Reviewerly/exHarmony huggingface dataset:
Multi-Modal Virtual-Real Fusion based Transformer for Collaborative Perception

Automobile intelligence and networking have become the inevitable trend in the future development of the automotive industry. Existing intelligent and connected vehicles rely on single-agent intelligence to perform the basic perception, which is still weak in dealing with the problem of accurate recognition and positioning in complex traffic scenes such as small and far away objects. To tackle this issue, we propose a multi-model virtual-real fusion Transformer for collaborative perception. Specifically, to possess the complementary information from both RGB images and LiDAR point clouds, we propose the multi-model virtual-real fusion (MVRF) method, which generates virtual points and compensates for the lack of point information on sparse locations. Furthermore, the heterogeneous graph attention network (HGAN) is constructed to capture the inter-agent interaction and adaptively incorporate multiple agents’ features. The HGAN contains a series of encoder layers, each of which has a heterogeneous inter-agent attention module and a multi-scale self-attention module, which motivates to learn different relationships based on various agents’ types and simultaneously capture the global and local spatial attention. Extensive experiments demonstrate that the proposed method gains superior performance as compared with state-of-the-art methods.
Design of a Federated Learning System for IT Security: Towards Secure Human Resource Management

Federated learning is a new, decentralized type of machine learning in which the models, rather than the data, are shared to maintain privacy in machine learning. This paper aims to investigate the design of a federated learning system to increase IT security in Human Resource Management and, in particular, recruiting processes while complying with business needs and General Data Protection Regulation. We propose a federated learning system and a novel approach to identifying malware throughout the recruiting process. The combination of Design science, Service Science, and the reference modeling method guide our research design. This paper presents the results of the first design iterations with the identification of service design elements and the design of a recruiting process with a federated learning system inside.
Analysis of Development Performance and Product Quality with Scrum Design Thinking Development (Case Study: E-Recruitment)

Software quality is the most important aspect of user expectations and is a major factor in software success. When the product meets the needs of the user, it can be said that the product is of high quality and vice versa, when the user is not satisfied with the product, it can be said that the product is of low quality. Based on the survey, e-recruitment websites still have problems related to complicated user flow, less user-friendly designs and lack of learnability. Currently creating software with quality that can meet user needs is a challenge for the software development team. The researchers propose Design Thinking as a solution to these problems because it can create a solution-oriented analysis that is carried out repeatedly. However, Design Thinking lacks project management techniques. On the other hand, some studies combine the concept of Design Thinking with Scrum to address the shortcomings of Design Thinking. However, the process in Scrum often creates a series of deadlines and iterations that reduce software quality, cost and time due to decreased Development Performance. To prove this, this study will conduct an analysis related to Product Quality and Development Performance from the merger of these concepts. Based on the test results, it can be concluded that product development with Scrum Design Thinking results in a relatively long process but can offer much better product quality.
Teaching Iranian Sign Language via a Virtual Reality-Based Game

Nowadays, a considerable number of people have hearing disabilities. These people have difficulties communicating with a major part of society. This paper’s main goal is to create an attractive environment to teach Iranian Sign Language (ISL) to children to increase the number of people who can use sign language in the future. In order to achieve this purpose, a virtual reality game has been designed with the Unity game engine that the user plays on Oculus Quest 2, using eight ISL signs. These signs are detected by a webcam and processed in real time by a deep learning algorithm. After recognizing the performed sign, the command will be run in the game. The used deep learning algorithm includes two Deep Neural Network models. One is based on Convolutional Neural Network (CNN) and the other one is based on Long Short-Term Memory (LSTM) with test accuracies of 99.7% and 99.1%, respectively. The game has been tested on a number of participants. After analyzing their recorded data during the tests, it is indicated that considerable improvements have been occurred on their performances. It is observed that players could play 195.36% longer in the second half of their tries compared to the first half.
A Comparison between Rapidly Randomized Tree and Efficient Frontier Methods for Autonomous Mobile Robot Exploration

