Reviewerly/exHarmony huggingface dataset:
An Adaptive Hint System for Puzzle Games: A Multimodal-based Approach

For a puzzle game, the embedded hint system is a vital medium to improve the quality of the gameplay. A well-designed hint system can help balance the difficulty of the puzzle game and offer the player a better game experience. However, according to the questionnaire-based survey and the industrial investigation we made for puzzle games, two significant problems with the current hint systems are discovered: a) Most hint systems are rule-based. Many players consider that the timing of the hints cannot match the actual states of their game flows. b) In addition, the presentation modes of the hints are sometimes too abstract or too straightforward to follow and even leak the upcoming puzzles. Hence, to improve the game experience for the players when involved in puzzle games, we design and develop a multimodal-based adaptive hint system to help players get through the puzzles more smoothly with timely hints that are presented in proper modes. To test the system's effectiveness and usefulness, we conducted a control experiment based on three puzzle games, with a control group playing the original games and an experimental group playing the games with the multimodal-based adaptive hint system. The experimental results showed that our system has a significant effect in helping players improve their game experience and balance the difficulty of the game.
Joint Original Space and Latent Space for Multi-view Spectral Clustering

We propose a novel multi-view spectral clustering model, called Joint Original space and Latent space for Multi-view clustering (JOLM). Different from most existing multi-view clustering methods, which usually improve clustering performance by developing original or latent features of multi-view data, the proposed JOLM method integrates both original features and latent features into a framework to improve clustering performance. Specifically, we learn the similarity graph matrix from original multiple features and latent features respectively, and obtain the global graph by minimizing the errors between them, so as to better utilize the rich information from multiple views. An effective iterative algorithm is proposed to optimize the objective function. Finally, abundant experiments show the effectiveness of our proposed method.
Efficient Heuristic Algorithm to Speed up GraphCut in GPU for Image Stitching

GraphCut algorithm has been widely utilized to solve various types of computer vision problems. Its expensive computational cost encouraged many researchers to improve the speed of the algorithm. Recent works proposed schemes that work on parallel computing platforms such as CUDA. However, the problem of low convergence speed prevents the usage of GraphCut for real time applications. In this paper, we propose global suppression heuristic to boost the convergence process of the algorithm. A parallel implementation of GraphCut algorithm on CUDA designed for the image stitching problem is introduced. Our method achieves up to 3× time boost on the graph of size 80×480 compared to the best sequential GraphCut algorithm while achieving satisfactory stitched images, suitable for panorama applications. Our source code will be soon available for further research.
Leveraging Graph to Improve Lexicon Enhanced Chinese Sequence Labelling

Recently BERT has been employed for encoding a sequence of input characters in state-of-the-art Chinese sequence labelling models. However, Chinese sequence labelling often faces the lack of explicit word boundaries, which is well-noticed and more challenging problem. To alleviate this problem, we adopt the containing relation between characters and self-matched words from external lexicon to construct graph and incorporate lexicon-based graph information into the lower layers of BERT. We evaluate our model on ten Chinese datasets of three classic tasks containing Named Entity Recognition, Word Segmentation and Part-of-Speech Tagging. The experimental results demonstrate the effectiveness of our proposed method.
The Fast Paillier Decryption with Montgomery Modular Multiplication Based on OpenMP

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.
An Obstacle Avoidance Control Strategy for DELTA Parallel Manipulator via Improved Genetic Algorithm

Aiming at the requirement of the DELTA parallel manipulator for performing grasping operations, a method for obstacle avoidance based on a genetic algorithm is proposed. Firstly, the classical DELTA parallel manipulator is analyzed mathematically, and its model is simplified. Then, the motion space and inverse kinematics solutions of the DELTA parallel manipulator are solved by using the spatial geometry method. Secondly, a cylindrical envelope surface is added to the manipulator’s end for distance calculation. This envelope surface ensures that obstacles do not collide with the manipulator during obstacle avoidance. Thirdly, an improved genetic algorithm is proposed for obstacle avoidance. Finally, the simulation results are given to verify the effectiveness and reliability of the proposed algorithm.
Generating High Quality Titles in StackOverflow via Data Denoising Method

