and may help replace people in hazardous, risky, and inhumane occupations. An autonomous mobile robot can carry out a variety of functions in search and rescue operations, monitoring, relief efforts, extra-terrestrial investigation, transport, warehousing etc. Thus, an intelligent mobile robot that is capable of navigating a variety of situations, both static and dynamic, may prove essential and instrumental in contemporary industrial applications. The objective of navigation is to find optimal or sub-optimal ways from a specified point A to another point B while avoiding as many obstacles as possible. Some experiments that evaluate navigation capabilities also examine how well a robot is able to track a well-defined trajectory and synchronize its steering wheel so as to enhance mobility and power usage. Mobile robot navigation may be achieved in numerous ways so that the developed device may have obstacle avoidance, path planning, steering wheel synchronization, and line tracking capabilities. Several studies shed light on this topic. However, a comprehensive review of all the existing strategies, approaches, and applications does not yet exist. This study's objective is to examine the published writings on mobile robots capable of path planning, trajectory tracking, and driving wheel synchronization and then highlight the most important and effective approaches which might assist future research in autonomous mobile robot navigation.
Classification Of Javanese Script Using Convolutional Neural Network With Data Augmentation

Javanese is a language widely used by people living on the island of Java. The Javanese language was initially compiled using the Javanese script, not Latin. Many historical objects use Javanese script, but with the low level of recognition of Javanese script, people are not familiar with even Javanese script. Therefore we need a method of recognizing Javanese characters. For its implementation, there are several challenges, such as the many variations of Javanese script forms, the selection of the right architecture, and the many training data needed so that the system can predict accurately. Convolutional Neural Network (CNN) is an effective deep learning in image processing. The CNN model was trained using a Javanese script dataset. The dataset consists of 10220 data with 20 classes, namely the HA TO NGA script, with 8160 used as training, 2040 as validation, and 20 predictions. In this research, optimization is carried out using data augmentation, which helps add variations to the training data. The test results achieved 98% accuracy on training data and 92% data validation and can classify predictions with accurate results.
Weakly Supervised Object Detection with Position Information of Convolution Neural Network

Weakly Supervised Object Detection (WSOD) is a task that training object detector with only image-level label which typically means the category label. In this task, it is difficult to train a separate position regressor due to the lack of bounding box label. To solve this problem, some methods based on Multiple Instance Learning which establishes an instance (box) classifier by learning information from bag (image) are proposed by transforming the detection task into classification one. However, classifiers tend to find the most discriminative local region of an object, which leads to the problem of local optimization. In this paper, we utilize a regularization restriction method to obtain more global attention information by using the multi-layer features of convolutional neural network. Further, we use the feature enhanced by attention to locate more complete object. Meanwhile, the method is like a model plug-in which is easy to implement without additional computation. The result of the proposed method on PASCAL VOC2007 is better than baseline method, which has proven its effectiveness.
Building LLVM and GCC, with Amake

This paper describes the author's exploratory experience of porting the build systems of two large software distributions, the LLVM and GCC programming-language translation systems, to the Amake build tool. Amake is an enhanced derivative of GNU Make. Amake adds automatic language-independent dependency analysis and site-wide heterogeneous target caching. Amake also supports GNU Make's parallel-build capabilities. This experience included (mostly) expected changes to these build systems, but somewhat surprising changes to the design and implementation of Amake. A description of the former is hoped to encourage the migration of other build systems to Amake; the latter is Amake's latest set of improvements.
An Improved Weighted KNN Algorithm About Text Classification Based on Spark Framework

K-nearest neighbor classification algorithm can quickly deal with the classification problem in this paper, but when calculating the similarity, it will assign the same weight to all distances, and does not pay attention to the impact of small distance on classification accuracy. At the same time, the k-nearest neighbor classification algorithm will be affected by the number of samples and dimensions, which will affect the efficiency of the classification algorithm. Therefore, an improved weighted KNN classification algorithm based on spark framework is proposed, which can improve the operation efficiency of the algorithm by cutting and reducing the dimension of sample data. Experimental results show that the algorithm has better accuracy and speedup ratio than the parallel algorithm based on Hadoop platform, and can process large-scale text data quickly and accurately.
Development of Final Year Project System (FIPOS) Based on Website with One-Time Password

The final Project is one of the processes for graduation from college studies. However, problems arising during guidance are the limited communication time and the suitability of the schedule between students and lecturers, so the frequency of offline meetings is reduced or even non-existent. We introduced a final-year project system called web-based FIPOS (FInal year PrOject System) to tackle the problems. FIPOS not only maintains communication between lecturers and students but also maintains the confidentiality of the year-end guidance process. Based on OWASP 2021, SQL Injection is still in the top 10. In applications using logins, SQL Injection must be anticipated. Therefore, the development of FIPOS implements security using a two-factor authentication scheme that utilizes email, password, and one-time passcode in the login process. And also implement secure coding to reduce the impact of SQL injection attacks. In addition, because FIPOS is an application that final-year students often use, its usefulness is tested with UAT at XYZ University. From the results, FIPOS can secure the login authentication process and data from SQL injection attacks and useful applications by getting a UAT value of 97.7%, which means that FIPOS can run well and be user-friendly.
Comparison of LSTM and GRU in Predicting the Number of Diabetic Patients

Diabetes is one of the chronic diseases that many people have. This diabetes disease experienced a significant increase during the pandemic, which could cause numerous deaths. One way to help hospitals prevent too many diabetic patients is to predict the number of diabetic patients. We used the LSTM (Long Short-Term Memory) method to predict diabetic patients. The study was conducted using patient data from the Modopuro Health Center, Mojokerto Regency. The prediction process manually calculates the data, then looks for the correlation of the data according to the LSTM method, namely identifying the autocorrelation coefficients at two to three different time lags. The data observed is daily from January 2, 2021, to April 20, 2022, with as many as 345 data. From the calculation results, the RMSE value is 3.184, while the GRU produces an RMSE of 1.727. It concluded that the GRU could better predict the number of visits of diabetic patients in internal medicine polyclinics.