Traditionally, an available off-line map was used and more recently, by using novel techniques, robots could automatically generate a map of their environment. These techniques provided major improvements in the navigation process. Exploration of an environment consists of leading a mobile to some intermediate points where the robot expands its gathered map until the whole environment map becomes known. There are many environment exploration methods, two of which are more common; Rapidly Randomized Tree and Efficient Frontier (based on boundary search) Exploration Methods. This article aims to investigate the performance of these two methods in real environments by implementing them first within the Gazebo simulation environment and then on a commercial TurtleBot® mobile robot. The simulation and experimental results of both methods are demonstrated in terms of some crucial parameters, including explored area dimensions and traveling time and distance to determine the efficiency of each exploration technique. While the output parameter of time indicates whether the related method is quick enough for the desired application or not, the distance traveled by the robot is an indicator of energy consumption. Lastly, it is shown that the second method improves the robot’s performance in exploration for all two independent parameters.
Sentiment Analysis on Movie Review from Rotten Tomatoes Using Word2Vec and Naive Bayes

Along with the development of the movie industry and the internet, watching movies has become accessible to the public. Nowadays, it is easy to watch movies, and people need to know whether a film is good or not through a collection of movie reviews. The extensive collection of movie reviews spread across many sites makes it difficult for the public to find valid reviews of existing films. From these problems, the public can search for valid and valuable information about film reviews. Therefore, it is necessary to use sentiment analysis on movie reviews to make it easier for the public to find valid and valuable information. The method used in this research is Naive Bayes as classifier and Word2Vec as feature extraction. Word2Vec is chosen because this method can group words with the same meaning in a vector form. Naive Bayes is chosen as a classifier because the method works quickly and is easy to implement. The performance result from this research produces an accuracy value of 72.23%.
Flutter Framework Code Portability Measurement on Multiplatform Applications with ISO 9126

Multiplatform development aims to develop applications that are able to run on multiple platforms. However, multiplatform development usually has issue where the application does not run exactly the same on every platform. Development using the Flutter framework allows multiplatform applications to run from a single codebase. This research utilizes this framework to develop a multiplatform application, called Ahulang, a simple attendance check-in app as the experimental subject. This paper aims to determine the benefits of using the Flutter framework by analyzing the application portability based on ISO 9126. This study proposes several metrics to measure portability using several quality factors described in ISO 9126. These metrics are then used to evaluate the portability performance of Flutter framework. The final result shows that Flutter effort portability is 0.81 and other portability subfactors showing relatively high scores, such as an adaptability score of 0.59, an installability score of 100%, and a coexistence score of 100%. From the obtained results, it can be concluded that the Flutter framework is suitable for developing multiplatform applications as shown by have high portability scores.
The Impacts of Test Driven Development on Code Coverage

Testing is one of the most important phases in the software development life cycle. To emphasize this importance, Test Driven Development (TDD) is developed and proposed to be applied in the software development process. TDD method requires developers to determine the business processes first and software design needed as test cases, then implement them by writing the code. One of the benefits of implementing TDD is the high code coverage value, that means all methods and functionalities will be tested. To measure the quality of the written code, code coverage can be used as a metric. The higher code coverage result, it indicates the better the quality of the application. This study aims to determine the impacts of applying the TDD method in the software development process to the code coverage results. To run our experiment, we use a case study that involved the development of a point of sales application, namely dRetail. From the test results, the code coverage values are 99% instruction coverage, 88% branch coverage, 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> coverage for both line and method coverage. This code coverage value is higher than the code coverage value obtained from software development without using the TDD method.
Detection of Pill Intake Associated Gestures using Smart Wearables and Machine Learning

Gesture identification represents one way of monitoring adherence to medical treatment for cognitive-impaired individuals, dementia-related conditions being severely dependent on precise medication. This paper proposes a gesture identification algorithm used to detect the pill ingestion, that runs on an inexpensive smart wearable device. We use techniques pertaining to supervised machine learning and the data set is processed with the Keras framework. Data collected is represented by acceleration values supplied by the wearable and is proceed on the wearable device itself, showing high accuracy results in identifying the pill intake gesture. The trained model is deployed in a resource constrained embedded device and the inferences is carried locally onto the device.
Data Security Structure of a Students’ Attendance Register Based on Security Cameras and Blockchain Technology