StackOverflow is one of the most popular question-and-answer platforms on the internet and whether posts on StackOverflow will be answered largely depends on their titles’ quality. Based on recurrent neural networks (RNN) or transformers, previous studies have attempted to use real posts from StackOverflow to generate better titles. However, the challenge of noise in existing data has been ignored, leading models can’t generate higher quality titles. To address this issue, we propose the K-clusters confidence learning for code titles (KCL-CT) model, which contains code clustering and confident learning (CL) denoising components. Specifically, the code clustering component is used to capture the word order and semantic information in code and classify code into different functional categories. The CL denoising component receives the output from the code clustering component and employs a heuristic method based on a confidence threshold to prune raw datasets. We conducted experiments based on Java, Python, JavaScript, SQL and C# datasets, the results of which indicated that in terms of the BLEU and ROUGE scores, the proposed KCL-CT model can outperform previous state-of-the-art models by 2.0%–11.1% and 2.5%–14.0%, respectively.
On the Use of Deep Graph CNN to Detect Vulnerable C Functions

Software vulnerabilities are a problem in most software systems. If left unchecked, they can be exploited by malicious third parties to compromise the system, which can result in hazardous consequences. Over the years, several techniques have been proposed to tackle the problem of automatically detecting vulnerabilities. However, in spite of the efforts, they usually issue a large number of false alarms, which create a large overhead for the development team to analyze them. In this work, we study the viability of using a static technique (originally developed to classify classes of malware) to detect vulnerable C functions. This technique uses the Control Flow Graph (CFG) of the functions, features related to the structure of the graph, and the code sequence. Different from the malware classification problem, we also extract memory management-related features. All of the features are processed by a Deep Graph Convolution Neural Network (DGCNN). To do that, we use vulnerable and non-vulnerable functions of the open-source Linux Kernel project. Results show that a high recall can be obtained using this approach at the cost of a low precision.
A Study of Hadoop and Mapping Approach Techniques on Big Data Strategies

Research is an art of scientific examination. The advance learner’s vocabulary of current English lays down the meaning of research as “A careful exploration and enquiry especially through search for new facts in any branch of knowledge. Bradman and Morry define research as “A standardize efforts to increase new knowledge”. Research is, thus an original contribution to existing stock of knowledge making for its advancement. It is detection of truth with the help of study, observation, comparison, and experiments. The technologies that give support to the entire process of cost-effectively storing and processing data, and utilize internet technologies in a scattered way have arisen in the past few years. NoSQL and Cloud computing are the renowned ones that improve the potential offered by Big Data Technologies. Map Reduce is a software manufacture introduced by Google to act upon parallel processing on large datasets supercilious that large dataset storage is distributed over a large number of machines. Each machine computes data stored locally, which in turn contributes to distribute and parallel processing. This paper focuses on the Big data and Cloud services using impact of Map Reduce Algorithm and very advantageous for the researchers and corporate sectors who are using Map Reducing System technology.
A Context-Aware System to Support Personalized Clinical Pathways Using OWL and SWRL

The healthcare sector is crucial for the health and well-being of citizens, and many research efforts are being carried out which aim to support people's health and ultimately improve their quality of life. As a result, there is a growing need for technology-based solutions to support people in providing health services. In this chapter, research is presented which aims to develop a conceptual framework that supports personalized healthcare for pervasive computing that allows monitoring the health and wellbeing of patients anytime and anywhere. Thus, the author proposes virtual health mentor (vhMentor), an ontological and a rule-based model for the representation of knowledge and the exportation of the required interpretations. In case vhMentor detects a health problem, it will send a notification to the patient with instructions, while alternatively caregivers will be notified to investigate the problem and provide help. The system is expected to offer assisted living to long-term patients, improve their quality of life, and to reduce health costs and the caregivers' overwork.
Getwit as an API-based Acquisition Tool for Twitter