The latest, modern security camera systems record numerous data at once. With the utilization of artificial intelligence, these systems can even compose an online attendance register of students present during the lectures. Data is primarily recorded on the hard disk of the NVR (Network Video Recorder), and in the long term, it is recommended to save the data in the blockchain. The purpose of the research is to demonstrate how university security cameras can be securely connected to the blockchain. This would be important for universities as this is sensitive student data that needs to be protected from unauthorized access. In my research, as part of the practical implementation, I therefore also use encryption methods and data fragmentation, which are saved at the nodes of the blockchain. Thus, even a DDoS (Distributed Denial of Service) type attack may be easily repelled, as data is not concentrated on a single, central server. To further increase security, it is useful to constitute a blockchain capable of its own data storage at the faculty itself, rather than renting data storage space, so we, ourselves may regulate the conditions of operation, and the policy of data protection. As a practical part of my research, therefore, I created a blockchain called UEDSC (Universities Data Storage Chain) where I saved the student's data.
A method for obtaining user requirements based on NLTK and knowledge graph

The traditional product design scheme can no longer fully meet the current user needs. To solve some problems that live in the period of small and medium batch products' design in traditional solutions, such as long cycles and a lot of differences between design direction and user needs as well as the high technical difficulty of current deep learning-assisted products. This paper proposes a rapid update design method for small batch products based on a knowledge graph, combining data mining and Python module methods. This method aims to reasonably reduce technical requirements, obtain user comments with intelligent crawler plug-ins, complete the extraction of demand information with two key modules of NLTK and Stanford CoreNlp, and realize visualization with a knowledge graph.
Industrial Operation Training Technology Based on Panoramic Image and Augmented Reality

Augmented reality training based on real scenes has problems such as hardware dependence and environment dependence, which makes most of the augmented reality complex industrial operation training unable to be carried out smoothly. In view of the above problems, this paper proposes an augmented reality complex industrial operation training method based on panoramic images. Based on panoramic images control technology, knowledge point mapping technology and training knowledge dynamic guidance technology, a complex industrial operation training system based on panoramic augmented reality has been formed. Based on wiring operation training, the feasibility of the technology and the effectiveness of the training effect have been verified. Compared with traditional training, This technology can effectively improve the training effect of complex industrial operations.
An Improved SURF Algorithm with FREAK Descriptor for BGA Component Image Stitching

This paper presents an image stitching method of BGA component for industrial operation. The mounter is the core equipment of the surface mount system, and it currently has no way to place components larger than the camera's field of view. Therefore, a high-speed image stitching method of BGA component is still not available. In this method, the hu invariant moment is used to determine the overlapping area of the image and reduce the range of feature point extraction. After obtaining the feature points, the differences between the SIFT (Scale Invariant Features Transform) algorithm, the SURF (Speeded Up Robust Features) algorithm and the improved SURF algorithm are compared. The improved SURF algorithm combines the SURF feature extraction method and the FREAK (Fast Retina Keypoint) descriptor is developed. After the feature points are processed by BFMather matching and RANSAC algorithm, the image transformation matrix of the two images is calculated. Finally, the perspective transformation is performed on the image to complete the image stitching. This image stitching method is shown to be simple and effective for high-speed industrial application. The procedure taken can be extended to other similar IC component and to other machine.
Autonomous Navigation and Obstacle Avoidance using Self-Guided and Self-Regularized Actor-Critic

This paper presents a deep reinforcement learning (DRL) algorithm for autonomous navigation and obstacle avoidance of a mobile robot. Deep Reinforcement learning algorithms (DRL) have achieved great success in sequential decision-making problems and control tasks. One of the controlling elements of deep reinforcement learning (DRL) is the target network which alleviates the divergence when learning the Q function. But, target networks slow down the learning process due to delayed learning updates. So, this makes learning unstable and induces poor performance in high-dimensional domains. As a result, it is challenging to train and deploy robots in real-time using existing Deep Reinforcement learning algorithms, which require a large number of training examples to converge or perform better in various scenarios. To tackle this issue we have used an algorithm called self-guided and self-regularized actor-critic (GRAC) which doesn't require target networks for learning state-action values in high dimensional continuous state space and continuous actions. Where we have developed an experimental test bed in the ROS-Gazebo simulator by implementing self-guided self-regularized actor-critic (GRAC) algorithm for goal-directed navigation and obstacle avoidance tasks using the Turtlebot3 robot. The simulated experiments were conducted in two different environments. In comparison to existing algorithms such as DDPG, the simulation results demonstrate that the proposed algorithm exhibits improved performance and faster convergence in navigation and obstacle avoidance of a mobile robot in high dimensional continuous state space and continuous actions.
DSGA: Distractor-Suppressing Graph Attention for Multi-object Tracking