Twitter usage until 2022 is very high, making Twitter data very fast growing. The data contains information related to users and user activities through each feature on Twitter. The data can be used as one of the pieces of evidence to identify a person. Some features on Twitter cannot be obtained using forensic tools due to the limited scope of these tools. Research in data acquisition on the server-side shows that API can be used to support the acquisition process. Therefore, this study uses the acquisition method by utilizing the API. The results of this study provide evidence on how the Twitter API can assist digital investigators in collecting Twitter data. This API is used in our application called Getwit to perform automatic collection. As a result, Getwit is able to obtain user data and calculate its hash value which is important in digital investigations of Twitter-related incidents.
Federated Learning in Indonesia: Challenges and Opportunities

A single client of traditional Machine Learning (ML) limits the data sharing capabilities among different models. A centralized ML scheme leads to less data provision and confinement of a certain problem. In realistic applications, multiple learning clients need to work together to jointly train the model to enable a variety of solutions to scalability problems. However, the topic is relatively new. In this research, related open cases in a developing country are discussed, focusing on the exploration of emerging opportunities and challenges for the next generation of ML in the field of smart city, smart industry, and smart health service with case-studies from Indonesia. Federated Learning (FL) is a new paradigm of ML with a multi-client scheme. FL makes it possible to train ML algorithms with distributed training data across multiple devices or servers without any data exchange. Several large industries and companies in Indonesia were observed in this research to find out which sectors could most realize and increase the effectiveness and excellence of FL in advancing ML. Then possible solutions to deal with these challenges are proposed, one of which is the internet of things for smart cities and industries based on the federation mechanism which has great sector potential in Indonesia. Some areas of government and industry where FL can be integrated seamlessly are also listed. This research contributes in providing ideas for implementing FL in various fields to accelerate the development of FL in Indonesia. Finally, this research can briefly answer the question ‘What are the opportunities and challenges of FL in Indonesia?’.
Aerial Load Transportation With Obstacle Avoidance in Observed Environment

In this article, the load transportation is investigated in an unknown environment with obstacles. A mathematics model of the system consists of a quadrotor and slung load is driven by Euler-Lagrange method so that the dynamic of cable is also considered. In here, the main control part is done by creating an imaginary leader according to the load position and velocity. Indeed, the created leader by the error of load location and velocity not only guide the system to transfer the load to its desired location directly by passing through unknown environment but also it avoids the collision of the system based on the artificial potential field strategy. Then, A sliding mode control is designed for quadrotor to follow the imaginary point which is created in previous control step as well as to cope with disturbances and load swings. Moreover, the asymptotically stability of position control step is proven by Lyapunov approach. Finally, the numerical results are presented to verify that appropriate performance of the system in the load transportation operation.
Dynamic reconfiguration of costmap parameters with Fuzzy controllers to enhance path planning

Autonomous mobile robots are widely used in industries, and these robots utilize various methods to find their path in complex environments. Path planning methods aim to choose the shortest and fastest route to the target. However, working alongside humans needs a better intuition of the surrounding environment. In this paper, Fuzzy controllers have been designed to reconfigure the costmap parameters of mobile robots, considering the danger of collision in their environment. The Fuzzy controller has been tuned by tracking the behavior of mobile robots in several experiments, and it is observed that conveying the result of these observations to robots would significantly improve their navigation behavior. The results of this work are applicable to all path planning methods working based on costmaps.
Forensic Investigation Analysis of WhatsApp Messenger and Telegram Messenger on Android Based Device

This study provides a descriptive explanation to the discovery of artifacts related to digital forensic evidence in instant messenger applications i.e., WhatsApp and Telegram. It also explains the method to obtain evidence containing crucial information for forensic analysis in a cybercrime investigation on Android-based WhatsApp and Telegram applications. The experiments to obtain the digital evidence in this study were carried out by simulating the exchange of conversations from the victim to the perpetrator in an online crime of fraudulent buying and selling transaction. The results of the experiment were data such as the type of sent messages, user contact info, timestamps and chronology of the conversations, etc.
Application of Eye-Tracking for a 3-DOF Robot Assisted Medical System