Multiple object tracking (MOT) methods based on single object tracking are of great interest because of their ability to balance efficiency and performance on the strength of the localization capability of single-target tracking. However, most of the single object tracking methods only distinguish foreground and background. They are susceptible to the influence of similar interfering objects during localization, while in multiple object tracking scenarios, there are more interfering objects and the influence is more severe. Therefore, we propose a Distractor-Suppressing Graph Attention (DSGA) to learn more discriminative attention by reducing the influence of distractors on learning attention weight features. Furthermore, DSGA is embedded into the basic MOT framework “SiamMOT” formed as DSGA-SiamMOT and applied to multiple object tracking to verify its effectiveness. We conduct experiments on the MOT Challenge benchmark with "public detection", and obtain MOTA 66.65%, IDF1 62.2% accuracy on the MOT17 dataset with 14fps.
Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks

This paper presents the development of a system for controlling a robotic arm to deliver an object depending on the gender identity (male or female) of a human recognized in front of the robot to demonstrate essential gender identification-based applications for physical Human-Robot Interaction. For this, we developed a convolutional neural network-based model for identifying genders. With the recognition result, the control of the robotic arm with six degrees of freedom was implemented using a Jetson Nano embedded computer, OpenCV, ROS, and TensorFlow libraries. The developed gender identification model achieved a 96.5% of accuracy and a loss of 3.5% during training and validation using a gender database composed of 50K gender images. The final real-time prototype obtained a 98.2% accuracy and a margin of error of 1.8% during testing. This proof of concept indicates that more complex applications based on the gender of the user could also be developed in the future.
DynaBARN: Benchmarking Metric Ground Navigation in Dynamic Environments

Safely avoiding dynamic obstacles while moving toward a goal is a fundamental capability of autonomous mobile robots. Current benchmarks for dynamic obstacle avoidance do not provide a way to alter how obstacles move and instead use only a single method to uniquely determine the movement of obstacles, e.g., constant velocity, the social force model, or Optimal Reciprocal Collision Avoidance (ORCA). Using a single method in this way restricts the variety of scenarios in which the robot navigation system is trained and/or evaluated, thus limiting its robustness to dynamic obstacles of different speeds, trajectory smoothness, acceleration/deceleration, etc., which we call motion profiles. In this paper, we present a simulation testbed, DynaBARN, to evaluate a robot navigation system's ability to navigate in environments with obstacles with different motion profiles, which are systematically generated by a set of difficulty metrics. Additionally, we provide a demonstration collection pipeline that records robot navigation trials controlled by human users to compare with autonomous navigation performance and to develop navigation systems using learning from demonstration. Finally, we provide results of four classical and learning-based navigation systems in DynaBARN, which can serve as baselines for future studies. We release DynaBARN open source as a standardized benchmark for future autonomous navigation research in environments with different dynamic obstacles. The code and environments are released at https://github.com/aninair1905/DynaBARN.
On the Development of Tethered, Modular, Self-Attaching, Reconfigurable Vehicles for Aerial Grasping and Package Delivery

Unmanned Aerial Vehicles (UAVs) are quickly becoming the future of payload delivery. Over the past few years, many designs have surfaced with a variety of different solutions to this problem. Current systems are often bulky and designed for a specific purpose. This makes them difficult to adapt to new environments or payloads. This paper details the design and development of a novel, reconfigurable vehicle system that is able to adapt to a variety of environments and payload dimensions. The system consists of multiple individual mobile modules equipped with rotors, which work collaboratively to grasp a single payload. Each module has the capability to act independently of one another and can move along a 2D plane in any direction, like a mobile robot. Grasping is accomplished using a tethering system which joins adjacent modules together and allows them to clamp onto a payload. The payload becomes the backbone that offers rigidity to the formed drone. Thus, upon reconfiguration, the system is essentially a rigid body with the modules on the exterior surrounding the payload. The system could then takeoff and transport the package to a different location. Significant testing was carried out with the designed prototype, and open loop takeoff was achieved, proving the feasibility of the concept. The system has been experimentally tested to provide up to 14 N per vehicle with a theoretical capacity of 20 N. This results in each module having an estimated payload of 500 g with 25% thrust capacity still available.