Eye-tracking systems have evolved into robust data acquisition tools for a variety of use cases. Some cost-effective and demonstrably accurate systems are available on the market. The primary purpose of such systems remains the detection of gaze points on surfaces to determine the interest of individuals. Very few applications attempted to use this technology to control devices. In this paper, we analyze the possibilities of controlling robotic devices by eye-tracking in a medical context. In particular, this could be useful as an aid for paralyzed individuals. We propose a system that uses inertial measurement in addition to eye tracking to measure head movements and the main sub-functions of the system are tested. The tracking and guiding a system has been studied, indicating that eye-tracking can generally be used for controlling robotic devices. It has been shown that different head movements are distinguishable and therefore can be used to switch between different functions of the device.
Evaluation and User Interface Design Recommendation Using User Centered Design Method in Finpay Money Application

Amid rapid economic growth and development, people create various technologies to facilitate all related activities. One of those is E-money. E-money is a digital platform that supports transactions digitally using the internet. One of the e-money applications in Indonesia is Finpay Money which is a brand product of PT. Finnet Indonesia. Among the advantages, this application has several comments on Playstore in 2021, such as impractical applications, difficulty in upgrading premium accounts, and buttons that could not be clicked. Based on those comments, the Finpay Money rating is quite low (3.1 out of 5). From a survey conducted on 20 respondents using the system usability scale (SUS) method on the Finpay Money application, the score was 54.25 and entered the F predicate. The assessment shows the application is ineffective, inefficient, and unsatisfying to the user and motivates researchers to provide design recommendations for this application and review the design. The research applied the User-Centered Design (UCD) method for design recommendation. Afterward, the created design will be re-rated to view customer satisfaction. Customer satisfaction was obtained using the SUS method. Based on the result from 20 respondents, the rate of satisfaction was 86.75/100. The score signifies the redesign of Finpay Money classified in grade B with an excellent adjective rating (usability) and acceptable for the user. This result concluded the Finpay Money application is acceptable to users and follows user needs.
Redesigning Phillip's Online Electronic Mart System (POEMS) Investment Services Application using The User-Centered Design (UCD) Method

The Indonesian Central Securities Depository (KSEI) stated that the number of stock investors in Indonesia is increasing yearly. One of Indonesia's oldest and most widely used investment service applications is the POEMS application. Questionnaires were distributed to the POEMS application user with feedback on problems on the learnability aspect for novice investors. This study uses the User-Centered Design (UCD) method because the target user for improving the POEMS application is specific to novice investors who are different from professional investors. Initial testing tested nine essential tasks and resulted in 5 tasks that failed to be carried out by one or more respondents. Respondents' pain points and needs during interviews, observations, and initial testing simultaneously gave rise to an additional new essential task. Six essential tasks had to be tested in the second stage. After making a design solution by following each stage of UCD, all respondents managed to do 5 out of 6 tasks. In this way, the design solution has succeeded in improving the user experience for novice investors when they first use the POEMS application by referring to the interface guidelines and measuring specific metrics for the learnability aspect.
Optical encryption of gray image based on CS and CGH with double chaotic system

In this paper, a novel image encryption algorithm is proposed by combining Logistic chaotic map, 4D NHS hyperchaotic system, compressed sensing (CS), computer generated hologram (CGH) and two-dimensional discrete cosine transform (DCT). Firstly, the gray image is sparse represented by two-dimensional discrete cosine transform (DCT). The sparse matrix obtained by index sort scrambling is scrambled. For further encryption, the pixel scrambling uses the Logistic chaotic system to generate the scrambling vector. Subsequently, CGH encryption was performed and the Logistic chaotic map and 4D NHS hyperchaotic system were used as double keys to obtain the final encrypted picture.
Drowsiness Detection System for Masked Face Based on Deep Neural Network and Haar Cascade

Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system. In this study we propose drowsiness detection system utilizing current novel classification model such as Deep Neural Network (DNN), combined with Haar Cascade. The DNN is utilized to detect face, while Haar Cascade is utilized for detecting the eyes and its state on the detected face. In this study, due to Covid19 pandemic, we focused on developing the classifiers for detecting the face with mask. Apart from that, our proposed classifiers are also capable of identifying non-masked faces. The experimental result showed that by utilizing DNN and Haar Cascade, our proposed system could reach accuracy, precision, recall, and f1 measure as much as 81%, 88%, 80%, and 84%, respectively.